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13th IEEE International Conference on CYBER technology in Automation, Control, and intelligent systems, CYBER 2023

作者： Han, Qiaoni Cheng, Guowen Tang, Zhenning Liu, Chang School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China

ISBN: (纸本)9798350315196

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security. © 2023 IEEE.

关键词： Fuzzy control

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Advanced Scale-aware Attention for Enhanced Tiny Object Detection in Remote Sensing

Advanced Scale-aware Attention for Enhanced Tiny Object Dete...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Tingting Qiao He Chen Bingqian Chai Wenchao Liu Guijie Qi Yizhuang Xie National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recent advancements in remote sensing object detection have progressed, but distinguishing objects from complex backgrounds remains difficult, especially for tiny objects. To address this, we introduce the Advanced Scale-Aware Attention (ASA) module, which retains key channel information while minimizing computational cost. This module reorganizes channels into the batch dimension and splits them into multiple sub-features. The ASA module contains three branches: the first encodes weights based on scale and channel relationships. The second and third branches use different convolution kernel sizes to capture features across various receptive fields. These branches collaborate to enhance pixel-level relationships, improving foreground-background contrast. On the AI-TOD dataset, our method improved AP50 by 8.6% over state-of-the-art approaches.

关键词： Image resolution Convolution Object detection Feature extraction Data processing Sensors Object recognition Kernel Remote sensing Signal resolution

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Three-Channel Graph Convolutional Network 24

Three-Channel Graph Convolutional Network

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Meng, Lei Ye, Zhonglin Zhao, Haixing Yang, Yanlin Chen, Yang Wang, Zhaoyang College of Computer Qinghai Normal University Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China

ISBN: (纸本)9798350319934

At present, as an emerging direction of artificial intelligence, graph neural networks (GNNs) have developed rapidly and can solve many graph data problems efficiently. The real world is composed of a large number of networks. Compared with the widely studied single-relationship feature information networks, the real-world networks are mostly composed of multi-relationship feature information networks in which different types of objects are interconnected through complex relationships. Complex structural information and rich semantic information are included by multi-relationship feature information networks, thereby the real network can be restored more realistically. However, one type of relational feature of the network is merely considered by most of the existing GNNs, and the information of multi-relationship features and the hidden information between multi-relationship features are ignored in the network. In order to deal with these restrictions, a novel graph convolutional network (TCGCN) based on an attention mechanism is proposed, which aims to extract different relational features in multi-relationship feature networks and the hidden information between them more comprehensively, thereby improving the performance of GNNs in downstream tasks. In this paper, an improved attention mechanism is used to aggregate different types of single relational features, and the aggregated multiple single relational feature network information is fused with the mixed relational feature network information into the final node representation. The proposed TCGCN outperforms most other methods in extensive experiments conducted on three real-world datasets. © 2022 IEEE.

关键词： Graph neural networks

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DIR-YOLOv5: A real-time Drone-perspective Infrared object detection method based on YOLOv5

DIR-YOLOv5: A real-time Drone-perspective Infrared object de...

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2023 Applied Optics and Photonics China: Infrared Devices and Infrared technology;and Terahertz technology and Applications, AOPC 2023

作者： Yuexing, Wang Chuangang, Xu Jianzhong, Su Jinwen, Tian National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Tianjin Jinhang Institute of Technical Physics Tianjin300308 China

ISBN: (纸本)9781510672260

With the advancement of drone technology, object detection from the perspective of drones has found extensive applications in various fields, including surveillance, search operations, and reconnaissance tasks. Currently, most drones in the market are equipped with visible light imagers, while some high-end drones are equipped with infrared imaging detectors capable of performing infrared object detection tasks. Infrared imaging utilizes a passive imaging mode, enabling it to detect thermal radiation emitted by objects. As a result, it offers the distinct advantage of continuous operation without being restricted by daylight conditions. In comparison to visible imaging, infrared imaging uses longer wavelengths and possesses a certain level of penetration capability through clouds and smoke. Consequently, infrared object detection represents a significant research area within the field of object detection. However, detecting infrared objects, especially small ones, remains challenging due to the complexity of background information, lower resolution compared to visible images, and the lack of shape and texture information in infrared images. In response to these challenges, this study proposes a real-time drone-perspective infrared (IR) object detection method based on the YOLOv5 framework, known as DIR-YOLOv5. To effectively address the challenge of infrared vehicles occupying fewer pixels in the drone's perspective image and making objects difficult to detect, the coordinate attention (CA) for feature enhancement is introduced. we also introduce a Spatial-Channel dynamic and query-aware sparse attention mechanism (SCBiFormer), which is optimized based on BiFormer. Additionally, we redefine the loss function as the Repulsion Loss function to tackle the problem of infrared vehicle objects gathering and overlapping occlusion in scenarios like parking lots. Furthermore, we expand the ISVD infrared image object detection dataset to include multiple scenarios and conduct

关键词： Image enhancement

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Adaptive Neural Consensus Tracking Control for Nonlinear Multiagent systems with Dead Zone and Sensor Faults 42

Adaptive Neural Consensus Tracking Control for Nonlinear Mul...

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42nd Chinese Control Conference, CCC 2023

作者： Huang, Chengjie Chen, Ci Liu, Zhi Li, Zhenni Guangdong University of Technology Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581543

In this paper, an adaptive neural tracking control design is investigated for uncertain multiagent systems (MASs) with actuator dead zone and sensor faults. The adaptive control technique based on neural networks is employed to handle the uncertainty in the systems, which, however, results in a bounded tracking error from most existing works. To achieve the asymptotic tracking performance, we proposed a direct adaptive strategy via the tuning function method. Technically, a Lyapunov function with a series of smooth functions is designed, which benefits proposing of an asymptotic controller. Then, under the framework of direct adaptive control, an inverse compensation scheme, as well as a fault-tolerant mechanism, are considered for disposing of the actuator dead zone and sensor failures. As a result, user-preset tracking performance is achieved and the system error in the MASs converges to a predefined range. An example in the simulation section illustrates the effectiveness of the proposed method. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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SC-DVNet: A Dual-Branch Recognition Network for Synthetic Aperture Radar Images Based on Scattering Features

SC-DVNet: A Dual-Branch Recognition Network for Synthetic Ap...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Fan Chen Hao Shi Jingfei He Yifei Yin Fanyu Meng Liang Chen National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Ship classification using Synthetic Aperture Radar (SAR) images is a crucial component in marine monitoring. With the rise of deep neural networks (DNN), the abstract feature maps obtained by CNN-based methods have significantly improved classification performance. However, most current DNN-based methods are predominantly inherited from optical imagery, designed only for SAR intensity images, which may present challenges for further improving recognition performance. Therefore, this paper proposes using the point Scattering Center (SC) model for scattering feature extraction combined with graph convolutional networks for ship classification tasks. Comprehensive ablation experiments were carried out to demonstrate the efficacy of the proposed approach. Finally, experimental results on OpenSARShip1.0 dataset verify that the performance of the suggested approach notably outperforms DNN-driven techniques.

关键词： Integrated optics Image recognition Scattering Artificial neural networks Feature extraction Optical imaging Adaptive optics Optical scattering Marine vehicles Synthetic aperture radar

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A Hardware Implementation of Spaceborne SAR BP Algorithm Based on Precise Region Focusing and Hardware-Software Collaboration

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14160-14174页

作者： Li, Yongrui Chen, Liang Zheng, Pengnan Ding, Jie Zhang, Zhihan Yang, Zhu Xie, Yizhuang Li, Han Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Institute of Technology Zhuhai519088 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China

Onboard imaging of Spaceborne Synthetic Aperture Radar (SAR) is a key technology in the field of high-timeliness remote sensing applications. It adopts the hardware platform deployed on the satellite to convert the SAR radar echoes into visual images. Combined with image detection and recognition, it can rapidly downlink and distribute the effective information. At present, high-resolution and wide-swath imaging is the development trend of spaceborne SAR imaging. The Back Projection (BP) algorithm is an excellent high-resolution imaging algorithm for spaceborne SAR, but it demands large storage resources and involves heavy computations. Given the limited resources of the satellite's hardware platform, the traditional hardware implementation of the algorithm can hardly support large-scale scenes imaging. Even if it can, the imaging time is extremely long. This paper proposes an improved Cartesian Factorized Back Projection (CFBP) algorithm based on precise region focusing. By dividing the imaging sub-regions and using a stepped data reading method, the demand for storage can be significantly reduced. Moreover, a software-hardware collaboration method based on scheduling acceleration is put forward. Through constructing an ultra-long projection pipeline, the continuity and speed of the algorithm execution can be enhanced. According to the proposed method, a Projection Intellectual Property (IP) and a complete imaging system are constructed using the Xilinx VU9P Field Programmable Gate Array (FPGA) board. Experimental results show that the Projection IP can save up to 40.78% - 85.09% of Random Access Memory Blocks (BRAM) , and the projection calculation time can be reduced by up to 54.54%. The complete imaging system can process the improved CFBP projection algorithm for images of sizes 32K × 32K, 16K × 16K, and 8K × 8K within 2308.11 seconds, 598.07 seconds, and 167.81 seconds, respectively. Compared with other FPGA-related research, it supports large-size scene imagi

关键词： Random access storage

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Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track

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Science China(information Sciences) 2023年第5期66卷 196-214页

作者： Yu WANG Jiaojiao YAN Wei HUANG Leszek RUTKOWSKI Jinde CAO School of Mathematics Southeast University Jiangsu Provincial Key Laboratory of Networked Collective Intelligence Southeast University Intelligent Transportation System Research Center Southeast University Systems Research Institute of the Polish Academy of Sciences Institute of Computer Science AGH University of Science and Technology Information Technology Institute University of Social Sciences Yonsei Frontier Lab Yonsei University

Rutting deformation is typical damage to asphalt pavement and can be quantitatively described as the time-varying mechanical deformation feature of the asphalt mixture during the creep process. First,a nonlinear viscoelastic creep model is established based on the Caputo variable-order fractional *** fractional order in this model is not fixed and is defined as a time-varying function. The time-dependent mechanical properties of asphalt mixtures are efectively reflected. Based on the modified Burgers model, the variable-order function is revised to a linear form. Second, the variable-order fractional Burgers rutting model is obtained by fitting the test data of the RIOHTrack full-scale track. The Levenberg-Marquardt optimization algorithm is used to analyze the test data and the corresponding parameters to obtain more accurate and applicable variable-order fractional rutting mechanics and empirical models for diferent asphalt pavement structures. Finally, according to the classification of the pavement structure, structures III and VI are taken as examples. The rutting prediction and comparison results of several representative pavement structures are obtained to verify that the proposed rutting prediction model can also accurately predict all road sections of RIOHTrack. The new model is also compared with the rutting model of the mechanistic-empirical pavement design guide, the fractional-order Burgers rutting model, and the modified Burgers rutting model. The fitting results are evaluated using the determination coefficient R2. The results show that the variable-order fractional Burgers rutting model proposed in this study exhibits a better fit and is more applicable to describe the rutting deformation of asphalt pavement.

关键词： RIOHTrack full-scale track Caputo fractional derivative variable-order fractional modified Burgers model rutting prediction

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laboratory Power Conversion System for Controlling and Managing Renewable Energy Chains: Design and Hardware Implementation 2nd

Laboratory Power Conversion System for Controlling and Manag...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Benzazah, Chirine Mrabet, Najoua El Akkary, Ahmed Rerhrhaye, Fathallah LPFAS Laboratory of Fundamental and Applied Physics Polydisciplinary Faculty of Safi Cadi Ayyad University Safi Morocco LASTIMI Laboratory Systems Analysis Information Processing and Industrial Management Higher School of Technology of Sale Mohammed V University of Rabat Salé Morocco

ISBN: (纸本)9783031704109

The increasing demand for renewable energy sources underscores the need for efficient DC/AC power converter. This paper offerings the scheme and implementation of controlled two-level three-phase inverter aimed at regulating and supplying power from renewable sources such as solar, wind, and hydroelectric. Utilizing IGBT technology, the inverter is customized to provide power to 3-phase balanced star connected load through a passive filter. Its operation is governed by two regulation loops: one for the voltages produced at the AC output and the other for the currents flowing through the input inductor of passive filter. The control approach, developed using Matlab-Simpower systems, is executed on the eZdsp TMS320F2812 DSP board. Experimental validation involves comparing the effectiveness of bidirectional DC/AC converter with those of a compact industrial module, 7MBR15SA120. The comparison demonstrates a close alignment between the experimental waveforms of the two prototypes, confirming the effectiveness of the proposed control strategy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Insulated gate bipolar transistors (IGBT)

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Short-term load forecasting for buildings based on dynamic weighted edRVFL model

Short-term load forecasting for buildings based on dynamic w...

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2024 China Automation Congress, CAC 2024

作者： Xie, Junwei Yuan, Meng Xu, Zhezhuang Liu, Chi Xia, Yuxiong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou350000 China

ISBN: (纸本)9798350368604

Short-term load forecasting of buildings plays a key role in demand management, however, when dealing with massive data, the prevalent practice of training and testing the system as a whole overlooks pattern variations inside data and potentially limits predictive performance. To address this issue, this paper proposes an ensemble deep random vector functional link (edRVFL) model that combines adaptive ensemble clustering and dynamic weighting strategy for load forecasting of an industrial building. Firstly, the adaptive ensemble clustering that combines ordering points to identify the clustering structure and K nearest neighbor is employed to determine the electricity load usage patterns. Subsequently, the improved particle swarm optimization algorithm is applied to adjust the weights of load curves in different typical day scenarios. Finally, a dynamic weighting strategy is designed to dynamically assign model weights to two clusters, where one matches the type of predicted load pattern for the day being forecasted and another for the cluster of the previous day. The experiment in a real building verifies the effectiveness of the proposed algorithm, which has higher prediction accuracy than other comparison algorithms including one-week interval and different ensemble clustering. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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