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2024 International Conference Optoelectronic information and Optical Engineering, OIOE 2024

作者： Li, Xiaoyang Wang, Rui Tong, Xing Niu, Lianqiang Shenyang University of Technology Shenyang China Key Laboratory of Network Control Systems Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

In order to improve the detection accuracy and efficiency of surface defects on ceramic tiles, a detection model based on DeepLabV3+and combined with MobileNetv2 for feature extraction was constructed. Using ECA attention mechanism and multi branch receptive field cascade fusion structure to expand the sampling range of global and local element features for the empty space pyramid pooling module ASPP of the original model. Meanwhile, the model decoder uses depthwise separable convolutions instead of the original standard convolutions. Experimental results have shown that the improved model has an MPA value of 96.12%, a MIou value of 97.29%, an F1 value of 90.03%, and an Accuracy value of 99.89%. Compared with the previous model, the improved model's indicators have increased by 4.07%, 4.44%, 2.04%, and 1.02%, respectively. The segmentation accuracy and detection speed have been significantly improved. The experimental results fully demonstrate that the algorithm proposed in this article can effectively complete the task of detecting surface defects on ceramic tiles. © 2025 SPIE.

关键词： Image segmentation

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A Fully Distributed Hybrid Control Framework For Non-Differentiable Multi-Agent Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1792-1800页

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Yue Wei the Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the School of Electronic and Information Engineering Tongji UniversityShanghai 200082 the Peng Cheng Laboratory Shenzhen 518055China

This paper develops a fully distributed hybrid control framework for distributed constrained optimization *** individual cost functions are non-differentiable and *** on hybrid dynamical systems,we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization *** proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump *** the semistability theory of hybrid dynamical systems,the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different *** simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.

关键词： Distributed algorithm hybrid framework multiagent network non-differentiable optimization

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ASD (Autistic Spectrum Disorder) Preschool Children Socialization Using Experimental Monitored Virtual Environments

ASD (Autistic Spectrum Disorder) Preschool Children Socializ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Ksenia S. Vasilyeva Guzel Ya. Guzelbaeva Anton A. Boltushkin Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The paper presents the development and testing of a virtual reality (VR) application for the development of social skills in preschool children with autistic spectrum disorders (ASD). The application includes a system of adaptive learning scenarios based on traditional methods of working with children with ASD, an interactive virtual assistant with an advanced navigation system and based on hand gesture recognition. Special attention was paid to the development of an automatic report generation system based on the Childhood Autism Rating Scale (CARS), which analyses more than 10 different parameters of user interaction with the virtual environment to track the dynamics of the condition. These reports form the basis for analysing user interaction with the virtual environment, which is especially important for the therapy of children with ASD, as it allows to adapt the training methodology to the individual characteristics of each child and adjust the therapeutic process based on objective progress indicators. The network interaction possibility was implemented to control the therapy process in real time, including visualisation of the user's movement trajectory and analysis of the visited zones of the virtual space. Initial testing was carried out on a control group of three normotypical 4–5 year old children to collect reference values of interaction with the virtual environment.

关键词： Training Navigation Virtual assistants Virtual environments Process control Clinical trials Real-time systems Trajectory Monitoring Testing

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Image Segmentation Algorithm of Ischemic Stroke based on Wavelet Pooling Neural Network 2

Image Segmentation Algorithm of Ischemic Stroke based on Wav...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Zhu, Huiyuan Zhai, Yao Wu, Jinghe Lu, Chuwei Zhang, Xianchao Li, Ruide Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China College of Medicine Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Department of Nursing Jiaxing China Beijing Institute of Technology School of Information and Electronics Beijing China Aviation Industry Corporation of China Avic Xi' An Flight Automatic Control Research Institute Xi'an China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing China

ISBN: (纸本)9798331515669

Ischemic stroke is a high-risk brain disease that causes disability or death in adults worldwide. Rapid diagnosis and treatment, as well as segmentation of ischemic lesions from stroke medical images so that doctors can evaluate and develop treatment plans, are crucial to reducing brain damage. Currently, doctors' manual segmentation of lesions is time-consuming and labor-intensive and prone to missed or misdiagnosed diagnoses, delaying the best time for treatment. A well-trained neural network is considered as an effective aid for image segmentation tasks. However, in the traditional neural network down-sampling process, the pooling layer loses feature information, and the lost information significantly impacts the edge regions. While for medical images, edge regions are crucial features in the segmentation process. Therefore, to address the above issues, in this paper we improve upon the existing stroke lesion segmentation algorithm U-net, we design a segmentation network based on wavelet pooling and attention mechanisms. The network was tested on ISLES 2018 and ISLES 2022 datasets, and the Dice coefficient improved by 2.13% and 1.93% compared to baseline network, respectively. © 2024 IEEE.

关键词： Image segmentation

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Domain-Adaptive Power Profiling Analysis Strategy for the Metaverse

Domain-Adaptive Power Profiling Analysis Strategy for the Me...

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作者： Li, Xiang Yang, Ning Liu, Weifeng Chen, Aidong Zhang, Yanlong Wang, Shuo Zhou, Jing Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Institute of Semiconductors Chinese Academy of Sciences Beijing China College of Robotics Beijing Union University Beijing China Research Center for Multi-Intelligent Systems Beijing Union University Beijing China Information System Laboratory Beijing Microelectronics Technology Institution Beijing China

In the surge of the digital era, the metaverse, as a groundbreaking concept, has become a focal point in the technology sector. It is reshaping human work and life patterns, carving out a new realm of virtual and real interaction. However, the rapid development of the metaverse brings along novel challenges in security and privacy. In this multifaceted and complex technological environment, data protection is of paramount importance. The innovative capabilities of high-end devices and functions in the metaverse, owing to advanced integrated circuit technology, face unique threats from side-channel analysis (SCA), potentially leading to breaches in user privacy. Addressing the issue of domain differences caused by different hardware devices, which impact the generalizability of the analysis model and the accuracy of analysis, this paper proposes a strategy of portability power profiling analysis (PPPA). Combining domain adaptation and deep learning techniques, it models and calibrates the domain differences between the profiling and target devices, enhancing the model's adaptability in different device environments. Experiments show that our method can recover the correct key with as few as 389 power traces, effectively recovering keys across different devices. This paper underscores the effectiveness of cross-device SCA, focusing on the adaptability and robustness of analysis models in different hardware environments, thereby enhancing the security of user data privacy in the metaverse environment. © 2024 John Wiley & Sons Ltd.

关键词： Data privacy

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Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1140-1152页

作者： Zhenhao Xu Wen Ma Peng Lin Yilei Hua Geotechnical and Structural Engineering Research Center Shandong UniversityJinan250061China School of Qilu Transportation Shandong UniversityJinan250061China Institute of Robotics and Intelligent Systems Wuhan University of Science and TechnologyWuhan430000China

An intelligent lithology identification method is proposed based on deep learning of the rock microscopic *** on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were *** shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be *** the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative *** expanded a self-built dataset by using transfer learning and data ***,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model *** results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing ***,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology *** proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.

关键词： Deep learning Rock microscopic images Automatic classification Lithology identification

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Interactive Health Care Robot to Measure Vital Signs

Interactive Health Care Robot to Measure Vital Signs

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IEEE International Conference on systems, Man and Cybernetics

作者： Ryo Saegusa Kensuke Ohno Rikuto Okonogi Department of Information Systems Kanagawa Institute of Technology Atsugi Japan Department of Robotics and Mechatronics Kanagawa Institute of Technology Atsugi Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In order to maintain the health of aged people living in care facilities, care staffs need to check the vital conditions of the residents frequently. For the facility staffs, however, taking vital signals of residents is one of the time-consuming tasks, and the operation is strongly expected to be supported by modern robot technologies. In this study, we propose an interactive health care robot that measures vital signals of residents. In this interactive vital measurement, the health care robot gently approaches a resident and operates its arm to reach the resident's hand. The robot then interactively requests the resident to grab the haptic vital handle. The novelty of our method is in the design of human-robot interaction. On the surface of the haptic vital handle, the vital sensors and pressure sensors are arrayed in order to detect physical gripping. The vital signals of the blood oxygen saturation and heart rate are recorded only when the robot is detecting pressure patterns and confident of the hand gripping. This mechanism allows the open contact measuring without clipping resident's fingers. We performed experiments to measure vital and pressure signals from healthy adults and evaluated the reliability of the measurement in the different configurations of the body postures denoted as the stance, on-chair, on-wheelchair, and on-bed.

关键词： Heart rate Hands Medical services Robot sensing systems Reliability engineering Manipulators Pressure measurement Haptic interfaces Robots Sensor arrays

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A Lightweight YOLOv5 Model Integrating GhostNet and Attention Mechanism 4

A Lightweight YOLOv5 Model Integrating GhostNet and Attentio...

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4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Dou, Xinke Wang, Ting Shao, Shiliang School of Information Engineering Shenyang University of Chemical Technology Shenyang China Shenyang Institute of Automation State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350326444

Aiming at the problems of complex structure of neural network, large number of parameters and difficult deployment to terminal devices, this paper proposes an improved lightweight object detection model for the purpose of building a model with high classification accuracy and low complexity. First, the one-stage detector yolov5 is used as the base network. Considering the excessive redundant features of feature extraction, Ghost module is used to replace the traditional convolution, effectively allocate computing resources, to reduce the size of the model and improve the speed of model detection. When designing model bottleneck structure, Normalization-based Attention Module (NAM) is added to improve training efficiency and reduce model complexity. Experimental results show that compared with YOLOv5, the calculation speed of the proposed model is increased by 17% and the accuracy is reduced by less than 2%, which is of great significance in the environment of low-performance computing equipment. © 2023 IEEE.

关键词： Object detection

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Model Predictive Torque Control of Six-Phase Switched Reluctance Motors Based on Improved Voltage Vector Strategy

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7650-7661页

作者： Yang, Yifei Sun, Xiaodong Dianov, Anton Demidova, Galina Prakht, Vladimir Wang, Yong Han, Shouyi Suzhou Vocational University School of Mechanical and Electrical Engineering Suzhou215104 China Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China ITMO University Faculty of Control Systems and Robotics Saint Petersburg197101 Russia Ural Federal University Department of Electrical Engineering Yekaterinburg620062 Russia Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China

To rapidly and accurately establish the model of switched reluctance motors (SRMs) and enhance torque control performance, this article proposes a model predictive torque control (MPTC) strategy based on the optimized voltage vector. First, a fourth-order Fourier series is used to calculate the flux and torque models, and a complete nonlinear model of the SRM is constructed using the Kriging model. Second, the torque characteristics are used to divide the sectors, thereby reducing the number of candidate voltage vectors (CVVs) and effectively decreasing the computational burden of predictive control. Finally, an adaptive adjustment algorithm for the sector boundary angle is proposed, where the position of the sectors is determined based on variations in speed and load. The proposed method reduces torque ripple and enhances dynamic response capability. The effectiveness of this approach is validated through experiments on a 12/10 pole six-phase SRM prototype. © 2015 IEEE.

关键词： Vectors

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Deep Learning-based Paddy Doctor for Sustainable Agriculture 7

Deep Learning-based Paddy Doctor for Sustainable Agriculture

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7th International Conference on Image information Processing, ICIIP 2023

作者： Garg, Vijay Agarwal, Stuti Sharma, Sahil International Institute of Information Technology Bangalore India Panjab University Chandigarh India Ulster University School of Computing Engineering And Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798350371406

In promoting sustainable agriculture, the prompt and precise identification of plant diseases and pests becomes critical in mitigating economic setbacks via timely interventions. Utilizing EfficientNet, a deep learning-based convolutional neural network (CNN), we categorized images using open-source datasets on various rice strains. Our empirical findings confirm that our suggested model architecture attains a commendable accuracy of 91% with imbalanced classes. Discarding blurry images and employing data augmentation techniques enhanced the model's precision in rice disease categorization by an additional 8.4%. The corresponding code will be publicly accessible here. © 2023 IEEE.

关键词： Convolutional neural networks

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