To address the high rate of missed detections and low accuracy in traditional shipboard fire alarm systems, this paper proposes a ship compartment fire alarm system based on the fusion of multi-sensor data. The hardware components of this system include environmental data modules, wireless communication modules, display modules, and alarm system application software, enabling the acquisition of compartment environmental data, real-time display of parameters, and backdraft warning functions. In terms of algorithm, a combination of BP neural network and fuzzy reasoning is adopted to enhance system performance and address the issue of high false alarm rates in traditional ship fire alarm systems. Experimental results demonstrate that this algorithm can accurately identify the actual status of fires, predict fire scales and development trends, and exhibit high reliability, fully meeting the expected requirements of the system.
Microplastic (MP) pollution presents a significant challenge to environmental protection and requires rapid detection and classification methods. We utilize machine learning methods coupled with fluorescence spectroscopy detection to improve the accuracy of MP detection and classification. To comprehensively explore MP classification, Principal Component Analysis (PCA) and PCA-SVM methods are used to analyze 2400 spectral data samples of six types of MPs. Each MP category is divided into a training set comprising 200 spectra and a test set containing 200 spectra to ensure robust evaluation. The initial SVM model achieves 100% classification accuracy for the test set, the associated computational burden is significant, with a training time of 42.14 seconds and a prediction time of 8.23 seconds. To enhance efficiency, we integrate the PCA algorithm, which reduces feature dimensionality without compromising accuracy. The integration of PCA significantly reduces training time to 9.46 seconds and prediction time to 0.05 seconds while maintaining a 100% classification accuracy rate. These results highlight the efficacy of our methodology in efficiently classifying MPs. Combining machine learning and fluorescence spectroscopy, our research provides a promising solution to the pressing challenge of monitoring MP contamination.
Laser-induced fluorescence spectroscopy plays an important role in rapid detection and identification of oil spills at sea. The fluorescence spectra of different thicknesses of oil films of the same type of oil are very similar, which makes it difficult for the method to detect and recognize the thickness of oil films. In this paper, a laser-induced fluorescence detection method based on convolutional neural network algorithm is proposed to measure the thickness of oil films. Experimental studies of laser-induced fluorescence were conducted for different oil spill film thicknesses, and fluorescence spectral data were obtained for oil-free water and for 10 different oil film thicknesses. The recognition of 11 samples with different oil film thicknesses was realized by Matlab software using convolutional neural network algorithm. It was verified that the method can completely recognize different thicknesses of oil film with high accuracy and low training set error. Based on the convolutional neural network algorithm, the oil spill volume can be calculated by detecting the oil spill thickness, which has certain application value for the protection of marine environment.
The chlorophyll content in seawater seriously affects the inherent optical properties of seawater and the light transmission characteristics. This paper investigates the relationship between inherent optical properties and chlorophyll concentration in seawater applicable to class 1 seawater and part of class 2 seawater. The transmission of 405 nm violet and 532 nm green laser beams in seawater with a chlorophyll concentration range of 0-12 mg/m3 was simulated using the Monte Carlo method. The light field distribution, the on-axis laser energy, and the variation of the laser spot size with the transmission distance were obtained. The simulation results show that the transmission characteristics of 405 nm light are better for near-shore seawater with high chlorophyll concentration, while the transmission distance is longer for 532 nm light in the clearer deep-sea region. The study of the effect of chlorophyll content in seawater on light transmission characteristics can provide theoretical guidance for marine laser communication, lidar and laser underwater imaging, and other marine equipment.
A portable laser induced fluorescence (LIF) lidar is designed and developed for oil pollution monitoring. A 405nm violet light is used as excitation light source. A dual Amici prism spectroscopic structure to split the fluorescence signal in the spectral range of 450-1000nm, and an image intensifier combined with array CCD to achieve high sensitivity detection. The system integrates control, data acquisition and data processing modules, which can realize the real-time detection of the composition and content of pollutants. The fluorescence spectra of seawater, diesel oil, oil and crude oil were measured at 30m. The spectra of diesel oil, engine oil and crude oil with concentration at 0.05, 0.01 and 0.01mg/L are measured. The LIF oil pollution monitoring system can accurately distinguish oil spill pollutants such as type and grade. It has small volume, light weight, high integration, high detection sensitivity and good stability characteristics, which can be loaded in small aircraft, unmanned aerial vehicles, ships for oil spill remote sensing.
Visible light-based positioning (VLP) is envisioned to be a promising solution to indoor positioning, orientation, and navigation because of the widespread use of light-emitting diodes for illumination. At present, several VLP schemes have been proposed, with most researchers using the line-of-sight channel and ignore the multipath reflection. However, some research works have shown that multipath reflections degrade the performance of VLP systems greatly. We propose an optimization positioning method based on reflected light depolarization characteristics to subtract the multipath reflection signals from the received signals, which can eliminate or mitigate the impact of multipath reflection to improve positioning accuracy. Further, a model is established theoretically, and simulations are carried out in a 0.82 m × 0.82 m × 0.62 m area. The simulation results show that the average positioning error and maximal error are reduced by 94.9% and 85.1%, respectively, when using the method. In the experiments, the performances are assessed and match well with our simulation results, achieving an average positioning error of 4.2 cm and a maximal positioning error of 8.4 cm, corresponding to a decrease of 62.5% and 66.0%.
In this paper, a low-cost signal delay generator which can be used for lidar range gating is developed and implemented by MC100EP195 delay chip and STM32 microcomputer. LabVIEW is used to write the upper computer control software, and 4 delay chips cascade is adopted to realize 0-40ns delay range. By controlling the encoder and the upper computer, the delay accuracy of 10ps can be obtained. The inherent delay of the generator is 10ns, which can meet the requirements of lidar range gated detection and other high precision applications.
A portable laser induced fluorescence (LIF) lidar system (total weight about 5 kg) was developed for real-time remote sensing oil in aquatic environment. LIF lidar consists of 405nm semiconductor laser, receiving telescope, double Amici prism spectrometer and ICCD detecting system, which can detect fluorescence signal in the spectral range of 400-750 nm. In the laboratory, the fluorescence spectra of water samples with different oil concentration were investigated. The contents of oil were calculated using the ratio between fluorescence and intensity. The LIF lidar system has the advantages of compact configuration and low cost, which is promising for monitoring water quality rapidly. The portable system can be installed on small aircrafts, unmanned aerial vehicles, ships, and shore platform for remote monitoring aquatic environments.
We present an optical receiving system for LIF lidar using a direct view spectrometer based on holographic grating prism. The proposed receiving optical system consists of receiving telescope, slit, collimating lens, holographic grating prism, objective lens and ICCD camera. The receiving optical system based on this dispersion structure can not only reduces the optical distortion to offer a high optical efficiency, but also has a more compact structure which is very suitable for spectral dispersion of remote target. The system adopted an intensifier coupled a CCD to make up an ICCD camera. Based on real-time background subtraction algorithm, 60fps fluorescence spectrum can be obtained in real time. System validation experiment uses a semiconductor laser as excitation source to illuminate oil target to radiate fluorescence at a distance of 30 m. The fluorescent signal is received by the set up LIF lidar receiving optical system, and clear spectrum image is obtained. The designed in-line, direct view configuration holographic grating prism spectrometer owns the advantages of high light throughput, less optical distortions, compact structure, small volume and easy operation, which make a practical portable receiving optical system.
In this paper, we present a prism spectrometer that exploits a double Amici prism dispersion structure. The system consists of a slit, a collimating lens, a double Amici prism, an imaging lens and a CCD. The incident light enter into slit, and then is paralleled by a collimating lens to the double Amici prism. The double Amici prism is used to realize spectral dispersion. The dispersed light is collected by an imaging lens and image on the photosensitive surface of the CCD. The dispersion resolution is theoretical analyzed from the ray tracing point of view. In addition, the imaging position on CCD element at different wavelength is presented according to nonlinear curve of dispersion. The designed prism spectrometer can obtain a high light throughput and less optical distortion spectrum in the spectral range of 370-700nm. In experiment, we measured the spectral resolution of the designed prism spectrometer at five wavelength used a grating monochromator. The designed in-line, direct view configuration prism spectrometer owns the advantages of high light throughput, less optical distortions, compact structure, small volume and easy operation, which has important role in application of laser spectral measurement especially laser remote sensing spectral detection.
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