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Computing Challenges of UAV Networks: A Comprehensive Survey

Computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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Asynchronous Sliding-Mode control for Discrete-Time Networked Hidden Stochastic Jump Systems With Cyber Attacks

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IEEE Transactions on Cybernetics 2024年第3期54卷 1934-1946页

作者： Qi, Wenhai Zhang, Ning Zong, Guangdeng Su, Shun-Feng Cao, Jinde Cheng, Jun Qufu Normal University School of Engineering Rizhao276826 China Chengdu University School of Information Science and Engineering Chengdu610106 China Tiangong University School of Control Science and Engineering Tianjin300387 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Southeast University School of Mathematics Nanjing210096 China Yonsei University Yonsei Frontier Lab Seoul03722 Korea Republic of Guangxi Normal University College of Mathematics and Statistics Guilin541006 China

In this study, asynchronous sliding-mode control (SMC) for discrete-time networked hidden stochastic jump systems subjected to the semi-Markov kernel (SMK) and cyber attacks is investigated. Considering the statistical characteristic of the SMK, which is challenging to acquire in engineering, this study recognizes the SMK to be incomplete. Due to the mode mismatch between the original system and the control law in the operating process, a hidden semi-Markov model is proposed to describe the considered asynchronous situation. The main aim of this study is to construct an asynchronous SMC mechanism based on an incomplete SMK framework under the condition of random denial-of-service attacks so that the resulting closed-loop system can realize the mean-square stability. By virtue of the upper bound of the sojourn time in each mode, innovative techniques are developed for mean-square stability analysis under an incomplete SMK. Furthermore, an asynchronous SMC scheme is designed to achieve the reachability of the quasi-sliding mode. Finally, the effectiveness is verified using an electronic throttle model. © 2013 IEEE.

关键词： Markov processes

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Distributed Localization of Multi-Agent Systems Based on Local Bearing information

Distributed Localization of Multi-Agent Systems Based on Loc...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yunkai Lv Kai Rao Hao Zhang Huaicheng Yan School of Information Science and Engineering East China University of Science and Technology Shanghai China Department of Control Science and Engineering Tongji University Shanghai China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This work addresses localizability of local-bearing-based directed multi-agent systems, which are more general but also more challenging than bearing-based undirected one. By using the local bearing measurement information, an orientation estimation algorithm is proposed, which does not require relative orientation measurements. Combined with the orientation estimation, a distributed localization estimation scheme is proposed to solve the problems of multi-agent systems absolute localization estimation. The global convergence analysis is derived based on the cascade system. A distinctive advantage of the proposed distributed estimation scheme is that it can be adopted to realize accurate orientation and absolute position estimation merely using the local bearing measurements. Several numerical simulation results are provided to illustrate the effectiveness of the proposed algorithms.

关键词： Location awareness Automation Accuracy Simulation Estimation Position measurement Numerical simulation

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New ultrashort pulsewidth measurement technology based on interference jitter and FPGA platform

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Chinese Optics Letters 2022年第3期20卷 46-51页

作者： Jin Li Yanbo Dou Lixin Wang Jinhai Zou Yu Ding Hang Wang Qiujun Ruan Zhipeng Dong Zhengqian Luo Department of Electronic Engineering Xiamen UniversityXiamen 361005China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China

Conventional ultrashort pulsewidth measurement technology is autocorrelation based on second-harmonic generation;however,nonlinear crystals and bulky components are required,which usually leads to the limited wavelength range and the difficult adjustment with free-space light ***,we proposed a compact all-fiber pulsewidth measurement technology based on the interference jitter(IJ)and field-programmable gate array(FPGA)platform,without requiring a nonlinear optical device(*** crystal/detector).Such a technology shows a wide measurement waveband from 1 to 2.15μm at least,a pulsewidth range from femtoseconds to 100 ps,and a small relative error of 0.15%-3.8%.In particular,a minimum pulse energy of 219 fj is experimentally detected with an average-power-peak-power product of 1.065×10^(-6)W^(2).The IJ-FPGA technology may offer a new route for miniaturized,user-friendly,and broadband pulsewidth measurement.

关键词： pulse width measurement interference jitter FPGA platform optical device

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Adaptive Anomaly Detection in Industrial Systems: An EVT-DTS Approach with LSTM Autoencoders

Adaptive Anomaly Detection in Industrial Systems: An EVT-DTS...

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IEEE Instrumentation and Measurement technology Conference

作者： Bing Yu Jiakai Xu Gang Xiang RuiShi Lin LiGuo Zhao Yang Yu School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of System Engineering Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

The reliability of modern industrial systems is rigid; therefore, it is mandatory to monitor the system status and detect anomalies accurately. A long short-term memory (LSTM) network can be used to predict the trend of single-dimensional system test data and implement anomaly detection based on the prediction result. However, the structure of the modern system is complex, and strong dependencies may exist between different variables. The LSTM-based detection method cannot capture this dependency, and some anomalies can be ignored. Therefore, an anomaly detection framework based on the LSTM autoencoder is proposed in this paper. The auto encoder is applied to find the hidden dependency among variables by minimizing the reconstruction error of normal data, while the LSTM is used to capture the temporal dependencies in the time series. Moreover, a new dynamic error threshold selection strategy based on extreme value theory (EVT-DTS) is presented, which can avoid estimating the error distribution beforehand. The EVT-DTS method can dynamically adjust the error threshold according to the current input data error so that the overall optimal detection result can be obtained. Finally, we implement experiment on two industrial applications using the proposed method, which demonstrate its effectiveness in finding complex anomaly states in the system.

关键词： Adaptive systems Time series analysis Market research Reliability Long short term memory Anomaly detection Monitoring

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Intermittent fault diagnosis based on EMD decomposition and convolutional neural network

Intermittent fault diagnosis based on EMD decomposition and ...

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Electrical, Automation and Computer engineering (ICEACE), International Conference on

作者： Yu Peng Yuhang Liu Ruishi Lin Gang Xiang Shenhang Wang Zhiming Yang Department of System Engineering Beijing Aerospace Automatic Control Institute Beijing China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

The paper introduces an intermittent fault diagnosis method for analog circuits using EMD and CNN. The proposed method obtains the circuit output characteristics under different intermittent fault type using SPICE simulation. Then, EMD is used to extract the features and finally, the CNN model for fault detection and localization is built. The effectiveness of the method is validated through experiments in a three-phase SVPWM circuit.

关键词： Fault diagnosis Space vector pulse width modulation Training Neural networks Analog circuits Feature extraction SPICE Software Convolutional neural networks Testing

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Audio Signal Compression in a Surround Environment Using Wavelet Transform

Audio Signal Compression in a Surround Environment Using Wav...

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International Conference on Sensing technology, ICST

作者： Miyuki Shirai Shotaro Yamamoto Tomoyuki Matsumoto Mikiko Sode Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Niihama Japan Department of Electrical Engineering and Information Sience National Institute of Technology (KOSEN) Niihama College Niihama Japan

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

When a machine emits an abnormal sound, it is often necessary to take measures such as stopping the factory line. Therefore, we have developed a system that detects machine failures using sound. The feature of the proposed system is that it converts voice data into images using wavelet transform, and uses the images as input to determine abnormalities using machine learning. The important thing in this system is the size of the voice data. We would like to compress the voice data to make it easier to send. In this paper, we discuss the compression of voice signals using wavelet transform. We consider reducing the size of voice data without removing abnormal sounds contained in the voice data. We apply several different transformation methods and compare the features of abnormal sounds on the time-frequency plane.

关键词： Wavelet transforms Time-frequency analysis Image coding Machine learning Feature extraction Production facilities Sensors

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Solar Radiation Forecasting Based on Random Forest and XGBoost 7

Solar Radiation Forecasting Based on Random Forest and XGBoo...

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7th International Conference on Green technology and Sustainable Development, GTSD 2024

作者： Nguyen, Nhat-Truong Tuong Dao, Thai Cat Tuyen Nguyen, Le Nhut Nguyen, Thanh-Dat Le, Minh-Vuong French, Isobel Timothea Doan, Van-Tuong Kim Vo, Van Hoang Tran, Nghi C. Pham, Duc-Hung Nguyen, Van-Trung Ho Chi Minh City University of Technology and Education Department of Renewable Energy Viet Nam Ho Chi Minh City Industry and Trade College Department of Control Engineering and Automation Viet Nam LILAMA2 International Technology College Dongnai Viet Nam Institute of Cognitive Neuroscience National Central University Taiwan College of Information and Communication Technology Can Tho University Viet Nam Hung Yen University of Technology and Education Faculty Electrical and Electronic Engineering Viet Nam

ISBN: (纸本)9798350386073

Renewable energy offers a viable alternative to depleting fossil fuels, contributing to global warming and greenhouse gas emissions. Thus, solar energy is widely favored as a renewable energy source. However, the output power of solar systems is unstable and is highly dependent on environmental circumstances such as radiation, humidity, temperature, and wind. Therefore, accurately estimating solar radiation is crucial for successful integration of the solar system into existing electrical grid systems. In this study, two different machine learning models, XGBoost and Random Forest, were used to predict solar radiation data in Ho Chi Minh City, Vietnam. The results show that RMSE and MAE of the Random Forest model (61.09 and 24.37) is smaller compared to XGBoost (61.31 and 25.08). Regression value of the two models is close to 0.955. In summary, this paper demonstrates that these two machine learning algorithms effectively forecast solar radiation, with the Random Forest model showing better performance. © 2024 IEEE.

关键词： Solar irradiance

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Sequential generation of self-starting diverse operations in all-fiber laser based on thulium-doped fiber saturable absorber

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Chinese Physics B 2022年第6期31卷 87-90页

作者： Pei Zhang Kaharudin Dimyati Bilal Nizamani Mustafa M.Najm S.W.Harun School of Electrical and Information Engineering Huaihua UniversityHuaihua 418008China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua 418008China Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Advanced Technology and Multidiscipline Airlangga UniversitySurabaya 60115Indonesia Institute of Computer Science and Digital Innovation UCSI UniversityKuala LumpurMalaysia

Self-starting Q-switching,Q-switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first *** central wavelengths of Q-switching,Q-switched mode-locking and mode-locking operation modes are 1569.7 nm,1570.9 nm,and 1572 nm,*** mode-locking operation of the proposed fiber laser generates stable dark soliton with a repetition rate of 0.99 MHz and signal-to-noise ratio of 65 *** results validate the capability of generating soliton pulse by doped fiber saturable ***,the proposed fiber laser is beneficial to the applications of optical communication and signal processing system.

关键词： all-fiber laser Q-switched mode-locking dark soliton fiber saturable absorber

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Effective Use of Machine Learning for Analyzing Sentiments of Social Media Users

Effective Use of Machine Learning for Analyzing Sentiments o...

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MIT Art, Design and technology School of Computing International Conference (MITADTSoCiCon)

作者： P. S. Vikhe V. V. Mandhare C. B. Kadu K. S. Nagale Department of Instrumentation & Control Engineering Pravara Rural Engineering College Loni India Department of Computer Engineering Pravara Rural Engineering College Loni India Department of Information Technology Pravara Rural Engineering College Loni India

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

The impact of stress and depression on society is significant due to their widespread recognition and handicapping nature. To enhance detection of depression and stress through social networks, automatic monitoring of health play a vital role. The identification of feelings can be achieved through sentiment analysis, which involves use of processing natural language along with content mining approaches. The creation and investigation of systems and devices that have the capability to sense, comprehend, process, and imitate human effects is what computing is all about. Using sentiment analysis and deep learning, it is possible to create powerful technique and frameworks for appraisement and observation of mental issues. This paper focuses on analyzing sentiment through deep learning approach for detecting and monitor depression and stress. A multimodal model for identifying stress and depression is included in fundamental plan, which combines investigation and processing of full- feeling has been proposed in this research work.

关键词： Deep learning Sentiment analysis Art Social networking (online) Computational modeling Natural languages Psychology

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