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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Lamperti, Gianfranco Zhao, Xiangfu Department of Information Engineering University of Brescia Brescia25123 Italy School of Computer and Control Engineering Yantai University Yantai264005 China

Automated diagnosis has always been a challenging task to AI. When model-based diagnosis is adopted, a model of the system is required in order to generate a set of diagnoses based on a collection of observations, where a diagnosis is a set of faulty components or, more generally, a set of faults ascribed to components. An active system (AS) is an asynchronous, distributed discrete-event system, whose model consists of a topology (how components are connected to one another), and a communicating automaton for each component (the mode in which a component reacts to events). A problem afflicting all model-based approaches to diagnosis is a possibly large number of diagnoses explaining the observations, which may jeopardize the task of a diagnostician in charge of monitoring the system, owing to the cognitive overload raised by an overwhelming number of faulty scenarios to examine. This is exacerbated in critical application domains, where, under uncertain conditions, an artificial agent is supposed to perform recovery actions in real-time, even in the order of milliseconds, to possibly restore the system. To make diagnosis of ASs viable in critical, real-time application domains, a Smart Diagnosis Engine is presented, which is grounded on two heuristics: (1) if a diagnosis δ is a superset of a diagnosis δ′, then δ is ignored (minimality);(2) if the cardinality (number of faults) of a diagnosis δ is lower than the cardinality of a diagnosis δ′, then δ is generated before δ′ (sorting). Consequently, the diagnosis output consists in a sequence of minimal diagnoses that are generated in ascending order by cardinality. As indicated by the experimental results, the overall improvement is twofold: most likely diagnoses are generated upfront, thereby supporting real-time recovery actions;also, the abductive search in the behavior space of the AS is reduced considerably, owing to the pruning of the trajectories that will not generate minimal diagnoses, thereby resulting in an

关键词： Finite automata

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Combining Multi-Scale Feature Fusion and Attention Mechanism to Achieve Pulmonary Nodule Classification 17

Combining Multi-Scale Feature Fusion and Attention Mechanism...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： He, Naixin Dong, Zhengyu Li, Lin Wang, Jinxia Ma, Chaoqing School of Computer and Control Engineering Yantai University China Yantaishan Hospital Affiliated Binzhou Medical University China

ISBN: (纸本)9798331507398

Lung cancer is the deadliest form of cancer, with most cases originating from small malignant nodules. However, the early symptoms of malignant nodules are not obvious, which can lead to misdiagnosis and delay the optimal treatment time. Therefore, differentiating between benign and malignant lung nodules is crucial for the early diagnosis and treatment of lung cancer. The advent of convolutional neural networks has made it possible to achieve rapid classification of pulmonary nodules. Most classification networks use a convolution kernel size of 3, but due to the limited receptive field, the classification of benign and malignant nodules remains inaccurate. To address this, we use larger convolution kernels to increase the receptive field, capture more detailed feature information, and enhance the ability of model to represent complex patterns and details. Furthermore, we fuse features from multiple scales to mitigate the impact of scale differences. We also employ an attention mechanism to obtain global information, highlight important areas, and suppress irrelevant parts. We use the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to evaluate our model. Compared to other classification methods, our approach achieved superior performance, with an accuracy of 0.9855 and an F1score of 0.9901. © 2024 IEEE.

关键词： Lung cancer

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Data Security Storage Mechanism Based on Blockchain Network

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computers, Materials & Continua 2023年第3期74卷 4933-4950页

作者： Jin Wang WeiOu Wenhai Wang RSimon Sherratt Yongjun Ren Xiaofeng Yu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Control Science and Engineering Zhejiang UniversityHangzhou310058China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Business Nanjing UniversityNanjing210093China

With the rapid development of information technology,the development of blockchain technology has also been deeply *** performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage *** the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the *** traditional blockchain system uses theMerkle Tree method to store *** verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and *** order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication *** realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

关键词： Blockchain cryptographic commitment smart contract data storage

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The Estimation Method of Multi-target Parameters for OTFS Radar Sensing

The Estimation Method of Multi-target Parameters for OTFS Ra...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Chang, Quanran Wang, Jixuan Tian, Tuanwei Deng, Hao Du, Xiaolin Henan University School of Physics and Electronics Kaifeng475001 China Yantai University School of Computer and Control Engineering Yantai264005 China

ISBN: (纸本)9798350375909

In high mobility beyond 5G communications, orthogonal time frequency space (OTFS) is a promising alternative to orthogonal frequency division multiplexing. In addition to the superior communication performance, OTFS is also a natural choice for radar sensing. In this work, we investigate the estimation problem with multi-target parameters for the OTFS-based radar sensing system. To begin with, we recap the fundamental concepts in OTFS and present the input-output relation of OTFS symbols in the delay-Doppler domain. Then, we conceive a multitarget parameter estimation algorithm for range and velocity parameter estimation. Finally, numerical results demonstrate that the proposed algorithm can get the multi-target parameters with a high estimation accuracy. © 2024 IEEE.

关键词： Radar target recognition

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Advancing Epilepsy Disease Classification through Machine Learning and Deep Learning Models Utilizing EEG Data. 17

Advancing Epilepsy Disease Classification through Machine Le...

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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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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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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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