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Concussion case classification based on brain strain waveforms using dynamic time warping and cluster analysis

Journal of Biomechanical Science and engineering 2024年第2期19卷 1-13页

作者： MIYAZAKI, Yusuke MASUDA, Hiroki Department of Systems and Control Engineering Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo152-0052 Japan

In sports, concussion prevention measures require the development of concussion evaluation criteria for improved head protective gear or early intervention through impact measurement in the field. The peak value of the brain strain has been used as a brain strain metric to evaluate the risk of brain injury. However, it extracts the instantaneous moment of strain history experienced by the brain tissue;therefore, it lacks the characteristics of the entire strain waveform experienced by the brain tissue during a head impact. This study developed a new method for classifying concussion cases based on the overall characteristics of the brain strain waveform. First, 53 head collision cases tagged with the concussion status in American football were simulated using a finite element head model, and the brain strain waveforms in four regions (right brain, left brain, brainstem, and cerebellum) were obtained. By applying the dynamic time-warping distance and clustering method to the brain strain waveforms in each case, a concussion classification method that considers the overall characteristics of the waveforms was constructed. The classification evaluation results showed that the concussion classification method had a higher performance than the conventional method using the peak value of brain strain. Brain strain waveforms in concussion cases have multiple high peaks, and it is important to understand the characteristics of the entire brain strain waveform, not just the instantaneous peak value, to classify concussion cases. © (2024), (Japan Society of Mechanical Engineers). All Rights Reserved.

关键词： Finite element method

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Remote control of Cart Platform for Alter Ego Robot by Gaze Input 13

Remote Control of Cart Platform for Alter Ego Robot by Gaze ...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Mori, Kai Shibasato, Koki Kumamoto College Electronics and Information Systems Engineering Course 2659-2 Suya Kumamoto Koshi Japan Kumamoto College Department of Control and Information Systems Engineering 2659-2 Suya Kumamoto Koshi Japan

ISBN: (纸本)9798350355079

In general, people with severe physical challenges have difficulty getting into an electric wheelchair. Therefore, an "alter ego robot"is proposed in order to realize telepresence of the challenged person. In this research, a system that uses an electric wheelchair as basic research for the cart platform of the alter ego robot is developed and enables an operator to teleoperate the robot by using gaze input even when the operator is in a supine position. In the proposed system, there are extended display and an eye tracker in the operator side. Accordingly, a new interface is devised so that the operator can use the system naturally with gaze input. This system aims to improve the quality of life of physically challenged persons. © 2024 IEEE.

关键词： Wheelchairs

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Response probability density of a single-degree-of-freedom linear system under non-Gaussian random excitation with dominant frequency 14

Response probability density of a single-degree-of-freedom l...

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14th International Conference on Recent Advances in Structural Dynamics, RASD 2024

作者： Tsuchida, Takahiro Department of Systems and Control Engineering Tokyo Institute of Technology Ookayama 2-12-1 Meguro-ku Tokyo152-8552 Japan

The response probability density function (PDF) of a single-degree-of-freedom linear system under non-Gaussian random excitation with a non-zero dominant frequency is investigated. The non-Gaussian excitation is a zero-mean stationary stochastic process prescribed by a first-order PDF and a power spectral density (PSD) with bandwidth and dominant frequency parameters. Uniform and Laplace distributions are considered for the excitation PDF. Monte Carlo simulations are performed to obtain the stationary displacement and velocity PDFs of the system. The simulation results indicate that the effects of excitation non-Gaussianity on the response PDFs vary greatly, depending on the bandwidth and dominant frequency of the excitation PSD. Particularly, the excitation non-Gaussianity appears strongly in the response PDFs when the excitation has a narrow bandwidth and the excitation dominant frequency matches the natural frequency of the system. Next, it is shown that when the excitation and response waveforms are similar, the response PDFs take a shape similar to the excitation PDF;on the other hand, when the waveforms are different, the response PDFs are nearly Gaussian, irrespective of the shape of the excitation PDF. Furthermore, as a quantitative index of waveform similarity, the maximum absolute value of the normalized cross-correlation function of excitation and response is introduced. The correspondence between the index value and the non-Gaussianity of the response PDF is revealed. © 2024 Institute of Physics Publishing. All rights reserved.

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Real-Time Intrusion Detection Based on Decision Fusion in Industrial control systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 143-153页

作者： Xue, Yawen Pan, Jie Geng, Yangyang Yang, Zeyu Liu, Mengxiang Deng, Ruilong Information Engineering University Zhengzhou450001 China Singapore University of Technology and Design ITrust 487372 Singapore The University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS10 2TN United Kingdom Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Industrial control systems (ICSs) are becoming increasingly interconnected as the rapid convergence of information technology (IT) and operation technology (OT) networks, and meanwhile massive attack surfaces have been exposed. However, traditional intrusion detection systems (IDSs) are difficult to be directly deployed in ICSs due to the hard real-time requirement and rare patching chance. Besides, the design of effective and practical IDSs is hampered by the lack of benchmarking ICS cybersecurity datasets. To bridge the gaps, this paper makes the first attempt by open-sourcing the developed ICS cybersecurity datasets and proposing a decision fusion based real-time IDS. Firstly, we design a customized cybersecurity dataset in a full-hardware and high-fidelity platform, including 7 types of cyber threats tailored for ICSs. The collected dataset includes network traffic, sensor readings, actuator status, and system parameters, providing the state-of-the-art benchmark dataset for ICSs consisting of cross-layer characteristics. Furthermore, we design an online decision fusion-based IDS by strategically integrating 4 widely-used machine learning models. The proposed IDS is deployed on a real-time running ethanol distillation, surpassing the performance of single detection models in terms of precision and F1-score, which substantially enhances intrusion detection accuracy and cybersecurity of ICS. © 2023 IEEE.

关键词： Intrusion detection

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Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks: A Multiobjective Approach

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IEEE Internet of Things Journal 2025年第11期12卷 17753-17764页

作者： Chu, Kai-Fung Chen, Tianlun Xie, Yue Lam, Albert Y. S. Song, Yue Iida, Fumiya University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Department of Geography Hong Kong Fano Labs Hong Kong Tongji University Department of Control Science and Engineering Shanghai201804 China Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai200120 China

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this article, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multiobjective mixed-integer program (MIP). To solve the optimization problem, we develop a hybrid heuristic approach based on nondominated sorting genetic algorithm II (NSGA-II) and branch-and-bound (BnB) algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors. © 2014 IEEE.

关键词： Vehicle-to-grid

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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

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Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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Data-Driven LIDAR Feedforward Predictive Wind Turbine control

Data-Driven LIDAR Feedforward Predictive Wind Turbine Contro...

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Dinkla, Rogier Oomen, Tom Wingerden, Jan-Willem Van Mulders, Sebastiaan P. Delft University of Technology Delft Center for Systems and Control Faculty of Mechanical Engineering Mekelweg 2 Delft2628 CD Netherlands Eindhoven University of Technology Control Systems Technology Group Department of Mechanical Engineering Eindhoven5600 MB Netherlands

ISBN: (纸本)9798350335446

Light Detection and Ranging (LIDAR)-assisted Model Predictive control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework. © 2023 IEEE.

关键词： Model predictive control

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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Enhancing 3D Human Skeleton Key-point Detection through Weakly Supervised Learning and Multi-level Attention Mechanisms

Enhancing 3D Human Skeleton Key-point Detection through Weak...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Xu, Meng Gong, Yuanhao Dogramadzi, Sanja University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS10 2TN United Kingdom Shenzhen University Faculty of Electronics and Information Engineering China

ISBN: (纸本)9798350383386

With the soaring interest in understanding the dynamics of human body skeletons for applications such as action recognition and video understanding, the significance of precise 3D key-point detection has become increasingly prominent. Despite the advancements, existing approaches struggle to address the issues of occlusions and limited annotated data. This paper proposes a novel framework integrating a multilevel attention mechanism and weakly supervised 3D key-point generation to tackle these prevalent issues, enhancing both the accuracy and efficiency of human pose estimation. © 2023 IEEE.

关键词： Musculoskeletal system

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RGB-D Image-Based Deep Neural Network Body Shape Classifier

RGB-D Image-Based Deep Neural Network Body Shape Classifier

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

ISBN: (纸本)9798350383386

Leveraging the untapped potential of depth information in RGB-D images, this study introduces a deep neural network classifier for advanced body shape classification. Going beyond traditional RGB image analysis, our method innovatively employs multi-task learning, simultaneously performing body shape classification, posture estimation, and body part segmentation to achieve superior accuracy. This approach promises to revolutionize personalization avenues in healthcare, fashion, and entertainment industries, establishing a new benchmark in body shape analysis. © 2023 IEEE.

关键词： Deep neural networks

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