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arXiv 2024年

作者： Zhu, Huizhi Xu, Wenxia Huang, Jian The Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

In this paper, we propose a graph neural network, DisGNet, for learning the graph distance matrix to address the forward kinematics problem of the Gough-Stewart platform. DisGNet employs the k-FWL algorithm for message-passing, providing high expressiveness with a small parameter count, making it suitable for practical deployment. Additionally, we introduce the GPU-friendly Newton-Raphson method, an efficient parallelized optimization method executed on the GPU to refine DisGNet’s output poses, achieving ultra-high-precision pose. This novel two-stage approach delivers ultra-high precision output while meeting real-time requirements. Our results indicate that on our dataset, DisGNet can achieves error accuracys below 1mm and 1deg at 79.8% and 98.2%, respectively. As executed on a GPU, our two-stage method can ensure the requirement for real-time computation. Codes are released at https://***/FLAMEZZ5201/DisGNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Newton-Raphson method

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Fast Node Selection of Networked Radar Based on Transfer Reinforcement Learning 5th

Fast Node Selection of Networked Radar Based on Transfer R...

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5th IFIP TC 12 International Conference on Intelligence science, ICIS 2022

作者： Cao, Yanjun Wang, Yuan Guo, Jingjing Han, Li Zhang, Chao Zhu, Jin Zhang, Tianyang Zhang, Xiangrong The 54th Research Institute of China Electronics Technology Group Corporation Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Xian710071 China National Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9783031149023

The networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and network collaboration. However, due to the large number and wide range of nodes in the networked radar system, there exists a redundancy problem in radar node assignment, which causes additional resource consumption and slows down the task execution speed of radar node selection. To solve the above problem, this paper proposes a fast radar node selection method based on transfer reinforcement learning to quickly select the optimal and minimum node resources. The proposed method devises a novel reward function for the Monte Carlo Tree and a different termination state of iteration to select the minimize the number of radar nodes. In order to further accelerate the selection of radar nodes, transfer reinforcement learning is presented to fully leverage the previous knowledge. Experimental results show that our proposed method can quickly select the optimal and minimum radar nodes in a brief period, significantly improving the speed of radar node selection in the networked radar. © 2022, IFIP International Federation for Information processing.

关键词： Reinforcement learning

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A new technique for high-fidelity cutting technology for hydrate samples

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Journal of Zhejiang University-science A(Applied Physics & Engineering) 2022年第1期23卷 40-54页

作者： Hai ZHU Jia-wang CHEN Zi-qiang REN Pei-hao ZHANG Qiao-ling GAO Xiao-ling LE Chun-ying XU Kai HE Peng ZHOU Feng GAO Yu-ping FANG Institute of Ocean Engineering and Technology Ocean CollegeZhejiang UniversityZhoushan316021China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou511458China Key Lab of Digital Signal and Image Processing of Guangdong Province School of EngineeringShantou UniversityShantou515063China

Designing a high-fidelity cutting device is one of the difficulties in hydrate samples pressure-holding *** to the limitations of the existing mechanical system,there is much damage to the cut surface of hydrate samples,with many chips produced,which seriously affects the quality of *** this paper,a new cutting device utilizes two servo motors to achieve a high degree of *** the Archimedes spiral,it achieves low disturbance of the cut surface and provides accurate control of the *** addition,due to the operation of the sample long-stroke push unit,cutting hydrate samples of any length with almost no chips within a short cutting time can be *** and sea tests have achieved all design requirements of the equipment and strongly demonstrate its benefit and *** is concluded that this new high-fidelity cutting technology is practically *** physical state of the hydrate can be maintained to the greatest extent,and thus the new equipment provides significant support for the exploration and development of hydrate resources.

关键词： High-fidelity Cutting technology Hydrate samples Automation

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A demonstration of vector symbolic architecture as an effective integrated technology for AI at the network edge 2

A demonstration of vector symbolic architecture as an effect...

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Artificial Intelligence for Security and Defence Applications II 2024

作者： Bent, Graham Davies, Cai Vilamala, Marc Roig Li, Yuhua Preece, Alun Di Caterina, Gaetano Sola, Alex Vicente Kirkland, Paul Pearson, Gavin Tutcher, Benomy Neurosynapse Ltd. United Kingdom Security Crime and Intelligence Institute Cardiff University United Kingdom Neuromorphic Sensor Signal Processing Lab. EEE Dept. University of Strathclyde United Kingdom Defence Science and Technology Laboratory Porton Down United Kingdom Frazer-Nash Consultancy Ltd. Bristol United Kingdom

ISBN: (纸本)9781510681200

Vector Symbolic Architecture (VSA), a.k.a. Hyperdimensional Computing has transformative potential for advancing cognitive processing capabilities at the network edge. This paper presents a technology integration experiment, demonstrating how the VSA paradigm offers robust solutions for generation-after-next AI deployment at the network edge. Specifically, we show how VSA effectively models and integrates the cognitive processes required to perform intelligence, surveillance, and reconnaissance (ISR). The experiment integrates functions across the observe, orientate, decide and act (OODA) loop, including the processing of sensed data via both a neuromorphic event-based camera and a standard CMOS frame-rate camera;declarative knowledge-based reasoning in a semantic vector space;action planning using VSA cognitive maps;access to procedural knowledge via large language models (LLMs);and efficient communication between agents via highly-compact binary vector representations. In contrast to previous ‘point solutions’ showing the effectiveness of VSA for individual OODA tasks, this work takes a ‘whole system’ approach, demonstrating the power of VSA as a uniform integration technology. © 2024 SPIE.

关键词： Photons

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Adversarial attacks and defenses in physiological computing:a systematic review

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National science Open 2023年第1期2卷 62-90页

作者： Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China

Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

关键词： physiological computing brain-computer interfaces health informatics biometrics machine learning adversarial attack

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On the use of DPCA in process fault detection and identification

On the use of DPCA in process fault detection and identifica...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Mohamed Guerfel Hassani Messaoud Electronics Department Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Laboratory of Automatic Control Signal and Image Processing (LARATSI) National Engineering School of Monastir University of Monastir Monastir Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

This paper proposes an innovative Dynamic Principal Component Analysis (DPCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method is proposed to fix the data lag and the number of principal components to retain. A new structuration method is introduced to identify the process fault. It is based on the computation of common angles between residual subspaces of different modes. This structuration yields a concise set of detection indices, specifically sensitive to certain process faults while remaining unsensitive to others. The proposed FDI scheme is successfully applied to a three tank affected by several process faults.

关键词： Fault diagnosis Automation Fault detection Computational modeling Systems modeling Data models Principal component analysis

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Memristor-Based Circuit of Sequential Associative Memory with Memory Interactions and Stimulus Similarity Effect

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Xiong, Dongdong Wang, Xiaoping Jiang, Yiming Yang, Chao Jiang, Man Lai, Jingang Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Braininspired Intelligent Systems Wuhan430074 China Huazhong University of Science and Technology Institute of Artificial Intelligence School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Huazhong University of Science and Technology Department of Physiology School of Basic Medicine and Tongji Medical College Wuhan430030 China

Most proposed memristor-based circuits of associative memory consider various mechanisms in only one associative memory. Few works on circuit design of sequential associative memory have been reported. In this paper, a memristor-based circuit of sequential associative memory with memory interactions and stimulus similarity effect is designed. Two associative memories, the prior associative memory (PAM) and the later associative memory (LAM), are formed successively. The PAM modifies the rate of the formation of the LAM, and the LAM, in turn, affects the strength of the PAM, which are two interactions in sequential associative memory, called transfer and retroaction. In addition, the magnitudes of transfer and retroaction are determined by the similarity of the conditioned stimuli. The above functions are realized by the ring input processing module, memory module, transfer module and retroaction module. It has been identified in circuit analysis that the proposed circuit is power efficient and has good robustness. Furthermore, the proposed circuit has promising applications for fire rescue robots. © 2016 IEEE.

关键词： Associative storage

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A hybrid renewable energy system for Hassi Messaoud region of Algeria: Modeling and optimal sizing

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Energy Storage and Saving 2025年第1期4卷 56-69页

作者： Bourek, Yacine Messini, El Mouatez Billah Ammari, Chouaib Guenoune, Mohamed Chabira, Boulerbah Saha, Bipul Krishna Department of Electrical Engineering Faculty of Applied Sciences University of Ouargla Ouargla30000 Algeria Laboratoire LAGE Faculty of Applied Sciences University of Ouargla Ouargla30000 Algeria School of Production Engineering and Management Technical University of Crete Chania73100 Greece Sonatrach - Algerian Petroleum Institute - Hassi Messaoud School Hassi Messaoud30001 Algeria Department of Renewable Energy Faculty of Hydrocarbons Renewable Energy Science Earth and Universe University Kasdi Merbah-Ouargla Ouargla30000 Algeria Laboratory of Image Processing and Radiation University of Sciences and Technology Houari Boumediene Algiers 16111 Algeria Indian Institute of Science Bangalore560012 India

The growing global energy demand and the need to mitigate greenhouse gas emissions have driven the exploration of sustainable and efficient energy solutions. In Algeria, where the energy sector relies heavily on fossil fuels, integrating renewable energy systems is essential for enhancing energy security and reducing environmental impacts. This study focuses on optimizing a hybrid renewable energy system (HRES) for off-grid applications in the Hassi Messaoud region of Algeria to balance technical performance, economic viability, and environmental sustainability. A hybrid system consisting of photovoltaic (PV) panels, wind turbines (WTs), fuel cells (FCs), and diesel generators (DGs) was modeled and optimized using a genetic algorithm (GA). The optimization process aims to minimize the annual cost of the system while ensuring high reliability, as measured by the loss of power supply probability, and maximizing the use of renewable energy. A particle swarm optimization (PSO) approach was also implemented for comparison, highlighting the advantages of the GA in terms of cost distribution and system reliability. The optimized HRES demonstrated that renewable sources (PV and WT) provided 77% of the total energy demand, with an overall system cost of 0.18080 $·kWh−1, significantly lower than recent studies, which reported costs between 0.213 and 0.609 $·kWh−1. FCs contributed 14% to the load, whereas DGs were limited to 8% to minimize emissions, resulting in annual CO2 emissions of 10,865 kg and a relative emission rate of 3.608 gCO2eq·kWh−1. Economic analysis showed that DGs and FCs accounted for 44% and 24% of the annual cost, respectively, highlighting the impact of backup systems in ensuring reliability. Sensitivity analysis under varying load demands and renewable energy availability confirmed the robustness of the system, and the GA approach was found to be more effective than PSO in maintaining cost efficiency and reliability. Additionally, the social analysis high

关键词： Clean energy

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Feature Extraction through MS Lesion and Cerebral Lobe Segmentation for Enhanced EDSS Prediction

Feature Extraction through MS Lesion and Cerebral Lobe Segme...

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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Nada Haj Messaoud Neirez Zina Manel Mili Rihab Ben Dhia Asma Ben Abdallah Mohamed Hedi Bedoui Medical Technology and Image Processing Laboratory Univ. of Monastir Tunisia Higher Institute of Computer Science and Mathematics of Monastir Monastir Tunisia Faculty of Medicine of Monastir Univ. of Monastir Tunisia

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

In this paper, we present a hybrid approach for segmenting multiple sclerosis (MS) lesions and predicting disability progression. Using Concat-UNet for accurate MS lesion segmentation and a multi-class UNet for brain lobe segmentation, we enable regional analysis of lesion impact. By fusing MRI, clinical, and demographic data, we enhanced EDSS prediction, with LightGBM achieving a high R2 score (0.96 ± 0.07)

关键词： Multiple sclerosis Accuracy Magnetic resonance imaging Feature extraction Lesions Informatics

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Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

arXiv

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arXiv 2024年

作者： Chen, Xiaoqing Wang, Ziwei Wu, Dongrui The Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs. To mitigate this problem, we propose alignment-based adversarial training (ABAT), which performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary. The integration of data alignment and adversarial training can make the trained EEG classifiers simultaneously more accurate and more robust. Experiments on five EEG datasets from two different BCI paradigms (motor imagery classification, and event related potential recognition), three convolutional neural network classifiers (EEGNet, ShallowCNN and DeepCNN) and three different experimental settings (offline within-subject cross-block/-session classification, online cross-session classification, and pre-trained classifiers) demonstrated its effectiveness. It is very intriguing that adversarial attacks, which are usually used to damage BCI systems, can be used in ABAT to simultaneously improve the model accuracy and robustness. © 2024, CC BY.

关键词： Adversarial machine learning

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