Photoelectric Detection Technology Research Articles

Electronic and optical properties of CsSnI3/MoS2 heterostructure as ultrabroadband photoelectric device

Infrared photoelectric detection and imaging technology has a wide range of applications in satellite remote sensing, safety monitoring, biomimetic vision, autonomous driving and other fields. Due to CsSnI3 having a lower bandgap, it has great application potential in the field of infrared and near-infrared photoelectric detection. The CsSnI3/MoS2 heterostructure is first designed to achieve absorption from mid infrared to ultraviolet. In this study, we systematically investigated the structural, electrical, and optical properties of CsSnI3/MoS2 heterostructure using Density Functional Theory (DFT). All of these heterostructures exhibit type-II band alignment. γ-CsSnI3/MoS2 shows more efficient charge transfers than α-CsSnI3/MoS2 and the γ-(110)-CsI/MoS2 heterostructures transfer the most electrons and have the biggest band offset, indicating a higher open circuit voltage and lower dark current. The γ-(110)-CsI/MoS2 heterostructures is stable and have the best enhancement effect compared to other heterostructures and enhances the absorption of the latter in ultraviolet to mid-infrared regions. This demonstrates that heterostructures composed of the specified termination surface and the two-dimensional material may achieve different functions, providing a guideline for experimentation to produce high-performance optoelectronic devices.

Fluorescent Photoelectric Detection of Peroxide Explosives Based on a Time Series Similarity Measurement Method.

Due to the characteristics of peroxide explosives, which are difficult to detect via conventional detection methods and have high explosive power, a fluorescent photoelectric detection system based on fluorescence detection technology was designed in this study to achieve the high-sensitivity detection of trace peroxide explosives in practical applications. Through actual measurement experiments and numerical simulation methods, the derivative dynamic time warping (DDTW) algorithm and the Spearman correlation coefficient were used to calculate the DDTW-Spearman distance to achieve time series correlation measurements. The detection sensitivity of triacetone triperoxide (TATP) and H2O2 was studied, and the detection of organic substances of acetone, acetylene, ethanol, ethyl acetate, and petroleum ether was carried out. The stability and specific detection ability of the fluorescent photoelectric detection system were determined. The research results showed that the fluorescence photoelectric detection system can effectively identify the detection data of TATP, H2O2, acetone, acetonitrile, ethanol, ethyl acetate, and petroleum ether. The detection limit of 0.01 mg/mL of TATP and 0.0046 mg/mL of H2O2 was less than 10 ppb. The time series similarity measurement method improves the analytical capabilities of fluorescence photoelectric detection technology.

WT-YOLOM: An Improved Target Detection Model Based on YOLOv4 for Endogenous Impurity in Walnuts

Since impurities produced during walnut processing can cause serious harm to human health, strict quality control must be carried out during production. However, most detection equipment still uses photoelectric detection technology to automatically sort heterochromatic particles, which is unsuitable for detecting endogenous foreign bodies with similar colors. Therefore, this paper proposes an improved YOLOv4 deep learning object detection algorithm, WT-YOLOM, for detecting endogenous impurities in walnuts—namely, oily kernels, black spot kernels, withered kernels, and ground nutshells. In the backbone of the model, a lightweight MobileNet module was used as the encoder for the extraction of features. The spatial pyramid pooling (SPP) structure was improved to spatial pyramid pooling—fast (SPPF), and the model size was further reduced. Loss function was replaced in this model with a more comprehensive SIoU loss. In addition, efficient channel attention (ECA) mechanisms were applied after the backbone feature map to improve the model’s recognition accuracy. This paper compares the recognition speed and accuracy of the WT-YOLOM algorithm with the Faster R-CNN, EfficientDet, CenterNet, and YOLOv4 algorithms. The results showed that the average precision of this model for different kinds of endogenous impurities in walnuts reached 94.4%. Compared with the original model, the size was reduced by 88.6%, and the recognition speed reached 60.1 FPS, which was an increase of 29.0%. The metrics of the WT-YOLOM model were significantly better than those of comparative models and can significantly improve the detection efficiency of endogenous foreign bodies in walnuts.

Design of Amplifier Circuit of Optical Fiber Sensor and Its Application in Cloud Computing of Mechanical Vibration Fault

Photoelectric detection technology has been widely used in industry, agriculture, medicine, and other fields. The signal collected by photoelectric signal detection equipment is weak and vulnerable to noise interference. Therefore, a preamplifier circuit with good performance is needed to amplify the original signal effectively. Firstly, considering that a single-mode fiber coupler is more sensitive to the length of the coupling region, a coupled optical fiber vibration sensor is designed. Secondly, the appropriate operational amplifier (Op-amp) is chosen based on the photodetector. By listing and selecting the OPA656 Op-amp produced by TI Company, the basic current and voltage conversion circuit of photoelectric detection equipment is analyzed. In addition, a basic trans-impedance circuit with a phase compensation function is proposed and the calculation method of optimal phase compensation capacitance is derived. Finally, in the process of monitoring the vibration data of equipment, cloud computing technology (fuzzy C-means clustering algorithm+MapReduce model) is used for distributed processing of mechanical vibration data. In the test, the coupled optical fiber vibration sensor designed with an amplifier circuit and moving coil vibration sensor are placed on the ship equipment at the same time, and the rubber hammer is employed to hit the surface of the ship machinery. The test results of the two sensors are the same, which proves that the optical fiber sensor designed in this study meets the requirements of vibration measurement.

Data Processing Approaches to Measure Velocity of Electromagnetic Gun on Laser Screen in Complex Environment.

The exit velocity of the armature is an important indicator in measuring the launching performance of the electromagnetic gun. The non-contact photoelectric detection technology with the use of a laser screen was applied to the measurement of the armature velocity of the electromagnetic gun. By means of taking the signals that pass through the laser screen obtained by the velocity measurement system as the research object, we solved problems such as the harsh test environment of the launch armature velocity of the electromagnetic gun, the interferences on the armature signal passing through the laser screen unavoidably caused by various factors such as vibration, electromagnetic interference, shock wave, flare, smoke and fragments, and even the non-recognition of the signal passing through the laser screen in severe cases. A data-processing algorithm that combines the Ensemble Empirical Mode Decomposition (EEMD) with Correlation Algorithm (CA) was proposed, with the aim of processing the signals passing through the laser screen, while using the maximum slope point as the time passing through the laser screen so as to calculate the velocity of the armature passing the laser screen. This method can effectively reduce the influence of interference on the test results, and the test results from two sets of velocity measuring systems show that the velocity obtained by the proposed approach is highly consistent.

Research and development of Wide-Field-of-View bionic compound eye photoelectric imaging detection technology

Research and development of Wide-Field-of-View bionic compound eye photoelectric imaging detection technology

Real-time in situ detection and source tracing of different soot

Coal combustion produces large amounts of carbon dioxide, sulfur dioxide and metal ions. They are the main causes of greenhouse effect, acid rain and haze-fog respectively. It brings a great threat to the environment and human health. As a new photoelectric detection technology, laser-induced breakdown spectroscopy (LIBS) has become an important method in the field of environmental monitoring. In this paper, LIBS is applied to real-time in situ detection of soot from burning different coal. It is found that the main elements in soot are Al, Fe, Ca, Mg, Mn, Si, Sr and Na. In addition, the CN molecular bands can be observed. By comparing the spectra of different samples, differences between them are discussed. What’s more, principal component analysis (PCA) is used for data dimensionality reduction. After that, the data is distributed in a three-dimensional space, and then different kinds of soot are classified successfully. Combined with error back propagation training artificial neural networks (BP-ANN), the source tracing of soot from different coal are conducted, and the results are very good. The recognition accuracy is more than 92%. It is proved that this method is very promising for the detection and identification of soot. It is of great benefit to the environment and human health.

The Application and Optimization of Phase Method in Laser Distance Measurement

At present, photoelectric detection technology has become a hot spot in the field of automation technology, which is very active in the process of industrial automatic assembly detection. Laser ranging technology is a kind of technology that USES photoelectric technology to measure the distance without contact. In order to improve the flexibility of assembly and the adaptability of products, this paper adopts the non-contact detection method of laser ranging for the distance and length detection of the interior decoration covering parts of the emu passenger room. This method can avoid the disadvantages of manual detection and realize the purpose of high-speed, high-precision and non-contact detection.

Preface

The 2020 International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP 2020) and Intelligent Oil Field is jointly organized by Xi’an Shiyou University and AEIC Academic Exchange Center, which was successfully held in Xi’an, Shaanxi, China during December 04-06, 2020.The conference mainly focused on the development of intelligent oil field and measurement and control technology and intelligent instrument, photoelectric detection and measurement technology, intelligent control and automation, signal and information processing, artificial intelligence and robot and other research fields of oil and gas fields. The conference aims to provide an exchange platform for experts and scholars, engineers and research personnel of related fields.We are greatly honored to have invited Prof. Tiantai Li, President of Xi’an Shiyou University to serve as our Conference Chair. There were over 80 experts and scholars in the area of Intelligent Control, Measurement and Signal Processing representing different famous universities and institutes around the globe to attend the conference.The conference model was divided into two sessions, including oral presentations and keynote speeches. In the first part, some scholars, whose submissions were selected as the excellent papers, were given 15 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In the keynote speeches part, we have invited eight industry elites as our keynote speakers. Prof. Jie Li, from University of Tsukuba, Japan. His current research interests are in big data, cloud, wireless and mobile networking, network security, IoT, OS, modeling and performance evaluation of information systems. The second keynote speaker, Prof. Zhenmao Chen, from Xi’an Jiaotong University. Dr. Tuan Guo, from Jinan University. His main research fields are advanced photonic devices, optical fiber sensing, biphotonic and energy photonics. The others keynote speakers as list: Prof. Maode Yan, from Chang’an University; Prof. Bo Wang, from Xi’an Shiyou University; Prof. Jie Chen, from Northwestern Polytechnical University; Prof. Chun-Sheng Jia, from Southwest Petroleum University; Prof. Chenglin Liu, from China University of Petroleum, Beijing. By discussing scientific research achievements and advanced technology, we can understand the trend of academic development, broaden our horizon in research, strengthen the level of academic research and discussion, and promote the intelligent level of oil field development and the industrialization of academic achievements.ICMSP 2020 is to bring together innovative academics and industrial experts in the field of Intelligent Control, Measurement and Signal Processing to a common forum. Therefore, we compiled a lot of excellent papers featuring the latest advanced studies on the following topics: Measurement Control Technology and Intelligent Instrument, Signal and Information Processing, Intelligent Digital Oil Field, Machine Learning Approach for Oil Well Location, 5G Oil & Gas Field Infrastructure and other related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard.We would like to acknowledge all of those who have supported ICMSP 2020. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the organizing committee for their valuable advices in the organization and helpful peer review of the papers.List of Committee Member is available in this pdf.

Photoelectric Detection of Hole Shape and Size for Large Plate Parts

The measurement of hole shape and size of large plate parts has always been a technical problem. The article proposes a technology to use non-contact photoelectric detection technology for online detection of the shape and position of large plate-shaped workpieces. The detection principle is: the control system controls the photoelectric detection system to move in a straight line along the guide rail at a uniform speed, and the large plate-shaped workpiece the hole shape and size are measured, and the lighting system moves synchronously with the photoelectric detection system to provide the light source for it. The images collected by the photoelectric detection system are processed through digital image processing and image splicing technology to realize the hole shape and size of large plate workpieces. Non-contact measurement.

Photoelectric Detection System for Large Board Parts

In-depth analysis of the current shortcomings in the detection of large board parts, a photoelectric detection method based on image processing technology is proposed, the overall structure of the detection system is designed in detail, the mechanical transmission system and the photoelectric detection system are specifically designed, and at the same time, the analysis the implementation steps of image processing for large-scale class parts using photoelectric detection technology are given. The specific implementation process is given, including: image acquisition, image stitching and image edge extraction, and finally the photoelectric online detection of large-scale board parts is completed.

Optimization and Practice on the Course Content and Teaching Methods of Optoelectronic Testing Technology

The research aimed at the characteristics and teaching requirements of the photoelectric detection technology course, optimized the teaching content and teaching methods of the photoelectric detection technology course, explored how to use scientific research results as the teaching content, and practiced how to put the new teaching method into the course and the teaching process. The practice has shown that the use of scientific research results in teaching is conducive to maintaining the rapid development of high and new technology and a dynamic atmosphere of the times; new teaching methods can improve students’ interest in learning and the quality of teachers’ teaching, and help to cultivate students’ innovative consciousness and ability.

Preface

The 2019 International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP 2019), jointly organized by Xi’an Shiyou University and China Petroleum Logging Co., Ltd, was grandly held during December 27-29, 2019, in Xi’an, China. ICMSP 2019, held in two days, was a high level and high standard academic conference. Under the full supports of the hosts, every session of the conference was successfully completed by the joint efforts of all representatives attending the conference and all staff.We are honored to invite Dr. Jie Li from University of Tsukuba, Japan, Prof. ZhenMao Chen from Xi’an Jiaotong University, Prof. Zahriladha Zakaria from Universiti Teknikal, Malaysia, Prof. Alex Noel Josephraj from College of Engineering, Shantou University, and Dr. Tuan Guo from Jinan University as the keynote speakers to professionalize this conference. The spotlights of the conference lie on the great progress in the fields of Measurement and Control Technology and Intelligent Instruments, Photoelectric Detection and Measurement Technology, Intelligent Control and Automation, Signal and Information Processing, Artificial Intelligence and Robots and other interdisciplinary areas. This conference aims to encourage more young academic researchers to share their new ideas, thoughts and research results, enhance academic research and exploration, and promote the transmission of academic results.At last, ICMSP 2019 would like to thank all guests and speakers for sharing their wonderful talks and all participants coming from distant places without complaints for attending this conference in Xi’an. We also want to thank all sponsors for offering the materials and funds to this conference. Thanks to your great supports, we’re sure International Conference on Intelligent Control, Measurement and Signal Processing can be held better in the future!Prof. Jie WuCollege of Electronic Engineering, Xi’an Shiyou UniversityThe Chairman of the Organizing Committee

Research and Development of Gasoline Direct Injection Image Acquisition System

Both industry standards and international standards in the field of automobiles recommend the image test method to detect the spray performance of the fuel injector. It is necessary to accurately determine the time when the fuel injector starts spraying and capture the spray image at the standard time for analysis. An injector spray image acquisition system based on photoelectric detection technology is proposed. The infrared photoelectric sensor is used to determine the initial starting time of the injector. The FPGA chip receives the trigger signal of the photoelectric sensor and then sends driving pulses to the industrial camera in the external trigger acquisition mode to realize spray image acquisition. Then, it is transmitted to the upper computer through gigabit network interface for subsequent image processing. The experimental results show that the system has stable performance and high quality of collected images, fully meets the high precision requirements of spray detection, and can be extended to droplet velocity measurement and other fields.

A 3D topological Dirac semimetal/MoO3 thin film heterojunction infrared photodetector with a current reversal phenomenon

Photoelectric detection technology is extremely significant on account of its extensive applications in commercial and military fields, but high-performance array photodetectors based on Dirac semimetal thin film materials are rarely reported.

新型光电探测技术在精确制导武器上的应用研究（特约）

新型光电探测技术在精确制导武器上的应用研究（特约）

Application of Vacuum Photoelectric Detection Technology in Super-Resolution System

Due to the wave characteristics of light, diffraction occurs when the light passes through the optical system, so that the resolution of the ordinary far-field optical system is limited by the size of the Airy disk diameter. There are various factors that cause image quality degradation during system detection and imaging, such as optical system aberrations, atmospheric inter-ference, defocusing, system noise and so on. Super-resolution optical imaging technology is the most innovative breakthrough in the optical imaging and detection field in this century. It goes beyond the resolution limit of ordinary optical systems or detectors, and can get more details and information of the structure, providing unprecedented tools for various fields. Compared with ordinary optical systems, super-resolution systems have very high requirements on the signals to be detected, which cannot be met by ordinary detection techniques. Vacuum photoelectric detection and imaging technology is equipped with the characteristics of high sensitivity and fast response. It is widely used in super-resolution systems and has played a great role in super-resolution systems. In this paper, the principles and structure of the image-converter streak camera super-resolution system, scanning electron microscopy super-resolution system and laser scanning confocal super-resolution system will be sorted out separately, and the essential role of the vacuum photoelectric detection technology in the ultra-microscopic sys-tem will be analyzed.

Bond states, moiré patterns, and bandgap modulation of two-dimensional BN/SiC van der Waals heterostructures

This study aims to determine moiré patterns on the two-dimensional (2D) BN/SiC heterojunction at lattice strains of 5% and 7% and different incident angles, which can be applied to photoelectric detection technology.

Fire detection with infrared images using cascaded neural network

The existing equipment of civil aircraft cargo fire detection mainly uses photoelectric smoke detectors, which has a high false alarm rate. According to Federal Aviation Agency’s (FAA) statistics, the false alarm rate is as high as 99%.1In the cargo of civil aircraft, the traditional photoelectric detection technology cannot effectively distinguish interference particles from smoke particles. Since the video smoke detection technology has proven to be reliable in many large scenarios, a deep learning method of image processing for fire detection is proposed. The proposed convolutional neural network is constructed of front end network and back end network cascaded with the capsule network and the circularity computation for the dynamic infrared fire image texture extraction. In order to accurately identify whether there is a fire in the area and give the kind of burning substances, a series of fuels are selected, such as n-heptane, cyclohexane, and carton for combustion reaction, and infrared camera is used to take infrared images of all fuel combustion. Experimental results show that the proposed method can effectively detect fire at the early stage of fire which is applicable for fire detection in civil aircraft cargoes.

Photoelectric detection technology of laser seeker signals

Photoelectric detection technology of laser seeker signals