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IEEE Conference on Global communications (GLOBECOM)

作者： Tao Wang Yichen Wang Hong Liu Haotian Liu Zhou Su School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China North Automatic Control Technology Institute Taiyuan China School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In the upcoming sixth generation (6G) era, dynamic routing relying on link state information update is crucial for global data service in large-scale low-earth orbit (LEO) satellite networks. However, the existing dynamic routing methods use a static link state update scheme where all satellites distribute their link state information with the same fixed period, while the link state of the satellites are different and vary dynamically. This makes it difficult to achieve the balance among various network performance metrics such as link state update accuracy, signaling overhead, network throughput, and energy efficiency. To solve this issue, we propose an adaptive link state update scheme for the LEO satellite network, where each satellite can dynamically adjust its own link state distribution interval according to the observation on the inter satellite links (ISLs). Based on the proposed scheme, we define the information deviation to characterize the accuracy of the link state update and derive the signaling overhead of link state distribution. To improve further the network performance, a multi-objective optimization problem (MOP) is formulated to minimize the information deviation and the signaling overhead simultaneously. By applying the weighted sum method, we convert the formulated MOP into a single-objective optimization problem (SOP). Then, we adopt the distributed reinforcement learning approach and develop the deep Q-network (DQN) algorithm for each satellite to learn its optimal link state distribution decision strategy based on local information. Simulation results demonstrate the superiority of the proposed scheme.

关键词： 6G mobile communication Satellites Adaptive systems Accuracy Simulation Low earth orbit satellites Routing Deep reinforcement learning Orbits Optimization

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SBRDF Hyperspectral Ray Tracing Rendering for 3D Representation Based on Target Surface Material 8th

SBRDF Hyperspectral Ray Tracing Rendering for 3D Representat...

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8th International Symposium of Space Optical Instruments and Applications, ISSOIA 2023

作者： Yi, Jia Jiang, Huilin Wang, Xiaoyong Ying, Du Graduate School Changchun University of Science and Technology Changchun130022 China School of Physics Changchun University of Science and Technology Changchun130022 China Beijing Institute of Space Mechanics and Electricity Beijing100190 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China Fundammontal Science On Space-Ground Laser CommunicationTechnology Laboratory Changchun University of Science and Technology Changchun130022 China Key Laboratory of Education Ministry Optoelectronics Measurement and Control and Optical Shapingba China

ISBN: (纸本)9789819767175

Spectral technology is one of the key means for long-distance non-contact detection and identification of materials. It is an important force for ground-based detection satellites which can play a role in monitoring satellite operation status and safety, and identifying satellite types except for radar. However, for complex targets detected at ultra long distances, the detection object is equivalent to a point source, and the distribution of surface materials is not clear. Usually, material spectral unmixing can only focus on the distribution of planar materials, making it difficult to analyze the target structure. This article uses the spectral BRDF function to construct a hyperspectral ray tracing rendering 3D representation method based on the relationship between illumination, target surface normals, and detection points. It obtains a 3D rendering of a single material on a composite target, laying the foundation for analyzing the structural features of long-distance point target detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rendering (computer graphics)

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Study of aircraft skin defect detection and characterization methods

Study of aircraft skin defect detection and characterization...

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International Conference on Information, Communication and Networks (ICICN)

作者： Xinfeng Wang Qing Liu Kun Jia Yan Zhang Haiyin Zhang Jia Zhen Kaida Shang School of Automation and Information Engineering Xi’an University of Technology Xi’an China Xi’an Aerocomm Measurement and Control Technology Co. Ltd Xi’an China

With the development of modern vehicle functions and technologies, surface geometry defects such as dents and cracks generated during the assembly and service of a vehicle not only endanger flight safety and reduce load capacity but also limit its functional characteristics. Accurate detection of surface geometry defects is critical, and these defects must be controlled within a small margin of error. However, conventional methods have difficulty meeting practical requirements in terms of detection accuracy and measurement size. In this paper, based on the analysis of the structural characteristics of the aircraft surface, an aircraft skin defect detection and characterization method is proposed, which processes the defect point cloud data, combines the segmentation algorithm of area growth to detect defects, and analyzes the geometric analysis and characterization through the depth of defects to achieve the accurate measurement of defects, and the experimental results show that the measurement of round holes with different depths of standard parts, the measurement results and the true value of The error are less than 0.05mm; the measurement of the outer surface of the aircraft engine compartment can detect different types of defects such as scratches or dents, and the method in this paper can be extended to the defect detection field of other large industrial equipment

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Artificial Intelligence in Next Generation IoT Infrastructure: Challenges and Opportunities 1

Artificial Intelligence in Next Generation IoT Infrastructur...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Arabelli, Rajeshwarrao Chotaliya, Dhwanit K. Gadhave, Rajashree Tukaram Haritha, B. Kasireddy, Lakshmi Chandrakanth Soni, Mukesh School of Engineering SR University Department of Electronics and Communications Engineering Warangal India GEC-Rajkot Department of Instrumentation and Control Engineering Gujarat Rajkot360005 India Pillai HOC College of Engineering and Technology Rasayani Tal. Khalapur MH 410207 India St. martin's Engineering College Department of ECE Hyderabad India ThoughtSpot Inc Enterprise DevSecOps Architect R&D - Engineering FranklinTN United States Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331518578

Numerous facets of the public realm could be revolutionised by the convergence of the Internet of Things (the Internet of Things) and Artificial Intelligence (AI). This project developed a based-on data predictive maintenance system for manufacturing production lines. One of these anticipated improvements that can be included into the factory's production system is 'AI of Things (AIoT),' which refers to the use of the IoT to carry out intelligent tasks with the aid of AI integration. By using the information created by IoT sensors progressively, the system utilizes machine learning (ML), a part of AI, to recognize expected blunders preceding they happen. The viability of the innovation was surveyed utilizing IoT information from genuine assembling frameworks, and the results of the appraisal demonstrated that the prescient support structure was effective in distinguishing potential failure indicators and could assist with forestalling some production stops. Comparing the performance of ML techniques revealed that frameworks of the bagging ensemble technique Random Forest and the boosting technique XGBoost appeared to outperform each of the techniques in the assessment. The factory's production system now uses the best-performing ML techniques from the present research. © 2025 IEEE.

关键词： Predictive maintenance

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Satellite Power System Anomaly Detection based on Spatio-Temporal Uncertainty Fusion

Satellite Power System Anomaly Detection based on Spatio-Tem...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Zhipeng Wang Yanjiao Gao Jingyue Pang Yuchen Song Yu Peng School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China School of Artificial Intelligence Chongqing Technology and Business University Chongqing China

Satellites have been widely used for military and civil applications. The power system is critical for the satellite’s operating safety. However, satellite power system anomaly monitoring is faced with challenges like large amounts of data, insufficient anomaly samples, and complex operating conditions. To address these challenging issues, this paper proposes a data-driven anomaly detection method that fuses the time-related historical data and spatial-associated sub-system monitor data. Firstly, the probabilistic prediction model is used to establish the prediction model based on time and space The corresponding prediction uncertainties of the temporal and spatial models are quantitatively represented. Then, the stochastic distribution model is introduced to fuse the two uncertainties to obtain the predicted mean value and fusion uncertainty of the current power system state. The mean value is taken as the expected result of the state, and the fused uncertainty interval is taken as the threshold of abnormal judgment to realize the detection of the abnormal condition. The telemetry data of the actual satellite power system are used for experimental and comparative analysis. The results show that the false negative rate of anomaly detection results can be effectively reduced, and the detection accuracy can be improved through the fusion of spatio-temporal uncertainty.

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State-of-charge Estimation of Li-ion Battery Cells based on Distribution of Relaxation Times and Gaussian Mixture Model

State-of-charge Estimation of Li-ion Battery Cells based on ...

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Impedance Spectroscopy (IWIS), International Workshop on

作者： Dhia Neifar Ahmed Yahia Kallel Ferdaous Chaabane Olfa Kanoun Higher School of Communications of Tunis Carthage University Ariana Tunisia Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany COSIM Research Lab Higher School of Communications of Tunis Carthage University Ariana Tunisia

Li-ion batteries are gaining popularity and are widely used today. However, the current methods used to estimate the state of charge (SoC) of these batteries are quite limited and prone to drift-related errors, resulting in higher inaccuracies as the battery cell ages. Despite efforts to improve the material, the problem of determining the SoC of Li-ion batteries remains challenging. This paper presents a new method for SoC determination based on direct impedance spectroscopy measurement to address this issue. The proposed method involves applying the Distribution of Relaxation Times (DRT) and fitting a Gaussian mixture model (GMM) to the DRT results to obtain machine-interpretable data. Afterward, standard scaling is performed before classifying the SoC using a Support Vector Machine. measurement data from four Li-ion battery cells at three different temperatures and 22 cycles were used to validate this method. The results showed that the SoC can be obtained with an accuracy of 98.4% ± 1.2% without relying on open-circuit voltages and currents.

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Minimizing Data Collection Latency for Coexisting Time-Critical Wireless Networks with Tree Topologies

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2354-2369页

作者： Zhang, Jialin Liang, Wei Yang, Bo Shi, Huaguang Liang, Ying-Chang Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Northwest A and F University College of Information Engineering Yangling712100 China Henan University School of Artificial Intelligence Zhengzhou450046 China University of Electronic Science and Technology of China Center for Intelligent Networking and Communications Chengdu611731 China

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot. © 2004-2012 IEEE.

关键词： Scheduling algorithms

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Research on Resonance Suppression Method of Multi-Inverter Parallel System Based on Improved Fcs-Mpc

SSRN

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SSRN 2024年

作者： Chunhui, Liang Li, Jinfa Zuo, Xiaoyang Huang, Chenglong Liu, Renjie School of Electrical and Information Engineering Changchun Institute of Technology Changchun130025 China National local joint Engineering Research Center for Measurement and Control and Safe Operation Technology of Intelligent Distribution Network Changchun130103 China

A mathematical model of the multi-NPC three-level LCL inverter system is constructed with the goals of suppressing the resonance of the multi-inverter grid-connected system, improving the output power quality, and improving the pure output current waveform on the grid side of the system. Firstly, The system's resonance characteristics are studied by the equivalent NORTON model of multiple inverters in parallel. Secondly, because of the resonance existing in the multi-inverter parallel system, by integrating active damping and improving the Finite control Set Model Predictive control (FCS-MPC) method, realize the harmonic design in both the low- and high-frequency domains. It can successfully reduce the resonance phenomenon and raise the system's general stability. Meanwhile, It is possible to greatly lower the harmonic distortion rate and enhance the output current waveform. Finally, two parallel NPC three-point flat LCL filters inverters were built for simulation analysis, and the suggested analysis method's viability and accuracy were confirmed. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis

arXiv

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arXiv 2025年

作者： Bai, Runci Xu, Guibao Shi, Yanze Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing100080 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China

Brain tumors can lead to neurological dysfunction, cognitive and psychological changes, increased intracranial pressure, and seizures, posing significant risks to health. The You Only Look Once (YOLO) series has shown superior accuracy in medical imaging object detection. This paper presents a novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The SCConv module optimizes convolutional efficiency by reducing spatial and channel redundancy, enhancing image feature learning. We examine the effects of different attention mechanisms with YOLOv9 for brain tumor detection using the Br35H dataset and our custom dataset (Brain_Tumor_Dataset). Results indicate that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art performance in brain tumor detection. Copyright © 2025, The Authors. All rights reserved.

关键词： Health risks

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Multi-Pedestrian Tracking by Decoupling Confidence Weighting and Hierarchical Appearance Embedding Updates

Multi-Pedestrian Tracking by Decoupling Confidence Weighting...

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Communication technology and Information technology (ICCTIT), International Conference on

作者： Guopei Zeng Luping Wang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd Zhejiang China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

The advent of powerful object detectors has led to the widespread adoption of the “tracking-by-detection” paradigm in multi-object tracking (MOT). However, challenges such as association errors during occlusions continue to hinder tracking performance. To address this issue, we propose three key improvements. First, we leverage the confidence scores generated by a detector with decoupled heads to weight the cost matrix. Second, we introduce a height-constrained intersection-over-union (IoU) distance metric. Finally, we classify the appearance features of objects in the current frame into two levels—high and low—based on their respective confidence scores. These features are then matched and updated against stored appearance embeddings from previous frames. Extensive comparative and ablation experiments conducted on the MOT17 dataset demonstrate that the proposed method significantly improves tracking performance in multi-object tracking scenarios.

关键词： measurement Costs Detectors communications technology Information technology

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