检索结果-内蒙古大学图书馆

A clustering-Based Coverage Path Planning Method for Autonomous Heterogeneous UAVs

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 25546-25556页

作者： Chen, Jinchao Du, Chenglie Zhang, Ying Han, Pengcheng Wei, Wei Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China

Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.

关键词： Path planning Task analysis Unmanned aerial vehicles Planning Training clustering algorithms Search problems Coverage path planning unmanned aerial vehicle clustering-base method autonomous heterogeneous UAVs

来源：评论

学校读者我要写书评

暂无评论

Multiview Spectral clustering With Bipartite Graph

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 3591-3605页

作者： Yang, Haizhou Gao, Quanxue Xia, Wei Yang, Ming Gao, Xinbo Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Westfield State Univ Dept Math Westfield MA 01086 USA Westfield State Univ Dept Comp & Informat Sci Westfield MA 01086 USA Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Multi-view spectral clustering has become appealing due to its good performance in capturing the correlations among all views. However, on one hand, many existing methods usually require a quadratic or cubic complexity for graph construction or eigenvalue decomposition of Laplacian matrix;on the other hand, they are inefficient and unbearable burden to be applied to large scale data sets, which can be easily obtained in the era of big data. Moreover, the existing methods cannot encode the complementary information between adjacency matrices, i.e., similarity graphs of views and the low-rank spatial structure of adjacency matrix of each view. To address these limitations, we develop a novel multi-view spectral clustering model. Our model well encodes the complementary information by Schatten p-norm regularization on the third tensor whose lateral slices are composed of the adjacency matrices of the corresponding views. To further improve the computational efficiency, we leverage anchor graphs of views instead of full adjacency matrices of the corresponding views, and then present a fast model that encodes the complementary information embedded in anchor graphs of views by Schatten p-norm regularization on the tensor bipartite graph. Finally, an efficient alternating algorithm is derived to optimize our model. The constructed sequence was proved to converge to the stationary KKT point. Extensive experimental results indicate that our method has good performance.

关键词： Tensors Matrix decomposition Laplace equations clustering algorithms Computational modeling Adaptation models Eigenvalues and eigenfunctions Multi-view clustering graph fusion large scale data

来源：评论

学校读者我要写书评

暂无评论

Path Tracking and Handling Stability Coordinated Control of 4WS and DYC for Distributed In-Wheel Motor Drive Electric Vehicle Under Extreme Conditions

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 18402-18417页

作者： Zhang, Haichuan Wang, Shu Zhao, Xuan Zheng, Zichen Changan Univ Sch Automobile Xian 710000 Shannxi Peoples R China

How to improve the path tracking ability and handling stability under critical maneuvers is an important research issue for distributed in-wheel motor drive electric vehicle (IWMDEV). This article proposes a novel coordinated control architecture of active four-wheel steering (4WS) system and direct yaw moment control (DYC) system for IWMDEV. Firstly, a novel shared steering control framework for 4WS system is built, which includes two players, namely, the active front steering (AFS)and active rear steering (ARS). The non-cooperative game theory-based control strategy between AFS and ARS is designed to provide more lateral stability for the path following control. Secondly, a linear time-varying model predictive control (LTV-MPC) is used to determine the direct yaw moment of the vehicle. Then, on the basis of comprehensive consideration of system characteristics and control objective performance requirement, the coordination controller based on K-means Density Peak clustering and Particle Swarm Optimization (KDPC-PSO) algorithm is put forward to calculate optimal weight coefficient between the ARS system and DYC system. Finally, the Carsim/Simulink co-simulation and Hardware-in-the Loop (HIL) experiment results show that the proposed control system can effectively improve the path tracking ability and handling stability of IWMDEV under extreme conditions.

关键词： Stability criteria Wheels Control systems Vehicle dynamics clustering algorithms Tires Game theory In-wheel motor drive electric vehicle (IWMDEV) path tracking handling stability coordinated control

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Understanding of Train Delays With Delay Evolution Pattern Discovery: A clustering and Bayesian Network Approach

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15367-15381页

作者： Huang, Ping Spanninger, Thomas Corman, Francesco Swiss Fed Inst Technol Inst Transport Planning & Syst CH-8093 Zurich Switzerland Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 610031 Peoples R China

Train delay evolutions exhibit different patterns (i.e., increasing delays, decreasing delays, or unchanged delays), because of the effects of stochastic disturbances and pre-scheduled supplement/recovery times. The dynamics and uncertainty of the train delay evolution make train delay prediction a challenging task. This study presents a hybrid framework, called context-driven Bayesian network (CDBN), composed of a delay evolution pattern discovery model, i.e., a K-Means clustering approach, and a train delay prediction model, i.e., Bayesian network (BN), to address this problem. The clustering algorithm is used to uncover the delay evolution patterns, and classify the data into different categories, based on the delay jumps, i.e., the change of a delay from one station to a consequent station. The BN model, which considers the delays in previous stations to overcome the Markov property assumption, is used as the predictive model of train delays. The data in each category (classified by the clustering model) are used to train and test the BN model separately. We evaluated the BN model, the clustering algorithm, and the CDBN model, by comparing against their counterparts, respectively. The results show that: (1) the proposed BN structure has advantages over the common delay prediction models built on Markov property;(2) the clustering is effective, and it can extensively improve the accuracy of the predictive model;and (3) the CDBN outperforms the existing delay prediction models in wide usability, because of its more profound understanding of the delay evolution patterns.

关键词： Delays Predictive models Markov processes Data models Bayes methods Rail transportation clustering algorithms Train delays delay evolution pattern discovery K-Means clustering Bayesian network

来源：评论

学校读者我要写书评

暂无评论

A Novel Modeling Framework Based on Customized Kernel-Based Fuzzy C-Means clustering in Iron Ore Sintering Process

引用

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第2期27卷 950-961页

作者： Hu, Jie Wu, Min Chen, Luefeng Pedrycz, Witold China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada

This article proposes a customized kernel-based fuzzy C-means (CKFCM) clustering, which provides an original framework for data interpretation and data analysis and delivers an effective solution for an accurate division of actual run data under multiple operating conditions in an iron ore sintering process (IOSP). The improvement of CKFCM clustering is achieved by including an adjustment factor introduced into the kernel-based fuzzy C-means clustering. The adjustment factor only needs to consider a small amount of labeled data that are determined based on expert experience. Subsequently, the CKFCM clustering is applied to the modeling of the IOSP, and the Spearman's rank correlation coefficient method is utilized to determine input variables of the model under different operating conditions. Furthermore, the broad learning model is employed to build the prediction model for each operating condition. Finally, we conducted an in-depth analysis of the presented clustering method. Its performance has been experimentally demonstrated by many publicly available datasets. Meanwhile, simulation results involving actual run data of the IOSP demonstrate the superiority and effectiveness of the developed model in carbon efficiency prediction. We show that the developed model outperforms the state-of-the-art prediction models in achieving a good balance between simplicity and accuracy.

关键词： Data models clustering algorithms Predictive models Mechatronics Iron IEEE transactions Input variables Actual run data broad learning model carbon efficiency prediction customized kernel-based fuzzy C-means (CKFCM) iron ore sintering process (IOSP) multiple operating conditions

来源：评论

学校读者我要写书评

暂无评论

Progressive Self-Supervised clustering With Novel Category Discovery

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 10393-10406页

作者： Wang, Jingyu Ma, Zhenyu Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Astronaut Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

These days, clustering is one of the most classical themes to analyze data structures in machine learning and pattern recognition. Recently, the anchor-based graph has been widely adopted to promote the clustering accuracy of plentiful graph-based clustering techniques. In order to achieve more satisfying clustering performance, we propose a novel clustering approach referred to as the progressive self-supervised clustering method with novel category discovery (PSSCNCD), which consists of three separate procedures specifically. First, we propose a new semisupervised framework with novel category discovery to guide label propagation processing, which is reinforced by the parameter-insensitive anchor-based graph obtained from balanced K-means and hierarchical K-means (BKHK). Second, we design a novel representative point selected strategy based on our semisupervised framework to discover each representative point and endow pseudolabel progressively, where every pseudolabel hypothetically corresponds to a real category in each self-supervised label propagation. Third, when sufficient representative points have been found, the labels of all samples will be finally predicted to obtain terminal clustering results. In addition, the experimental results on several toy examples and benchmark data sets comprehensively demonstrate that our method outperforms other clustering approaches.

关键词： clustering methods Bipartite graph Optimization Data models clustering algorithms Matrix decomposition Laplace equations Anchor-based graph progressively selected strategy pseudolabel representative points self-supervised clustering semisupervised framework

来源：评论

学校读者我要写书评

暂无评论

Ensemble Graph and Device clustering Method based on Attention Mechanism for Decomposing Monolithic to Microservices 22

Ensemble Graph and Device Clustering Method based on Attenti...

引用

22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Deng, Qingyong Li, Qiuming Zuo, Qinghua Tian, Shujuan Long, Saiqin Guangxi Normal University School of Computer Science and Engineering The School of Software Guilin China Xiangtan University School of Computer Science Xiangtan China Jinan University College of Information Science and Technology Guangzhou China

ISBN: (纸本)9798331509712

Existing methods for decomposing monolithic applications into microservices in cloud environments primarily rely on the call relationships within itself. However, these methods are difficult to apply directly in resource-constrained and distributed edge network scenarios without considering the heterogeneity of the device. Therefore, this paper proposes a clustering method that ensembles graph structures and device features based on attention mechanism, which utilizes attention encoders to learn node embeddings and employs a spectral clustering algorithm to obtain decomposition results, optimizing the affinity and matching degree between microservices and devices. Experimental results demonstrate that the proposed method exhibits excellent performance in terms of functional independence, modularity, and adaptability of microservices. © 2024 IEEE.

关键词： clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Design of Iterative Fuzzy Radial Basis Function Neural Networks Based on Iterative Weighted Fuzzy C-Means clustering and Weighted LSE Estimation

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第10期30卷 4273-4285页

作者： Roh, Seok-Beom Oh, Sung-Kwun Pedrycz, Witold Wang, Zheng Fu, Zunwei Seo, Kisung Univ Suwon Sch Elect & Elect Engn Hwaseongsi 18332 South Korea Linyi Univ Res Ctr Big Data & Artificial Intelligence Linyi 276005 Shandong Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Polish Acad Sci Syst Res Inst PL-00034 Warsaw Poland King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia Seokyeong Univ Dept Elect Engn Seoul 02173 South Korea

In this article, a reinforced iterative fuzzy radial basis function neural networks (IFRBFNN) is introduced as an augmented FRBFNN architecture generated through the iterative refinement process of weighted fuzzy C-means (WFCM) clustering and weighted least square error estimation. It is well known that the location of the clusters have an effect on the classification performance of the FRBFNN. The underlying idea behind this article is how to define the center points of clusters based on the data distribution analysis as well as the improvement of classification performance. In a nutshell, while the fuzzy C-means clustering to define the positions of radial basis functions (RBFs) based in the unsupervised learning manner is usually used in a FRBFNN, the parameters (cluster centers and their ensuing polynomial coefficients) refinement of FRBFNN through the proposed iterative method lead to superb classification performance by relocating the positions of RBFs over the input space implied by supervised learning. The idea of the proposed approach is to relocate the centers (prototypes) of the fuzzy clusters by using WFCM clustering algorithm and to re-estimate the coefficients by weighted least square estimation with the aid of the cross-entropy loss values of data so that the classification performance is improved. The weight related to each data is determined by some auxiliary information (i.e., the modified version of the cross-entropy loss value of each data). When it comes to the estimation of the coefficients of the consequent polynomial in IFRBFNN, they are estimated by the weighted least square error estimation technique, where the weights of the coefficients are defined based on the cross-entropy loss values. The auxiliary information such as the weights for relocation of the centers of RBFs and the weights for the re-estimation of the polynomial coefficients should be defined from the viewpoint of enhancement of the classification performance. The cross-entro

关键词： clustering algorithms Radial basis function networks Fuzzy neural networks Estimation Prototypes Classification algorithms Training data Cross-entropy loss value iterative radial basis function neural networks iterative weighted fuzzy c-means (WFCM) clustering radial basis function neural networks (RBFNNs) weighted least square error (WLSE) estimation

来源：评论

学校读者我要写书评

暂无评论

Enhanced Multiview Fuzzy clustering Using Double Visible-Hidden View Cooperation and Network LASSO Constraint

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第11期30卷 4965-4979页

作者： Deng, Zhaohong Liang, Ling Yang, Hongtan Zhang, Wei Lou, Qiongdan Choi, Kup-Sze Zhang, Te Zhou, Jin Wang, Shitong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Key Lab Computat Neurosci & Brain Inspired Intell Shanghai 200433 Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China Univ Nottingham Sch Comp Sci Lab UncertaiNty Data & Decis Making Nottingham NG7 2RD England Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China

Multiview clustering is an important topic in multiview learning, where the cooperation of different views is used to improve clustering performance. Although multiview clustering has made considerable progress, most existing methods only utilize the information of the original visible views, or only consider some hidden space information shared by different views. Two of the challenges are: 1) insufficient exploitation of cooperative learning between visible and hidden information despite some preliminary attempts, and 2) inadequate consideration of topological information for improving multiview clustering. To meet the challenges, we propose the cooperation enhanced multiview fuzzy clustering method (CE-MVFC) in this article. First, we characterize multiview data with two hidden views, which are obtained by adaptive multiview non-negative matrix factorization (NMF) and fuzzy partition information of each sample in different clusters. Then, we integrated the hidden views and the original visible views to realize visible-hidden cooperation learning. Furthermore, we establish a similarity matrix for each visible view and the hidden view obtained through NMF to describe the data topology in these views. Based on the spatial topological relationship of the samples and the representation of hidden view obtained by fuzzy partition, the network least absolute shrinkage and selection operator is constructed to constrain multiview learning. Finally, we develop the multiview clustering method by exploiting the visible-hidden information cooperation and the spatial topological information constraints. Experiments on benchmark multiview datasets are conducted to demonstrate the highly competitive performance of the proposed CE-MVFC against the state-of-the-art methods.

关键词： clustering methods Feature extraction clustering algorithms Optimization Collaboration Partitioning algorithms Data models Double hidden views multiview clustering network LASSO constrained learning visible-hidden view cooperation

来源：评论

学校读者我要写书评

暂无评论

Image clustering Using an Augmented Generative Adversarial Network and Information Maximization

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7461-7474页

作者： Ntelemis, Foivos Jin, Yaochu Thomas, Spencer A. Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Natl Phys Lab Teddington TW11 0LW Middx England

Image clustering has recently attracted significant attention due to the increased availability of unlabeled datasets. The efficiency of traditional clustering algorithms heavily depends on the distance functions used and the dimensionality of the features. Therefore, performance degradation is often observed when tackling either unprocessed images or high-dimensional features extracted from processed images. To deal with these challenges, we propose a deep clustering framework consisting of a modified generative adversarial network (GAN) and an auxiliary classifier. The modification employs Sobel operations prior to the discriminator of the GAN to enhance the separability of the learned features. The discriminator is then leveraged to generate representations as to the input to an auxiliary classifier. An objective function is utilized to train the auxiliary classifier by maximizing the mutual information between the representations obtained via the discriminator model and the same representations perturbed via adversarial training. We further improve the robustness of the auxiliary classifier by introducing a penalty term into the objective function. This minimizes the divergence across multiple transformed representations generated by the discriminator model with a low dropout rate. The auxiliary classifier is implemented with a group of multiple cluster-heads, where a tolerance hyper-parameter is used to tackle imbalanced data. Our results indicate that the proposed method achieves competitive results compared with state-of-the-art clustering methods on a wide range of benchmark datasets including CIFAR-10, CIFAR-100/20, and STL10.

关键词： Training Generative adversarial networks Feature extraction Mutual information clustering algorithms Entropy Deep learning Deep neural models generative adversarial network (GAN) mutual information maximization unsupervised learning virtual adversarial training

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：