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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Chu, Shiqiang Hu, Yanjun Wang, Yi Ministry of Education Anhui University Key Laboratory of Intelligent Computing Signal Processing Hefei China

ISBN: (纸本)9781538683392

The ultra dense networks (UDN) are considered as a key technology of 5G for its ability to increase communication capacity. However, the problem of constrained backhaul and the lack of energy which is caused by micro base station without power supply equipment are two mainly bottlenecks of UDN. In order to solve the problem of low micro base station's energy efficiency (MIEE) due to constrained backhaul, we propose an energy efficiency optimization algorithm based on the factor for the portion of time division (FPTD). The optimization problem considers a joint design of downlink precoding, power allocation and the FPTD, meanwhile, we utilize first order Taylor convex approximation (FOTCA), convex slack variable and one-dimensional search method (ODS) to solve. Simulation results show that the proposed FPTD-based energy efficiency optimization algorithm (FEEOA) is improved with much better energy efficient compared with the primitive scheme without FPTD, and more throughputs can be transmitted at the same energy consumption. © 2018 IEEE.

关键词： Base stations

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Android Oriented Image Visualization Exploratory Search 5th

Android Oriented Image Visualization Exploratory Search

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Ouyang, Jianquan He, Hao Chu, Minnan Chen, Dong Tang, Huanrong College of Information Engineering Xiangtan University Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education College of Information Engineering Xiangtan University Xiangtan411105 China

ISBN: (纸本)9789811501203

When using traditional image search engines, smartphone users often complain about their poor user interface including poor user experience, and weak interaction. Moreover, users are unable to find a desired picture partly due to the unclear key words. This paper proposes the word-bag co-occurrence scheme by defining the correlation between images. Through exploratory search, the search range can be expanded and help users refine retrieval of the expected images. Firstly, the proposed scheme applied the bag of visual words (BoVW) vector by processing images on Hadoop. Secondly, similarity matrix was constructed to organize the image data. Finally, the images in which users were interested was visually displayed on the android mobile phone via exploratory search. Comparing the proposed method to current methods by testing with image data sets on ImageNet, the experimental results show that the former is superior to the latter on visual representation, and the proposed scheme can provide a better user experience. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Visualization

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Bernstein Bezout Matrix with Applications to the Generalized Lienard-Chipart Stability Criterion

Bernstein Bezout Matrix with Applications to the Generalized...

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第三十八届中国控制会议

作者： Huazhang Wu Xiaoting Shen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Mathematical Sciences Anhui University

In polynomial and linear control systems, the Lienard-Chipart stability criterion plays an important role in the judgment of the zeros of a real polynomial based on the inertia of a Bezout matrix. In this paper we consider the case in the Bernstein polynomials basis. First, the Bernstein Bezout matrix and some important properties are introduced, and then a generalized perturbations of a real polynomial under the Bernstein polynomials basis is considered. Finally, a generalized Lienard-Chipart stability criterion in terms of the Bernstein Bezout matrix is established.

关键词： Bernstein polynomials basis Bernstein Bezout matrix perturbation Lienard-Chipart stability criterion

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A niching-based nondominated sorting for multimodal multiobjective optimization with local pareto fronts

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Applied Soft computing 2025年 179卷

作者： Deng, Qi Zou, Juan Yang, Shengxiang Liu, Yuan Yu, Fan Xie, Tianbin Zheng, Jinhua Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Computer Science and School of Cyberspace Science of Xiangtan University Hunan Province Xiangtan China Faculty of School of Computer Science and School of Cyberspace Science of Xiangtan University Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom School of Traffic and Transport Engineering Central South University Changsha China

In multimodal multiobjective optimization problems (MMOPs), there may be local Pareto optimal solutions (PSs). Finding both global PS and local PS simultaneously can provide additional options for decision makers. However, since individuals on the global Pareto front (PF) dominate individuals on the local PF, traditional multimodal multiobjective evolutionary algorithms (MMEAs) cannot effectively handle MMOPs with local PF (MMOPLs). In this paper, we improve our previous dynamic-niching-based Pareto domination (DNPD) approach to search for local Pareto optimal solutions. The new version is called niching-based non-dominated sorting (NNSL). There are two improvements to NNSL. First, the dynamic niche is changed to a fixed niche to increase the diversity of the population. Second, individuals with poor convergence in the non-dominated layer are deleted to ensure the convergence of the population. NNSL can be combined with Pareto dominance-based algorithms. We apply NNSL to solve MMOPLs (MMF_e and IDMP_e series). Experiments show that NNSL can help traditional MMEAs find global PF/PS and local PF/PS simultaneously. © 2025

关键词： Multiobjective optimization

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Mechanical properties of individual core-shell-structured Sn O2@C nanofibers investigated by atomic force microscopy and finite element method

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Science China(Technological Sciences) 2018年第8期61卷 1144-1149页

作者： LIU Xing LI KeFeng DUAN XiaoLu HUANG YuTing LI Shuai LIU YiLin XU Fu LI Zheng ZHANG Ping DING YanHuai Institute of Rheological Mechanics Xiangtan University School of Science Harbin Institute of Technology at Shenzhen Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University

Although SnO_2-based nanomaterials used to be considered as being extraordinarily versatile for application to nanosensors,microelectronic devices, lithium-ion batteries, supercapacitors and other devices, the functionalities of SnO_2-based nanomaterials are severely limited by their intrinsic vulnerabilities. Facile electrospinning was used to prepare SnO_2 nanofibers coated with a protective carbon layer. The mechanical properties of individual core-shell-structured SnO_2@C nanofibers were investigated by atomic force microscopy and the finite element method. The elastic moduli of the carbon-coated SnO_2 nanofibers remarkably increased, suggesting that coating SnO_2 nanofibers with carbon could be an effective method of improving their mechanical properties.

关键词： SnO2 nanofiber core-shell structure elastic modulus atomic force microscopy finite element method

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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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dense feature aggregation and pruning for RGBT tracking

arXiv

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arXiv 2019年

作者： Zhu, Yabin Li, Chenglong Luo, Bin Tang, Jin Wang, Xiao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei Anhui China

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Multiplicative reconstruction based on the transport of intensity equation 4

Multiplicative reconstruction based on the transport of inte...

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4th International Conference on Fuzzy Systems and Data Mining, FSDM 2018

作者： Cheng, Hong Wang, Jincheng Shen, Chuan Zhang, Cheng Zhang, Fen Bao, Wenxia Education Ministry Key Laboratory of Intelligent Computing and Signal Processing Anhui University HefeiAnhui230039 China

ISBN: (纸本)9781614999270

In order to effectively recover the phase information of the object and reconstruct the three-dimensional shape of the object, a multiplicative reconstruction based on the transport of intensity equation (TIE) is proposed. Firstly, combining with the relationship between the light field moment imaging and the TIE, the phase information and the first moments of the light field are solved between the two differently focused intensity images, and the phase perspective can be recovered. In this way, the phase can be observed from different slight angles, and the three-dimensional shape of the phase of the object is reconstructed in combination with the multiplicative technique . At the same time, the object is rotated at different angles for the purpose of reconstructing the three-dimensional shape of the object phase under these angles. Finally, the correctness and effectiveness of the algorithm are verified by experimental results. © 2018 The authors and IOS Press. All rights reserved.

关键词： Fuzzy systems

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Electric Load Forecasting Based on Sparse Representation Model 13th

Electric Load Forecasting Based on Sparse Representation Mod...

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13th International Conference on Green, Pervasive, and Cloud computing, GPC 2018

作者： Huang, Fangwan Zheng, Xiangping Yu, Zhiyong Yang, Guanyi Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030150921

Accurate electric load forecasting can prevent the waste of power resources and plays a crucial role in smart grid. The time series of electric load collected by smart meters are non-linear and non-stationary, which poses a great challenge to the traditional forecasting methods. In this paper, sparse representation model (SRM) is proposed as a novel approach to tackle this challenge. The main idea of SRM is to obtain sparse representation coefficients by the training set and the part of over-complete dictionary, and the rest part of over-complete dictionary multiplied with sparse representation coefficients can be used to predict the future load value. Experimental results demonstrate that SRM is capable of forecasting the complex electric load time series effectively. It outperforms some popular machine learning methods such as Neural Network, SVM, and Random Forest. © 2019, Springer Nature Switzerland AG.

关键词： Electric load forecasting

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