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2023 International Workshop on Signal Processing and Machine Learning, WSPML 2023

作者： Qu, Zhiguo Zhong, Zhi Cao, Xinghui Shan, Mingguang Xie, Yongqiang College of Information and Communication Engineering Harbin Engineering University Harbin China Laboratory of Survey Control Xinjiang Institute of Technology Akesu China Institute of Systems Engineering AMS PLA Beijing China

ISBN: (纸本)9781510671928

Classification of reflected signals from surface sediments can improve our understanding of the properties of these sediments. In this paper, we propose a method for classifying reflection signals using deep learning techniques. The method uses a pulse compression algorithm to convert reflection signals into reflection compressed data, and then uses a one-Dimensional Convolutional Neural Network - Double Long Short-Term Memory (1DCNN-DLSTM) network to classify these data. The advantage of this method is that the pulse compression algorithm can improve the resolution of the stratigraphic reflection signal, thus better capturing the details of the signal. Meanwhile, 1DCNN can effectively extract the spatial features of reflection compression signals and capture the differences between different sediment types. DLSTM, on the other hand, can capture the temporal dynamic features of the signals, which is very advantageous for modeling temporal information. By fusing these two network structures, it is possible to categorize deep-sea surface sediments in a more comprehensive way. To verify the feasibility of the method, we conducted experiments using reflection data from surface sediments on the South China Sea continental slope. The experimental results show that the method is feasible in classifying the reflection signals from deep-sea surface sediments. We obtain high classification accuracy by training and testing different types of reflection compression data. This indicates that the method can effectively distinguish different types of deep-sea surface sediments, which helps us to better understand the deep-sea environment and related geological processes. © 2023 SPIE. All rights reserved.

关键词： Deep learning

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Enhanced Responsivity of Solar Blind Ultraviolet Photodetector by PEALD Deposited Zn-Doped Ga2O3Thin Films

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IEEE Transactions on Electron Devices 2024年第1期71卷 664-669页

作者： Fan, Hui-Chen Wang, Chen Ruan, Yu-Jiao Shen, Kun-Ching Wu, Wan-Yu Wuu, Dong-Sing Lai, Feng-Min Lien, Shui-Yang Zhu, Wen-Zhang Xiamen Institute of Measurement and Testing National Measurement and Testing Center for Flat Panel Display Industry Xiamen361024 China Fuzhou University College of Physics and Information Engineering Fuzhou350108 China National United University Department of Materials Science and Engineering Miaoli36063 Taiwan National Chi Nan University Department of Applied Materials and Optoelectronic Engineering Nantou54561 Taiwan Xiamen University of Technology Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-Electronic and Communication Engineering Xiamen361026 China Da-Yeh University Department of Biomedical Engineering Changhua51591 Taiwan

In this article, the tunable Zn-doped Ga2O3 films were obtained by introducing ZnO intercalation during the film deposition using plasma-enhanced atomic layer deposition (PEALD). The effect of the ZnO cycle ratio on the performance of solar-blind photodetectors (PDs) was first analyzed. Compared to the pure Ga2O3, an obviously enhanced performance of Zn-doped Ga2O3 films PDs was observed by the introduction of Zn dopants, especially photocurrent or responsivity, even if the light intensity is low. A high sensitivity of Zn-doped Ga2O3 PD has been achieved with superior photoelectric characteristics for an extremely low dark current of 2.22 × 10-13 A, an ultrahigh light ON/ OFF current ratio of 2.89 × 106, and a satisfactory responsivity of 104 mA/W when the ZnO cycle ratio is 5%. The above excellent results imply that the Zn-doped Ga2O3 PD can be greatly beneficial to the application in deep-ultraviolet (DUV) detection. © 1963-2012 IEEE.

关键词： II-VI semiconductors

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Analysis and Simulation of Interference Effects on CSK Modulation Systems 15

Analysis and Simulation of Interference Effects on CSK Modul...

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15th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2022

作者： Guan, Lele Xu, Zhan Tian, Lu Shi, Chenrui School of Information and Communication Engineering Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

As the space environment of information transmission becomes more and more complex, the accuracy of communication becomes a new challenge. In order to study the anti-jamming performance of code shift keying (CSK), this paper mainly simulates the performance of CSK and binary phase shift keying (BPSK) under 5 signal interferences, and discusses it according to the bit error rates (BER) obtained by simulation. In the face of different forms of signal interference, CSK has better anti-jamming performance than BPSK. Under various forms of signal interference, CSK has the strongest anti-interference ability to multi-tone interference, and its anti-interference ability to pulse interference, 50% narrowband interference, single-tone interference and channel noise decreases successively. © 2022 IEEE.

关键词： Binary phase shift keying

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Design and Implementation of Indoor Visual Autonomous Navigation System for Quadrotor UAV Based on Binocular Camera and IMU 3

Design and Implementation of Indoor Visual Autonomous Naviga...

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3rd International Conference on Computer Science, Electronic Information engineering and Intelligent control technology, CEI 2023

作者： Tang, Jiaze Guo, Tianxing Liudan Li, Junbao Sun, Jinwei Huang, Wenbin Wang, Qisong Electrical Measurement Technology and Intelligent Control Institute School of Instrumention Science and Engineering Harbin Institute of Technology Harbin150080 China Schools of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9798350372052

Addressing the challenge of unmanned aerial vehicles (UAVs) navigating indoors without satellite positioning, this study introduces a novel method combining binocular camera and inertial measurement unit (IMU) data. This approach overcomes the structural differences between visual and inertial information, integrating them effectively for enhanced indoor autonomous navigation. The research presents a gradient-free path planning framework, pivotal for enhancing the UAV's ability to execute efficient and stable flight in intricate indoor settings. The cornerstone of this system is an advanced path planning algorithm, adept at swiftly identifying optimal routes and adapting to environmental shifts. The system's efficacy was rigorously evaluated through simulations and real-world tests, confirming its precision in positioning and path planning without reliance on satellite data. The findings demonstrate the system's capacity for precise localization and effective route determination, utilizing an integration of visual and inertial inputs. This research significantly contributes to the autonomous flight capabilities of UAVs, offering a valuable reference for future UAV autonomous navigation systems. By delving into visual-inertial fusion navigation technology, the study paves the way for expanded indoor UAV applications. © 2023 IEEE.

关键词： Antennas

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Calibration of Piezoresistive Pressure Sensors in a Wide Temperature Range

Calibration of Piezoresistive Pressure Sensors in a Wide Tem...

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2024 International Conference on Actual Problems of Electron Devices engineering, APEDE 2024

作者： Svetlov, Mikhail S. Nikiforov, Alexandr A. Lvov, Peter A. Filina, Ekaterina V. Jburi, Osama K. Kareem, Mohammed H. Laboratory of Control and Automation Problems in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russia Institute of Institute of Electronic Engineering and Instrumentation Yuri Gagarin State Technical University of Saratov Saratov Russia Electronic Design Laboratory Digital Measurement Technologies Ltd Saratov Russia Institute of Applied Information Technologies and Communication Yuri Gagarin State Technical University of Saratov Saratov Russia

ISBN: (纸本)9798350393316

Piezoresistive pressure sensors (PPS) are widely used in various microelectronic devices applied in modern technologies. The performance of these sensors are highly dependent on the ambient temperature. Unfortunately, known methods of PPS calibration do not allow measuring pressure with the required accuracy in the wide temperature range typical, for instance, in aviation from –60 to 140 ◦C. In this paper, a conventional approach is used, when the dependence of the output voltage on pressure is represented using a polynomial of a relatively low order, and the dependences of the coefficients of this polynomial on temperature are specified by the cubic splines. The paper describes in detail the identification method of the polynomial model under consideration and obtains expressions for correcting the readings of the pressure gauge during pressure measurements in wide temperature ranges, when the temperature dependence model is specified using cubic splines. The results of experimental studies of specific pressure sensors are presented. © 2024 IEEE.

关键词： Splines

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Deterministic Message Flow Scheduling in Industrial Internet: Message Middleware Use Case 24

Deterministic Message Flow Scheduling in Industrial Internet...

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24th IEEE International Conference on communication technology, ICCT 2024

作者： Wang, Xin Zhou, Chonghan Jiang, Jing Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing China State Grid Economic and Technological Research Institute co. Ltd Beijing China

ISBN: (纸本)9798350363760

This article mainly focuses on message flow scheduling for industrial message middleware in the integration of deterministic network with industrial internet, which is to find the optimal matching between telecom operator and message requests from industrial users. However, current deterministic network is limited to two or three layers, and the whole transmission reliability and latency involving industrial networking and service layers cannot be guaranteed, resulting in uncontrollable message scheduling reliability, deterministic latency, low channel resource utilization etc. To this end, an integer linear programming (ILP) model is formulated for message flow scheduling issue, with multi-objectives optimization of resource utilization and profit under customized message sending latencies, and deterministic reliability. A heuristic algorithm is also proposed. Furthermore, we present a Deep Reinforcement Learning based Message Flow Scheduling algorithm (DRL-MFS), which not only solves complex message flow scheduling problems, but also achieves the self-optimization automatically. Proposed message flow scheduling approaches are simulated based on data from real cloud messaging service platforms. Simulations results prove that proposed DRL-MFS is superior to heuristic, and is close to ILP model in multi-objective optimization under three urgency levels of message sending. The reliability of three methods is proven to be deterministic. © 2024 IEEE.

关键词： Middleware

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Zero-Sample Multiple Fault Diagnosis of Diesel Engine Based on Attribute Prediction 43

Zero-Sample Multiple Fault Diagnosis of Diesel Engine Based ...

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43rd Chinese control Conference, CCC 2024

作者： Su, Qian Lv, Xuantao Qiu, Zhengxing Liu, Zhenxing Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Wuhan University of Science and Technology School of Information Science and Engineering/School of Artificial Intelligence Wuhan430081 China Wuhan Maritime Communication Research Institute Wuhan430205 China

ISBN: (纸本)9789887581581

A zero-sample fault diagnosis method based on fuzzy fault feature representation for attribute prediction is proposed to address the challenge of insufficient fault data samples covering all types of multiple faults in actual diesel engine operations. Through simulation analysis of single and multiple fault operational states of diesel engines using AVL-boost ledR simulation software, along with the variation patterns of single fault thermodynamic parameters and the coupling patterns of multiple faults, category information for multiple fault diagnosis is provided. Gaussian membership functions are employed to describe fault features, and an SVM attribute trainer is established using single fault data to predict attribute values for multiple faults without data samples. The nearest neighbor search algorithm is utilized to infer the most suitable multiple fault category. Experimental results demonstrate that the proposed method exhibits excellent performance in the diagnosis of multiple fault in diesel engines, with a prediction accuracy of 96.62% for fault attributes and a final multiple fault diagnosis accuracy of 97.31%, thereby providing a basis for zero-sample multiple fault diagnosis in diesel engines. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Diesel engines

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Multiclass Classification of Remote Sensing Images Using Deep Learning Techniques

Multiclass Classification of Remote Sensing Images Using Dee...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tahir Arshad Zhang Junping Qingyan Wang School of Electronics and Information Engineering Harbin Institute of Technology China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology

Deep learning has strong learning ability to extract the features from Image datasets. In recent years, deep networks especially deep convolutional neural networks have revolutionized this field. When exposed to a huge number of datasets and their labels, deep learning techniques like Convolutional Neural Networks (CNNs) can produce precise categorization results. However, employing CNNs with scant labeled data might have a number of issues, including the problem of heavy overfitting. Convolutional Neural Networks are the backbone of modern deep learning architectures for the purpose of image classification. We solved image classification problem with different architectures and compare their performances. The objective of this work lies in the approach to study the given datasets. We use domain adaptation approach to highlight the underlying characteristics of these datasets and the different parameters (activation function, weights, regularization, neural simulation etc.) associated with these architectures. In our work, we use three datasets—RS19, UC Merced, and EuroSat images—were utilized in the CNN implementation to training the suggested model. The obtained results effectively demonstrate the local representation capacity of CNNs. Furthermore, this work shows that transfer learning improves classification outcomes in optical remote sensing images, particularly when the training sample is small.

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Research on Fault Prediction and Health Management of Power Supply Board Method Based on Mahalanobis Distance 43

Research on Fault Prediction and Health Management of Power ...

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43rd Chinese control Conference, CCC 2024

作者： Li, Jiaying Su, Qian Qiu, Zhengxing Zhao, Min Liu, Zhenxing Wuhan University of Science and Technology School of Information Science and Engineering School of Artificial Intelligence Wuhan430081 China Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Wuhan Maritime Communication Research Institute Wuhan430205 China

ISBN: (纸本)9789887581581

In recent years, with the widespread application of complex electronic systems in large aircraft, high-speed railways, and ocean-going vessels, research on fault prediction and health management of electronic systems has become increasingly important. Traditional fault detection techniques bring cumbersome detection processes, high maintenance costs, and economic losses caused by repairs. To address electronic system faults and health issues caused by load status, capacitor failure, and temperature effects in power supply boards, a feature extraction method based on probability density modeling is adopted to establish a health evaluation index for power supply boards, thereby achieving health assessment and fault prediction for electronic circuit boards. The proposed method in this paper can effectively reduce the frequency of periodic monitoring and maintenance, provide guidance for the production and ordering cycle of replacement parts, and avoid the occurrence of post-repair maintenance. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electronic System Fault Detection Health Management Mahalanobis Distance

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Robust Federated Learning Algorithm Based on Chaotic Encryption and Dual-Server Detection

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IEEE Internet of Things Journal 2025年

作者： Kang, Shouqiang Wu, Yuxuan Wang, Yujing Wang, Qingyan Liang, Xintao Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin150080 China

Federated learning allows users to collaboratively train models, but studies have shown that local model parameters may leak users’ privacy. Furthermore, the global model in federated learning could be compromised by Byzantine attacks. To address these issues, a robust federated learning algorithm based on chaotic encryption and dual-server detection (CDRFL) is proposed. First, CDRFL introduces a lightweight chaotic encryption method, where ciphertexts have a unique property of mutual cancellation, allowing the plaintext global model parameters to be derived without decryption. Moreover, CDRFL allows users to encrypt with different keys, further reducing the risk of privacy leakage. Next, a dual-server detection scheme is designed to defend against Byzantine attacks while preserving user privacy. Lastly, a reputation score mechanism with a forgetting function is proposed to fairly distinguish Byzantine participants. Experimental results demonstrate that, compared with popular robust aggregation schemes, CDRFL improves testing accuracy by 3.26% to 9.06%. When compared to existing chaotic encryption federated learning schemes, CDRFL enhances the ability to defend against Byzantine attacks and reduces time and communication overhead by 73.63% and 95.40%, respectively. © 2014 IEEE.

关键词： Federated learning

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