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Multi-granularity manifold hybrid adaptive near-neighbor preserved clustering for hyperspectral image band selection

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Li, Qingyan Luo, Xiaobo Wang, Yaxu Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing400065 China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing400065 China

Band selection is an effective method to reduce the dimensionality of hyperspectral images (HSIs). However, most existing band selection methods usually consider each band as an overall feature and neglect to analyze the intrinsic discriminative components of each band. Worse still, these methods also assume that the spatial features of bands in HSIs are distributed only in Euclidean or non-Euclidean space, which may lead to the limited discriminability of the selected bands. To address these problems, a novel multi-granularity manifold hybrid adaptive near-neighbor preserved (MMANP) clustering method is proposed for band selection. Specifically, we first transform the problem of analyzing the intrinsic discriminative components of bands into a feature decoupling problem and propose a fine-grained manifold collaborative (FGMC) feature decoupling model, which can obtain spatially discriminative features and important features with the fine-grained manifold structures in each band. Then, the hybrid adaptive local neighborhood (HALN) block clustering model is proposed to interact with the spatial effective information of the obtained fine-grained important features of each band in the Euclidean and non-Euclidean spaces, which helps to improve the HSI’s adaptive clustering of similar bands. Next, multi-view cross-granularity learning (MVCL) feature selection model is proposed to harmoniously integrate information at different levels of granularity in HSIs into a unified framework to perform adaptive band selection for each cluster from the perspectives of both the different subsets and the whole HSI. Extensive experimental results on four datasets demonstrate that MMANP outperforms other state-of-the-art comparative methods. © 1980-2012 IEEE.

关键词： Nearest neighbor search

CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization

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arXiv

arXiv 2023年

作者： Wei, Feng-Feng Chen, Wei-Neng Guo, Xiao-Qi Zhao, Bowen Jeon, Sang-Woon Zhang, Jun The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The State Key Laboratory of Subtropical Building Science With The Guangdong-Hong Kong Joint Innovative Platform of Big Data and Computational Intelligence South China University of Technology Guangzhou510006 China The Pazhou Lab Guangzhou510006 China Guangzhou Institute of Technology Xidian University Guangzhou510006 China The Department of Electronics Engineering and Communication Engineering Hanyang University Ansan15588 Korea Republic of

Crowdsourcing is an emerging computing paradigm that takes advantage of the intelligence of a crowd to solve complex problems effectively. Besides collecting and processing data, it is also a great demand for the crowd to conduct optimization. Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crowdsourcing-based evolutionary computation (CEC) paradigm for distributed optimization. EC is helpful for optimization tasks of crowdsourcing and in turn, crowdsourcing can break the spatial limitation of EC for large-scale distributed optimization. Therefore, this paper firstly introduces the paradigm of crowdsourcing-based distributed optimization. Then, CEC is elaborated. CEC performs optimization based on a server and a group of workers, in which the server dispatches a large task to workers. Workers search for promising solutions through EC optimizers and cooperate with connected neighbors. To eliminate uncertainties brought by the heterogeneity of worker behaviors and devices, the server adopts the competitive ranking and uncertainty detection strategy to guide the cooperation of workers. To illustrate the satisfactory performance of CEC, a crowdsourcing-based swarm optimizer is implemented as an example for extensive experiments. Comparison results on benchmark functions and a distributed clustering optimization problem demonstrate the potential applications of CEC. Copyright © 2023, The Authors. All rights reserved.

关键词： Crowdsourcing

CYRUS Soccer Simulation 2D Team Description Paper 2022

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arXiv

arXiv 2022年

作者： Zare, Nader Firouzkouhi, Arad Amini, Omid Sarvmaili, Mahtab Sayareh, Aref Rad, Saba Ramezani Matwin, Stan Soares, Amilcar Institute for Big Data Analytics Dalhousie University Halifax Canada Institute for Computer Science Polish Academy of Sciences Warsaw Poland Memorial University of Newfoundland St. John's Canada Amirkabir University of Technology Iran Qom University of Technology Iran Shiraz University Iran

Soccer Simulation 2D League is one of the major leagues of RoboCup competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one coach compete against each other. The players are only allowed to communicate with the server that is called Soccer Simulation Server. This paper introduces the previous and current research of the CYRUS soccer simulation team, the champion of RoboCup 2021. We will present our idea about improving Unmarking Decisioning and Positioning by using Pass Prediction Deep Neural Network. Based on our experimental results, this idea proven to be effective on increasing the winning rate of Cyrus against opponents. © 2022, CC BY.

关键词： Deep neural networks

Experimental Investigation and Thermodynamic Assessment of Phase Equilibria in the Al-Ta-V Ternary System

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SSRN 2023年

作者： Lu, Yong Wang, Cuiping Zheng, Debin Zheng, Zhangcan Wu, Lianzhang Le, Jianping Guo, Yihui Huang, Yixiong Zhang, Jinbin Liu, Xingjun College of Materials Fujian Key Laboratory of Surface and Interface Engineering for High Performance Materials Xiamen University Xiamen361005 China Xiamen Key Laboratory of High Performance Metals and Materials Xiamen University Xiamen361005 China School of Materials Science and Engineering Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen518055 China

The phase equilibria in the Al-Ta-V ternary system at 1000 °C and 1200 °C have been studied by using electron probe microanalysis and X-ray diffraction. τ-Al2.9Ta2.7V1.4 phase was found in the Al-Ta-V ternary system in both two isothermal sections. The addition of V makes the Al69Ta39 phase be stable at 1000 °C. The linear compound Al3(V,Ta) phase (D022-type) forms a continuous solution from the Al-V side to the Al-Ta side at both temperatures. Based on our experimental results, reported liquidus projection of the ternary Al-Ta-V system and thermodynamic data of the binary systems of Al-V, Al-Ta and V-Ta, a thermodynamic evaluation of Al-Ta-V system was carried out by CALPHAD technique. A set of reliable thermodynamic parameters for the Al-Ta-V system was obtained. The current calculation results agree well with the available experimental data. The invariant reaction scheme of Al-Ta-V ternary system was presented. The present study could provide essential experimental and thermodynamic data for establishing a comprehensive Co-based alloy database. © 2023, The Authors. All rights reserved.

关键词： Ternary systems

An improved bat algorithm based on hybrid with ant lion optimizer 13th

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An improved bat algorithm based on hybrid with ant lion opti...

13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019

作者： Dao, Thi-Kien Chu, Shu-Chuan Pan, Jeng-Shyang Nguyen, Trong-The Ngo, Truong-Giang Nguyen, Trinh-Dong Tran, Huu-Trung Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

ISBN: (纸本)9789811533075

Bat Algorithm (BA) is one of the fundamental algorithms for solving optimization problems. However, the BA still exists weaknesses in terms of exploitation and exploration. In this paper, an enhancing capability of exploration and exploitation for BA by hybridizing BA with Ant Lion Optimizer (ALO) is proposed for the global optimization problems. In the experimental section, several benchmark functions are used to test the performance of the proposed approach. Compared results with other algorithms literature show that the proposed method provides a new competitive algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Global optimization

Trust Secure data Aggregation in Wsn-Based Iiot with Single Mobile Sink

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SSRN 2021年

作者： Liu, Xiaowu Yu, Jiguo Yu, Kan Wang, Guijuan Feng, Xingjian School of Computer Science Qufu Normal University Rizhao276826 China Big Date Institute Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Labortary of Computer Networks Jinan250014 China Big Data Institute Qilu University of Technology Shandong Jinan250353 China Big Data Center of Weifang City Shandong Weifang261000 China

Wireless Sensor Networks (WSNs), as the fundamental infrastructure, are indispensable for Industrial Internet of Things (IIoT). In particular, the security and effectiveness of WSNs in IIoT are the universal and inevitable issues. In this paper, a WSN-based IIoT Model (WIM) is designed characterizing by a single mobile sink. Based on WIM, a high robust aggregation tree algorithm and an outlier elimination scheme are proposed in a virtual grid network with the mobile sink, which can acquire data in a real-time and accurate manner. Moreover, a trust secure data aggregation mechanism is applied in WIM which takes both the direct trust and the indirect trust into consideration to perform the secure data aggregation without losing the effectiveness of network even if the sink moves randomly. The simulation results show that the proposed model and algorithms can promote the performances of WSNs in terms of accuracy, effectiveness and delay. © 2021, The Authors. All rights reserved.

关键词： Wireless sensor networks

Exploiting web images for fine-grained visual recognition by eliminating noisy samples and utilizing hard ones

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arXiv

arXiv 2021年

作者： Liu, Huafeng Zhang, Chuanyi Yao, Yazhou Wei, Xiu-Shen Shen, Fumin Tang, Zhenmin Zhang, Jian The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China The Global Big Data Technologies Center University of Technology Sydney Australia

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. Therefore, in this paper, we propose a novel approach for removing irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. The data and source code of this work have been made publicly available at: https://***/NUST-Machine-Intelligence-Laboratory/ Advanced-Softly-Update-Drop. Copyright © 2021, The Authors. All rights reserved.

关键词： HTTP

Unique in the Metro System: The Likelihood to Re-Identify a Metro User with Limited Trajectory Points

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SSRN 2023年

作者： Yang, Hongtai Ping, An Wei, Hongmin Zhai, Guocong School of Transportation and Logistics Institute of System Science and Engineering National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities Shenzhen518060 China School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Department of Civil & Environmental Engineering Old Dominion University 129C Kaufman Hall NorfolkVA23529 United States

Though the collection of metro smart card data could help improve the operations of the metro system, the release of such data might lead to privacy issues. Few studies have quantified the probability to re-identify a user from the smart card data using very limited trajectory points. Thus, this study investigates this topic by analyzing eight-day metro smart card data of Chengdu, China. Results reveal that, on the macro level, three random trajectory points with a temporal resolution of one minute and one hour are enough to identify over 90% and 67% of the users. Even when the resolution is reduced to one day, 20% of the users could be still be identified by three points. On the individual level, three carefully selected points with a temporal resolution of one minute, one hour, and one day could lead to a re-identification risk no less than 0.5 for 99%, 89%, and 52% of the users. The effects of number of points, number of users, and other temporal resolutions are also thoroughly evaluated. These findings emphasize the great privacy issues involved in the release of metro smart card data and remind metro operators to take proactive measures to enhance privacy protection. © 2023, The Authors. All rights reserved.

关键词： Smart cards

Initial Result of Design-to-Test Method for Developing Architecture of the Large-Scale Complex Industrial Software

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SSRN 2022年

作者： Chen, Ge Zhang, Junbo Cui, Wei Ma, Mengcheng School of Electric Power South China University of Technology Guangzhou510641 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China School of Big Data and Artificial Intelligence Guangdong Polytechnic of Water Resources and Electric Engineering Guangzhou510635 China

In the process of developing large-scale complex industrial software, defects may occur at any point. Cost of correction will increase exponentially if the defects are not detected until later stages, especially the architectural defects. To address this problem, an architecture design-to-test solution is proposed in this paper to find the architectural defects in the design stage, so it can effectively reduce the potential rework cost. First, we introduce a mathematical model for service-based software architecture testing, and determine that the testing content is to check the correctness of business processes the architecture can achieve. The paper then applies a pruning technique to cut down on the quantity of business processes, and proves its effectiveness of reducing test cost. Finally, an implementation method is proposed for architecture testing, including the process encoding, the process decoding, and a search algorithm considering some constraints, to search out all the business processes the architecture can achieve. We test the proposed method in an architecture design of typical industrial software, and the results show the method can indeed help us find architectural defects and reduce the potential rework cost. © 2022, The Authors. All rights reserved.

关键词： Defects