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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

2023 Asia communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Gu, Huaxin Liu, Shuaiqi Yu, Feihong Xu, Deyu Chen, Xingwei Shao, Liyang Wu, Yu Zhang, Haifeng College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Research Institute of Tsinghua University Advanced Measurement and Control and Equipment R&d Center Chengdu China

ISBN: (纸本)9798350312614

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events. © 2023 IEEE.

关键词： convolutional aliasing model DAS FastIVA time-frequency entropy

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Reflection signal classification of deep-sea surface sediment based on 1DCNN-DLSTM networks

Reflection signal classification of deep-sea surface sedimen...

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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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Kalman Filter-based Cycle-Consistent Adversarial Learning for Time Series Anomaly Detection

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 790-803页

作者： Liu, Shu-Tang Fan, Ji-Jie Wang, Rui-Dong Han, Han Zhang, De-Yang Faculty of Humanities and Arts Macau University of Science and Technology 999078 China Academic Affairs Office Minjiang University Fuzhou350108 China Kyrgyz National University Named after Jusup Balasagyn Bishkek720033 Kyrgyzstan School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China Henan Provincial Institute of Scientific and Technical Information Zhengzhou450003 China

Anomaly detection in time series is challenging for machine learning due to the uncertainty and complex pattern in time series. In this paper, we propose a Kalman filter-based cycle-consistent adversarial learning framework (KFCGAN) for time series anomaly detection, assuming that the abnormal pattern prevents the model from reconstructing the original and filtered signals. Specifically, KFCGAN comprises a Kalman filter module and a cycle-consistent adversarial learning module that utilizes an autoencoder as its generator. Firstly, the Kalman filter module generates the filtered signals from the original signals as the target domain. Then, the forward direction of the cycleconsistent adversarial learning module learns the transformation of the original time series domain to the filtered time series domain. At the same time, the backward direction optimizes it by transforming the target domain to the original domain. Finally, anomaly detection can be modeled as the signal reconstruction problem between the original and target domains. The experimental results on five real-world datasets show that the proposed method outperforms the start-of-the-art, demonstrating that KFCGAN can effectively capture abnormal time series patterns. © 2024, J. Network Intell. All rights reserved.

关键词： Time series

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Study the influence of using guide vanes blades on the performance of cross-flow wind turbine

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Applied Nanoscience (Switzerland) 2023年第2期13卷 1115-1124页

作者： Yahya, Waled Ziming, Kou Juan, Wu Al-Nehari, Mohammed Tengyu, Li Qichao, Ren Alhayani, Bilal College of Mechanical and Vehicle Engineering Taiyuan of University of Technology Taiyuan 030024 China Shanxi Province Engineering Technology Research Center for Mine Fluid Control Taiyuan 030024 China Yildiz Technical University Electronics and communication department Istanbul Turkey

A cross-flow wind turbine has a high torque coefficient at a low tip speed ratio;therefore, it is a good candidate for a self-starting turbine. This study aims to investigate the best configuration between guide vanes and cross-flow vertical axis wind turbine. The experiment test was carried out to determine the turbine with the highest power coefficient. The cross-flow turbine has 14, 18, and 22 blades with using 6,10 and 14 blades of guide vane (GV) was developed in this study, employing 15°, 25°, 35°, 45°, 55°, 65°, and 75° of tilt angles in fifth different wind speed conditions 4 m/s, 6 m/s, 7.5 m/s, 9.20 m/s, and 11 m/s. The turbine has 22 blades with 14 GV blades at 55° of tilt angle blades producing more remarkable turbine performance improvement than other blades. The highest power coefficient (CP) of cross-flow using 14 GV blades at 55° was 0.0162 at 0.289 TSR, which increased around 59% of the turbine's performance using GV. © 2021, King Abdulaziz City for Science and technology.

关键词： Cross-flow wind turbines Guide vanes (GV) Power coefficient (CP) Tilt angles

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Artificial Intelligence-Aid OTFS Receiver: Design and Performance Evaluation

Artificial Intelligence-Aid OTFS Receiver: Design and Perfor...

引用

2023 International Conference on Ubiquitous communication, Ucom 2023

作者： Wang, Chenxi Qian, Li Ping Zhu, Huijie College of Information Engineering Zhejiang University of Technology Hangzhou China The 36th Research Institute of China Electronics Technology Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9798350340433

OTFS technology is attracting much attention due to its strong adaptability to high-speed mobility and high spectrum efficiency. Studies show that applying deep learning (DL) to traditional communication systems significantly improves their performance. To this end, this paper proposes an AI-Aid OTFS receiver to enhance the performance of OTFS systems in high-speed mobile communication scenarios. The proposed receiver employs two networks-a channel estimation network called TFNet and a decoding network called RecovNet. In the channel estimation module, TFNet performs more accurate channel estimation based on the results of traditional channel estimation methods. In the decoding module, RecovNet decodes the signal based on the equalized signals. To validate the superiority of our proposed receiver, we conduct comparative testing with a traditional receiver under the integer Doppler channel. The experimental results demonstrate that integrating DL technology in OTFS systems effectively enhances their performance. © 2023 IEEE.

关键词： Decoding

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The Reconstruction of Seismic Reflection Events by Optimized Matching Pursuit Algorithm

The Reconstruction of Seismic Reflection Events by Optimized...

引用

Intelligent control, measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Pingyang Yu Yanping Liu Defu Wan Xue Yao Dept. of Measurement and Control Technology and Instrument The College of Electronic Engineering of Xi’an Shiyou University

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

This paper mainly introduces an optimized matching pursuit algorithm for the reconstruction of seismic reflection events. It adopts intelligent optimization algorithms of genetic algorithm (GA) and particle swarm algorithm (PSO) to matching pursuit algorithm for improving the processing effect and efficiency. Through experiments on synthetic seismic records and field seismic data, it demonstrates that both GA-MP and PSO-MP can reconstruct the reflection events effectively and efficiency, while PSO-MP performs better especially in waveform reconstruction.

关键词： Seismic measurements Matching pursuit algorithms Signal processing algorithms Process control Reflection Frequency measurement Noise measurement Particle swarm optimization Frequency control Genetic algorithms

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Identification and Location of Leakage Aperture of Underground Water Supply Pipeline in Coal Mine

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Science technology and engineering 2025年第8期25卷 3296-3303页

作者： Du, Jing-Yi Chen, Zhen Zhang, Jia-Wei Li, Chen Gao, Rui Wang, Peng College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an710054 China College of Communication and Information Engineering Xi'an University of Science and Technology Xi'an710054 China College of Mechanical Engineering Xi'an University of Science and Technology Xi'an710054 China

In order to quickly identify the location of the leakage point and the leak aperture in the coal mine, a model was proposed for identifying and locating the leak aperture by using the pressure and flow signals generated when the water supply pipeline leaked. Modal energy entropy and genetic algorithm combined with envelope entropy were used to optimize the parameters of variational mode decomposition (VMD), and then VMD was used to denoise the pressure signal. Convolutional neural network(CNN) was used to extract the deep feature sequence of pressure and flow signal, and the long short-term memory network(LSTM) was used to extract the time sequence of deep feature sequence to identify and locate the leak aperture. The experimental results show that compared with Kalman filter, mean value filter and low-pass filter, the variational modal decomposition with optimized parameters has higher root-mean-square error (RMSE), mean absolute error(MAE), signal-to-noise ratio(SNR) and normalized cross correlation(NCC), which indicates that it can effectively reduce noise components and retain effective signals. Compared with LSTM, the MAE, mean absolute percentage error (MAPE) and RMSE of CNN-LSTM in leak location decrease by 65. 97%, 61. 22% and 59. 11% . In the identification of leak aperture, MAE decreases by 12. 04%, MAPE decreases by 22. 45%, and RMSE decreases by 3. 29%, which proves that CNN-LSTM can make full use of the spatial and temporal characteristics of pipeline pressure and flow signals to identify the leak location and aperture, and its detection effect is more accurate and stable than LSTM. © 2025 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Variational mode decomposition

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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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Journal of Bionic engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

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Reusable magnetically-modified Enteromorpha prolifera-based biochar hydrogels: competitive removal mechanism for metal-organic dye composite contaminants

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Carbon Research 2024年第1期3卷 1-20页

作者： Song, Zihan Liu, Yonglin Liu, Lin Yang, Chuanxi Tian, Wei Duan, Baorong Fang, Xu Ren, Yunke Zhang, Mingkun Xiong, Si Gong, Yuzhuo Sun, Haofen Wang, Weiliang School of Environmental and Municipal Engineering Qingdao University of Technology Qingdao266525 China Academy of National Food and Strategic Reserves Administration Beijing100037 China College of Chemistry and Chemical Engineering Yantai University Yantai264005 China Shandong Think-eee Environmental Technology Co. Ltd. Jinan250100 China Qingdao Jiaming Measurement and Control Technology Co. Ltd. Qingdao266000 China Qingdao Zhuoyan Information Technology Co. Ltd. Qingdao266000 China

Dyes and heavy metals pollute the environment. Biochar-based hydrogel is an excellent adsorbent, but the competitive adsorption mechanism associated with the removal of pollutants using biochar is yet to be understood in detail. Biochar was prepared following the process of high-temperature lysis of marine green tide (Enteromorpha prolifera). The prepared biochar was cross-linked with water-soluble chitosan and compounded with nano-Fe3O4 to synthesize magnetically-modified Enteromorpha prolifera-based biochar hydrogel (MM-EBC-HD). The competitive removal performance of the hydrogel was studied, and the pollutant removal mechanism was analyzed against a binary system consisting of common environmental pollutants (methyl orange [MO] and hexavalent chromium [Cr (VI)]. The physical and chemical properties of the composites were studied before and after contaminant removal, and the associated pollutant removal mechanisms were analyzed by SEM, EDS, FTIR, XRD, and XPS techniques. The effects of pH, temperature and initial pollutant concentration on the adsorption performance of the materials were examined. The maximum adsorption of MO on MM-EBC-HD was 71.18 mg g−1, and adsorption equilibrium was attained at approximately 60 min. Electrostatic forces, hydrophobic bonds, and hydrogen bonds were exploited for MO adsorption. And the maximum adsorption amount of Cr (VI) was recorded to be 115.41 mg g−1, and equilibrium was attained in approximately 10 min. Electrostatic and ion exchange effects were exploited to adsorb Cr (VI) efficiently. The MO and Cr (VI) adsorption processes could be explained by the second-order kinetic model and Langmuir adsorption isotherms, respectively. The adsorption performance recorded for the binary adsorption system was poorer than that recorded for the single adsorption system for both pollutants. MO and Cr (VI) adsorption decreased from 74.88% to 47.65% and from 62.33% to 42.4%, respectively. Competition between MO and Cr (VI) in the dual system

关键词： Hydrogen bonds

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MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE BASED ON MONTE-CARLO TREE SEARCH 10

MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE...

引用

10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Dai, Juan Jing, Yanzhang Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China School of Automation Beijing Information Science & Technology University Beijing China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

With the rapid development of artificial intelligence technologies, multi-robot cooperative processing tasks are becoming more and more common in various applications. However, traditional single-robot planning algorithms show obvious limitations in dealing with complex social interactions and dynamic environments in the real world. To address this challenge, this paper applies Monte Carlo Tree Search (MCTS) to multi-robot path planning and proposes a collaborative obstacle avoidance motion planning algorithm based on MCTS. Specifically, the algorithm prunes the Monte Carlo tree by excluding cases that do not meet the robot speed constraints and potential collisions, which significantly improves the computational speed and success rate of the algorithm, making it applicable to the multi-robot path conflict problem. In addition, we design a policy space that conforms to the robot's operation and a cost function that meets the requirements of robot path planning to plan an efficient cooperative path for each robot. Through a series of simulation experiments, we demonstrate the algorithm's path planning and obstacle avoidance performance in a variety of conflict scenarios. The results show that the method is highly cooperative and effective in complex environments. © The Institution of engineering & technology 2024.

关键词： Cost functions

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