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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Saleem, Adnan Khan, Muhammad Ali Yousaf, Hafiz Muhammad NCEPU School of Control and Computer Engineering Beijing China University of Lahore Department of Software Engineering Pakistan

ISBN: (纸本)9798350381320

Epilepsy disease is a neurological condition marked by recurring seizures that has a big effect on people's life. Effective management and therapy depend on a prompt and correct diagnosis. The traditional methods, including medical history checks and MRI-based diagnoses, are costly, time-consuming, and often create incorrect results. In order to overcome the drawbacks of conventional and expensive diagnostic techniques, this work offers a machine-learning and deep learning-based method for the classification of epilepsy disease utilizing EEG signal data. The proposed study uses the EEG signal dataset collected from the UCI machine learning repository. This study shows the potential for automating the classification of epilepsy disease by using EEG signal data, opening up a potentially new path for the early detection of disease. Proposed CNN model achieved the highest accuracy of 98.4% among all classifiers, which is quite good. © 2023 IEEE.

关键词： Deep learning

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Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

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Chinese Physics B 2023年第10期32卷 273-282页

作者：马璐陈梅辉何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health *** physiological complexity of the cardiac system is related to age,disease,*** the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time *** propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological *** obtained results suggest that these two network measures show distinct changes between different *** is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac ***,the complexity of the cardiac system is *** that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed *** of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two ***,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

关键词： electrocardiogram signals multivariate recurrence networks cross-clustering coefficient entropy multiscale analysis

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A review of intelligent optimization for group scheduling problems in cellular manufacturing

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Frontiers of engineering Management 2023年第3期10卷 406-426页

作者： Yuting WANG Yuyan HAN Dunwei GONG Huan LI School of Computer Science Liaocheng UniversityLiaocheng 252059China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Given that group technology can reduce the changeover time of equipment,broaden the productivity,and enhance the flexibility of manufacturing,especially cellular manufacturing,group scheduling problems(GSPs)have elicited considerable attention in the academic and industry practical *** are two issues to be solved in GSPs:One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each *** a number of studies on GSPs have been published,few integrated reviews have been conducted so far on considered problems with different constraints and their optimization *** this end,this study hopes to shorten the gap by reviewing the development of research and analyzing these *** literature is classified according to the number of objective functions,number of machines,and optimization *** classical mathematical models of single-machine,permutation,and distributed flowshop GSPs based on adjacent and position-based modeling methods,respectively,are also *** but not least,outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.

关键词： cellular manufacturing group scheduling flowshop literature review

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Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

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Chinese Physics B 2023年第11期32卷 401-407页

作者：马璐任彦霖何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free ***, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

关键词： epilepsy EEG signal horizontal visibility graph complex network

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Research on Frequency Regulation with Dynamic Trajectory Planning of Participation Factors

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Distributed Generation and Alternative Energy Journal 2024年第4期39卷 751-776页

作者： Hu, Hongbin Zhao, Jiayu Li, Qiang Jing, Zhibin Guo, Qi Yang, Zhihao Co. Ltd Hohhot010020 China Control and computer engineering school North China Electric Power University 102206 China Co. LTD Hohhot010020 China

The energy transformation has led to a high proportion of new energy entering the grid. New energy has high randomness, which challenges the frequency stability of the power system. A new approach to improve frequency based on dynamic trajectory planning of participation factors is proposed in this work. In the frequency regulation interval, the trajectory and termination time of participation factors are regarded as the variables to be optimized. Then, taking the frequency regulation performance and economy as the goals, a multi-objective optimization problem is constructed. The participation factors are used to dispatch the output power of different units, which breaks the constraint that the output characteristics of different units are consistent in the original regulation process. Therefore, the generator units with different regulation characteristics in the system achieve complementary coordination on the second time scale. In simulation cases, frequency oscillation is reduced, while the overall economy of the system is improved. The numerical evaluation results show that the frequency performance is improved by 2.7% at most and 2.9% of the economy is improved at most simultaneously. The above results demonstrate the effectiveness of the proposed approach. © 2024 River Publishers.

关键词： Frequency stability

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Annotated survey and perspectives on rail transport energy system RAMS evaluation technology

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Green Energy and Intelligent Transportation 2024年第3期3卷 75-90页

作者： Ruifeng Shi Lingzhi Zhang Feng Lin Jin Ning Limin Jia Kwang Y.Lee School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China China Institute of Energy and Transportation Integrated Development Beijing 102206China State Key Lab of Rail Traffic Control&Safety Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering Baylor UniversityWacoTX 76798USA

The rail transit system plays a crucial role in modern *** the increasing demand for clean and green energy in the transport sector,its energy system is expected to achieve low-carbon and highly efficient energy utilization in rail ***,the gradual development of the rail transport energy system has led to an increase in its complexity,and the rising difficulty of system assessment has faced the limitations of traditional assessment ***,it is essential to develop effective assessment *** paper begins by providing a systematic review of the development status of Reliability,Availability,Maintainability and Safety(RAMS)assessment and analyzing the shortcomings of traditional RAMS assessment technology in the context of rail transit energy ***,based on the four fundamental properties of RAMS,it summarizes the current state of key assessment technologies in the field of rail ***,the paper delves into the challenges and potential solutions concerning the implementation of RAMS assessment technology for rail transit energy ***,the paper offers an outlook on the future development of RAMS assessment for rail transport energy *** comprehensively analyzing these aspects,the paper aims to contribute valuable insights into optimizing the rail transit energy system,promoting its sustainable and efficient operation in the context of clean and green energy utilization.

关键词： Integration of energy and transportation RAMS evaluation Rail transit energy system

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PSRMER: Proactive Services Recommendation Driven-by Multimodal Emotion Recognition

PSRMER: Proactive Services Recommendation Driven-by Multimod...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Zhizhong Huang, Guangyu Chu, Dianhui Sun, Yuhang Yantai University School of Computer and Control Engineering Yantai China Harbin Institute of Technology School of Computer Science and Technology Weihai China

ISBN: (纸本)9798350304855

In the past three years, global COVID-19 pandemic not only impacted people's physical health but also significantly affected their mental health, which resulting in rapid increase of psychological problems. Emotions are a common manifestation of psychological changes, and some services (such as music, video, or psychological counseling services) can help users to adjust their emotions in a timely manner, thus to avoid bringing extreme events (e.g., running away from home or committing suicide). Therefore, how to perceive users' real-time emotions and then recommend the most appropriate services to users has become a challenge. To address this issue, this work proposes an approach for proactive services recommendation driven-by multimodal emotion recognition (named as PSRMER). Specifically, PSRMER first actively identifies a user's emotion with a multimodal emotion recognition model based on BiGRU and Transformer;Then, considering the user's emotion and preferences, PSRMER selects the optimal services based on an index-graph linking different emotions and various services;Finally, PSRMER proactively recommends the selected optimal service to the user. Extensive experiments have been conducted and the effectiveness of our proposed method have been proved. Moreover, the proposed method can also be used in smart education, smart transportation, smart elderly care and other modern industry fields. © 2023 IEEE.

关键词： COVID-19

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Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation 38

Efficient Adaptation of Pre-trained Vision Transformer via H...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dong, Wei Sun, Yuan Yang, Yiting Zhang, Xing Lin, Zhijun Yan, Qingsen Zhang, Haokui Wang, Peng Yang, Yang Shen, Hengtao College of Information and Control Engineering Xi'an University of Architecture and Technology China School of Computer Science Northwestern Polytechnical University China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Tongji University China

A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adaptation matrix. This matrix is decomposed into a product of down-projection and up-projection matrices, with the bottleneck dimensionality being crucial for reducing the number of learnable parameters, as exemplified by prevalent methods like LoRA and Adapter. However, these low-rank strategies typically employ a fixed bottleneck dimensionality, which limits their flexibility in handling layer-wise variations. To address this limitation, we propose a novel PEFT approach inspired by Singular Value Decomposition (SVD) for representing the adaptation matrix. SVD decomposes a matrix into the product of a left unitary matrix, a diagonal matrix of scaling values, and a right unitary matrix. We utilize Householder transformations to construct orthogonal matrices that efficiently mimic the unitary matrices, requiring only a vector. The diagonal values are learned in a layer-wise manner, allowing them to flexibly capture the unique properties of each layer. This approach enables the generation of adaptation matrices with varying ranks across different layers, providing greater flexibility in adapting pre-trained models. Experiments on standard downstream vision tasks demonstrate that our method achieves promising fine-tuning performance. © 2024 Neural information processing systems foundation. All rights reserved.

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Object Tracking Algorithm Using Siamese Attention Mechanism based Key Point Network 23

Object Tracking Algorithm Using Siamese Attention Mechanism ...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Zhang, Feng Liu, Guifu Yuan, Shuai Kong, Xiangjie Geng, Jinyu School of Electrical and Control Engineering Shenyang Jianzhu University China School of Computer Science and Engineering Shenyang Jianzhu University China

ISBN: (纸本)9798400708237

The tracker based on Siamese network usually adopts the cross-correlation of convolutional features between the object branch and the search branch to describe the visual object tracking task as a similarity matching. However, most trackers apply backbone networks to represent multi-scale features in a hierarchical manner, which has limitations in distinguishing similar object. In addition, the feature information on each channel and spatial region is treated equally in the calculation process. In fact, the feature importance of these locations is not the same, and the anchor box detection mechanism requires setting a large number of anchor boxes with redundant hyperparameters, which can lead to computational and memory overload. As for these problems, this paper proposes an object tracking algorithm using siamese attention mechanism based key point network, called SAMR (Siamese Attention Mechanism Reppoints). First of all, the backbone network res2net is used to represent fine-grained multi-scale features on a hierarchical basis for increasing the receptive field of each layer. Then the channel and spatial attention information of the attention mechanism is to enhance the discriminant ability and positioning ability of the feature map. Finally, a set of key points anchor-free frames is adopted to detect and track the object. Experiments on challenging datasets such as VOT2018, VOT2019, and OTB100 illustrate that the SAMR proposed in this paper is superior to other advanced trackers in tracking accuracy. © 2023 ACM.

关键词： Object detection

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Accurate Modeling of CHP Plant by Digital Twin and Transformer Neural Network 3

Accurate Modeling of CHP Plant by Digital Twin and Transform...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Hou, Guolian Zhang, Tianhao Guo, Zhiqiang Huang, Ting Li, Qingwei North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798350318470

This paper proposes an accurate modeling method for CHP plant by combining the digital twin(DT) and Transformer. Firstly, the actual operating data of the CHP plant is real-time affined to the simulation platform through DT. As Transformer has the advantages of strong robustness, parallel computing and wide applicability. It is used to establish the accurate model of supercritical CHP plant The innovation of this modeling method lies in the successfully combination of DT and Transformer, which has important reference value for promoting the flexible operation capacity of CHP plant. Finally, the effectiveness of the proposed model method is confirmed through the simulations on a 350 MW CHP plant. © 2023 IEEE.

关键词： Simulation platform

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