This PDF file contains the front matter associated with SPIE Proceedings Volume 12702, including the Title Page, Copyright information, Table of Contents, and Conference Committee information

In order to promote the implementation of the national technical system of Chinese herbal medicine industry and accelerate the industrialization, scale and modernization of regional medicinal plants, this paper will take medicinal plants as the research object, innovatively introduce WebGIS technology into the production division and planting management of medicinal plants, and combine ArcGIS Server application platform and Ajax technology to complete the construction of medicinal plant planting management information system in Java language environment. The whole system adopts B/S architecture, and the Web Server is built by Spring framework, which can support users to log in and use the system by using the client browser, and can also control MySQL database to complete the storage and call of various data by JDBC. With the help of JSP page of the system and Openlayers framework, the prepared electronic map will be displayed, and the map will be visually integrated with the data of various elements in the process of planting medicinal plants, thus realizing the integration of information resources. Through systematic analysis, it provides necessary decision-making assistance for planting management, effectively improves the efficiency of medicinal plant planting management, and is conducive to the promotion and implementation of ecological planting mode.

We have put forward an approach to control street lamps which is based on Long Short-Term Memory Network (LSTM for short) and Bayesian optimization. This approach imports meteorological conditions, natural light intensity, and other factors to train the network model in order to adaptively adjust the brightness of road network lighting equipment. The brightness of lighting equipment would transform according to the weather and natural light intensity conditions. Compared with MLP, XGBoost and LSTM, the model built by this method has the characteristics of small error and high precision.

At present, the prescription processing in hospitals is generally done manually, with high intensity, error-prone and low efficiency. Compared with other countries, China has a large population, so hospital pharmacies are more prone to patients' concentration of medical treatment, which leads to a large number of drug distribution during the working hours of pharmacies. In addition, the heavy work for the pharmacy staff caused great pressure, resulting in a waste of human resources, in some cases may be due to human causes to the patient to take the wrong medicine accident. In order to reduce such waste, the development of automated pharmacy system is of great significance. In the medical and health care, it introduces automation technology, computer and information technology into the pharmacy, greatly reduces the human prescription error accident, and provides efficient and safe medication services for patients. In terms of science and technology, the automated pharmacy system draws on the three-dimensional warehouse system. Its research and development experience will also provide important reference for the development of three-dimensional warehouse, three-dimensional garage, logistics information, Internet of Things, cloud service medical care and so on.

As an emerging manufacturing technology, 3D printing has the characteristics of forming complex structures, mass customization and rapid prototyping. It is widely used in biomedical, aerospace, construction, and other fields. Problems such as poor printing effect, wire drawing, and nozzle blockage in the process of 3D printing are often closely related to its control system. Therefore, the 3D printing system should have an excellent motion control system and temperature control system, which is of great significance for improving the quality of 3D printing. This paper mainly summarizes the types, methods, and characteristics of motion control and temperature control in the control mode of existing 3D printing equipment and prospects for the optimization of the control scheme in the control system.

With the improvement of people's living standards, the public has become more and more concerned about healthy diet. A healthy diet is the first line of defense for human health. The existing recommendation services on catering mostly consider personal preference and usually ignore the people's demand for healthy diet. To solve such problem, we consider the healthy diet recommendation system with balanced healthiness and personalization, where the online and offline interactions of food and beverage are more contextualized and diversified, and the personalized needs of users are context sensitive. In particular, we study the process and system implementation of healthy diet recommendation, and consider the actual user needs of healthy diet recommendation system. By integrating two algorithms of knowledge filtering and collaborative filtering by process, we design a modular system architecture framework and realize the acquisition of contextual information and ontology module with rule-based inference.

The design of a double-layer transposable IoT-based elevated stereo garage is introduced, whose four-link design can freely access the parked vehicles using the classic four-link elevated structure, and the intelligent system and face recognition system turn the traditional mechanized garage into an intelligent garage capable of identity authentication and safe use. The test results of the system show that the stereo garage system can meet the parking needs of the community courtyard type residents, and has high application value for the development of stereo garage intelligence.

In view of the problems of numerous targets, diverse types of precision strike ammunition and pressing operational time in modern warfare, which make it difficult for commanders to make decisions, this paper puts forward the military application requirements of intelligent task planning system for precision strike ammunition, designs the system operation process according to three stages: target processing, task planning and deduction evaluation, and adopts the design idea of combining object-oriented and structured methods to build the architecture of intelligent task planning system for precision strike ammunition. The system can realize the combined use of multiple munitions to strike multiple targets, and give full play to the operational effectiveness of munitions, achieving the effect of 1+1>2. It can show the whole process of ammunition from launching, guidance and hitting the target, which is beneficial to the research of ammunition combat process. It can realize the independent analysis of the order of attacking targets, the intelligent planning of attacking tasks, and the accuracy of evaluation and correction, and can effectively improve the speed and accuracy of commanders' decision-making, which is of great significance for winning intelligent and information-based wars.

The control of PH and EC in the nutrient solution of hydroponic plant factories is a key factor affecting the yield and quality of plants. In this paper, a cloud-based system for environmental factors in plant factories is studied for the need of real-time, remote, automatic and accurate monitoring and controlling of PH and EC values of nutrient solution in multi-species vegetable hydroponic plant factories. The system includes information acquisition module, data transmission module, remote monitoring module, local control and execution module, which can realize information monitoring and control of nutrient solution environmental data of multi-equipment and multi-species vegetables in plant factories. Real-time data monitoring and control of PH and EC values in nutrient solution can be carried out both remotely and on site. Meanwhile, the automatic control of PH and EC values in nutrient solution based on PID algorithm ensures that each environmental factor is in the target range suitable for plant growth. The test results show that the system is stable and reliable, and can realize intelligent detection and control of nutrient solution environment in multiple areas of plant factories.

Detecting the driver's vital signs can prevent safety hazards caused by sudden illness or drowsiness, which is essential for safe driving. In the traditional vital signs detection system for drivers, the fixed detection sensitivity will not adapt better to the complex and variable vehicle environment. Moreover, the complex vehicle environment will lead to the problem of the low SNR ratio of the detection signal, and the amplification will cause a distortion problem. The low SNR of the detection signal in complex environments and the signal distortion generated by the amplification. In this paper, the phase discrimination coefficient of the phase comparator is adjusted to make the system match different application scenes. It provides the flexibility to change the sensitivity and improve the dynamic detection capability. In addition, a digital controlled bias amplifier is used in the data processing part to adjust the bias and amplification of the signal. The impact of DC offset on the extraction of the effective signal is suppressed.

In order to solve the problem that the traditional decision system is difficult to fully mine the data generated in the production and operation process of manufacturing enterprises, which makes it difficult to make production and operation decisions, a decision support system for manufacturing enterprises based on data warehouse is proposed. In this paper, the function module of enterprise production and operation decision is designed first, and then the manufacturing enterprise production and operation decision data warehouse is established. Finally, based on the improved decision tree algorithm, the mining of production and operation data in the data warehouse is completed, which provides data analysis services for the production and management of manufacturing enterprises. The experimental results verify that the proposed system can manage the production management information of production enterprises in a unified way and improve their comprehensive management level.

The development of the construction of the airport brings numerous projects of treatment of the foundations, in which a variety of methods such as rotary jet, cement mixing stack, etc. are used. These methods are hidden in such a way that there are blind spots for supervision, and the workload and management difficulty is great. In order to solve these management flux, The digital monitoring system based on the Internet of things, geographic information systems and satellite positioning began to be applied to the pile drivers construction process control. The system monitors process parameters such as positioning, inclination, flow, pressure, etc. in real time by installing sensors on construction equipment. Thus, optimize the management mode from "post hoc inspection" to "process control" that actually curb the phenomenon of Jerry-building and cutting materials, and improve the management efficiency and quality of the project.

This paper analyzes the drawbacks and challenges in pandemic for the current hospital logistics management, proposes and designs an IoT-based Hospital Logistics Management System. The system covers the logistics business of three modules: Transportation, Cleaning and Engineering Maintenance. It uses the IoT, Cloud Computing technology to achieve real-time processing information and tasks, employee collaboration, statistical reports generating and approval through mobile terminal. By deploy the IoT-based Hospital Logistics Management System, a digital hospital logistics management which interconnect people, equipment and data will be established. In this system, people from all departments in hospital can participate in the work anytime and anywhere, management efficiency been dramatic improved, costs reduced and achieve hospital sustainable development.

The communication and control system of an oilfield in the South China Sea has been modified and optimized in order to complete the remote control production of an offshore oilfield during typhoons. The oilfield communication antenna is equipped with glass fiber reinforced plastic sheath to reduce the vibration of satellite antenna caused by strong winds. The oilfield thermal backup and terminal power amplifier ensure continuous connection. Through the remote control transformation of on-site equipment, control system optimization, the construction of land remote monitoring center and other ways to complete the remote control of production during the typhoon. Meanwhile, through the automatic control of one-button seawater displacement function to ensure the replacement of pipelines after the production of oil fields, fire and gas fire fighting system during the typhoon production is still normal operation, to ensure the safety of on-site equipment. In the process of typhoon remote control, due to the remote communication control, there is a certain delay, the operator needs to minimize the man-machine interface of the operation control system, and try to make the equipment run automatically. The stable operation of typhoon remote control production is ensured through the transformation and optimization of the oilfield.

When designing Orthogonal Frequency Division Multiplex(OFDM) system, high PAPR has become an important factor to be considered due to the superposition of signals on different subcarriers. This paper presents a FPGA scheme of DFTs- OFDM based IM-DDO-OFDM system including the transmitter and the receiver part which can effectively restrain PAPR of signals. Specific encryption sequences and high-order modulation are also introduced in order to promote the system security. The simulation and test results proves that PAPR of transmission signals are effectively reduced compared to the traditional OFDM system.

To address the problems of data acquisition and integration of heterogeneous equipment such as CK40 CNC lathe, KUKA industrial robot and Siemens PLC for logistics control, interconnection and interoperability between heterogeneous equipment and visual management of equipment data in the discrete manufacturing workshops, this paper proposes a data acquisition and integration method based on OPC UA and studies a data visualization and management method for equipment KPI. This study establishes an OPC UA information model based on the characteristics of discrete manufacturing heterogeneous equipment, develops an OPC UA aggregation server and client using the OPC UA open source library, and develops an equipment data management visualization system for intelligent KPI evaluation using Java. Finally, based on the field experiment platform, it is verified that the system is feasible in dealing with data acquisition and integration of heterogeneous equipment in discrete manufacturing workshops, equipment KPI evaluation, and data visualization.

The carrier signal is subject to interference and attenuation in the channel transmission process. In this paper, a high-performance anti-interference method for the modulation and demodulation of phase modulation QDPSK signals is proposed, and the programming and waveform simulation of the QDPSK system are conducted based on an analysis of the module. In addition, good modulation and demodulation effects are achieved in the waveform experiment, indicating the correctness and practicality of the QDPSK system.

In order to meet the requirements of real-time intelligent decision-making simulation for the integrated Coal Supply Chain of large energy group, the existing business scope and characteristics are summarized and analyzed to solve the problems caused by the characteristics of large-scale, complex structure, multiple influencing factors, and rapid dynamic changes of complex systems. First, the application scenarios of the simulation business environment are analyzed. Then, based on the Multi-Agent Modeling method, the simulation business environment of real-time dispatching and intelligent decision-making is constructed, and the functional architecture, process, and interface prototype of the simulation environment are designed. Moreover, verification experiments based on typical scenarios are also established for the application practice of the model in the business environment. The verification results reflect the excellent adaptability and generalization of the simulation model to the complex system. Finally, based on these results, the future engineering application of the simulation system is forecasted.

In view of the dynamic simulation requirements of the construction process, using plug-in, GLSL rendering, key frame animation and collision detection technology, design and development of SiPESC animation editing and rendering engine module, in order to solve the dynamic simulation problems of the construction process, to verify the feasibility of the construction scheme. This paper discusses the design idea, software framework and key technologies of construction process dynamic simulation system. The practical application examples of the software show its practicability in engineering field.

In order to strengthen the promotion of model-based system engineering (MBSE) in the development and production of civil aircraft, this paper adopts model-based system engineering method based on MBSE to establish a functional architecture model of pressure control system based on SysML language to carry out architecture design and verification. In the early stage of system development, the correctness of functional logic and data flow is checked through the model, unexpected errors in the logic are identified, and missing interfaces for logic operation are captured. Realize early exposure of design problems, avoid downward transmission of design problems, reduce system design changes, improve work efficiency, and reduce costs.

In order to improve the forecasting accuracy of monthly electricity sales and the refined operation management of power users. A management method of power transformer operation system is proposed. Through the establishment and use of the management and control working model of the special transformer, the forecasting accuracy of monthly electricity sales is improved, the early warning of electricity charges is realized to avoid arrears, and abnormal situations such as current loss and voltage loss of the special transformer users can be found and handled in time, thus preventing the loss of electricity and current. Using the mobile Internet and GPS positioning, the staff can query the equipment and geographical information of distribution GIS system in real time while patrolling the distribution circuit. Combined with the regular inspection of special transformer users, a series of measures have been implemented to improve the refined level of power transformer user management and provide better and better power supply services for special transformer users.

The task of quickly finding target information from a huge database is required in the era of big data. Association rule algorithms, which mainly carry out research in terms of dimensionality of data, abstraction level and types of processing variables, have become an effective method for solving data mining problems. Data mining techniques can discover a lot of information hidden behind a large amount of data and provide effective data support for important decisions. Association rule-based data mining techniques are one of the most important research areas, and the use of association rule algorithms can identify the key factors affecting the research object. In this paper, the classical association rule algorithm Apriori and its improvement algorithm are studied to investigate the factors influencing teaching quality in vocational institutions using data mining techniques. The article combines the association rule algorithm of data mining technology and database technology, and constructs a database matching the association rule mining through the process of data collection and data pre-processing. The key factors of teaching quality are analyzed by using association rule algorithm, and the teaching quality evaluation system based on data mining technology is studied in depth.

With the expansion of the scale of the power grid, the types and quantities of new equipment put into operation continue to increase. It is necessary to develop an intelligent, standardized and universal operation management system to meet the development needs of the large power grid and ensure safe and stable operation objectives. Therefore, a smart grid new equipment startup management system is proposed. The intelligent dispatching platform adopts the B/S architecture system, intelligently obtains various data, analyzes the operation types of new equipment startup and the characteristics of the startup scheme, intelligently forms the new equipment startup operation scheme, and completes the preparation, review and verification functions through the system, thus building an intelligent management system for the startup of new equipment in the power grid. The practice shows that the system can significantly reduce the workload of operators, improve the risk control level of new equipment startup, and play an important role in ensuring the safe and stable operation of the power system.

In the present work, a distributed network-based remote indoor temperature control and energy conservation system is proposed. The system provides such functions as remote data transfer and remote control, as well as a distributed information sharing and communication platform. Meanwhile, the system integrates an intelligent control system and a database management system, on which the data obtained from the Internet are first assessed by the expert panel and then processed by the intelligent control system. In this way, real-time intelligent control is achieved to allow the user to control the indoor temperature and production conditions of the greenhouse through the network. During the design of the system, a wireless monitoring system is developed as per the specific location and geographic conditions for remote surveillance. The field test shows that compared with the control plot, the experiment plot reduced the gas consumption by 2802.4 m³, reduced energy consumption by 24935.0 kW·h. The work is expected to provide a solution for development of remote indoor temperature control and energy conservation systems in real-world scenarios.

Library is an irreplaceable part of the public cultural service system. Nowadays, the informatization of library reservation management is the general trend. Many university libraries and social public libraries have begun to introduce management systems such as library reservation systems, in order to save manpower and material resources while improving the efficiency of the library. This system is developed using C# language, adopts B/S structure, Asp.Net Core MVC web application framework for development, uses SQL Server as the database, and applies microservice architecture. The system realizes the reasonable allocation of library space resources, saves users' time, and makes the library's reservation management more efficient and convenient.

The current knee joint rehabilitation devices are quite cumbersome and uncomfortable for the majority of knee joint rehabilitation patients. In view of this practical problem, this research has developed a lightweight and flexible knee joint flexion monitoring system. Using flexible components as the sensing elements, a small embedded system is established, providing a significantly improved comfort level and a more humanized user experience, while also facilitating further intelligent expansion of related functions.

Nowadays, the global epidemic prevention and control has entered a normalization, but the temperature detection task in traditional epidemic detection is heavy, and the detection speed of common equipment in the market is slow and the supervision is inconvenient. In order to reduce the work intensity of human body temperature detection, reduce the probability of virus infection, and improve the screening and interrogation efficiency of abnormal people, a non-contact intelligent temperature acquisition and monitoring system based on FPGA is designed. In this system, FPGA is used as the main control unit of the temperature measurement system, and the infrared temperature sensor MLX90614 based on the principle of infrared radiation is used for non-contact temperature measurement to collect temperature data, and then the corresponding temperature data is displayed on the OLED display. At the same time, the ESP8266 WIFI module is used to synchronize the temperature data to the host computer to realize remote temperature monitoring. When the temperature exceeds a certain value, the host computer sends out an alarm of excessive temperature. The system has good practicability, suitable for hospitals, schools, shopping malls and other crowded areas, convenient management. It can also be used for real-time inspection of temperature changes of products and materials in the factory, research and development of production areas.

In this paper, an asynchronous advantage actor-critic (A3C) based intelligent temperature control method is proposed for Continuous Stirred Tank Reactor (CSTR) system. Firstly, the overall framework of CSTR system is composed of global network, thread network, environment model and interaction mechanism is designed. Secondly, the process of interaction is designed: the thread network is trained to interact with environmental models by using an asynchronous multi-threaded approach, the trained parameters are updated and synchronized to the global network. Finally, the effectiveness and stability of A3C algorithm is verified by the simulation experiments. The simulation experiments show that the trained A3C network can control the output temperature of the CSTR system to reach the preset temperature and keep it the preset temperature, so the temperature control of the CSTR system is achieved in the A3C algorithm.

In order to detect the growth environment indicators of farmland more comprehensively and improve the collection efficiency, a multi-channel data scanning detection system was developed based on Arduino, which integrated temperature and humidity detection, light intensity detection and soil moisture detection. The communication data mainly used MQTT protocol and relied on the Topic mechanism provided by Ali Cloud Internet of Things platform to publish, and the above data can be displayed in real time on the PC side. Based on the Yolov5 development environment, the development of the main interface, serial port configuration and debugging interface, data distribution interface, and entering the monitoring system interface was carried out, and the received index data was saved to the PC side. Through two-dimensional color painting, the data distribution of each area plot was displayed in real time. Field monitoring experiments show that the system runs stably and reliably, and achieves efficient collection and visual detection.

In this note, we investigate cooperative robust consensus problem for a class of linear heterogeneous multi-agent systems (MASs) with parameter uncertainty in agents. All the agents can have nonidentical dynamic dimensions. By combining the communication network, a distributed dynamic compensator is established based on only the measurable output information of the exosystem, which provides an estimated value of the state of the exosystem for each agent. The distributed dynamic output feedback protocol (DDOFP) is proposed by utilizing the exosystem compensator, through which the cooperative robust consensus is achieved under switching topology by following output regulation framework. Here, we introduce the stabilization scheme for a class of liner switched systems to analyse the closed-loop stability, which avoids the dependence on the eigenvalues of Laplacian matrix, such that it is a fully distributed control strategy without any global information. Finally, the simulation example is illustrated to verify the feasibility and effectiveness of the theory results.

For the current design of power transmission and transformation engineering design review system, there are some problems, such as poor functionality and processing capacity, and long system response time. The design review system of power transmission and Transformation Engineering Based on Spring + Struts + Hibernate (SSH) framework is designed. According to the definition and characteristics of SSH framework, this paper puts forward three classical frameworks: Struts, Spring and Hibernate, and integrates SSH framework. Through data cleaning, conversion and standardization, the evaluation data of transmission line engineering are simplified, and the evaluation data of transmission and transformation engineering are clustered by clustering algorithm. Analyze the overall requirements and functional requirements of the system, adopt SSH framework and design the overall system architecture in combination with specific business. Divide the system function structure from the user use type. Through the design of database structure and data table, the design of power transmission and transformation engineering design review system based on SSH framework is realized. The experimental results show that all functional modules of the system designed by the proposed method are in normal state, with an average throughput of 94.8mbps and a response time of only 17.2s. The proposed method has better functionality and processing capacity, and shorter system response time.

If the capacity of a hydropower unit in a certain area is several times the capacity of the thermal power unit, the characteristics of the hydropower unit speed regulation system have a great impact on maintaining the stability of the power grid frequency, according to which this paper builds a water-fire hybrid power system based on simulink, and subdivides the thermal power unit into two categories: no reheating unit and no reheating unit, so as to accurately simulate the role played by different types of units in the water-fire hybrid system in the frequency regulation process. In order to achieve a better FM effect, the text first improves the ordinary particle swarm algorithm to solve the problem of "precociousness"; Secondly, the traditional PID parameters are optimized with the improved particle swarm algorithm, and finally the quantization factor and scale factor in the fuzzy PID are optimized. The above-mentioned fuzzy PID control strategy is applied to the influence of different primary frequency regulation effects on the frequency stability of the system when the capacity proportion of the hydropower unit is different. From the simulation results, it can be seen that the fuzzy PID controller optimized by the improved particle swarm has the characteristics of fast response, good stability, small oscillation amplitude and strong anti-interference ability. And the larger the proportion of hydropower unit capacity, the more prone to more large oscillations in the system frequency, which is not conducive to the recovery of frequency, but can improve the stability of the system frequency.

This paper designs a system that can give an audible alarm when someone illegally enters the user's home. This system uses STC12C5A60S2 single-chip microcomputer as the main control chip, sets the anti-theft mode and cancels the antitheft mode through the keyboard. With the help of thermal infrared sensors, the system can detect whether there are illegal intruders. When there is an intruder, the system can broadcast the illegal intrusion location through the voice module, and notify the user through the GSM module or Bluetooth module. The system can be used in the field of family safety. Voice broadcasting, Bluetooth communication and GSM communication make the system more convenient.

There are many hybrid transmission lines consisting of power cables and overhead lines. When a fault occurs, it is difficult to determine the exact location of the fault. In order to prevent further damage caused by reclosing when the cable faults, this paper proposes to build a differential protection for the cable line to realize the judgment of the internal fault of the cable line based on 5G communication technology. If it is judged that the fault is inside the cable line, then send the blocking signal to the relevant protection devices in the substations on both sides to block the function of reclosing, thereby preventing reclosing from cable faults. The fault discrimination algorithm of the system in different types of hybrid transmission lines and the key technology of the system are introduced.

The new generation of power consumption information acquisition system has increasingly high requirements for local communication business bandwidth, acquisition frequency, and reliability. However, high-speed power line communication(HPLC) is prone to performance degradation due to the influence of new measurement terminals electromagnetic compatibility(EMC). This paper proposes the analysis and improvement method of the influence of new measurement terminals EMC on HPLC. First, the hardware architecture and the characteristics of the new measurement terminals EMC are introduced. Then, the influence of new measurement terminals EMC on HPLC is studied, and the test environment of the influence of new terminals on HPLC is established; Finally, combined with the typical EMC cases analysis of new measurement terminals, the effect of the improvement of the Influence of new measurement terminals EMC on HPLC is verified. The research results show that the HPLC attenuation performance by referring to the method in this paper is significantly improved, and achieves the similar effect as the standard I concentrator. The method in this paper can provide effective technical support for the construction of a new generation of power consumption information acquisition system.

Multi source data fusion plays a great role in promoting the application of basic geographic information in various industries, and this method makes up for the limitations of surveying and mapping data attribute collection. This paper expounds and analyzes the problems faced by the data integration of multi scale, multi format and multi topic settings in the construction of electric power public service platform, puts forward the research on multi-source data fusion methods in the construction of electric power public service platform, and discusses and studies the key technologies.

CubeSats are gaining popularity in low-Earth orbit networks due to their low cost, fast response, and the potential and ability to form constellations. But establishing optical communication links between CubeSats is a daunting task. Laser transmission links require very strict beam pointing stability, and for small satellites with strict size, weight, and power (SWaP) requirements, it is imperative to research light and miniaturized laser communication terminals. Electrostatic micro-electromechanical systems (MEMS) mirrors are currently the most effective devices for realizing SWaP terminals in the field of optical communication. Therefore, this paper mainly studies the servo control of electrostatic MEMS mirrors for micro-miniature laser communication terminals. By using the position error feedback of the photodetector, the servo control requirements of high response speed and high robustness are realized.

With the successful global networking of Beidou satellite navigation system, Beidou system has been able to provide all-weather navigation, positioning and timing services to users worldwide. In order to compare and analyze the positioning performance of BDS-3 with GPS and their combinations under observation environment of woods, based on the unified GPS/BDS-3 combined positioning spatio-temporal datum, a GPS/BDS-3 combined pseudo-range single-point positioning observation model is established, the positioning solution results are solved by the least squares method, and the SPP data processing software (LsmSPP) is developed based on Visual Studio 2022 platform using C/C++ programming language. The experimental results show that the BDS-3 system is better than the GPS system in tracking satellites under observation environment of woods, and the BDS-3 pseudo-range single-point positioning accuracy is better than 2.50m in the E direction, 2.15m in the N direction, and 5.98m in the vertical direction, which is slightly worse than the GPS/BDS-3 combined positioning accuracy in the E and N directions, but slightly better than the dual-system combined positioning accuracy in the vertical direction, and the positioning accuracy of BDS-3 system is better than that of GPS system in all directions.

With the development of agricultural science and technology, there are problems of information overload and data explosion in the process of finding educational resources through the network. This paper selects talent training in the field of agriculture as the goal to explore the intelligent push of network resources in the field of agricultural education. Combined with the establishment of collaborative association rules and the AutoSVD hybrid recommendation model, the K-Means algorithm was adopted to cluster the agricultural teaching resources information in the teaching resources retrieval model obtained in the early stage. Different types of resource clusters were obtained, and the resources with the most user preferences in each cluster were also obtained. Finally, the users in the same cluster were recommended. RMSE and MAE are calculated for comparison. Experiments verify that the performance of the hybrid model proposed in this paper is superior to that of the current mainstream models. The development and exploration of the theory of personalized learning will be beneficial from it. Thus, this study has some innovations.

The method of spacecraft’s Ex-factory transportation (the transportation from AIT facility to launch site) is an important part of satellite inter-system design; it is the key constraint of satellite configuration and structure. In this paper, the ocean shipping of large satellite in recent years is investigated, and its technical characteristics are analysed from the aspects of applicable satellite situation, transport ship performance, port and other supporting facilities, so as to provide technical reference for the related work for Chinese satellites.

One of the scenes where smart transportation enables smart community is smart parking, which involves vehicle recognition The principle of vehicle recognition technology is analyzed and the scheme of license plate recognition system is determined. The overall design of vehicle recognition application system is carried out, the research from network architecture, software architecture, camera scheme, AI smart terminal(ST), terminal software, content management system (CMS), scene optimization, etc. is conducted, the main design scheme and software module functions are explained, and a community-level smart parking management system based on video/image automatic vehicle recognition and network connection is built. The system is piloted and applied in some communities in a large city, and the operating status shows that it can fully cover three scenes: single passage one-way, single passage dual-way, and dual passage dual-way, and the license plate recognition rate of standard entrance and exit scenes is>99.7%. The system follows the development trend of networking and information management, and has the value of promotion and application in the industry.

The wireless sensor network is widely used in the Internet of Things. As its core technology, the clustering routing algorithm effectively prolongs the network life cycle. An energy balance algorithm for heterogeneous wireless sensors based on bilevel decision and data distribution is proposed. Firstly, a more suitable cluster head is selected through the bilevel decision-making and competition mechanism. Secondly, A multi-objective decision-making strategy is used to synthesize multiple factors for data distribution. It can be seen from the simulation results that the routing algorithm has a great improvement in the network life cycle, energy utilization rate and data transmission.

In the process of realizing the visualization of 3D model files through middleware, it is very important to convert the format of 3D model files into the format acceptable to the middleware. This paper analyzes the ASSIMP library, uses QT as the application development framework, to realize the conversion problem between file formats by the ASSIMP library.

Simulation-based design plays an important role in the study and manufacturing of weapon equipment, in which effectiveness evaluation is one of essential tasks. While a variety of effectiveness evaluation approaches have been proposed, they are becoming incapable with the increase in the complexity of weapon equipment systems. In this case, data driven methods have been explored to address the effectiveness evaluation issue. In this paper, we propose a new CatBoost based data driven method for assessing and predicting the effectiveness of equipment and give a procedure for guiding the use of this method. We illustrate our method with an air defense missile weapon system, which demonstrates the validity of our method compared with state-of-the-art methods.

Key performance indicators (KPIs) are of great significance in the monitoring of wireless network service quality. The network service quality can be improved by adjusting relevant configuration parameters (CPs) of the base station. However, there are numerous CPs and different cells may affect each other, which bring great challenges to the association analysis of wireless network data. In this paper, we propose an adjustable multi-level association rule mining framework, which can quantitatively mine association rules at each level with environmental information, including engineering parameters and performance management (PMs), and it has interpretability at each level. Specifically, We first cluster similar cells, then quantify KPIs and CPs, and integrate expert knowledge into the association rule mining model, which improve the robustness of the model. The experimental results in real world dataset prove the effectiveness of our method.

In order to improve the efficiency of text recognition of converter station operation, this paper studies the intelligent text recognition system combined with computer image acquisition technology. Through the visual recognition algorithm developed by VS2010 and OpenCv to extract the font outline, combined with the developed outline edge feature extraction algorithm to identify and locate the font, the intelligent recognition statistics are realized. Because the real-time performance of human body detection using gradient histogram features is poor, the method of feature extraction is improved in this paper. The experimental results show that the improved human detection algorithm has good real-time performance. In the experiment, the success rate is over 95%, and the time spent is less than 28s. There is a huge space for practical application and promotion.

Energy consumption in various occasions is required to be managed and controlled finely. Traditional micro circuit breakers are mainly used for overload and short-circuit protection of distribution lines, which cannot meet the requirements of fine management and control of energy consumption. In order to achieve this goal, an intelligent micro circuit breaker is proposed in this paper to meet the requirement of new power systems and integrate functions of wireless communication, power measurement and equipment diagnosis. The circuit breaker can be remotely controlled according to users’ command, and the power consumption can also be monitored and measured in real time. The intelligent micro circuit breaker has a temperature measurement module for defect self-diagnosis such as poor contact of connectors of the circuit breaker. These functions indicate that the proposed design has good practical application value.

A MHP-RGA (Multiple Hierarchical Projection Grey Relational Analysis) method is proposed for consistency and effectiveness assessment between validation results, which is focus on ADAS or autonomous driving functions under designed test case. The complex and interactive systemic output trace could be projected and calculated to the normalized relevance degree from multi-dimensional concerned levels such as the system architecture, component constitution, functional behavior, and measurement approaches. It can identify the inconsistencies sources between different validation types or test rounds (e.g simulation vs. open-road testing) with quantification. The experiment of FCW (front collision warning) scenario shows its abilities and the wide application prospect.

In order to solve a class of Multi-Depot Vehicle Routing Problems With Soft Time Window (MDVRPSTW). We present a Tabu Search based on Variable Neighborhood (TSVN).The proposed algorithm applies a granular local search mechanism in the intensification phase and a tabu shaking mechanism in the diversification phase of Variable Neighborhood Search. Furthermore, it allows the violation of problem-specific constrains throughout the search in an attempt to escape from local optima and to converge to a high-quality feasible solution. TSVN is an effective algorithm, compared with the Tabu Search before the improvement, it has some improvement in the cost of objective function. The average cost (AC) is decreased by 28.75%,compared with the other algorithms used in the MDVRPSTW problem, such as Discrete Particle Swarm Optimization Algorithm (DPSO) and Genetic Algorithm (GA).

In order to ensure that the center and surface of the new energy heavy truck gear can meet different performance requirements, the final heat treatment process is carburization and low temperature tempering after quenching, which involves the determination of many process parameters. At present, these parameters are mainly determined manually, but the trial and error period is too long to ensure the effect. In order to solve this problem, this study proposes an intelligent decision-making method. On the basis of accurately describing the local similarity of process characteristic parameters, information entropy is introduced to express the impact of process characteristic parameters on decision-making, and entropy weight method is used to objectively weight process characteristic parameters. Then, the global similarity of process characteristic parameters is determined according to their local similarity and weight. The practical application shows that this method can realize the intelligent decision-making of the final heat treatment process parameters of new energy heavy truck gears.

With the gradual expansion of network scale, network security problems caused by intrusion attacks and Trojan horse viruses follow. Network attacks are highly targeted and diverse. With a large number of Internet of Things devices in the power system connected to the Internet, the interaction of heterogeneous information and the rapid change of network structure drive the dynamic development of the Internet of Things environment. It further expands the attack surface that may be threatened, and constantly generates new weaknesses and threats. In this paper, a rule-based reasoning method for multi-source knowledge in the security of the Internet of Things was proposed. First, a description logic-based language to represent the classes in the model was adopted. Reasoning rules were designed to supplement the semantic representation ability of the description language, which is to realize the reasoning of implicit facts from multi-source heterogeneous knowledge and data in the security field of the Internet of Things. Compared with the entropy method, the model is proved to be effective in predicting the actual network security situation and has certain practical guiding significance for the actual network security management.

Chaotic signals can be applied to confidential communications because of their noise-like, broad-spectrum and unpredictable properties. However, using the internal parameters of the laser as the key for communication is not only not easy to change, but also the key parameter space is limited. Therefore, increasing the key space is crucial for the security of chaotic confidential communication systems. In this paper, we propose to increase the key space of chaotic optical communication system by using multi-section semiconductor laser (MSSL), which increases the number of key parameters and increases the key space of the system. Finally, the validity of the ideas proposed in this paper is verified through experiments.

With the rapid development of modern information technology, big data technology has become an important technical support in the process of digital development in all walks of life, effectively promoting the efficient reform of work models in different fields. In the management work of local governments, relying on big data technology to build an optimized network security management is to give full play to the core advantages of big data technology in the management work, so that the efficiency of security management can be effectively improved under the background of data. In view of the risks of hacker attack and tampering, virus or Trojan program invasion and theft of computer network information, this paper puts forward a computer network information security management method. This paper uses big data clustering algorithm to evaluate its computer network information security and verify its simulation. The research results show that the proposed method is more suitable for the nonlinear characteristics of computer network security, and can accurately reflect the state of computer network security operation, thus providing an effective idea and guarantee for the protection work.

The current conventional communication data security storage platform mainly adopts a centralized approach to data storage, but the over-centralized storage approach tends to have an impact on the platform performance. In this regard, the design of a Hadoop-based secure storage platform for communication data is proposed. Combined with the Hadoop distributed storage architecture, the overall structure of the platform is designed, and the storage data tables are designed and the file storage function is designed in combination with HBase. The experimental results show that the proposed storage platform has a high data throughput and a better data processing performance.

The standard clauses used in the electric power industry often contain inconsistencies, making it difficult for professionals to select appropriate terms in their work. In this paper, we propose a discrepancy discrimination model based on knowledge graph and natural language processing, using standard clauses of power equipment as the dataset. Furthermore, we construct an ensemble learning model to assist decision-making in the electric power field. The experimental results show that the proposed method is effective in terms of precision and recall. The discrepancy discrimination model has a precision rate of 76.45% and recall rate of 84.72%, while the auxiliary decision-making model has a precision rate of 75.3%. The method is able to effectively identify inconsistencies in equipment standards and provide decision-making advice, providing a valuable reference for standardizing the content of standard terms in the industry.

Mars is the important goal in the exploration of the solar system. The maximum distance between Mars and earth is over 400 million kilometers, which means 22 minutes are needed for the earth station to transmit the telecommand to the spacecraft on Mars or receive the telemetry from them. Therefore, the real time control of the spacecraft on Mars is such a difficulty for earth station and a total automatic communication network is needed for orbiters and rovers themselves. The international common communication protocol for extraterrestrial planets is CCSDS proximity-1, which is widely used in various Mars probes of the United States, Europe and China. However, the CCSDS proximity-1 always operates between fixed pairs of spacecraft that usually belong to the same mission, which means the callers and the responders between these spacecraft are usually predefined, even when change is allowed, are pre-scheduled. For the possible hailing interferences from the spacecraft of other missions, the traditional method is “random back-off strategy”, which postpones the hailing process randomly when the failure of hailing is detected. The drawbacks are obvious: 1) the valuable communication time is wasted especially when the arc segment time is limited; 2) the random back-off cannot guarantee that the second time or third time will be successful; 3) the more the spacecraft of different missions are, the high the failure possibility is. In this paper, the multi-node efficient anti-interference communication method based on CCSDS protocol for Mars proximity space communication network is presented, which can monitor not only the return link, but also the side lobe of the forward link. Besides, the merging judgment mechanism is implemented in the data service sublayer of the CCSDS architecture, which can adjust the hailing process properly when the collision occurred and no extra time will be wasted. The method will be used in Chinese TW-3 probe. By using the conception products which simulate 5 orbiters and 5 rovers of different missions, real test shows that the presented method performs well in Mars proximity space communication network.

Diffraction-free beam propagation in free space can keep the light intensity distribution constant, but the complex system and bulky devices bring inconvenience to the experiment. In this paper, we propose an effective approach to generate a terahertz diffraction-free beam from diffractive transmitted deformed axicon. The main research methods are phase folding technique and three-dimensional (3D)-printing technique. Based on the existing research, the refractive transmitted axicon is compressed into a lighter diffractive element to implement a 0.1THz beam with 344.75mm diffraction-free length. The effect of element thickness on the phase folding efficiency is investigated, and the results of the refractive axicon and diffractive deformed axicon are compared to demonstrate the advantages of diffractive optical elements in generating diffraction-free beams.

When a vehicle is negotiating a curve of track, a kind of wheel squeal tends to be generated. In order to predict and mitigate wheel squeal, a model of wheel squeal based on modal coupling and negative damping theory is developed. The sensitivity of angle of attack, rolling velocity, negative slope coefficient and wheel damping is analysed based on this model, and the results correlate well with experimental measurements, indicating that this model is effective. Furthermore, these parameters of wheel-rail system are optimised based on this model using genetic algorithm. When these optimised parameters are applied, the result shows that the wheel vibration amplitude decreases evidently, and the sound pressure level is reduced by 16 dB, indicating that the optimisation using genetic algorithm based on this model is effective.

With the development of satellite networking technology, low-orbit satellites are gradually facing security threats caused by abnormal traffic. Since some types of attack traffic do not pass through ground stations, we are trying to migrate ground anomaly detection capabilities to satellites. In prior arts, many deep learning methods have been applied to network traffic anomaly detection. However, most of them have deep layers of neural networks and are unsuitable for satellites with limited energy and computing capacity. In this paper, we present SAT-NTAD, an effective low-orbit satellite network traffic anomaly detection method that combines shallow Convolutional Auto-encoder (CAE) and Bidirectional Simple Recurrent Unit (Bi-SRU) to extract the spatial and contextual features of traffic flows, and detects anomalies by utilizing dynamic threshold. Extensive experimental evaluations show that our method can stably maintain the high performance for abnormality detection, which outperforms state-of-the-art approaches, and effectively reduces the computational cost, which is more suitable for the low computing capacity environment of satellites.

The research on reconfigurable intelligent surface (RIS)-aided visible light communication (VLC) has been widely investigated in recent years. However, traditional research on this area only concentrates on one kind of VLC RIS (such as a mirror-based RIS or a liquid crystal (LC)-based RIS), however, dismissing the combination of different kinds of VLC RISs. Consequently, in consideration of the huge potential of the combination for VLC RIS, this paper proposes a trial combing a mirror array-based RIS with a liquid crystal-based RIS in an indoor visible light communication system. The optimization problem is formulated with achievable data rate maximization as the objective function, and meanwhile, the parameters of the two types of RIS is regarded as the independent variables. As for the non-convexity of the proposed optimization problem, in order to determine the optimal configuration of parameters for the two types of RIS, the particle swarm optimization (PSO) algorithm is adopted. Finally, the PSO algorithm is demonstrated to be an effective method to solve the optimization problem in the simulation results, and meanwhile, the combination of a mirror array-based RIS and a liquid crystal-based RIS is able to enhance communication performance to a greater degree compared with the RIS-aided VLC system which employs only one type of VLC RIS.

The rapid development of high-speed rail vehicles has promoted the continuous progress of railway transportation, and vehicle lighting control system is an important part of rail vehicle control. LED is the fourth-generation green lighting source. Compared with traditional fluorescent lamps, LEDs are energy-saving and environmentally friendly, have a long lifespan, and have a luminous effect that is more than three times that of traditional fluorescent lamps. However, LEDs have not been effectively utilized on rail vehicles. Therefore, this paper proposes a mathematical modeling for the efficient utilization of LED lighting modules in passenger compartments of rail vehicles, providing a model basis for future research on LED lighting control systems and their fault detection parts.

Reconfigurable intelligent surface (RIS) is famous for high energy-efficiency and treated as one of the potential technologies for green network. We analyze the performance of RIS-assisted vehicular network at urban intersections under Weibull fading. We derive expressions for the outage probability, the average achievable rate and the average symbol error probability. Simulation results show that the performance of the communication system can be improved by increasing the number of reflective elements. In addition, the theoretical results that we derive agree well with the Monte Carlo simulations, which proves the correctness of our derivation. It is enlightening that the performance of the communication system can be improved by adequately deploying RIS when the direct path is blocked.

In order to realize meet the requirements of real-time and efficient processing of massive low-voltage power distribution grid service operation data, this paper constructs a cloud-edge collaboration architecture to greatly relieve the pressure of the uplink. Then, a cloud-edge collaborative-based task segmentation mechanism for low-voltage distribution grid service operation is proposed to minimize the total transmission and computing delay. Simulation results show that the proposed mechanism can improve the total delay performance and achieve better resource utilization rate compared with two comparison algorithms.

In order to detect and diagnose the existence of fatigue cracks in the transmission shafting of rotating machinery equipment as early as possible, the influence of mass eccentricity factor on the cracked rotating shaft was investigated, this paper built a cracked rotor comprehensive fault simulation test bench, conducted experimental research on typical transmission shafting samples with straight cracks, and obtained its response signals under different noncentral mass (1g and 2g) and different noncentral phase angles (0°, 90° and 180°). Time domain diagram, frequency domain diagram and axis locus diagram. The results show that the increase of noncentral mass and noncentral phase angle will lead to the increase of 1^st frequency, but has little effect on 2^nd frequency.

Anomalies are rare items that differ significantly from the majority of the data and raise suspicion. Time series anomaly detection is of great significance in industrial applications, data mining and other fields. In this paper, we propose an autoencoder-based anomaly detection model. In the encoder part, the dependency relationship between different time series is mined through the graph attention network, the time domain data features are extracted through the GRU, and the frequency domain features are extracted through the convolutional neural network. The temporal data is reconstructed using a decoder consisting of a recurrent neural network in the encoder part. The residual between the reconstructed data and the original data is used to further judge anomalies. Data cleaning is performed in the preprocessing part to improve model performance. The effectiveness of our method is demonstrated on three publicly available datasets, and our method is found to outperform four other common anomaly detection methods.

With massive access of distributed photovoltaic, distributed energy storage station, and other equipment, the number of power distribution network devices growth rapidly. To satisfy differentiated QoS requirements of different businesses, distribution network communication needs to choose the appropriate communication method according to the characteristics of different devices business. Hence, Business differentiation-aware intelligent converged communication orchestration technology for distribution network under uncertain global state information was proposed in this paper, which dynamically learning communication orchestration strategy by adopting historical information to perceive business differentiation to maximize the utility function of the devices. Finally, simulation results demonstrates that compare with two existing algorithms, the proposed algorithm has superior performances.

With the upgrading and transformation of power system intelligence, the number and kinds of power big data have begun to show an explosive growth trend, the development of power grid in our country has already entered into the era of big data. How to quickly collect and sort out useful information in the chaotic data pile is a great challenge for the current power system construction. Multi-source information fusion plays an important role in the research of power big data. It can fuse the data information belonging to the research object from various aspects together to realize the sorting out of the disorganized power grid data and obtain a more detailed and comprehensive analysis of the research object. And relying on the mature development of big data storage and processing technology, it provides a new opportunity for the research of multi-source information fusion of power big data.

In order to solve the problem of data transmission efficiency of combat information link in cluster mixed configuration, the OLSR protocol in wireless Ad Hoc network is briefly introduced, an improved OLSR routing protocol algorithm based on MAC layer transmission information is proposed, and the algorithm is simulated and analyzed on OPNET simulation platform. The results show that the algorithm has been improved in network delay and data delivery rate, which can be used as a reference for improving the combat capability of cluster mixed combat.

As an important part of the power grid, the safe and stable operation of the transmission line is related to the entire national economic construction and people's life. In order to identify the external damage factors around the transmission line in a timely and reasonable manner, this paper uses the existing satellite remote sensing image technology to detect and analyze. In this paper, the bulldozers and excavators of large construction machinery in the influence factors of transmission lines are analyzed and identified by means of 50% cross validation and experimental data comparison. The experimental data shows that the missed alarm rate is less than 2%. Therefore, the improved target detection algorithm proposed in this paper can effectively identify multiple targets in complex background, and then apply it to the detection of external damage factors of transmission lines.

At present, intelligent systems in the health field have been widely used in various departments and play an important role in medical, health care and other industries. This paper establishes a safe and reliable wireless temperature control system based on RFID technology. This system takes RFID as the core component to design a complete, stable and efficient environmental temperature monitoring and alarm control system, which uses sensors to collect data for analysis and processing, so as to detect and display human body temperature in real time. The current actual situation is fed back to the microcontroller through the LED display screen, and the corresponding parameters are adjusted according to the user's needs, so that it can work normally and meet the user's needs. Finally, the performance of the system is tested. The test results show that the main functions of each module of the health management intelligent management system based on RFID technology, the terminal acquisition module, need less than 5s in processing and collecting data, and can operate normally.

To solve the traditional water quality detection using ZigBee, GPRS, and other communication technologies having limited coverage distance, high power consumption, high cost, and complex deployment problems, according to the water quality testing real-time monitoring requirements, an NB-IoT-based water quality monitoring system platform is designed. The system uses the "end-tube-cloud" of the Internet of things mode, through the temperature, pH, dissolved oxygen, and other sensors to collect water quality parameters. M5310-A module will receive the sensor data uploaded to the OneNET cloud platform to achieve the remote transmission of water quality information and display functions, while the user can be in the application terminal real-time monitoring and directly read the water quality parameters of the measured waters. The system effectively improves the efficiency and modernization of water quality monitoring work, with good application prospects and economic benefits.

The intelligent A/D module is based on the CPCI bus. It mainly realizes the data acquisition and processing of the two-way resolver or auto aligner signals, and is used to collect the azimuth angle and pitch angle of the photoelectric instrument. The resolution is up to 20 bits and the precision is up to 5 for the highest, it also has two-way RS422 interface of gyro angle signal data acquisition and processing capability. The module completes data interaction with the host computer and other modules through dual-port RAM and PCI bus. The module needs to work in an environment where the system provides +5V, and can run stably under operating systems such as Vxworks and Windows XP.

With the Internet of Things and big data advancing so quickly, spectrum resources are increasingly in short supply. For unauthorized users, to achieve reliable and stable wireless communication, they must rely on spectrum sensing technology. Traditional spectrum sensing methods have poor performance in situations with a low signal-to-noise ratio, but deep learning's evolution can effectively solve this problem. However, with the improvement of perception accuracy in practical application, the neural network now has many layers and has many large model parameters., which makes the actual deployment of the algorithm require high computing power equipment. To address the issues with conventional spectrum sensing models' excessive resource consumption, poor real time performance, and limited detection accuracy, a deep learning and cyclic spectrum-based approach for blind spectrum sensing is suggested. In this method, a lightweight sensing network based on MobileNet V2 is built, and the fast Fourier transformation accumulation method is used to extract the features of the signals, which are normalized and sent into the network for training. Experimental results show that this algorithm can sense many kinds of digital modulation signals, and the perceived performance is better than other algorithms.

The data information of the traditional traceability platform is stored in the central database, which is prone to data loss and tampering. However, the decentralization, non-tampering, transparency, and traceability of the blockchain technology can solve the traditional precise control and credible traceability that can guarantee the quality and safety of agricultural products, this paper applies blockchain technology to the traceability system of agricultural products and conducts an overall design to ensure the realization of quality traceability in terms of architecture.

For the ad hoc network topology is changeable: the network scale becomes larger, which leads to difficulty in network management and lack of efficiency; The diversity of network services (text, pictures, audio, video, etc.) leads to the dynamic occupation of channels by nodes, which easily affects the end-to-end delay and throughput of the network. This study proposes a dynamic distributed TDMA protocol using theoretical analysis and network simulation verification methods. The full connection matrix of nodes is used for distributed scheduling. The influence of TDMA slot allocation on service delay is analyzed in detail. The simulation results of end-to-end delay and throughput of the network are given in the simulation experiment. The simulation shows that this directional routing protocol has some improvements in reducing end-to-end delay, reducing average packet hops, improving channel space reuse rate, and increasing network capacity.

In the data era, encrypted storage of files is a basic requirement for data security. Because most computing devices and storage devices lack data protection functions or their own data protection ability is poor, the data stored in them are at risk of being stolen. To solve this problem, this paper proposes an authentication storage scheme based on embedded Internet of things devices, which uses the Trusted Cryptography Module (TCM), mutual authentication technology, asynchronous file transfer technology, and disk partition encryption technology to realize the security storage function of host-side files on the device-side storage device, and enhances the security through the three security mechanisms of mutual authentication technology, access control of encrypted partition and state inspection of the encrypted partition. The function modules of the system based on the hardware platform of the Internet of Things are implemented and evaluated in this paper. The average experiment time of two-way authentication is 3242.42 ms. The result shows that the system has strong security and a good transmission rate.

Low-resource machine translation usually uses data augmentation or constrained hidden space representations to improve translation quality, which ignores the knowledge representation divergence between different languages in latent space. We propose a latent space knowledge representation enhancement to improve the translation effect by reducing the divergence of knowledge representation between the source language and target language. Firstly, this method splits knowledge and feature representation from text representation in each language. Then, it promotes the knowledge representation of mutual learning between the two languages to reduce divergence. Next, the optimized knowledge representation and feature representation are re-combined to obtain enhanced text representation. Finally, the enhanced text representation is translated and reconstructed to reduce the differences between knowledge representations further. Through extensive experiments on public low-resource datasets 'English-German' and 'English-Turkish,' The method can achieve better performance on the test set. The results show that the method can effectively improve the ability of low-resource machine translation by reducing the divergence in knowledge representation between languages.

To save the energy and extend the lifetime of the network, Cluster Routing Algorithm based on Agglomerative Hierarchical Clustering (CRAAHC) is proposed in wireless sensor networks. WSN is divided into a certain quantity of ring areas based on the energy consumption model. Clustering process is optimized by constraining the distance between clusters and the diameter of clusters to improve the quality of clustering algorithm. Cluster head selection strategy is improved by considering the residual energy of the node and energy consumption in communication with other nodes in one cluster. Simulation results show that CRAAHC algorithm can achieve energy efficiency and routing reliability.

SPatiotemporal ENcoding (SPEN) has good robustness to inhomogeneous magnetic fields and chemical shifts. However, the eddy current and gradient delay in the acquisition process may cause a certain phase difference between the odd and even line data in the phase encoding dimension, resulting in severe ghost in the reconstructed images. This work proposes a deep learning model for correcting ghost in single-shot SPEN. Experimental results show that the proposed model can effectively correct ghost.

In this paper, a W-band transmitter with 33dBm output power is designed. The transmitter consists of GaAs-based MMIC, GaN-based MMIC and the power control module. Each module is internally composed of MMIC chip, W-band micro-strip probe, wave-guide cavity structure and power bias conversion circuit. the whole modules cascaded is calculated first; then the micro-strip probe and MMIC module are modeled and simulated; Finally, the test results showing that the actual insertion loss of the micro-strip probe with wave-guide conversion structure is about 0.7dB, and the measured results are consistent with the simulation, indicating a good design. The saturation output power of the transmitter is above 33dBm in 90-96GHz band.

High data rates can be achieved with large-scale MIMO systems by using hybrid beamforming (HBF), which also simplifies and lowers MIMO systems' costs, but designing hybrid precoders is an extremely challenging task that requires handling CSI feedback and solving demanding optimization problems. With the RSSI-based unsupervised deep learning approach introduced in this paper, hybrid beamforming is designed for large-scale MIMO systems. In addition, there are two approaches provided: one for designing the codebook for the simulated pre-encoder, and another for designing the synchronization signal (SS) for the initial access (IA), and different scenarios and realistic channel models are used to evaluate the system performance.

When the network has burst high concurrent access traffic, the traffic allocated to each node in the cluster will exceed the maximum processing capacity of the machine, which needs to be solved by applying flow control technology. For this reason, this paper studies the flow control technology under the microservice architecture, and proposes an adaptive dynamic service flow control strategy based on the srTCM algorithm, which can dynamically regulate and differentiate the tasks entering the service node according to the request type and the running state of the machine. Experiments show that the research results of this paper are better than the common current limiting schemes.

Aiming at the problem of increased response delay of microservice clusters caused by the exponential growth of the number of Internet users. A load balancing algorithm suitable for microservice clusters is proposed. By introducing dynamic weights to the least active number algorithm, when the load balancer receives a user request, the load balancer will select the server with the smallest active number to execute. If the active number of multiple servers is the same as the least active number at this time, use The CRITIC method recalculates the weights of memory size, memory usage, number of processor cores, processor usage, disk size, and disk usage indicators for servers with the same least active number, and combines each indicator to obtain the real-time performance quantification value of the server. Select the server with the best real-time performance quantification value for service. The experimental results show that, compared with the least active number algorithm before improvement, the proposed algorithm can reduce the overall response delay of the microservice cluster.

At present, the traditional Unmanned Aerial Vehicle (UAV) power line inspection project mainly uses UAV to carry out inspection on transmission lines, substations and distribution lines. However, the traditional way has some problems such as fixed power lines and single inspection target. And because of the independence of power transmission, transformation and distribution departments, the total demand for UAV and UAV controllers is large, and it is difficult to realize the integration of equipment, personnel and technology. In order to further improve the level of lean management, maintenance efficiency of the whole system of transmission, transformation and distribution, and realize scheduling UAV inspection tasks autonomously, this paper establishes a mathematical model based on the genetic algorithm, taking the UAV inspection route planning as the core problem, and taking the shortest total route as the objective function, and iteratively calculates the collected data through the genetic algorithm to obtain the optimal solution, which is finally concluded by analysis and conclusion. By using this model algorithm to plan the route of multi-professional UAV inspection, the total route of UAV inspection has been dramatically reduced, human resources have been reasonably allocated, and the power line inspection has become comprehensive and efficient. Regarding the emergency response speed of UAV inspection, this study selects the nearest nest response to make the nests interact with each other. The hangars that intersect within the coverage can be intelligently coordinated through the task priority to achieve the fastest and most appropriate automatic execution of UAV tasks and effectively improve emergency response efficiency.

The current conventional routing optimization methods for low-orbit constellation networks have a large impact on satellite energy consumption, for which a link-state-based routing optimization method for low-orbit constellation networks is proposed. By constructing a state-space excitation function based on node energy and delay requirements, and constraining the function. The analysis of the experimental results shows that the routing strategy can effectively reduce the satellite energy consumption under the proposed optimization method.

The demand forecasting of power supply is an indispensable part of China's energy production and consumption. The scale of power grid construction in China has been expanding and the load growth rate has accelerated. Accidents, equipment failures and personnel unemployment often occur in the power system. BP neural network has certain algorithm advantages. Therefore, in order to ensure the safety and demand of residents and buildings, it is necessary to study the emergency materials storage system of power grid based on BP neural network. This paper mainly uses experimental testing and quantitative analysis methods to study the advantages and methods of BP neural network algorithm in the construction and application of electric emergency materials storage system. The experimental data shows that the learning rate of 0.06 selected by BP neural network is the most appropriate in the construction of storage system.

Fog computing is a derivative of cloud computing, which has been widely used to solve the problems of communication latency, high latency of user devices computing and insufficient power of user devices. The cooperation between nodes of fog system and the scheduling of computing offloading will improve the efficiency of the fog systems, the above problems have also received wide attention. Most papers only consider common metrics such as latency and energy. In practice, however, user mobility, fog node coverage, and reliability all need to be taken into account, which will further enhance the design difficulties of fog systems. And energy harvesting technology gets more attention, it can not only increase battery life, but also enable users to perform longer work-learning operations. Therefore, in consideration of all the above problems, this paper puts forward the energy collection scheme for task offloading, and comprehensively considers the multi-layer equilibrium problem, namely, energy and latency balance, and then further balance the reliability of fog node selection. Based on the multi time slot task collection, an algorithm based on Lyapunov optimization for joint optimization of latency, energy and reliability (LOLER) is proposed. By considering the ultimate computing power of the system, we adjusted the end date of the offloading task, the quantity and size of the offloading task, etc, and the algorithm is going to get an approximate optimal solution. We prove the effectiveness of our algorithm through a large number of experiments.

In the traditional campus network construction, the computer equipment in the campus of colleges and universities are fixed in the way of layout, and the fixed positions of offices, libraries, classrooms, computer rooms, etc., form the campus management system network through the integrated wiring network. This method of fixed deployment of computers has limitations such as inconvenient movement of terminal equipment and fixed information points, which is extremely inconvenient for information maintenance in university information management such as teachers' mobile teaching data and students' mobile information collection. The project makes full use of wireless park network technology to more effectively improve the coordination and operation of offices, educational administration, logistics and other relevant departments, solve the wireless internet demands of all kinds of personnel, and meet the needs of mobile data management in campus network construction.

The distance piano teaching mode is an important part of the modern distance music education. Through the cooperation of network and streaming media, it can accurately restore the details of piano performance in real time. The connection between Piano and Piano realizes the connection among people. How to build a safe, efficient and convenient network link to realize remote piano sharing and communication is a hot topic in current remote piano teaching. IPSec VPN has become one of the widely used VPN technologies with its security features. DSVPN realizes dynamic collection through NHRP protocol and establishes dynamic VPN tunnel to avoid transmission network delay. At the same time, the mGRE technology reduces the workload of configuring VPN tunnels and makes network maintenance more intelligent, meeting the needs of Internet bandwidth services, transmission link security and other aspects. Based on this, DSVPN integrates IPSec VPN technology to realize reliable network transmission of remote piano teaching. Through the establishment of simulation experiments, the security and applicability of the scheme are tested and analyzed. The results show that DSVPN has strong scalability and convenient maintenance. The security features of IPSec VPN data encryption, identity authentication and integrity verification can achieve perfect integration.

The transportation of containers plays an important role in the transportation of cargos. The effective loading of containers can greatly reduce the transportation cost of logistics cargos. The purpose of packing problem is to get a reasonable packing scheme, so using heuristic algorithm to obtain approximate solution in limited time has become the first choice to solve this kind of problem. Container packing problem is a classic three-dimensional packing problem. In this paper, the mathematical model of the container problem is established with the maximum utilization of container space as the loading optimization objective. A combination heuristic algorithm is proposed, and the actual data of a company is modeled and solved through python programming, and a visual model is established to obtain a loading scheme with the maximum utilization rate of container space of 95.2%. It proves the effectiveness and feasibility of the algorithm in a certain sense, and verifies the effectiveness of the optimization algorithm in this paper.

SLAM (Simultaneous Localization and Mapping) refers to the robot in the unknown environment through the sensor to detect the environment, and combined with its own movement to gradually build a map of the environment, and in this process to determine its own posture information, that is, positioning and map construction at the same time, is a more complex coupling problem. With the development of artificial intelligence and computer technology, there are a rich variety of solutions to the problem of simultaneous positioning and mapping of mobile robots. Firstly, the basic classification and principle of the existing SLAM scheme are introduced, and the laser SLAM is used as an example to detail the basic working framework of the system. Then, the mainstream laser and visual SLAM algorithms are described, and the current application and future development trend of SLAM are analyzed.

With the increasing popularity of GPS modules, the development of intelligent transportation applications based on trajectory data is also accelerating, such as traffic forecasting, vehicle navigation, and travel time estimation. These urban applications all require a large number of highly sampled trajectories as a basis. However, due to energy constraints and environmental interference, a large number of trajectories are collected at low sampling rates. In order to solve the uncertainty problem brought by low sampling rate trajectories, many trajectory recovery algorithms have been proposed. Currently, many existing works are based on sequence-to-sequence networks for trajectory recovery, that is, encoders are used to encode trajectories, and decoders are used to recover missing trajectory points. However, these works do not consider road network information and only use grid numbers to recover trajectories. Therefore, we propose a BERTbased multi-task learning framework, namely BTTrajRec, for trajectory recovery. BTTrajRec incorporates road information when encoding trajectories, and captures the bidirectional relationship between trajectory points, which can better characterize trajectory data. In addition, a constraint mask matrix is used to improve the recovery accuracy. We used multiple taxi data sets in the real world to verify the validity of the proposed method.

A field programmable gate array (FPGA) based universal modulating-demodulating module has been designed in order to fulfil the requirement of wireless data transmission radio with multimode and good anti-interference capability using in the maritime combat scene. This module is designed using the thought of software defined ratio and digital modulating-demodulating technology. It has realized the function of multimode and variable rate. Meanwhile, the direct sequence spread spectrum technique is utilized in this design to improve the anti-jamming ability and confidentiality. Software simulation is done for the module using differential quadrature reference phase shift keying (DQPSK) modulation mode with 64 bit direct sequence spread spectrum technique. The Signal Tap Logic Analyzer is used for online verification. The performance of the designed module is good, which could fulfil the communication requirements of shipborne data transmission radio working in the complex marine environment.

With the rapid development of software industry, the scale of software systems is increasing, and the complexity of software writing is also increasing. The software development progress is not easy to track, the development cost is not easy to estimate, and the development quality is not easy to supervise. This paper studies the basic idea and framework of software quality management system CMMI, and the role of CMMI and software metrics in software quality management. On the basis of previous studies, the problems of the company's quality management are analyzed and explored. Combined with CMMI and software metrics, this paper puts forward suggestions to further improve the quality of software products of the company, and puts forward implementable methods applicable to product quality management of software companies.

Ground-based space target pulse measurement radar searches and captures medium-and high-orbit space targets, and there are some problems in target radial range measurement, such as range ambiguity and low echo signal-to-noise ratio. In order to solve the above problems, a coherent accumulation range deblurring method based on pseudo code pulse phase modulation is proposed. This method makes full use of the good autocorrelation of m-sequence. Firstly, the transmitted pulse is modulated by m-sequence pseudo-code, and then the collected echo signal is compensated by pseudo-code phase modulation and space target motion compensation. When the phase compensation reference signal constructed by m-sequence is completely matched with the received phase modulated echo signal of m-sequence, the space target motion compensation and coherent accumulation are carried out to realize the convergence of signal energy. In order to determine the range ambiguity, all possible ambiguities are processed by coherent accumulation of sliding window. By comparing the accumulated signal energy in the accumulated sliding window, the cumulative sliding window movement number corresponding to the maximum energy is obtained, and the target range ambiguity can be solved.

With the rapid increase in Internet of Things (IoT) devices, the frequency of attacking on them has also risen. and access the network with virus, which bring a great burden to the network. As many devices lack robust security measures and access the network with virus, they bring a great burden to the network. This paper introduces a system for detecting IoT device firmware security issues using static analysis technology. This system is designed to automatically analyze the firmware of IoT devices and identify potential vulnerabilities by cross-referencing known Common Vulnerabilities & Exposures (CVE) and scanning the source code.

To address the problem that traditional network defense techniques are difficult to cope with scanning attacks launched by internal attackers, this paper proposes a Virtual Network Topology Deception Defense mechanism(VNTDD)based on the idea of deception defense. In order to solve the problem of lack of randomness and security of virtual network topology, we divide the virtual network topology into three categories: forwarding nodes, real nodes and virtual nodes, and analyze their deployment locations and numbers respectively to generate a random virtual network topology, and enables the deployment of virtual network topology on the underlying real network through traffic control mechanisms. Finally, through experimental analysis, the VNTDD deception defense mechanism proposed in this paper can effectively prolong the scanning process of the intranet by internal attackers.

The differential evolution algorithm is a random search algorithm. Aiming at the problems of premature convergence and slow optimization in differential evolution algorithm, a differential adaptive SA-DEPSO algorithm based on particle swarm optimization is proposed. First, the positioning problem is transformed into a function iteration optimization problem by using the least square method. Then the adaptive differential evolution strategy is fused on the basis of the particle swarm optimization algorithm. This algorithm can not only avoid the problem of premature convergence, but also improve the optimization speed and reduce the positioning error. Simulation analysis shows that when the number of iterations reaches 40, the algorithm in this paper reaches the optimal value and converges, saving the optimization time. Compared with DEPSO, SA-MCDE and literature 11, the average number of optimization runs is reduced by 75, 55 and 25 times; the average positioning error of the algorithm in this paper is reduced by 17.3%, 13.1% and 7.5% respectively.

CEP emulation provides the possibility of transmitting TDM services in packet-switched networks, where jitters in the transmission network causes the time of receiving a packet at the receiving end to vary constantly. In order to improve the performance of circuit emulation switching services, overcome the problems brought by packet networks for circuit emulation switching functions, and achieve a smooth transition of TDM signal transmission, this paper will study the principle and application of packet circuit emulation functions from the perspective of application in the network circuit emulation clock jitters caching technology, and propose a clock jitters caching scheme, whose main function is to absorb the packet reception jitters caused by network delay, and The main function of the clock jitters cache scheme is to absorb packet jitters caused by network delay, and to achieve chaotic reorder and packet loss compensation.

With advances in sequencing technology, long-read sequences in single molecular sequencing (SMS) technologies have more structural variation and sequencing errors than short-read sequences in next-generation sequencing (NGS). Most of the current gene compression schemes have been developed for short read sequences, and further research is needed to develop more suitable compressors for long read sequences. We propose a reference-free compression scheme for FASTQ file based on the construction of d-neighbourhood mapping graph, and obtain better experimental results.

When the robot grasps the objects in the stack, it fails because the objects block each other and cannot find the appropriate grasping point and grasping path. Humans usually separate objects from each other before grasping stacked objects, so how to let robots effectively separate objects is crucial to achieve stable grasping. At present, many traditional analysis methods have problems such as poor generalization effect and difficulty in establishing appropriate mathematical models. Therefore, a method based on deep reinforcement learning is proposed, which trains the robot end-to-end in the continuous action space of the robot, inputs the depth map of the scene, and outputs the robot's push action. At the same time, an algorithm for judging the separation degree of a pile of objects is proposed to judge the separation degree of objects before and after the push action. The experimental results show that our method can effectively separate a pile of objects.

Microgrid has strong flexibility, reliability and controllability. The application of microgrid in new power system can enhance the power system's ability of safe power supply, disaster resistance and fast recovery. This paper proposes a new type of grid optimization of power system based on meta-reinforcement learning. Firstly, the economic evaluation index of power grid is constructed, and the comprehensive evaluation index of survivability is proposed considering security and recovery. Considering the contradiction between economy and survivability in the construction of grid structure with microgrid, the structure of grid structure is optimized based on meta-reinforcement learning method. An example analysis shows that the proposed method can quickly obtain the optimal grid structure.

Traditional field gas station monitoring follows a pyramid control system scheme, and the system was divided into 4 layers: field, control, monitoring, and management. With the advance of technologies, two problems appear: 1). A variety of specialty controllers contribute to the control of a field gas station, each has its own HMI, the SCADA of the monitoring layer focuses on the integration of HMI functions, not on collection and analysis of big data. 2). To build an edge-cloud collaboration big data platform, data from all field gas stations are required, the traditional PLCs of the control layer and the SCADA system of the monitoring layer do not have enough computing power to collect and process data from all of the controllers. This paper proposed a software-defined PLC technology to integrate the control function of the control layer and the HMI function of the monitoring layer into an edge computing server with 5G support, that provide unified data acquisition, display, and data access functionalities on the premise, and support the required data collection for the cloud. This has been proven to be a consolidated and cost effective approach with improved customer values.

Some of the traditional power distribution network fault location, isolation, and service restoration (FLISR) tasks were conducted with feeder automation (FA), which is part of the distribution automation (DA) system. Existing FA scheme have issues such as long fault processing time, due to centralized decision making. In this paper, a FLISR scheme for distribution network based on edge computing and 5G is proposed. Three key technologies of the scheme are introduced: ① Edge computing servers with local terminal units (LTU); ② FLISR algorithms with software-defined control and edge-cloud collaboration; ③ 5G integrated with edge computing (MEC). The proposed scheme is the first case with integrated 5G and MEC in DA applications, which can effectively shorten the fault processing time as evidenced by the deployed system’s performance.

With the rapid development of new power systems and 5G communication technology, the demand for electric hybrid services becomes more complex and diversified. However, the existing 5G interface management and resource allocation methods are difficult to adapt to the application scenario of electric hybrid service. In this paper, the 5G uplink communication system adapted to electric hybrid service scenarios is modeled. 5G interface management and resource allocation algorithm based on reinforcement learning is adopted to maximize the uplink transmission rate of the overall network system. Simulation results show that the proposed algorithm has excellent performance compared to the existing algorithms.