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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Ming, Rui Liu, Xinyu Lin, Yuxuan Gong, Qian Minjiang University College of Computer and Control Engineering Fuzhou350108 China Fuzhou University College of Computer and Data Science College of Software Fuzhou350108 China

ISBN: (纸本)9798350331417

In UAV imagery, the intricate backgrounds combined with the high quantity and compact distribution of minute targets have consistently made target detection a formidable challenge in the realm of computer vision. This study introduces an enhancement over the YOLOv8 algorithm, wherein a sophisticated multi-scale convolutional layer, integrating depth-separable convolution, attention mechanisms, and multi-scale processing techniques, replaces the original model's convolution. Moreover, we introduce an attention mechanism for a Bi-Level Routing within the core component of the base model, and adjustments are made to the original model's loss function. Lastly, to confirm the viability of the enhanced model proposed in this paper, we conducted a validation of the metrics using publicly accessible datasets. The findings illustrate that the improved model outlined in this research substantially enhances target recognition accuracy in UAV images. Furthermore, the model exhibits superior performance in mitigating issues of duplicate detection and target omission. © 2023 IEEE.

关键词： Multi-scale Convolutions Target Detection UAV Aerial Imagery YOLOv8-based

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Identification of Important FPGA Modules Based on Complex Network

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computers, Materials & Continua 2024年第1期78卷 1027-1047页

作者： Senjie Zhang Jinbo Wang Shan Zhou Jingpei Wang Zhenyong Zhang Ruixue Wang School of Aeronautics and Astronautics University of Chinese Academy of SciencesBeijing101408China Technology and Engineering Center for Space Utilization Chinese Academy of SciencesBeijing100094China College of Control Science and Engineering Zhejiang UniversityHangzhou310063China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing ***,existing defense or detection approaches often require additional circuitry to perform security verification,and are thus constrained by time and resource *** the scale of actual engineering tasks and tight project schedules,it is usually difficult to implement designs for all modules in field programmable gate array(FPGA)*** studies have pointed out that the failure of key modules tends to cause greater damage to the ***,under limited conditions,priority protection designs need to be made on key modules to improve protection *** have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems,to identify key modules in FPGA *** the single FPGA designs,considering the topological structure,network characteristics,and directionality of FPGA designs,we propose a node importance evaluationmethod based on the technique for order preference by similarity to an ideal solution(TOPSIS)***,for the multi-FPGA designs,considering the influence of nodes in intra-layer and inter-layers,they are constructed into the interdependent network,and we propose a method based on connection strength to identify the important ***,we conduct empirical research using actual FPGA designs as *** results indicate that compared to other traditional indexes,node importance indexes proposed for different designs can better characterize the importance of nodes.

关键词： Hardware security FPGA circuits node importance interdependent network

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An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

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Modified Blood Oxygen Levels Detection 5

Modified Blood Oxygen Levels Detection

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5th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2023

作者： Sai Sunil Kumar, R.K. Ananthi, M. Mangayarkarasi, T. Darshini G, Jaya Kalaivani, U.V. Sri Sairam Engineering College Computer Science and Business Systems Chennai India Sri Sairam Engineering College Instrumentation and Control Engineering Chennai India

ISBN: (纸本)9798350325041

A gadget called an OXIMETER is used to measure blood oxygen levels. The oximeter does not produce correct results in people with scleroderma, those who have decreased tissue perfusion, such as is the case in anemia instances, and people who have digital ulcers. Scleroderma is an autoimmune illness in which the body attacks its own cells because it believes they are foreign invaders, making the skin rough and making it difficult to accurately measure blood oxygen levels. OXILASER is a gadget that combines an oximeter and a laser. The laser aids in penetrating through rough skin to easily and accurately measure blood oxygen levels. © 2023 IEEE.

关键词： Oximeters

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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Sequential Masking Imitation Learning for Handling Causal Confusion in Autonomous Driving 8th

Sequential Masking Imitation Learning for Handling Causal C...

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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Zhang, Huanghui Zheng, Zhi College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789819975891

Training agents for autonomous driving using imitation learning seems like a promising way since its only requirement is the demonstration from expert drivers. However, causal confusion is a problem existing in imitation learning, which is that with more features offered, an agent may perform even worse. Here, we aim to augment agents’ imitation ability in driving scenarios under sequential setting, by a novel method we proposed: Sequential Masking Imitation Learning(SEMI). First, we train a Vector Quantised-Variational AutoEncoder(VQ-VAE) to encode a sequence of images into a latent representation with discrete codes. After that we deploy several masks on the encoded images, the masks here will randomly hide some semantic objects in the encoded images. Finally, we design the behavior clone network as a predictor of expert action, using an encoded and masked image sequence as input, encouraging the network to make expert-like predictions when some partition of information about the environment is missing. The masking procedure in SEMI helps the imitator identify the contribution of each encoded feature to the expert’s prediction. We demonstrate that this method could alleviate causal confusion in driving simulation by deploying it to the CARLA simulator, and compared it with other methods. Experimental results show that SEMI can effectively reduce confusion in autonomous driving. The agent trained with SEMI method reduces the collision rate by 45 % compared to methods without masking procedure, and obtain the highest average survival timesteps among competing methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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An Unsupervised Leak Detection Method via Assembling Twin Attention-based Prediction Models for Natural Gas Gathering Pipelines

An Unsupervised Leak Detection Method via Assembling Twin At...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Hao Zuo, Zhonglin Zheng, Li Liang, Shan Lü, Qingguo College of Automation Chongqing University Chongqing China College of Control Science and Engineering Zhejiang University Hangzhou China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350368604

Accurately detecting leaks in natural gas gathering pipelines cannot only help enterprises and governments timely cope with the safety management problems, but also maintain the reliable operation of pipelines. The data-driven approaches for leak detection have become a preferred solution with the widely installation of sensors in pipeline network. However, these methods face challenges in achieving high identification accuracy due to the lack of labeled leak data and insufficient representation learning. To overcome this difficulty, an unsupervised leak detection method that leverages twin attention-based prediction models is proposed for these gathering pipelines in this article. First, attention-based point and sequence prediction approaches are designed by joint utilization of attention mechanism (AM) and the cascade of one-dimensional convolutional network (1D-CNN) and long short-term memory (LSTM) network, where the sequence one also employs the operation of sequence repetition and flipping for better prediction. Based on the designed attention-based point and sequence prediction approaches, an unsupervised twin attention-based prediction structure is then introduced to jointly establish the normal pipeline models. Specifically, the point prediction model is mainly used to learn the short-term dependency patterns whereas the sequence prediction model for the long-term ones from the multivariate time series. Next, a fusion strategy is presented to fuse the prediction errors generated from the twin attention-based models for the computing of overall leak scores as well as exploiting their complementary detection sensibility. To obtain clear status of pipelines without affected by abnormal points resulting from device anomalies or noises, the minimum covariance determinant (MCD) approach is adopted to attain the reliable leak scores of the fused errors. The experimental results on datasets derived from real-world gathering pipelines validate the effectiveness of

关键词： Long short-term memory

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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Interpretable Roadside 2D Object Detection Based on Axial Attention 24

Interpretable Roadside 2D Object Detection Based on Axial At...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Wu, Fan Song, Shaojing Hu, Jinyan Shanghai Polytechnic University Faculty of Intelligent Manufacturing and Control Engineering Shanghai China College of Computer and Informational Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350376647

Environmental perception plays a crucial role in the operation of autonomous vehicles. Recent studies have relied on single vehicle cameras for perception due to the lack of roadside view dataset, but this approach provides limited perception information. To address this limitation, there is a growing interest in studying roadside perception to complement vehicle-side perception information. However, existing methods face challenges in accurately recognizing and locating small objects or objects under occluding conditions in the roadside. And autonomous vehicles require millisecond-level decision-making for safe driving, yet many advanced object detection methods struggle to meet the need for high-speed reasoning. Additionally, deep learning models often make black-box decisions without providing explanations or understanding of their decision-making process. To tackle these issues, this study proposes an interpretable 2D object detection method for roadside based on the YOLO V8 with Axial Attention replacing the CNN network within the network architecture. The trained model is then input into HiResCAM to visualize the decision-making process of the roadside detection model. Experimental results on DAIR-V2X-I and Rope3D demonstrate that our proposed method not only improves mean Average Precision (mAP) but also enhances image processing speed by 1.5 to 2 times compared to existing methods. By visualizing the decision-making process of the roadside detection model, it make autonomous vehicle perception more transparent and credible. © 2024 IEEE.

关键词： Deep learning

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Interval Observer Design for Multi-Rate Systems with Multiplicative Noises

Interval Observer Design for Multi-Rate Systems with Multipl...

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2023 China Automation Congress, CAC 2023

作者： Cheng, Kaijie Li, Xin Zhu, Liangkuan Hong, Qige College of Computer and Control Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350303759

An interval observer is designed for multi-rate systems with multiplicative noises in this paper. First, a multi-rate sampling system is investigated. At the same time, the presence of multiplicative noise would couple with the multi-rate sampling mechanism, which brings a significant challenge for the estimate of the system states. To address this problem, some arithmetic rules are proposed to separate the noise-contaminated terms, and the multi-rate system is transformed into an equivalent system with a uniform sampling period using the lifting technique and augmentation methods. Based on this, an interval observer is developed to estimate the bounds of the system states, sufficient conditions to ensure the effectiveness of the interval observer, and the input-to-state stability (ISS) of the estimation error dynamics are derived. Finally, simulations verify the proposed method. © 2023 IEEE.

关键词： Phase noise

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