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SSRN 2024年

作者： Zhao, Qiang Chen, Libin Chen, Qiyue Han, Yinghua Sun, Jie College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China The State Key Laboratory of Rolling and Automation Northeastern University United States Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

Fault diagnosis in complex industrial processes has become a vital issue drawing the attention in the industrial field. With the increasing deployment of sensors in industrial process equipment, deep learning (DL) based approaches have become more feasible. However, most existing DL-based approaches have two main drawbacks. Firstly, there is complex coupling between variables in industrial processes. Most existing approaches have only one metric to build the network, which cannot fully explore the potential relationships between variables. Secondly, there are few studies on aggregating critical features of different variables and reducing the interference of irrelevant and redundant information. To overcome these challenges, this paper presents a new approach for industrial process troubleshooting, called the variational modal decomposition-multi-head graph pruning network (VMD-MHGPN). This approach draws inspiration from the attention mechanism and combines it with graph construction and pruning. Specifically, we use multiple attention mechanisms to learn the interactions between nodes. Previously, variational modal decomposition (VMD) is applied to extract fault features at different time scales and use them as subsequent graph nodes. To reduce the impact of irrelevant and redundant nodes on the fault feature representation, we introduce the graph pruning operation, which uses an attention mechanism to score nodes and discard relatively unimportant ones. The final representation of fault features is generated by computing the weights of different interaction graphs based on the attention mechanism and aggregating multiple interaction graphs according to the weights. The proposed approach for diagnosing faults in industrial processes proved efficient and superior based on experimental findings from a three-phase flow apparatus and a Tennessee Eastman process. © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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An enhanced 3DCNN-ConvLSTM for spatiotemporal multimedia data analysis

An enhanced 3DCNN-ConvLSTM for spatiotemporal multimedia dat...

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作者： Wang, Tian Li, Jiakun Zhang, Mengyi Zhu, Aichun Snoussi, Hichem Choi, Chang School of Automation Science and Electrical Engineering Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Computer Science and Technology Nanjing Tech University Nanjing China Institut Charles Delaunay University of Technology of Troyes Troyes France IT Research Institute Chosun University Gwangju Korea Republic of

At present, human action recognition is a challenging and complex task in the field of computer vision. The combination of CNN and RNN is a common and effective network structure for this task. Especially, we use 3DCNN in CNN part and ConvLSTM in RNN part. We divide the video into multiple temporal segments by average and compress each segment into one feature map by pooling layer. Adding the pooling layer, dropout layer, and batch normalization layer into ConvLSTM is our groundbreaking work. We test our model on KTH, UCF-11, and HMDB51 datasets and achieve a high accuracy of action recognition. © 2019 John Wiley & Sons, Ltd.

关键词： 3DCNN action recognition ConvLSTM

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Extracting Stress-Related EEG Patterns from Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 1817-1827页

作者： Su, Cheng-Hua Ko, Li-Wei Jung, Tzyy-Ping Onton, Julie Tzou, Shey-Cherng Juang, Jia-Chi Hsu, Chung-Yao Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Department of Biomedical Science and Environmental Biology Kaohsiung807 Taiwan University of California San Diego Swartz Center for Computational Neuroscience La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan University of California San Diego Institute for Neural Computation La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Biological Science and Technology Hsinchu300 Taiwan Kaohsiung Medical University Hospital Sleep Disorders Center Department of Neurology Kaohsiung807 Taiwan Kaohsiung Medical University College of Medicine Department of Neurology Kaohsiung807 Taiwan

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution. © 2001-2011 IEEE.

关键词： Entropy

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Image Dust Storm Synthetic Method Based on Optical Model 3rd

Image Dust Storm Synthetic Method Based on Optical Model

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Huang, Jiayan Li, Zuoyong Wang, Chuansheng College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9783030624620

To address the problem that the dusty image dataset is small and difficult to collect, this paper presents a synthetic method for generating dusty image based on a classical optical model. The proposed method first learns the physical process of generating dusty image according to the classical optical model. Then, the transmission map is estimated and combined with the presupposed dust storm color map as inputs for obtaining a synthetic dusty image. Finally, considering the impact of image scene depth on the synthesis of dusty image, the proposed method selects an appropriate value of input parameter to obtain final synthetic dusty image. Experimental results on an image dataset captured in clear weather show that the synthetic dusty images obtained by the proposed method can be used as a good substitute for real dusty images. © 2020, Springer Nature Switzerland AG.

关键词： Storms

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Experimental verification of the steering ellipsoid zoo via two-qubit states

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Physical Review A 2024年第3期109卷 032415-032415页

作者： Kai Xu Lijun Liu Ning-Ning Wang Chao Zhang Yun-Feng Huang Bi-Heng Liu Shuming Cheng Chuan-Feng Li Guang-Can Guo CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 People's Republic of China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei 230026 People's Republic of China College of Mathematics and Computer Science Shanxi Normal University Taiyuan 030006 People's Republic of China Hefei National Laboratory University of Science and Technology of China Hefei 230088 People's Republic of China The Department of Control Science and Engineering and Institute for Advanced Study Tongji University Shanghai 201804 People's Republic of China

The quantum steering ellipsoid visualizes the set of all qubit states that can be steered by measuring on another correlated qubit in the Bloch picture. Together with local reduced states, it provides a faithful geometric characterization of the underlying two-qubit state so that almost all nonclassical state features can be reflected in its geometric properties. Consequently, the various types of quantum ellipsoids with different geometric properties form an ellipsoid zoo, which, in this paper, is experimentally verified via measurements on many polarization-path photonic states. By generating two-qubit states with high fidelity, the corresponding ellipsoids are constructed to certify the presence of entanglement, one-way Einstein-Podolsky-Rosen steering, discord, and steering incompleteness. It is also experimentally verified that the steering ellipsoid can be reconstructed from using the 12 vertices of the icosahedron as measurement directions. Our results aid progress in applying the quantum steering ellipsoid to reveal nonclassical features of the multiqubit system.

关键词： Quantum correlations in quantum information Quantum correlations, foundations & formalism Quantum optics

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A Dynamic Time Slot Algorithm Based on Throughput Optimization in IEEE 802.11 Wireless Networks

A Dynamic Time Slot Algorithm Based on Throughput Optimizati...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Guonian Jin Shejie Lu Siping Hu Haihong Xu Jinfeng Gao Fang Liu Haitao Wu School of Computer Science and Technology Institute of Engineering and Technology Hubei University of Science and Technology Xianning PR China International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Huanghuai University Zhumadian PR China

In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system performance degradation. Through in-depth analysis of the root cause of this problem, this paper designs a simple and effective algorithm, which uses ARMA filtering algorithm to measure the conditional collision probability and then calculate the number of nodes in the network. The AP tells all nodes in the network in the form of broadcast when the number of nodes in the network changes, after all nodes receive the broadcast sent by the AP, they dynamically adjust the network parameters accordingly. Finally, NS2 simulation software is used to conduct simulation experiments in a variety of network scenarios, and the experimental results verify that the algorithm in this paper is simple and effective, with less changes to IEEE 802.11, and is suitable for use and promotion in wireless networks. It can optimize the network system performance based on the number of nodes, improve the system throughput, and significantly improve the network performance. According to the number and density of nodes, the algorithm reduces the collision probability by dynamically adjusting the timeslot, accordingly increases the total network throughput, and achieves the purpose of effectively improving the network performance.

关键词： Degradation Wireless LAN Heuristic algorithms Wireless networks System performance Software algorithms Throughput

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ERT-GFAN: A multimodal drug–target interaction prediction model based on molecular biology and knowledge-enhanced attention mechanism

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computers in Biology and Medicine 2024年 180卷 109012-109012页

作者： Cheng, Xiaoqing Yang, Xixin Guan, Yuanlin Feng, Yihan College of Computer Science and Technology Qingdao University Qingdao266071 China School of Automation Qingdao University Qingdao266071 China School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao266071 China Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of Education Qingdao University of Technology Qingdao266071 China

In drug discovery, precisely identifying drug–target interactions is crucial for finding new drugs and understanding drug mechanisms. Evolving drug/target heterogeneous data presents challenges in obtaining multimodal representation in drug–target prediction(DTI). To deal with this, we propose ‘ERT-GFAN’, a multimodal drug–target interaction prediction model inspired by molecular biology. Firstly, it integrates bio-inspired principles to obtain structure feature of drugs and targets using Extended Connectivity Fingerprints(ECFP). Simultaneously, the knowledge graph embedding model RotatE is employed to discover the interaction feature of drug–target pairs. Subsequently, Transformer is utilized to refine the contextual neighborhood features from the obtained structure feature and interaction features, and multi-modal high-dimensional fusion features of the three-modal information constructed. Finally, the final DTI prediction results are outputted by integrating the multimodal fusion features into a graphical high-dimensional fusion feature attention network (GFAN) using our innovative multimodal high-dimensional fusion feature attention. This multimodal approach offers a comprehensive understanding of drug–target interactions, addressing challenges in complex knowledge graphs. By combining structure feature, interaction feature, and contextual neighborhood features, ‘ERT-GFAN’ excels in predicting DTI. Empirical evaluations on three datasets demonstrate our method's superior performance, with AUC of 0.9739, 0.9862, and 0.9667, AUPR of 0.9598, 0.9789, and 0.9750, and Mean Reciprocal Rank(MRR) of 0.7386, 0.7035, and 0.7133. Ablation studies show over a 5% improvement in predictive performance compared to baseline unimodal and bimodal models. These results, along with detailed case studies, highlight the efficacy and robustness of our approach. © 2024 Elsevier Ltd

关键词： Forecasting

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Decentralized Nonconvex Robust Optimization over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and Privacy Analysis

arXiv

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arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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computer Systems science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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AI-Based UAVs 3D Coverage Deployment in 6G-Enabled IoV Networks for Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Du, Pengfei Xiao, Tingyue Cao, Haotong Zhai, Daosen Gadekallu, Thippa Reddy Piran, Md. Jalil Xihua University Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Chengdu610039 China Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China Northwestern Polytechnical University Xi an School of Electronics and Information Shaanxi 710072 China Lovely Professional University Division of Research and Development Phagwara India Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

The unmanned aerial vehicle (UAV) has been widely deployed for the emergency communication recovery in 6G-enabled internet of vehicles (IoV) networks for Industry 5.0 due to its advantages such as high mobility,low cost and on-demand deployment. However,in a practical three-dimensional (3D) space urban road scenario,the obstructions from the buildings or interchange bridges,as well as the limited transmit power and quantity of UAVs,cannot satisfy the vehicles' communication quality of service (QoS) for UAV-assisted IoV networks. To address these challenges,we propose a QoS-aware UAVs 3D coverage deployment algorithm (QUCDA) to maximize the number of vehicles covered by UAVs based on the machine learning algorithms. The QUCDA takes into account the unique 3D urban environment characteristics and the QoS requirements of IoV networks,which are crucial factors that traditional optimization algorithms often overlook. More specifically,the proposed QUCDA improves on traditional genetic algorithm (GA) and consists of two key steps: first,a novel approach is developed to generate the initial population utilizing the K-means clustering algorithm to improve the quality of the solution. Subsequently,we propose the K-means initialized grey wolf optimization (KIGWO) algorithm to improve the diversity through a random replacement strategy. The experimental evaluation is conducted using a realistic 3D urban environment dataset,including the road locations,heights,and other environment details. Abundant simulation results demonstrate that,compared with the traditional GA,the particle swarm optimization (PSO) and the sine cosine algorithm (SCA),the proposed QUCDA can make the communication coverage reach 100%,effectively improve the system capacity and the coverage rate. The result is expected to be useful for achieving future smart city in 6G-enabled IoV networks for Industry 5.0. © 1975-2011 IEEE.

关键词： Genetic algorithms

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