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Vision-Based Adaptive Prescribed-Time Control of UAV for Uncooperative Target Tracking with Performance Constraint

Journal of systems science & Complexity 2024年第5期37卷 1956-1977页

作者： SHE Xuehua MA Hui REN Hongru LI Hongyi School of Automation and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of TechnologyGuangzhou 510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou 510006China College of Electronic and Information Engineering and Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing 400715China

This paper discusses the uncooperative target tracking control problem for the unmanned aerial vehicle(UAV)under the performance constraint and scaled relative velocity constraint,in which the states of the uncooperative target can only be estimated through a vision *** the limited detection range,a prescribed performance function is designed to ensure the transient and steady-state performances of the tracking ***,the scaled relative velocity constraint in the dynamic phase is taken into account,and a time-varying nonlinear transformation is used to solve the constraint problem,which not only overcomes the feasibility condition but also fails to violate the constraint ***,the practically prescribed-time stability technique is incorporated into the controller design procedure to guarantee that all signals within the closed-loop system are *** is proved that the UAV can follow the uncooperative target at the desired relative position within a prescribed time,thereby improving the applicability of the vision-based tracking *** results have been presented to prove the validity of the proposed control strategy.

关键词： Nonlinear transformation performance constraint prescribed-time tracking uncooperative target vision-based measurement

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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Non-Autoregressive Sign Language Translation: A Transformer Encoder-only Approach for Enhanced Multimodal Integration 24

Non-Autoregressive Sign Language Translation: A Transformer ...

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2024 International Conference on Advanced Robotics, Automation engineering and Machine Learning, ARAEML 2024

作者： Du, Yao Xie, Pan Peng, Taiying Hu, Xiaohui School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400717116

Sign language translation aims to transform continuous sign language videos into text, thereby mapping sign language actions onto natural language. Current research primarily focuses on the Transformer's encoder-decoder architecture, employing an autoregressive (AR) approach for SLT. This one-by-one token generation method has a slow speed to translate. We design a Non-Autoregressive Sign Language Translation (NA-SLT) model based on Transformer Encoder. The proposed model consists of three components: The text-encoder, sign-encoder, and sign-Text-encoder. Both the text-encoder and sign-encoder utilize the Encoder to model single-modality data. The sign-Text-encoder effectively engages with multi-modal data, producing high-quality corresponding text after minimal refinement iterations. Moreover, during model training, we devise a double-mask strategy. In addition to traditional random masking, the schedule allocates mask probabilities based on each word's frequency, ensuring long-Tail tokens have an increased chance of being masked and receiving extensive training. We evaluate the model on PHOENIX-2014T, and the results indicate that NA-SLT approaches the performance of AR models on BLEU (21.06) and ROUGE(49.59) metrics while significantly improving translation speed (5.4x-7.9x). © 2024 ACM.

关键词： Metadata

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Clipping discrete multi-tone for peak-power-constraint IM/DD optical systems

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science China(information sciences) 2023年第5期66卷 227-237页

作者： Ji ZHOU Liangchuan LI Jiale HE Xiaofeng LU Yu BO Guanyu WANG Yuanda HUANG Gengchen LIU Yanzhao LU Shecheng GAO Yuanhua FENG Shancheng ZHAO Weiping LIU Changyuan YU Zhaohui LI Department of Electronic Engineering College of Information Science and Technology Jinan University Optical Research Department Huawei Technologies Co. Ltd. Department of Electronic and Information Engineering The Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

High peak-to-average power ratio(PAPR) is a major drawback of discrete multi-tone(DMT) in peak-power-constraint(PPC) intensity-modulation/direct-detection(IM/DD) optical systems. Symmetric clipping operation is a frequently-used and practical method for mitigating the PAPR of the DMT ***, clipping noise is inevitably induced by the symmetric clipping operation, thereby deteriorating the system's performance. In this paper, we study the statistical characteristics of the clipping noise in *** on the statistical characteristics, clipping-noise-cancellation(CNC) algorithms are proposed to mitigate the clipping noise in the clipping DMT systems. Theoretically, the clipping noise can be completely removed using the low-density-parity-check-assisted CNC algorithm(LDPC-assisted CNC algorithm). To verify the superiority of the clipping DMT with the CNC algorithm, we conduct an experiment with a50 Gb/s DMT system using 10G-class commercial devices for the scenario of PPC passive optical *** receiver sensitivity of the clipping DMT system with the CNC algorithm can reach-24 d Bm at a 20%soft-decision forward-error-correction limit, which is 2.5 d B higher than that of the DMT system without clipping operation. The clipping operation and the CNC algorithm are added after the original digital signal processing, thereby demonstrating the potential to efectively overcome the drawback in DMT signals and other high-PAPR signals.

关键词： discrete multi-tone peak-to-average power ratio peak-power constraint clipping-noise cancellation intensity-modulation/direct-detection optical systems

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Real-Time Event Recognition of Long-Distance Distributed Vibration Sensing With Knowledge Distillation and Hardware Acceleration

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IEEE Internet of Things Journal 2025年第11期12卷 17896-17905页

作者： Luo, Zhongyao Wu, Hao Ge, Zhao Tang, Ming Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Next Generation Internet Access National Engineering Laboratory Hubei Optics Valley Laboratory School of Optical and Electronic Information Wuhan430074 China

Fiber-optic sensing, especially distributed optical fiber vibration (DVS) sensing, is gaining importance in internet of things (IoT) applications, such as industrial safety monitoring and intrusion detection. Despite their wide application, existing post-processing methods that rely on deep learning models for event recognition in DVS systems face challenges with real-time processing of large sample data volumes, particularly in long-distance applications. To address this issue, we propose to use a four-layer convolutional neural network (CNN) as the student model with ResNet as the teacher model for knowledge distillation. Compared to the baseline CNN model, which achieves 83.41% accuracy on data from previously untrained environments, the distilled CNN improves accuracy significantly to 95.39%, demonstrating its superior generalizability and robustness. Additionally, we propose a novel hardware design based on field-programmable gate arrays (FPGA) to further accelerate model inference. This design replaces multiplication with binary shift operations and quantizes model weights, enabling high parallelism and low latency. Our implementation achieves an inference time of 0.083 ms for a spatial-temporal sample covering a 12.5 m fiber length and 0.256 s time frame. This performance enables real-time signal processing over approximately 38.55 km of fiber, about 2.14× the capability of an Nvidia GTX 4090 GPU. The proposed method greatly enhances the efficiency of vibration pattern recognition, promoting the use of DVS as a smart IoT system. The data and code are available at https://***/HUST-IOF/Efficient-DVS. © 2014 IEEE.

关键词： Multilayer neural networks

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Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

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Computers, Materials & Continua 2024年第10期81卷 685-706页

作者： Yali Si Feng Li Shan Zhong Chenghang Huo Jing Chen Jinglian Liu School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China Suzhou Industrial Intelligence and Big Data Laboratory Suzhou215000China School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang524088China

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning ***,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive *** better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal ***,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time ***,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active *** further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,*** experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation *** specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

关键词： Location-based social networks adaptive successive point-of-interest recommendation long short-term preference trajectory sequences

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Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan Business University 430056China Wuhan Research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China School of Information and Safety Engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN Deployment optimization Confident information coverage(CIC) Target perpetual coverage

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Fault-induced gear meshing modulation sideband extraction and evaluation for fault diagnosis of planetary gearboxes

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science China(Technological sciences) 2025年第1期68卷 335-351页

作者： Peng ZHOU Xiaoluo YU Yang YANG Qingbo HE Zhike PENG State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Ningxia UniversityYinchuan 750021China Science and Technology on Vehicle Transmission Laboratory China North Vehicle InstituteBeijing 100072China

Gearboxes,known for their compact size and stable transmission capability,are widely used as power transmission structures in various types of mechanical equipment,such as wind turbines,helicopters,and special ***,due to harsh and non-stationary working conditions,gear surfaces often deteriorate,leading to faults such as wear,pitting,and ***,it is vital to monitor the working status of gearboxes and diagnose gear faults as early as *** faults can induce characteristic modulation effects near the gear meshing frequency(GMF),resulting in the appearance of faultinduced sidebands in a vibration *** of these sidebands allows for the diagnosis of gear faults in a ***,when faced with a planetary gearbox having a complex configuration,strong background noise and additional faultunrelated sidebands can interfere with fault-induced sidebands,making accurate diagnosis *** address these challenging issues,this paper proposes a novel fault-induced gear meshing modulation sideband extraction *** the process,the sidebands directly related to the gear fault near the GMF are first identified and then extracted by a variational harmonic mode decomposition(VHMD)***,a fault-related gear meshing modulation component(GMMC)can be accurately reconstructed by summing up the extracted fault-induced *** the GMMC,the gear fault severity can be assessed by evaluating its amplitude modulation(AM)*** superior performance of the proposed method is finally demonstrated by experimental data.

关键词： planetary gearbox gear meshing frequency modulation sideband signal decomposition fault diagnosis

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Refined modeling and experimental verification of a torque motor for an electro-hydraulic servo valve

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Chinese Journal of Aeronautics 2023年第6期36卷 302-317页

作者： Zhichuang CHEN Shenghong GE Yulei JIANG Wenhao CHENG Yuchuan ZHU National Key Laboratory of Science and Technology on Helicopter Transmission Nanjing University of Aeronautics and AstronauticsNanjing 210016China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing Engineering Institute of Aircraft SystemsNanjing 210061China AVIC Nanjing Servo Control System Co. LtdNanjing 210032China

The Permanent Magnet Torque Motor(PMTM)is the key electro-mechanical conversion device in an Electro-Hydraulic Servo Valve(EHSV).In this work,a refined model of a PMTM is developed,considering the non-working air-gaps between the upper or lower yoke and the armature,the fringing effect at the limiting holes,and the nonlinear permeability of soft magnetic *** on the refined model,the influences of various factors on the calculation accuracy of the magnetic flux at the pole surfaces of the armature and the output torque are *** verifying the validity of the refined model,a Finite Element Analysis(FEA)of the PMTM is conducted,and a test platform is *** with existing models,the refined model can better reveal the intrinsic mechanism of the PMTM,and its calculations are more consistent with the FEA *** experimental results of the armature deflection displacement show that the refined model can accurately describe the output characteristics of the PMTM.

关键词： Calculation accuracy Electro-hydraulic servo valve Experimental verification Finite element analysis Refined model Torque motor

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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