This PDF file contains the front matter associated with SPIE Proceedings Volume 12344 including the Title Page. Copyright information, Table of Contents, and Conference Committee Page.

Starting from the case of the electric wheelchair driving on bumpy roads, the mathematical model of the electric wheelchair in this driving state is established, and the vibration generated when the wheel contacts the ground is used as the external excitation to study the linear and nonlinear driving of the electric wheelchair. The relationship between the driving speed and the acceleration of gravity, the longitudinal displacement and the amplitude of the road in this state was analyzed, and the dynamic characteristics of the electric wheelchair in the process of driving on the bumpy road were analyzed. The study found that the vibration frequency and amplitude of electric wheelchair increase with the increase of the driving speed when driving on bumpy roads. Taking the driving speed of 3.3ms-1 and 3.7ms-1 as examples, it shows that the electric wheelchair drives on the bumpy road. The nonlinear vibration that occurs on the wheelchair is highly sensitive to parameters (driving speed), and changes in the driving speed can cause discontinuous vibration responses, leading to the tipping of the electric wheelchair.

Nowadays, Deep Brain Stimulation (DBS) has been used in the surgical treatment of various neurological diseases, such as Essential Tremor (ET). For severe cases, surgical treatment with DBS can suppress the disease better than drug therapy. However, existing DBS devices require the physician to manually set the parameters frequently to achieve the desired therapeutic effect. In this case, adaptive DBS devices become a possible solution for this problem. Adaptive DBS equipment still faces some problems, such as the choice of implementation method, data security and how to ensure that it will not cause serious damage to human body in clinical trials. In this paper, three kinds of adaptive DBS are analyzed, including their mechanism and core algorithms. This paper can provide reference for those who study DBS devices, those who study the application of machine learning algorithms to adaptive devices, and those who want to know about adaptive DBS devices.

Aimed the design of a smooth path quickly and accurately for the cable on the launch vehicle, when the locations and directions of the two endpoints are known, which is frequently encountered. The article realizes the design of the entire cable path based on the cubic spline curve and the Bezier curve respectively, and develops the automatic rapidly design system of the cable on the launch vehicle, which is common connection types for the cable on the launch vehicle. After a comparative study of the two methods, the conclusion is that the cubic Bezier curve is more general. At the end of the article, an example is given to verify the effectiveness of the proposed method.

The distribution of seats in armored vehicles was crowded and the ergonomic relationship affected the performance of military equipment directly. Therefore a method that divided each seat as an independent operation unit was proposed. After designing the principle prototype, JACK software was used to conduct ergonomic simulation analysis. An engineering prototype was set up on the basis of the optimized design. By the mean of questionnaire the ergonomic evaluation and verification was carried out. The scheme was applied to the vehicle assembly finally. This method could recognize and evaluate the man-machine relationship intuitively before assembling the vehicle, which reduced the change of design after assembling effectively. It would shorten the research-development cycle, and save the production cost.

The variety of daily household garbage is complex, which is not easy to recycle. On this basis, the methods of household micro garbage classification, recycling treatment and heat energy utilization of unrecyclable garbage are put forward. A kind of household micro garbage processor is designed, which mainly includes garbage sorting module, recyclable processing module and unrecyclable garbage processing module. And experiments on garbage classification recognition were conducted to obtain model training data for the limitations of existing garbage image datasets, using crawling techniques to collect image data from the web, expanding the garbage categories in the dataset and each category of garbage image dataset. To better extract the features of different categories of garbage images, migration learning techniques are used to train the garbage classification model. Since the multi-category problem is addressed in the article, the model evaluation is performed using confusion matrix. The classification efficiency and identification accuracy of micro garbage disposal meet the daily household requirements. The device not only reduces the burden of waste disposal and the environment, but also can make further use of the energy of unrecyclable waste.

The civil aircraft water and waste water system (WWS) provides potable water for passengers on board and disposes the waste water. System freezing will cause loss of system functions and the inability of flights to dispatch; Based on the normal temperature flight test data, this paper proposes the method of equal difference of recovery temperature to obtain the system heating protection area; The power calculation technology of typical component heater is studied by means of numerical calculation. The flight test results show that the research results of this paper can prevent the system from freezing at low temperature, and can guide the design of anti-freezing of the WWS for civil aircraft.

Aiming at the problems of external disturbance such as underwater ocean current and joint friction of master-slave manipulator in underwater teleoperation system, the bilateral control algorithm of master-slave manipulator was optimized under the hybrid control structure of force feedback and position feedback. In order to track the interaction force between the master manipulator and the slave manipulator, the grey predictive fuzzy control with error compensation is used to adjust the force feedback error, and it is mixed with the traditional PID control to achieve better control effect. In order to track the position of the master for the slave manipulator, a fuzzy adaptive system that based on the traditional sliding mode control, is used to adjust the switching gain. Estimate the upper bound of the external interference, and a robust term is designed to suppress the error estimation. The stability and convergence of the optimized system are proved by Lyapunov stability theory. Comparative simulation experiments on MATLAB/Simulink platform were carried out to verify the effectiveness of the optimization algorithm.

As a result of the comprehensive development of new energy, the indicators used to assess the level of new energy development in a region involve a variety of aspects. The relatively redundant and complex data indicators and various factors with different impact scopes make it difficult to give an intuitive overall quantitative level of new energy development in a region, and the non-uniform evaluation indicators make the results obtained from different provinces or regions no longer comparable. Based on the principal component analysis method, this paper establishes a set of general and unified new energy development level indicator evaluation system for all provinces from quantitative analysis to qualitative analysis, which can intuitively and quickly evaluate the indicators and overall development level of major provinces or regions in a comprehensive way, thereby providing methods and practical basis for analyzing the distribution and laws of new energy development across the country.

This paper studies the color cognition characteristics of the elderly on the mobile phone interface, and provides a reference for the color design of the mobile phone interface suitable for aging. First, through the analysis of the cognitive characteristics of the elderly and the elements of color design of mobile phone interface, the color cognition mechanism of mobile phone interface for the elderly is clarified. Then, combined with the method of Kansei Engineering, the perceptual cognitive characteristics of the theme color and component color of the elderly were obtained respectively. Finally, combined with a health management APP, its cognitive characteristics are explained. The research results show that among the theme colors, yellow is more popular among the elderly. In the color layout of components, the difference in saturation has a significant impact on the perceptual cognition of the elderly. The difference in saturation and beating layout can be used as the first choice for the color layout of components in an aging interface, which can better satisfy the elderly’s cognitive characteristics of aesthetics, coordination and openness. Combined with a health management APP interface design scheme suitable for aging, the paper explains its color cognition characteristics, and the research has a good guiding significance for design practice.

The existing mobile handheld instrument focuses more on detection technology research, ignoring the interface interaction principles. In this context, improving the interface reading efficiency of such handheld instruments is essential to improve the user experience. Based on several common charts, this paper conducts interactive analysis through eye movement experiments to find a visual chart that is easy to read. Take the element analyzer as the research object to conduct case studies. Firstly, four types of commonly used chart structures are screened by investigating the presentation methods of existing mobile device charts. Then, through eye movement experiments, extract the eye movement indicators of 4 types of charts to verify the legibility. Finally, combined with the instrument operation background, the most suitable chart scheme is selected and refined. This paper analyzes the legibility of the existing mobile handheld instrument's interface through eye movement experiments, and finds a visual chart that is easy to read in usage scenarios, which can improve the data reading efficiency of operators in the work process and the operability of the equipment.

Aiming at the problems of insufficient utilization of production line resources and high equipment waiting time in the process of connecting rod forging, two optimal layout schemes for series and parallel production lines are designed. The calculation shows that the workshop utilization rate of the parallel scheme is 74%, which is slightly better than that of the tandem production line, whose workshop utilization rate is 73%, and it is adopted as the optimized plan. Through the production line resource balance analysis method, the optimized plan is compared with the production line unbalanced loss rate of the original layout and the production line smoothness index. The balance loss rate was reduced from the original 31% to 13%; and the production line smoothness index was reduced from the original 7.19 to 2.37, those show that the optimization scheme was effective.

Because the traditional sensors in PMSM may result in abnormal operation of the entire system, which may affect its reliability, safety, and stability. In this paper, we present the technology for controlling a three-phase PMSM position sensorless system, based on research and analysis. The dq-shaft PMSM mathematical model is first constructed. The fundamental ideas behind SVPWM and vector control are explained. The technique of injecting pulsed high-frequency voltage is investigated, and the response current of PMSM in the high-frequency injection process is calculated. The pulse vibration high-frequency voltage injection-based PMSM sensorless control system block diagram is constructed, and simulation study is conducted. The experimental findings confirm the viability of the control technique in this study since the system has a quick reaction time, high anti-interference ability, and stable operation.

In recent decades, the problem of marine pollution has become increasingly serious, which has affected people's normal life. In addition to the increasing amount of floating garbage, the effect of oil pollution is also deteriorating. In order to deal with the oil pollution on the sea surface effectively and economically, this study designs an oil pollution collection device based on sea wave power. Based on the sea wave power, this device converts the energy of the sea wave to mechanical energy to drive the whole structure to move. Through innovative mechanical structure design, the adsorption collection, and storage of oil pollution on the sea surface are realized. The device structure based on sea wave power designed in this study not only has the advantages of green and energy saving, but also is more reliable, economical, and higher cleaning efficiency, which is conducive to further application and promotion in the actual marine environment.

To systematically quantify users' perceptual perceptions of nursing beds, analyze and summarize the general rules, and translate users' perceptual needs into design elements for new products to explore innovative design methods for multifunctional nursing beds. Using the semantic differential method in perceptual engineering, the user's perceptual imagery of the care bed is decomposed and quantified in three aspects: "feeling", "function" and "appearance", and a semantic space is established. A questionnaire survey was conducted to obtain the perceptual imagery scores for each sample, and the imagery scales were drawn according to the scores to summarize the trends in the design of multifunctional care beds. The design of the multifunctional nursing bed based on the theory of perceptual engineering is a good match for the user's needs, and it is also important to clarify the cognitive model of imagery for this type of product, which in turn can assist in the design of innovative solutions.

The injection molding process of mobile phone case is complex, including temperature, pressure, time and other technological variables, and each quality index is interdependent and influential, so the influence of each attribute must be considered comprehensively. For mobile phone case injection molding process, multivariate quality control and diagnosis driven by principal component model were carried out, the correlation of various process parameters was clarified, and the cause of runaway was pointed out, which provided scientific decision support for quality improvement. Finally, orthogonal experiment was used to optimize the process parameters, and the optimal parameter combination was determined to improve the product quality.

The light sensor chip generates different signals through different lighting, and is widely used in light sensors, digital cameras, etc. Therefore, it is very important to test the developed photosensitive chip. In the process of photoelectric detection, light signals of different intensities are faced, and the intensities of these light signals may vary greatly in magnitude over time. In this case, it requires our photoelectric detection system to have the characteristics of high sensitivity and fast response speed; the photoelectric detection system should also have high-precision gain changing ability, so that the optical signal acquisition can be better presented. Aiming at the above problems of photoelectric detection, this paper deeply studies the method of gain adjustment of photoelectric detection system, and designs and realizes the gain control system of photoelectric detection system based on FPGA. The experimental results show that the photoelectric detection gain control system can stably and accurately adjust the gain of the multi-channel photoelectric detection system, and can help the photoelectric detection system to detect plasma signals with different amplitudes and large magnitude gaps.

In view of the leakage of a metal corrugated hose in use, fault location and mechanism analysis are carried out, and improvement measures are proposed. According to the analysis, the main reason of the failure is that the influence of long-time vibration and transportation conditions are unconsidered, which leads to improper selection of corrugated hose. Due to the failure to design effective bending radius and binding point during the layout process, the metal corrugated hose is overstressed and finally fatigue cracked. By analyzing the production process and working condition of hose, the main failure factors are found out. The leakage failure problem of metal corrugated hose after long-time using is solved by optimizing the batch forming and installation state of metal corrugated hose.

Analog modulation is one of the hot topics in current research. Digital technology cannot completely replace analog technology, and analog modulation is the basic method of communication. Researchers have found and proved that the anti-noise capability of the receiver can measure the reliability of the system, but there is still a gap in the research on the anti-noise performance of different modulation modes. Based on MATLAB, this study analyzes the influence of different modulation methods on the anti-noise performance by using program design and analyzes the anti-noise performance of each modulation method by contrast analysis and control variables, and compares the performance of each modulation method. Through the experiment of this study, it is found that the modulation regime gain G of different amplitude modulation modes is different, so each mode has different anti-noise performance. These different modulation modes have advantages and disadvantages, so in practical application, it is necessary to choose the appropriate modulation mode according to the demand.

To explore the evaluation index to improve the usability of the mobile phone navigation interface. Method Guided by usability theory, the analysis was carried out by combining the research results of usability evaluation and the characteristics of travel scenes. Using the analysis hierarchical method to collect expert data and carry out analysis, as well as the calculation of index weights, the construction method of the usability evaluation index system of mobile phone navigation interface was proposed, and the consistency test of the index evaluation system was carried out. Results Among four Tier 1 indexes and eleven Tier 2 indexes, it is proved that the reliability of the navigation interface has the greatest impact on usability, followed by ease of use. Finally, the usability evaluation index of walking navigation interface with satisfactory consistency is established, and a certain theoretical basis is provided for the usability research of navigation interface.

In this paper, the most optimal motion control and planning for a two-dimensional flying machine will be explored. A quadrotor will be taken as an example of a two-dimensional flying machine for examination, and all aspects of forces acting on the quadrotor are taken into consideration within the control algorithm in order towards achieving stable and optimal flight. On-off Controls and Proportional Controls are implemented through python programming and are visually graphed to provide guidance on evaluation of functionality. Finally, future applications of proportional algorithms will be discussed.

According to the problems such as the absence of an accurate model of Camellia oleifera fresh fruit, inaccurate simulation process of decladding, and experience-based development of decladding equipment, the study combined the feature of shape structure and material characteristics of Camellia oleifera fruit, and is based on the bonded-particle model of discrete element method (DEM). The spherical equation was used to establish the contour of Camellia fresh fruit, and the crack line structure model of shell was generated in meta-particle method. The flexible model of seed was constructed by the particle accumulation method, and the particle coordinate control program was compiled to construct the flexible composite DEM model of Camellia oleifera fruit. The load-displacement curve of compression in the simulation test of Camellia oleifera fruit is basically consistent with that of the physical experiment, and the accuracy of the DEM model of Camellia oleifera fruit is verified. The research results provide theoretical guidance for revealing the mechanism of Camellia oleifera fresh fruit decladding and optimizing technical parameters of decladding equipment.

The lane line detection and recognition are crucial research area for automatic driving. It aims at solving the problem of fuzzy feature expression and low time-sensitives of lane line detection based on semantic segmentation. This paper proposes to remove irrelevant background by dynamic programming region of interest while improving the lightweight neural network (U-Net). A group-by-group convolution and depth wise separable convolution in the backbone network are introduced, simplifies the branches of the backbone network, and atrous convolution is introduced into the enhanced path network with multi-level skip connection structure to retain the underlying coarse-grained semantic feature information. The full-scale skip connection fusion mechanism of the decoder is preserved, while capturing the fine-grained semantics and coarse-grained semantics of the feature map at full scale. The introduction of skip connections between the decoder and the encoder can enhance the lanes without increasing the size of the receptive field. The ability to extract line features and the ability to extract context improves the accuracy of lane lines. The experimental results show that the improved neural network can obtain good detection performance in complex lane lines, and effectively improve the accuracy and time-sensitives of lane lines.

Aiming at the requirements of the accuracy of human action intention recognition during the active training of lower limb rehabilitation training robot. Firstly, the mathematics model of surface EMG generation process was established and the motion perception principle with surface EMG characteristic frequency and variance was put forward. Then, the surface EMG signals from four muscles were sampled and the feature vectors were extracted. Finally, the least squares support vector machine mothed was used to establish the mapping model between feature vectors and three motions. The experimental results show that the average correct rate may reach 99%, which is 7.7% higher than the method using wavelet coefficients. It is believed that the method proposed is an efficient method.

In order to study the influence of flexible components on the motion characteristics of a series 6-DOF intelligent cooperative robot, the rigid joints and links of the robot are flexibly replaced. A multi flexible body dynamic model reflecting the mechanical characteristics of the robot is established by using the Lagrange equation method. Based on ADAMS and ANSYS software, virtual prototype technology is used to realize the simulation analysis of robot dynamics. The change rules of position, velocity and acceleration of each joint under different working conditions were obtained, as well as the time history of driving force and driving torque of joint 1 and joint 2 with large force. The coupling characteristics of the mechanism under different motion states are analyzed and compared with the fully rigid series robot. The results show that the motion trends of the end-effectors of the two models are consistent, but the flexibility of the components has a great influence on the positioning accuracy of the end-effector. The joint and connecting rod with large stress were obtained by multi-flexible body dynamic simulation, which provides a reference for the structural strength improvement and lightweight design of the working device. Therefore, the establishment of a correct rigid-flexible coupling dynamics model has a certain guiding significance.

The stooping and handling exoskeleton robot integrating multi-field technologies such as mechatronics, human-computer interaction, gait analysis and intelligence is an important development direction in the field of robot research and development in recent years. The boosting efficiency is the most critical evaluation index in the overall performance of the exoskeleton robot. In this paper, by analyzing the changes of the human lumbar spine force in the state of normal bending and carrying under the state of natural motion cycle, the state of wearing the exoskeleton without assistance, and the state of wearing exoskeleton assistance, this paper establishes a bending and carrying model, completes the force analysis, and deduces the power-assisting efficiency. Compare the calculation formula, and design a wearable stooping exoskeleton robot based on this, and conduct EMG experiment data in three states to collect the changes of the user's biomechanical signals. The percentage of EMG signal amplitude of the erector spinae muscles of the waist in the state decreased by 39.3%, verifying its beneficial effect on users.

According to the problems of low maintenance efficiency and high cost for regular maintenance and after-the-fact maintenance methods of the automatic magazine transport system of a naval gun, an expert system for fault diagnosis based on fault tree is proposed to improve the success rate of the system operation, extend the service life, and reduce maintenance difficulty and cost. According to the characteristics of the magazine system to build the fault tree model, combined with rules to build the diagnostic rule base of expert system, using mixed reasoning strategy to build the inference engine, and the human-machine interface for fault diagnosis is designed and built. Finally, the diagnosis effects are analyzed by examples. The results show that this fault diagnosis expert system has a good diagnostic effect and applicability. This kind of method has a certain versatility and provides reference for fault diagnosis of other systems

As an important role in the digital exhibition hall, an interpretive robot could perform guidance, interaction, reception, and other functions. It can replace repetitive explanation tasks, improve the digitalization level of the exhibition hall, and save labor costs and management costs. However, multimedia playback, automatic door, and other electrical equipment in the exhibition hall still need manual control, and real-time data-related questions still need to be answered by the narrator. To solve these problems, the multi-system-interaction robot is designed and optimized. The robot interacts with the central control system of the exhibition hall through a variety of communication methods and controls the operation of the equipment independently. At the same time, the robot can independently obtain real-time data from the server and answer real-time data-related questions. The test results show that the designed robot system can significantly reduce the workload of the interpreter in the process of explanation, and realize the whole process of unmanned operation with multi-system interaction.

Single Shot Multibox Detector (SSD) uses multi-scale feature maps to detect and recognize objects, which greatly improves the performance of single-stage approaches, but it is still not conducive to detecting small objects. Researchers focus on enhancing features on the feature pyramid. However, many networks simply merge several feature maps, ignoring fully aggregating the semantics among different scale features. On this basis, this paper proposes an efficient semantic aggregation module and a lightweight feature combination module, which can significantly improve the detection accuracy based on SSD. In the semantic aggregation module, the feature maps of different sizes are adjusted and integrated through different channels to obtain the enhanced features with rich semantic information, which can improve the discrimination and expression ability of the features. In the feature combination module, the detector can fully utilize the multi-scale convolution layers in the feature pyramid to produce more descriptive and representative enhanced features with rich semantics. Our proposed network with 512×512 input size can respectively achieve 82.6% mAP and 81.3% mAP (mean Average Precision) in VOC2007 test and VOC2012 test datasets. Some experiments and ablation studies show that this method is superior to many advanced detectors in accuracy and speed.

For most of people, the burden caused by COVID-19 is not only physical, but also spiritual. Several research studies have shown that the negative emotions caused by COVID-19 have increased significantly worldwide, but there are not effective emotional interventions for people in most countries. Therefore, this negative emotional trend is likely to expand further and cause more adverse effects, such as pathological depression. By applying python to crawl the posts of depressed patients on Chinese social media platforms, and using natural language processing to quantitatively analyze the words with high frequency, this paper summarizes the text characteristics and depression degrees of depressives' posts on Chinese social media platforms, so as to present a preliminary psychological intervention mode. Then, the paper proposes a more targeted psychological intervention mode for depressives with different characteristics based on the paradigm of human-computer interaction and the results of the questionnaire. The study finds that the patients with sufficient self-regulation abilities and the patients without self-regulation abilities do not meet the high-frequency word extraction results, while patients with insufficient self-regulation abilities do. Additionally, for these three types of patients, different psychological intervention modes can be established based on different interaction paradigms.

The new crown epidemic has made jumping rope exercise popular. However, during the jumping rope, the movements are repetitive and easily make people bored, which leads to the decline of exercise performance, and fitness people cannot insist on jumping rope exercise for a long time. This study combines the human-computer interaction theory and the goal feedback system to construct a quantitative feedback interaction system for rope skipping, aiming to improve the user experience and exercise performance during the jumping rope. The study concluded that the quantitative feedback system has a facilitating effect on jumping rope exercise performance through a completely randomized single-factor experimental design, which verifies the effectiveness of the interaction system. This is valuable for fitness people in the post-epidemic era. At the same time, it also provides a theoretical basis for the design of a multi-modal motor interaction feedback system for jumping rope exercise in the future.

The most practical methods to allow manipulators to work together in the same workspace and collaborate with humans on the same tasks are brain-computer interfaces (BCIs) and augmented reality (AR). This paper provides a theoretical introduction to AR and BCI technologies in human-robot collaboration (HRC), fully demonstrating the practicability of AR-HRC and BCI-HRC solutions in manufacturing workspaces, including key advantages and future interests. Results show that these solutions have great potential to improve the current manufacture situation and impact the future research of HRC.

Vision is the most important sensory organ of human and visual impairment might significantly reduce the quality of human life. There are several methods that can be used to restore human visual system such as surgery, artificial retina and so on. Among which, Brain Computer Interface (BCI) based visual recovery strategies have drew researches attentions for its outstanding advantages and it is now the most promising rehabilitation method for the blind. To clarify the main findings and trends of the BCI based visual rehabilitation methods, this article introduced the theoretical basis of the human visual system firstly and then analyzed the most prevalent visual recovery strategies based on BCI, including the subretinal implantation and epiretinal implantation, which may provide theoretical and practical reference for further development of artificial retina in the future.

Machine learning has played a very important role these days. A very common area related to daily life is auto chatbots, such as Apple Siri, Google Home, Mi Xiaoai, and others. These technologies all use deep learning as the foundation to achieve the goal of communicating with humans. Engineers and computer scientists put a massive amount of effort into trying to improve the quality of chatbots by improving the models that drive this feature. From previous research, KNN (K-nearest neighbors) and DNN (Deep Neural Network) are two widely used models in the machine learning area. To find out which is more efficient to manage auto-chat while understanding deeper how chatbots actually manage to recognize human language and quickly come up with corresponding answers, the two learning models were applied to a self-made chatbot. By comparing the effectiveness of applying K-nearest neighbors and Deep Neural Network, the paper finds that KNN runs faster and takes less space since it is a comparatively simpler algorithm. In terms of correctness, in this experiment, both algorithms turned out to have a similar percentage of correctness. This might be caused by a comparatively small dataset.

This study proposes deep learning called Convolutional Neural Network (CNN), which can enhance the classification performance of five different rehabilitation exercise scenarios based on brain-machine interaction (BMI). Also, it further presents the feasibility of helping stroke patients and healthy people to assist in rehabilitation exercise. This work designs a wearable hand robot and five different motor images (MI) for exercise guidance on the computer screen. A participant is also asked to set on a chair to acquire the cerebral response signals using the function near-infrared spectroscopy (fNIRS). Deep learning called convolutional neural network (CNN) is utilized to extract and classify the collected data and make commands to the wearable hand robot. The classification accuracy of the S_2 MI is the highest value for participant 1; the classification accuracy of the S_1 MI is the highest value for participant 2. Besides, the S_5 and S_3 MI showed the lowest classification accuracy for participant one and participant two, respectively. Propose a CNN framework with visual guidance to control wearable robots to reduce incorrect commands, and present a rehabilitation exercise feasibility to stroke patients and healthy people with less time.

This paper mainly studies the application of interactive concept in food packaging design. From the perspective of participation, fun and technology of food packaging, the case analysis method is used to explain how to perfectly combine the interactive design concept with food packaging design. Finally, it is found that the application of interactive concept in food packaging can not only innovate modern food packaging design, but also bring people a unique visual image and appropriate humanistic care.

With the rapid development of robotics, intelligent obstacle avoidance robots are receiving more and more attention. They are heavily used in all aspects of production and development. The framework we chose to develop this time is based on an open-source platform called Robot Operating System (ROS), which acts as a digital twin in a modular configuration. There are now a large number of robots that choose to complete their architecture under the Robot Operating System (ROS), such as disaster relief robots, somatosensory robots, delivery robots, and more. In this article, we will introduce the ROS architecture of our project. This architecture enables robots to map and navigate autonomously in different environments with different sensors. This article will analyze the architecture we have made and explain the choice to make such an architecture. At the same time, we will also analyze our performance and point out which parts can be improved.

The equipment structure and operation status of spinning machine were analyzed, and an interactive virtual system for spinning machines was developed. The 3D model required by the system was established by UG software, and the 3D model was optimized by 3DSMAX software. In this paper, Unity3D was chosen as the development environment of the system. According to the system functional requirements, the interactive functions of the system were developed through the C# language. The interactive function development based on gesture operation was realized by using the Kinect device. The movement of the model was controlled by recognizing the user's gesture action, the assembly process of the longitudinal drive device of the spinning machine was completed. Finally, a virtual assembly system including motion simulation function, interactive perspective function and virtual assembly function was developed. It has been verified by an example that the system has good interactivity and immersion, and effectively improved the assembly quality and design efficiency of products.

In order to improve health awareness and consciousness of tobacco control, the introduction of cloud service platform system design ideas, put forward the smoke-free university construction management system based on cloud services research ideas. Through the construction of three layers of cloud service construction and management mode of application layer, platform layer and facility layer, relying on SpringBoot, MyBatis, LFW, OpenCV and other technologies of server and VUE 2.x and other technologies of WEB side, the effective identification and data feedback of smoking behavior of teachers and students are realized, so as to realize the construction and management of smoke-free universities under cloud service. After testing, it is found that the designed system can realize the identification and application of synchronization segment, has practical application technology value, and provides cloud power for the construction of smoke-free campus universities.

With the rapid development of China's economy, the real estate industry has become an important topic of livelihood issues in China in recent years. This paper mainly takes the second-hand house price in Beijing as the data source, analyzes the status quo of the second-hand house price in each district, and studies the housing price prediction problem according to the properties of the second-hand house in the region, floor, year, layout and so on. In this paper, the housing price is predicted by linear regression model and decision tree regression model respectively, and the prediction results are scored by R2_Scroe scoring method, and the results are 0.66 and 0.79 respectively. Experimental results show that the prediction effect of decision tree regression model is better than that of linear regression model.

Digital filter is an important technique for human speech filter. Different digital filters produce different filtering effects. In this paper, by using MATLAB programming tools to design a variety of FIR digital filters and IIR digital filters, by observing the effect of the voice signal in different filters after adding noise. It is concluded that IIR filter has less computation and infinite impulse response to the human speech signal, and it has good stability and is clearer than FIR digital filter after many auditions. Under the same technical index, IIR filter can meet some requirements of the index with fewer orders than the FIR filter because of the feedback of output to input. After understanding the advantages and disadvantages of different filters, it is necessary to use a more appropriate filter for the variety of human speech.

Population aging has become an important trend in the development of the global population structure, and high-quality elderly care has become a new problem faced by all human beings. Walkers can bring many conveniences to the lives of the elderly and provide a lot of support for the development of social elderly care. At present, the walkers on the market cannot help the elderly solve travel problems very well. Most of them ignore the psychological feelings of elderly users, and cannot meet the growing multi-dimensional and high-quality elderly life needs of the elderly. To this end, we designed an all-round intelligent follow-up walker; firstly, we obtained the basic data of user needs through user interviews and questionnaires. Design practice of azimuth-oriented intelligent follow-up walker, and finally verified the practicability of the walker through the physical model test. The all-round intelligent follow-up walker can meet the needs of users for daily community travel, walking assistance and storage. It has a high degree of integration of existing technologies and has a wide range of application prospects. The design of the runner provides a research and development idea.

With the development of society, the power system is becoming larger and larger, which makes the analysis and processing of power data more and more complex. It is necessary to introduce modern data technology to make the power system develop rapidly in the new era and better serve people's production and life. From the perspective of cloud computing technology, this paper analyzes the smart grid big data processing in detail.

One of the important research directions in the field of target detection in computer vision, among which deep learning-based target detection can extract advanced features and has higher detection accuracy than traditional detection algorithms. The inference speed of convolutional neural networks in embedded platforms is low, and the practical application value is low. Therefore, for the background of optoelectronic device detection and camera detection, on the embedded platform NVIDIA Jeston Nano, the ResNet18 convolutional neural network is used to identify the photoelectric target. Use TensorRT to accelerate the process of network model simplification and engine construction, and accelerate the network inference time. Experimental results show that when the input image resolution is 640*480, the inference time of tensorRT technology after running the network on the NVIDIA Jeston Nano device is in the range of 0.04-0.06s, and the single-area photoelectric target detection inference is accelerated by 2.38 times and the multi-area photoelectric target detection inference is accelerated by 2.74 times, which provides support for practical applications.

A multi-objective optimal design scheme for tolerance combination based on improved NSGA-II algorithm is established to reduce the production cost, standardize the tolerance design and improve the qualification rate of the rear cover of a product. The production cost function and quality loss function are used as the objective function, and the genetic algorithm with elite strategy and improved genetic variation factor is used to optimize the simulation of the assembly tolerance combination and obtain the optimal Pareto solution set. The results of the tolerance combination show that the optimized assembly tolerance combination is used to reduce both production cost and quality loss cost.

In today's information age, digital signal processing technology with a new form of technology has been applied to various fields of society, and with its own significant advantages, in each field has played a powerful role. In order to ensure the air quality of living and working environment, the primary task is to analyze the main pollutants in the air. In addition to traditional chemical monitoring methods, digital signal processing methods are more common to detect pollutants, but filtering technology is needed to overcome the noise interference in the detection process. In this paper, the digital filter technology in digital signal processing is described in detail, and the model of digital signal in gas detection principle is simply abstracted, and the main application of digital signal processing technology in gas detection is described at last. At the same time, the paper analyzes the application of digital signal processing technology in gas detection, hoping to provide some help to the application of this technology and the good detection and treatment of gas pollution. Finally, a signal processing device for differential gas detection and its gas detection system are introduced.

In order to solve the problem of multi-UAV coordinated track planning in complex battlefield environment, this paper proposes a track planning method based on Multi-Strategy Improvement Symbiotic Organisms Search (MSISOS). Firstly, UAV track planning model is established. Then, the adaptive strategy is adopted in mutualism and commensalism phase, to balance the algorithm’s development and exploration, and the introduction of normal disturbance strategy in parasitism phase effectively avoids precocity. Finally, a distributed multi-UAV collaborative trajectory planning method is designed, which uses MSISOS algorithm to solve track planning problem, and harmonizes time and space constraints through the multi-UAV information interaction layer. The simulation results show that MSISOS algorithm compared with MSASOS, PSO and DE algorithms has the best accuracy and convergence speed, and solves complex multi-dimensional multi-UAV coordinated track planning issues.

With the development of digital economy, computers are playing an increasingly important role in our life. Computers are also increasingly mature in image processing technology, and widely used in the electronic interface, to provide good technical support for visual design. This paper mainly analyzes the advantages of computer image processing technology in mobile phone interface design, and compares the experiment of computer image processing technology and traditional design means, which shows that the application of computer image processing technology can make the interface design more efficient and refined.

Aiming at the low efficiency of trajectory planning and unstable operation of sorting robot, a particle swarm optimization (PSO) is proposed, which can dynamically adjust the learning factor. In this method, the trajectory of the sorting robot is fitted by piecewise polynomial interpolation, and the trajectory of the sorting robot is optimized by using the improved PSO as the fitness function of time. The piecewise polynomial interpolation function is effectively combined with PSO, and the complex process of the traditional PSO adaptation function is avoided. The problems that it is easy to fall into local extreme value in the early stage and slow convergence rate in the later stage are improved. Experiments show that this method can optimize the joint motion posture, speed and acceleration trajectory of the sorting robot, and effectively improve the operating efficiency and stability of the sorting robot.

In modern times, people usually use channel coding technology to ensure the effectiveness and reliability of information transmission. As a better coding method, convolutional coding is widely used in wireless communication standards such as GSM, cdma20000 and IS-95. This paper introduces the coding principle of convolutional code, uses Viterbi decoding, and verifies the performance of convolutional code through MATLAB simulation. Simulations have found that when the bitrate is 1/2, the performance of the convolutional encoding increases with the length of the constraint. And in the decoding phase, the soft judgment Viterbi algorithm due to the hard judgment.

The classical PID controller, which serves for controlling the revolutions per minute of brushless direct current motor (BLDCM), has limitations of long settle time, slow response speed and violent fluctuation. To remedy this matter occurred above, by virtue of the whale optimization algorithm WOA and the fuzzy neural network PID controller modeled on the elementary structure of BLDCM, a modified approach to adjust revolutions per minute is raised in our paper. At the outset, under the action of the nonlinear approximation of fuzzy neural network, the uncertain coefficients of PID controller are timely altered. Then, considering that the initial values of fuzzy neural network are stochastic, the WOA method is used to prepare the parameters for neural network and it is further refined via the Lévy flight perturbation method. Eventually, there are several simulations to test this controller, and results demonstrate that the enhanced controller put forward by us is able to have good effects on the properties of system accuracy, response speed and anti-disturbance capability.

In signal processing, interpolation, as one of the fundamental operations, is a significant step when up sampling images. Traditional linear interpolation methods include the nearest neighbor method, the bilinear method, and the bicubic method. Non-linear interpolation methods include wavelets method and methods based on edge detection. This paper compares the major three linear interpolation methods and two non-linear interpolation methods in details. By utilizing both the python code, the Canny method is found to provide the best performance when up sampling images.

This paper studies an identity recognition and verification system based on the DTW voiceprint recognition algorithm. The system hardware is composed of a TC264 single-chip control chip, and the MCP6022 microphone module is used as a sensor. Through AD conversion, the input voice is converted into an arithmetic matrix, and the MEL frequency is used. Cepstral coefficient and MFCC algorithm perform spectrum analysis on the voiceprints of different people, upload them to the data cloud to form a voiceprint database, and finally complete the synchronous transmission of the data through communication with the mobile phone software platform, and complete the identification and verification of the identity on the mobile phone.

In view of the strong subjectivity of traditional salt dome recognition methods and the poor effect of existing deep learning algorithms on salt dome edge recognition, this paper proposes a salt dome recognition algorithm based on reverse attention mechanism, which uses u-net model as the backbone network, adds reverse attention module at the jump connection to extract edge structure information, and finally uses feature splicing to fuse feature information to improve the segmentation performance of network model. Experimental results show that the network achieves good results in salt dome segmentation, and effectively improves the problem of unclear edge segmentation.

In order to solve the shortcomings of the spindle thermal error prediction model established by ELMAN neural network, such as low precision, slow convergence speed and easy to fall into the local optimal solution, k-means ++ algorithm and correlation analysis were used to optimize temperature measurement points and extract thermal sensitive points. Whale Optimization Algorithm (WOA) was used to determine the optimal node number, weight and threshold of hidden layer. The thermal error prediction models of spindle based on ELMAN and WOA-ELMAN networks were established respectively. The thermal error of spindle was measured by five-point method with a five-axis machining center of double turntable as the research object. The thermal error experiment results show that the k-means++ algorithm combined with Person, Sperman and Kendall correlation analysis can effectively reduce the multicollinearity between temperature variables. WOA-ELMAN model can predict spindle thermal error with higher prediction accuracy.

Knowledge base based question and answer method has achieved remarkable success by deep learning. However, the method research process remains challenging due to the difficulty of capturing the global information of the question, the inconsistency of the outputs in training and prediction, and the low recognition accuracy of the current topic entity detection. Here, we propose an improved model TRBAM based on BAMnet to efficiently and effectively solve above problems. TRBAM is improved by Transformer and R-dropout. In the problem feature extraction layer, we use BiLSTM and Transformer to extract features from the problem respectively, and fuses the two extracted problem features to obtain a new problem representation, so that the model can more fully capture semantic information in the problem; especially, we improve the generalizability by R-dropout. We improve the subject entity detection model entnet by R-dropout to improve the accuracy in recognizing the best subject entities. The experimental results show that the improved model TRBAM has some performance improvement compared with the BAMnet model, the improved entnet model has effectively improved the recognition accuracy of the best subject entities, and the overall method of this paper has some advantages compared with other methods.

The rapid economic growth has promoted the development of intelligent technology, which has been widely used in various fields, including education, and many intelligent teaching systems with good performance and sound functions have been developed. Through the application of this system, not only the teaching effect has been improved, but also the students have more knowledge, and the workload of teachers has been reduced, so that teachers have more time and energy to prepare for teaching. Therefore, this paper takes Japanese teaching as the research object and web technology as the core, and designs an intelligent Japanese teaching system to provide support for further improving Japanese teaching effect.