检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Zhang, Haifei Ge, Hongwei Yang, Jieming Su, Shuzhi Tong, Yubing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China Key Laboratory of Advanced Process Control for Light Industry Jiangnan University Ministry of Education Wuxi214122 China School of Computer Science and Engineering Anhui University of Science & Technology Anhui Huainan232001 China Medical Image Processing Group Department of Radiology University of Pennsylvania PhiladelphiaPA19104 United States

In the process of urban logistics distribution, how to achieve efficiently and timely logistics distribution within the distribution time limit has become an urgent problem to be solved in logistics management. Three-echelon logistics distribution is considered to be an effective strategy to alleviate urban congestion, improve liquidity and reduce pollution. In this paper, a mathematical model of three-echelon logistics distribution problem considering time window constraint is established based on directed graph, and a double-tier intelligent algorithm coordinated by Distance Entropy-based Attractor Propagation (DEBAP) clustering algorithm - Crossover-and-Selection based Differential Evolution (CSBDE) is proposed. DEBAP is responsible for the optimization of region partitioning and facility allocation. The obtained region partitioning and facility allocation scheme are transmitted to CSBDE for path planning. CSBDE feeds back the optimized path planning scheme to DEBAP, and the two-tier intelligent algorithm cooperates to complete the optimization of three-echelon logistics distribution system. In addition, this paper proposes a search strategy based on reachable distribution region and a facility allocation optimization strategy based on path length as weight to improve the efficiency of the algorithm. The example calculation shows that the mathematical model and combined intelligent algorithm proposed in this paper can effectively solve 3-echelon logistics distribution problem(3E-LDP), provide theoretical and methodological guidance for multi-echelon logistics distribution, and can be well implemented in practice to help logistics operators reduce operation costs and improve customer service. © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

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Resilient Distributed Predefined Time Secondary control for Cyber-Physical Microgrids

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IET Renewable Power Generation 2025年第1期19卷

作者： Junfeng Tan Fan Zhang Yanlu Huang Shuai Zhao Hongyu Su China Southern Power Grid Digital Grid Research Institute Co. Ltd. China Southern Power Grid Artificial Intelligence Technology Co. Ltd. Guangzhou China School of Artificial Intelligence and Automation and Technology and also with the Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China

This paper proposed a resilient distributed predefined-time sliding mode control for islanded AC microgrids with external disturbances caused by noisy circumstances or cyber-attacks. By utilizing the predefined-time convergence theory, the voltage regulation and frequency restoration as well as active power sharing can be achieved within a predefined time, which is directly equal to an adjustable parameter. Furthermore, based on the integral sliding mode control approach, the proposed method can completely compensate the external disturbance. Different from some voltage control methods based on complex second-order consensus, a novel secondary controller is designed by adopting the virtual control technique, such that the voltage regulation can be achieved under a first-order consensus with a corresponding tracking controller. In addition, the direct Lyapunov method is utilized to prove the stability of islanded AC microgrids under the proposed controller, and the analysis of predefined-time convergence is also given. Finally, case studies on a microgrid test system with four distributed generator is built in the MATLAB/SimPowerSystems software environment are conducted to demonstrate the effectiveness and superior performance of the proposed control scheme.

关键词： distributed power generation Microgrids robust control

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A novel TSK fuzzy system incorporating multiview collaborative transfer learning for personalized epileptic EEG detection

arXiv

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arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology

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Medicine-engineering interdisciplinary researches for addiction: Opportunities and challenges

Meta-Radiology

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Meta-Radiology 2023年第2期1卷

作者： Wen, Xinwen Du, Zhe Wang, Zhen Xu, Yu Wang, Kunhua Yu, Dahua Liu, Jun Yuan, Kai School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China Yunnan University Yunnan Kunming 650500 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou 014010 China Department of Radiology Second Xiangya Hospital Central South University Changsha 410011 China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'an 710126 China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China

Advances in neuroscience have suggested that addiction is not only an ongoing dynamic transaction between the person, their behavior, and the environment, but a disturbance of neurotransmitters in neurons involved in reward, memory, and cognitive control. Although the neural mechanism of addiction is complex and heterogeneous, previous addiction recovery and treatment studies have proven that addiction is amenable to treatment and recovery. At present, addiction treatment faces great challenges as well as many opportunities. The field of biomedical engineering is and will be at the forefront of these accelerating disciplines based on advances in imaging, genomics, systems medicine, molecular and cellular engineering, and other biomedical sciences. Thus, we propose several perspectives to accelerate the development of advanced and scientific approaches for addiction treatment: early prevention is better than cure and addiction treatment should focus on individualized regulation, whole-body interaction and the translation of effective animal models to human. © 2023 The Authors

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Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)

arXiv

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arXiv 2020年

作者： Liu, Ziang Jiang, Xue Luo, Hanbin Fang, Weili Liu, Jiajing Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Civil Engineering and Mechanics Huazhong University of Science and Technology China

Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervised, which means the sampling process must use some label information, or an existing regression model. This paper considers completely unsupervised ALR, i.e., how to select the samples to label without knowing any true label information. We propose a novel unsupervised ALR approach, iterative representativeness-diversity maximization (iRDM), to optimally balance the representativeness and the diversity of the selected samples. Experiments on 12 datasets from various domains demonstrated its effectiveness. Our iRDM can be applied to both linear regression and kernel regression, and it even significantly outperforms supervised ALR when the number of labeled samples is small. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Closer to Pre-trained Network Transfer Better

Closer to Pre-trained Network Transfer Better

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Siyu Chen Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan People’s Republic of China Image Processing and Intelligent Control Key Laboratory Education Ministry of China Wuhan People’s Republic of China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In recent years, Deep Neural Network (DNN) has been widely used in the domain of computer vision, but its further development is restricted because of the lack of train samples. Fine-tuning is one of deep transfer learning methods to solve this problem. Nowadays, some existing Fine-tuning approaches assume that if the fine-tuned network could be as close to the pre-trained network as possible, the source knowledge would be fully utilized to improve the study effect. Although these approaches work pretty well, the assumptions on which they are based have not been systematically proved. Due to the complexity of DNNs, offering a reasonable and accurate explanation of the performance of the network is significant for designing new algorithms. In this paper, we design a series of experiments with or without transfer learning respectively, to characterize the relationship between network differences and network performance. The results show that there is an obvious negative correlation between them under certain circumstances. This conclusion can not only provide a more reasonable explanation for the relevant transfer learning algorithm, but also provide support and inspiration for the future work.

关键词： Knowledge engineering Correlation Transfer learning Neural networks Task analysis Information technology Knowledge transfer

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui Hu Jiaming Cai Wangjie Zheng Yang Yang Hong-Bin Shen Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools.

关键词： Three-dimensional displays Feature extraction image reconstruction Task analysis Solid modeling Electron microscopy Convolutional neural networks

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Transfer Learning for Motor imagery Based Brain-Computer Interfaces: A Complete Pipeline

arXiv

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arXiv 2020年

作者： Wu, Dongrui Jiang, Xue Peng, Ruimin Kong, Wanzeng Huang, Jian Zeng, Zhigang Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence Hangzhou Dianzi University Hangzhou310018 China

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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Distributed urban freeway traffic optimization considering congestion propagation

arXiv

引用

arXiv 2021年

作者： Gao, Fengkun Yang, Bo Chen, Cailian Guan, Xinping Zhang, Yang Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China Shanghai Municipal Urban-Rural Construction and Transportation Development Institute China

Traffic optimization strategies are imperative for improving the performance of transportation networks. Most traffic optimization strategies only depend on traffic states of congested road segments, where congestion propagation is neglected. Therefore, we propose a distributed traffic optimization strategy for urban freeways considering the potential congested road segments caused by congestion propagation, called potential-homogeneous-area (PHA). Utilizing the historical traffic density data, we firstly quantify the effect of congestion propagation and identify PHA by applying the proposed spatio-temporal lambda-connectedness method. Meanwhile, dynamic capacity constraints of PHA are determined and are integrated with cell transmission model (CTM) in a centralized traffic optimization problem. To reduce computational complexity and improve scalability, we then propose a doubleconsensus- based alternating direction method of multipliers algorithm (DC-ADMM) to handle the neighbor coupling constraints and global coupling constraints for solving the problem in a fully distributed way. We prove that the proposed DC-ADMM algorithm converges to the optimal solution in the condition of convex objective function. Finally, simulations based on real traffic density data, collected in Inner Ring Road, Shanghai, China, reveal the effectiveness of our proposed strategy. © 2021, CC BY.

关键词： Roads and streets

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Dynamic Event-Triggered Consensus of Multi-agent Systems on Matrix-weighted Networks

arXiv

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arXiv 2021年

作者： Pan, Lulu Shao, Haibin Li, Dewei Liu, Lin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Electronic Information Engineering Shanghai Dianji University Shanghai201306 China

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless and leader-follower consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks, generalizing those results only applicable for scalar-weighted networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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