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Exploring changes in brain function in IBD patients using SPCCA: a study of simultaneous EEG-fMRI

Mathematical Biosciences and engineering 2024年第2期21卷 2464-2670页

作者： Zhang, Yin Wu, Xintong Sun, Jingwen Yue, Kecen Lu, Shuangshuang Wang, Bingjian Liu, Wenjia Shi, Haifeng Zou, Ling School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University Department of Radiology China School of Computer and Artificial Intelligence Changzhou University Jiangsu Changzhou213164 China Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province Hangzhou310018 China

Research on functional changes in the brain of inflammatory bowel disease (IBD) patients is emerging around the world, which brings new perspectives to medical research. In this paper, the methods of canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA), and sparsity preserving canonical correlation analysis (SPCCA) were applied to the fusion of simultaneous EEG-fMRI data from 25 IBD patients and 15 healthy individuals. The CCA, KCCA and SPCCA fusion methods were used for data processing to compare the results obtained by the three methods. The results clearly show that there is a significant difference in the activation intensity between IBD and healthy control (HC), not only in the frontal lobe (p ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Data fusion

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Student modeling considering learning behavior history with deep factorization machines 27

Student modeling considering learning behavior history with ...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Kakizaki, Toma Oeda, Shinichi Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan

To use an intelligent tutoring system (ITS) in an educational setting effectively, it is necessary to understand the skill status of students and recommend appropriate questions. Existing studies focused on improving the performance of ITS using student modeling to estimate the skill status of students. Knowledge tracing (KT) is the mainstream student modeling method, and deep learning approaches such as deep knowledge tracing and self-attentive knowledge tracing have been studied extensively in recent years. These models take only the questions solved by the student and the correct or incorrect answers to those questions as input;they do not assume the use of other features. In this study, we perform student modeling using DeepFM and FiBiNET that combines factorization machines and deep learning. Our results indicate that these models are more accurate than KT because of their ability to cope with sparse data and consider pairwise feature interactions, and more suited for real-world applications than KT. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： DeepLearning Educational data mining Factorization machines Intelligent tutoring system Knowledge tracing Student modeling

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A student modeling method combining Deep Learning and forgetting models with Knowledge Tracing 27

A student modeling method combining Deep Learning and forget...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Oeda, Shinichi Hasegawa, Shunichi Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan

Educational Data Mining (EDM) aims to enhance education by analyzing learners' skills and question difficulty levels using machine learning methods. Knowledge Tracing (KT), a subfield of EDM, utilizes Hidden Markov Models to estimate learners' abilities and predict their performance on unseen questions. While deep learning methods, such as bi-directional RNNs, have improved KT's accuracy, they may lack interpretability from an educational psychology perspective. Item Response Theory (IRT), widely used in educational statistics, offers greater explanatory potential. This study proposes a model that integrates the concept of forgetting into IRT for improved accuracy and explainability in Knowledge Tracing using bi-directional RNNs. The forgetting concept is based on Ebbinghaus' forgetting curve theory. Three experiments were conducted using synthetic data to compare a model from a previous study, a model based on the proposed method, and a model that combines the previous study's model with IRT but excludes the forgetting concept. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Deep Learning Educational Data Mining Intelligent Tutoring System Item Response Theory Knowledge Tracing Student Modeling

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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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Surveillance Video Key Frame Extraction Based on Center Offset

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computers, Materials & Continua 2021年第9期68卷 4175-4190页

作者： Yunzuo Zhang Shasha Zhang Yi Li Jiayu Zhang Zhaoquan Cai Shui Lam School of Information Science and Technology Shijiazhuang Tiedao UniversityShijiazhuang050043 China 2Department of Computer Science and Engineering Huizhou UniversityHuizhou516007 China 3College of Engineering California State UniversityLong BeachCA90185USA

With the explosive growth of surveillance video data,browsing videos quickly and effectively has become an urgent *** key frame extraction has received widespread attention as an effective ***,accurately capturing the local motion state changes of moving objects in the video is still challenging in key frame *** target center offset can reflect the change of its motion *** observation proposed a novel key frame extraction method based on moving objects center offset in this *** proposed method utilizes the center offset to obtain the global and local motion state information of moving objects,and meanwhile,selects the video frame where the center offset curve changes suddenly as the key *** processing effectively overcomes the inaccuracy of traditional key frame extraction ***,extracting the center point of each ***,calculating the center point offset of each frame and forming the center offset curve by connecting the center offset of each ***,extracting candidate key frames and optimizing them to generate final key *** experimental results demonstrate that the proposed method outperforms contrast methods to capturing the local motion state changes of moving objects.

关键词： Center offset local motion key frame extraction moving object detection

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controllable reactor based hybrid HVDC breaker

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High Voltage 2020年第5期5卷 543-548页

作者： Amir Heidary Mehdi Bigdeli Kumars Rouzbehi Department of Electrical Engineering Zanjan BranchIslamic Azad UniversityZanjanIran 2Department of System Engineering and Automatic ControlUniversity of SevilleSevilleSpain

Short circuit fault occurrence in high-voltage DC (HVDC) systems causes extremely high currents in a fast raising time that does not experience current zero-crossing. To protect HVDC systems/grids against fault current, fast HVDC breaker is an essential equipment. This study presents the design procedure of a novel HVDC breaker based on solid-state controllable reactor which is able to reduce the fault current's rate of rise and fault current amplitude to less than grid nominal current in the breaking process. The main achievement of the proposed HVDC breaker is that not only breaker does not encounter fault current, but also none of the series HVDC equipment is influenced by the fault. The designed breaker performance is studied by PSCAD/EMTPS, and then the simulation results are validated by the developed laboratory experimental setup.

关键词： fault EMTP nominal

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Robust control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

Robust Control of Feeding Speed in Coal Seam Drilling Proces...

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2024 China Automation Congress, CAC 2024

作者： Liu, Xiao Chen, Luefeng Lu, Chengda Fan, Haipeng Wu, Min Pedrycz, Witold School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey

ISBN: (纸本)9798350368604

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust H∞ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller’s effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction. © 2024 IEEE.

关键词： Coal deposits

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Nonlinear Model Predictive control for Autonomous Sailboat with Optimization of Sail Angle

Nonlinear Model Predictive Control for Autonomous Sailboat w...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Wu, Junzhuo Pan, Ya-Jun Shen, Chao Smith, Sean Witrant, Emmanuel Dalhousie University Advanced Control and Mechatronics Lab Department of Mechanical Engineering HalifaxB3H 4R2 Canada Carleton University Department of Systems and Computer Engineering OttawaK1S-5B6 Canada Université Grenoble Alpes-CNRS GIPSA-lab GrenobleF-38000 France Dalhousie University Departement of Mechanical Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798331540081

This paper studies trajectory tracking control problems of sailing vessels using a nonlinear model predictive control (NMPC) approach with a novel sail angle optimization approach. The proposed sail angle optimization method accounts for physical constraints during practical implementation such as operational sail angle bound and rate of change in sail angles. This technique also emphasizes the potential to use the sail to plan a safe trajectory, while a simple proportional-derivative (PD) controller is used for rudder angle regulation. The NMPC is implemented for trajectory tracking control in the simulation. Results show that the proposed controller can achieve excellent tracking performance in the presence of environmental disturbances in terms of ocean waves. © 2024 IEEE.

关键词： Water waves

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A distributed algorithm with network-independent step-size and event-triggered mechanism for economic dispatch problem

A distributed algorithm with network-independent step-size a...

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作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey School of Electrical Engineering Dalian University of Technology Dalian China

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.

关键词： Cost functions

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Efficient Augmentation of Human Action Recognition Datasets with Warped Windows

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Procedia computer Science 2022年 207卷 3018-3027页

作者： Dawid Warchoł Mariusz Oszust Department of Computer and Control Engineering Faculty of Electrical and Computer Engineering Rzeszow University of Technology W. Pola 2 35-959 Rzeszow Poland

In this paper, an approach to augment action recognition time series datasets, devoted to improving the accuracy of deep learning classifiers, is proposed. In the introduced method, two operators are sequentially introduced that perform linear and nonlinear modifications in the time scale of the input time series. The resulting data samples contribute to the variability within classes and allow a deep learning-based classifier to better capture their boundaries, leading to a significant improvement in the classification accuracy. The extensive experiments performed on eight publicly available action recognition datasets using the popular Bidirectional Long Short-Term Memory (BiLSTM) classifier reveal the superiority of the proposed algorithm over related approaches.

关键词： data augmentation time series skeletal data human action recognition long short term memory

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