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5th International Conference on Artificial Intelligence and computer Science, AICS 2023

作者： Gao, Han Li, Xu Cao, Jianzhao Luan, Feng Zhu, Xiaoyan Zhang, Dianhua The State Key Laboratory of Rolling and Automation Northeastern University Liaoning Shenyang110819 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China School of Computer Science and Engineering Northeastern University Liaoning Shenyang110169 China College of Engineering Training and Innovation Shenyang Jianzhu University Liaoning Shenyang110168 China

ISBN: (纸本)9781510668621

This paper proposes a low-latency method, based on FPGA, for edge extraction of hot-rolled strip. The method uses a global shutter camera module to capture strip images and detects the strip edge position by analyzing the intensity profile and the gradient of image gray values. The method has the advantages of low latency, strong robustness, and accurate results. The feasibility and effectiveness of the method are verified by FPGA simulation experiments, and a foundation is laid for the subsequent strip deviation automatic control algorithm. © 2023 SPIE.

关键词： Field programmable gate arrays (FPGA)

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Qian, Heng Li, Chuan Li, Chengli Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming China School of Physics and Electronic Information Yunnan Normal University Yunnan Key Laboratory of Opo-electronic Information Technology Kunming China

ISBN: (纸本)9798350312614

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad2/Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method. © 2023 IEEE.

关键词： distributed optical fiber sensing laser phase noise phase demodulation phase-sensitive optical time-domain reflectometry

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Community Search Based on Containment Control of Multi-Agent System with Opinion Leaders 43

Community Search Based on Containment Control of Multi-Agent...

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43rd Chinese Control Conference, CCC 2024

作者： Liu, Siyang Li, Tong Shao, Jinliang Zheng, Wei Xing University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9789887581581

Community search is the problem of identifying the community in which a given node resides. Different from traditional community search methods based on specific topological structures, this paper proposes a community search algorithm based on containment control of multi-agent system by studying the influence of opinion leaders with competitive relationships in community formation. Firstly, the Breadth-First Search method is used to locally sample the network. Secondly, containment control of multi-agent system is applied to obtain the final state of each node for local community partitioning. Finally, grounded in practical considerations, an effective leadership transfer mechanism is proposed to update the results and enhance search precision. Through experiments on real-world networks and synthetic networks in comparison with three local community detection algorithms, the effectiveness and rationality of our algorithm are demonstrated. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Multi agent systems

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Motion Sickness Alleviation Based on a Mindfulness Brain-computer Interface 16

Motion Sickness Alleviation Based on a Mindfulness Brain-Com...

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16th International Conference on Advanced Computational Intelligence, ICACI 2024

作者： Zhu, Jiawei Bao, Xiaoyu Huang, Qiyun Wang, Tao Huang, Li Han, Yupeng Li, Kendi Chen, Di Xu, Kailin Wang, Zijian Jiang, Ya Li, Yuanqing Pazhou Laboratory Dept. Research Center for Brain-Computer Interface Guangzhou China Huizhou Desay SV Automotive Co. Ltd. Huizhou China South China University of Technology Dept. School of Automation Science and Engineering China Pazhou Laboratory Dept. Research Center for Brain-Computer Interface China South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

ISBN: (纸本)9798350308501

Motion sickness is a common affliction that affects nearly half of the global population and poses challenges to comfortable travel experiences, necessitating diverse intervention strategies. Pharmacological interventions have shown promise but present limitations due to side effects. To date, there is no non- pharmacological alleviation method for motion sickness that has been proven effective in a large population. In this study, we developed a novel mindfulness brain-computer interface (mindfulness BCI) for alleviation of motion sickness. Thirty-one subjects who were susceptible to motion sickness participated in our experiments in a real-world car motion environment. The experimental results demonstrated that the proposed mindfulness BCI can effectively alleviate motion sickness symptoms, where more than 90% of subjects reported the effectiveness of the mindfulness BCI system and a significant reduction of Motion Sickness Susceptibility Questionnaire (MISC) scores was obtained when comparing the BCI-based meditation state with the resting state for the subjects (t30=4.968, P © 2024 IEEE.

关键词： Brain computer interface

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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MSDAC: A multi-source domain adversarial framework for motion prediction in intracortical brain-computer interfaces 46

MSDAC: A multi-source domain adversarial framework for motio...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Liu, Haozhou Yang, Banghua Guan, Shouliang Rong, Fenqi Guo, Mengao Fang, Ying Liu, Bingyu Gao, Yan Gu, Yong Shanghai University School of Mechatronic Engineering and Automation Research Center of Brain-Computer Engineering Shanghai200444 China Lingang Laboratory Shanghai200031 China National Center for Nanoscience and Technology Beijing100190 China Institute of Neurosciences Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9798350371499

Intracortical brain-computer interfaces (iBCIs) restore motor function in patients with paralysis by converting neural activity into control signals for external devices. However, the frequent recalibration required by current decoding methods due to turnover and loss of recording neurons poses a challenge for achieving stable online decoding. To address these issues, we propose a multi-source domain adversarial classification (MSDAC) framework for cross-day decoding that utilizes an out-of-distribution (OOD) generalization approach. This framework divides the historical data into source domains by date and employs adversarial networks to minimize the distribution discrepancies among multiple source domains, thereby achieving robust domain-invariant characteristics and superior performance on unseen test data. The MSDAC framework was evaluated using five months of monkey center-out neural activity data and demonstrated exceptional performance. Without relying on test day data for model calibration or parameter updating, the MSDAC achieved an average decoding accuracy of 84.38% (day-5 to day-150, 27968 trials). These results underscore that the MSDAC-based decoding framework can be an ideal choice for establishing stable iBCI systems. © 2024 IEEE.

关键词： Brain computer interface

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Foundation models in bioinformatics

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National Science Review 2025年第4期12卷 400-418页

作者： Fei Guo Renchu Guan Yaohang Li Qi Liu Xiaowo Wang Can Yang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Xiangjiang Laboratory Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Department of Computer Science Old Dominion University School of Life Sciences and Technology Tongji University Department of Automation Tsinghua University Department of Mathematics State Key Laboratory of Molecular Neuroscience and Big Data Bio-Intelligence Lab The Hong Kong University of Science and Technology

With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,model evaluation and *** demonstrate notable proficiency in managing large-scale,unlabeled datasets,because experimental procedures are costly and labor *** various downstream tasks,FMs have consistently achieved noteworthy results,demonstrating high levels of accuracy in representing biological entities.A new era in computational biology has been ushered in by the application of FMs,focusing on both general and specific biological *** this review,we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks,including genomics,transcriptomics,proteomics,drug discovery and single-cell *** aim is to assist scientists in selecting appropriate FMs in bioinformatics,according to four model types:language FMs,vision FMs,graph FMs and multimodal *** addition to understanding molecular landscapes,AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

关键词： foundation model bioinformatics genomics transcriptomics proteomics drug discovery single-cell analysis

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Integrating Trajectory Optimization in Quality-Diversity for Kinodynamic Motion Planning

Integrating Trajectory Optimization in Quality-Diversity for...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Konstantinos A. Asimakopoulos Konstantinos I. Chatzilygeroudis Department of Electrical & Computer Engineering Laboratory of Automation & Robotics (LAR) Patras Greece Department of Mathematics Computational Intelligence Laboratory (CILab) University of Patras Patras Greece

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Kinodynamic planning is a critical component of robotics, enabling robots to generate dynamically feasible trajectories while adhering to geometric constraints. Existing methods for kinodynamic planning primarily fall into two categories: (a) stochastic planners, such as Rapidly-Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM), which excel in state-space exploration due to their directed randomness, and (b) optimization-based approaches, which are well-suited for structured environments, producing smooth and optimal solutions. In this work, we present Hybrid Trajectory Exploration for Kinodynamic Planning (HyTraX), a hybrid framework that integrates the strengths of these two paradigms. Specifically, HyTraX enhances the effectiveness of the Go-Explore algorithm by incorporating trajectory optimization with random target selection as its core exploration strategy. We evaluate HyTraX on two challenging kinodynamic motion planning tasks: a car-like agent and a planar quadrotor navigating through maze environments. Our results demonstrate significant performance improvements with minimal task-specific tuning, highlighting the robustness and versatility of the proposed approach.

关键词： Legged locomotion Scalability Performance gain Probabilistic logic Robustness Planning Trajectory optimization Robots Tuning Quadrotors

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1st Workshop on Low-Cost Digital Solutions for Industrial automation Virtual sensor architecture for indoor air quality monitoring

1st Workshop on Low-Cost Digital Solutions for Industrial Au...

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2023 Low-Cost Digital Solutions for Industrial automation, LoDiSA 2023

作者： Merino, Jorge Ikeuchi, Daiki Moretti, Nicola Mukerjee, Anandarup Li, Luning Karatzas, Stylianos Pattinson, Sebastian Parlikad, Ajith Distributed Information and Automation Laboratory Department of Engineering University of Cambridge Institute for Manufacturing Alan Reece Building 17 Babbage Road Cambridge United Kingdom Computer Aided Manufacturing Laboratory Department of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom

Human exposure to poor air quality is a leading risk factor in the Global Burden of Disease (GBD) study, estimating 22,000 premature deaths related to indoor air pollution in 2019 in Europe. Diverse pollutants are found in manufacturing environments resulting from both combustion and non-combustion sources, including Particulate Matter and Volatile Organic Compound. Internet of Things (IoT) air quality monitoring can enhance awareness and support informed decision making towards better air quality. However, hardware sensors are not always capable of monitoring particular characteristics and behaviour of a pollutant, for instance, spatial limitations may impede deploying sensors close enough to the source of the pollutant. Virtual Sensors can extend hardware sensing options via signal processing and data integration. This paper presents an architecture for training and deploying virtual sensors. A virtual sensor is implemented using the architecture in the context of additive manufacturing to estimate the production of Volatile Organic Compounds (VOCs) of 3D printers and their transfer into the rest of the space. In the case study, the 3D printers are installed inside cabinets to limit the transfer of pollutants to the exterior. Several of these virtual sensors are deployed to monitor the VOCs produced by the 3D printers and the transfer rate out of the cabinets. The paper includes some early results and initial insights on the accuracy and usefulness of virtual sensors. Virtual Sensors can be cost-effective solutions when monitoring systems are escalated by reducing number of hardware sensors and complexity. © 2023 IET Conference Proceedings. All rights reserved.

关键词： 3D printing

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