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Chattering-Free Fault-Tolerant Cluster Control and Fault Direction Identification for HIL UAV Swarm With Pre-Specified Performance

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 183-197页

作者： Pei-Ming Liu Xiang-Gui Guo Jian-Liang Wang Daniel Coutinho Lihua Xie the Beijing engineering Research center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing the Shunde Graduate School of University of Science and technology Beijing IEEE Autonomous Intelligent Systems department Hangzhou Innovation Institute of Beihang University the department of Automation and Systems Universidade Federal de Santa Catarina the School of electrical and electronic engineering Nanyang Technological University

In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop(HIL) swarm unmanned aerial vehicles(UAVs) in the presence of possible nonidentical and unknown direction faults(NUDFs) in the yaw *** control strategy begins with the design of a pre-specified performance event-triggered observer for each individual *** observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer *** is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs' output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.

关键词： Chattering-free cluster consensus fault direction identification human-in-the-loop (HIL) nonidentical and unknown direction faults (NUDFs) pre-specified performance swarm unmanned aerial vehicles (UAVs)

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Multi-Agent Deep Reinforcement Learning for Cross-Layer Scheduling in Mobile Ad-Hoc Networks

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China Communications 2023年第8期20卷 78-88页

作者： Xinxing Zheng Yu Zhao Joohyun Lee Wei Chen department of electronic engineering and Beijing National Research center for Information Science and technology Tsinghua UniversityBeijing 100084China department of electrical and electronic engineering Hanyang UniversityAnsan 15588South Korea

Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and *** this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power *** effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement *** transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention *** the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different *** method is general and can be extended to various types of topologies.

关键词： Ad-hoc network cross-layer scheduling multi agent deep reinforcement learning interference elimination power control queue scheduling actorcritic methods markov decision process

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul Latif Md.Masud Rana department of electrical and Computer engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia center of Excellence in Intelligent engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia department of Nuclear engineering King Abdulaziz UniversityJeddah21589Saudi Arabia department of electrical and electronic engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

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National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko department of Mechanical engineering Seoul National University IEN center for human-Centric Interfaces and engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of electrical and Computer engineering Georgia Institute of Technology department of Chemical engineering Stanford University department of Mechanical engineering Ajou University George W.Woodruff School of Mechanical engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

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Machine Learning Approach to Enhance the Performance of MNP?Labeled Lateral Flow Immunoassay

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Nano-Micro Letters 2019年第1期11卷 132-146页

作者： Wenqiang Yan Kan Wang Hao Xu Xuyang Huo Qinghui Jin Daxiang Cui department of Instrument Science and engineering School of Electronic Information and Electrical Engineering Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentKey Laboratory of Thin Film and Microfabrication (Ministry of Education) Shanghai Jiao Tong University School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University department of Biomedical engineering JiLin Medical University State Key Laboratory of Transducer technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Faculty of electrical engineering and Computer Science Ningbo University

The use of magnetic nanoparticle(MNP)-labeled immunochromatography test strips(ICTSs) is very important for point-ofcare testing(POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin(HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL^(-1) and the ideal detection limit was 0.014 mIU mL^(-1), which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

关键词： Point-of-care testing Immunochromatography test strips Magnetic nanoparticles Machine learning Support vector machine

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An Improved Daugman Method for Iris Recognition

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Wuhan University Journal of Natural Sciences 2015年第3期20卷 229-234页

作者： PENG Zhiyong WANG Hongchen WU Jun LI Jun School of electrical engineering and Automation Guilin University of Electronic Technology Guangxi Experiment center of Information Science Guilin University of Electronic Technology Guangxi Key Laboratory of Automatic Detecting technology and Instruments Guilin University of Electronic Technology Editorial Board of Geomatics and Information Science of Wuhan University

An improved Daugman iris recognition algorithm is provided in this paper, which embodies in two aspects： 1 Improvement for iris localization and 2 The improvement for both iris encoding and matching algorithms. In Step 1, the localization and shape of the pupil are roughly determined in iris image, which is used as prior knowledge to quickly locate the inner and outer boundary of iris from rough to fine scale. Eyelids, eyelashes areas and the spot in the pupil are automatically detected and removed to improve the localization accuracy. In Step 2, the possible noise from residual eyelashes is further filtered by selecting a ＂pure＂ iris area as a reference and making a validation judgment pixel-wise. Furthermore, the validation flag for each pixel is introduced into the iris encoding and matching computation, as a result, the rejection rate of iris recognition is reduced. Compared with Daugman algorithm, iris recognition test on collected human eye images shows that our proposed algorithm has an obvious improvement both on boosting the speed and reducing the rejection rate.

关键词： iris recognition iris localization iris encoding

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EARSCTS:A Chinese Telephony Conversational Corpus for Speech Processing

EARSCTS:A Chinese Telephony Conversational Corpus for Speech...

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第八届全国人机语音通讯学术会议

作者： Pascale Fung Christopher Cieri human language technology center department of electrical and electronic engineering University of Science and TechnologyHong Kong Linguistic Data Consortium University of Permsylvania U.S.A

This paper describes the development and collection as well as initial analysis of the EARS (Effective, Affordable, Reusable Speech-to-text) Chinese telephony speech corpus (EARSCTS). The corpus contains 1206 ten-minute natural Mandarin conversations between either strangers or friends. The total amount of the corpus is 200 hours. There are 40 topics in all conversations and each conversation focuses on a single topic. All the speech data are recorded over public telephone networks, e.g., landline and cellular channels. All the speech data are annotated manually with standard Chinese characters (GBK) as well as specific mark-ups for spontaneous speech, time alignment information is also provided. This corpus can be used for conversational and spontaneous Mandarin speech recognition and other application-dependent tasks. The EARSCTS corpus is the largest and first of its kind for Mandarin conversational telephony speech, providing sufficient and diversified samples needed for speech training, testing, adaptation and development.

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Efficient Remote Identification for Drone Swarms

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Computers, Materials & Continua 2023年第9期76卷 2937-2958页

作者： Kang-Moon Seo Jane Kim Soojin Lee Jun-WooKwon Seung-Hyun Seo The department of electronic&electrical engineering Graduate SchoolHanyang UniversityAnsanKorea School of electrical engineering Hanyang University ERICAAnsanKorea

With the advancement of unmanned aerial vehicle(UAV)technology,the market for drones and the cooperation of many drones are *** swarms move together in multiple regions to perform their tasks.A Ground Control Server(GCS)located in each region identifies drone swarmmembers to prevent unauthorized drones from *** on drone identification have been actively conducted,but existing studies did not consider multiple drone identification ***,developing a secure and effective identification mechanism for drone swarms is *** suggested a novel approach for the remote identification of drone *** an efficient identification process between the drone swarm and the GCS,each Reader drone in the region collects the identification information of the drone swarmand submits it to the GCS for *** proposed identification protocol reduces the verification time for a drone swarm by utilizing batch verification to verify numerous drones in a drone *** prove the security and correctness of the proposed protocol,we conducted a formal security verification using ProVerif,an automatic cryptographic protocol *** also implemented a non-flying drone swarmprototype usingmultiple Raspberry Pis to evaluate the proposed protocol’s computational overhead and *** showed simulation results regarding various drone simulation scenarios.

关键词： Drone remote identification drone swarms multi-drone authentication

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Large-scale real-world radio signal recognition with deep learning

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Chinese Journal of Aeronautics 2022年第9期35卷 35-48页

作者： Ya TU Yun LIN Haoran ZHA Ju ZHANG Yu WANG Guan GUI Shiwen MAO College of Information and Communication engineering Harbin Engineering UniversityHarbin 150001China College of electronic Science and engineering National University of Defense TechnologyChangsha 410073China College of Telecommunications and Information engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China department of electrical and Computer engineering Auburn UniversityAuburn 36849USA

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems(6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system-Automatic Dependent Surveillance-Broadcast(ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning ***, we conclude this paper with a discussion of open problems in this area.

关键词： Signal recognition Radio signal dataset Automatic Dependent Surveillance-Broadcast(ADS-B) Deep learning Recognition benchmark

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A fine-grained deconfounding study for knowledge-based visual dialog

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Visual Informatics 2024年第4期8卷 36-47页

作者： An-An Liu Quanhan Wu Chenxi Huang Chao Xue Xianzhu Liu Ning Xu School of electrical and Information engineering Tianjin UniversityTianjinChina Tiandy Technologies Co. LtdTianjinChina National and Local Joint engineering Research center of Space Optoelectronics technology Changchun University of Science and TechnologyChangchunChina College of Opto-electronic engineering Changchun University of Science and TechnologyChangchunChina

Knowledge-based Visual Dialog is a challenging vision-language task,where an agent engages in dialog to answer questions with humans based on the input image and corresponding commonsense *** debiasing methods based on causal graphs have gradually sparked much attention in the field of Visual Dialog(VD),yielding impressive ***,existing studies focus on the coarse-grained deconfounding,which lacks a principled analysis of the *** this paper,we propose a fined-grained study of deconfounding on:(1)We define the confounder from two *** first is user preference(denoted as Uh),derived from human-annotated dialog history,which may introduce spurious correlations between questions and *** second is commonsense language bias(denoted as Uc),where certain words appear so frequently in the retrieved commonsense knowledge that the model tends to memorize these patterns,thereby establishing spurious correlations between the commonsense knowledge and the answers.(2)Given that the current question directly influences answer generation,we further decompose the confounders into Uh1,Uh2 and Uc1,Uc2,based on their relevance to the current ***,Uh1 and Uc1 represent dialog history and high-frequency words that are highly correlated with the current question,while Uh2 and Uc2 are sampled from dialog history and words with low relevance to the current *** a comprehensive evaluation and comparison of all components,we demonstrate the necessity of jointly considering both Uh and ***-grained deconfounding,particularly with respect to the current question,proves to be more *** studies,quantitative results,and visualizations further confirm the effectiveness of the proposed method.

关键词： Visual dialog Knowledge Deconfounding Causality

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