检索结果-内蒙古大学图书馆

Congress on Evolutionary Computation

作者： Zhenya Diao Fei Yu Jian Guan Meitong Liu Key Lab of Intelligent Optimization and Information Processing School of Physics and Information Engineering Minnan Normal University Zhangzhou China Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province Key Lab of Intelligent Optimization and Information Processing School of Physics and Information Engineering Minnan Normal University Zhangzhou China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In the original Particle Swarm optimization (PSO), the selection of parameters significantly impacts the search capability of PSO. The paper introduces an innovative algorithm called Fitness Landscape-based PSO (FLPSO) to address the impact of parameter selection on the search capability of PSO. The algorithm focuses on acquiring fitness landscape information and analyzing the population state to intelligently select appropriate parameters in each generation. FLPSO defines four primary states: exploration, exploitation, convergence, and escaping state. In the convergence state, an elite learning strategy is incorporated to help the algorithm escape potential local optima by targeting the global best particle. To validate the efficacy of FLPSO, the paper conducted experiments on 30 different characteristic test functions from CEC2017. FLPSO was compared with six existing algorithms: PSO, PSO-TVAC, APSO, AWPSO, RPSO and ASRPPSO. The experimental results showed significant improvements in FLPSO, providing promising evidence for its effectiveness.

关键词： Sociology Particle swarm optimization Statistics Convergence

来源：评论

学校读者我要写书评

暂无评论

Hybrid Consensus-based Algorithm for Distributed Economic Dispatch Problem

引用

IFAC-PapersOnLine 2017年第1期50卷 177-182页

作者： Liu X.-K. Wang Y.-W. Yan H. Wang X. Hu X. School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education China Key Laboratory of Advanced Control Optimization for Chemical Process of Ministry of Education China School of Information Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai 200237 China

Economic dispatch is one of the fundamental problems in the power system research. The existing algorithms are either discrete iterative algorithms or continuous-time dynamical algorithms. By virtue of the hybrid techniques, a hybrid consensus-based algorithm is proposed in this paper, which consists of a finite-time continuous-time algorithm and a discrete-time interacted scheme. Moreover, inspired by the cluster synchronization and the benefit of the projection operator, a supervised strategy is carried out to ensure that each generator unit will work within its power generation constraint. For a better understanding of the supervised hybrid distributed algorithm, the detailed pseudo-code is presented. Finally, a numerical example is given to illustrate the effectiveness of the algorithm. © 2017

关键词： consensus protocol distributed optimization economic dispatch hybrid system smart grid

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Images Semantic Segmentation Method Based on Improved Nested UNet

Remote Sensing Images Semantic Segmentation Method Based on ...

引用

2022 International Conference on Geographic information and Remote Sensing Technology, GIRST 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Geophysics Chengdu University of Technology Chengdu610059 China College of Computer Science Chengdu Normal University Chengdu611130 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Sichuan Chengdu611130 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China

ISBN: (纸本)9781510662186

With the development of remote sensing technology, remote sensing images of buildings are of great significance in urban planning, disaster response, and other directions. When we use a neural network containing batch normalization layers for semantic segmentation, the neural network is sensitive to batch size and has low segmentation accuracy for occluded and dense buildings. This paper proposes a method for building segmentation in remote sensing images based on Nested UNet (UNet++) deep neural network. First, the UNet++ network is used to extract features, and the Group Normalization (GN) method is used instead of Batch Normalization (BN) to alleviate the model's sensitivity to batch size. Then the weighted combination of Cross-Entropy Loss (CELoss) and DiceLoss is used as the loss function to improve the feature extraction ability of the neural network for unbalanced buildings. Finally, experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet++-GN) improves Mean Intersection over Union (MIoU) and Mean Pixel Accuracy (Macc) by 12.16% and 2.92%, respectively, compared with the original model (UNet++-BN). © 2023 SPIE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Research on the identification of obstacle image based on convolutional neural network

Research on the identification of obstacle image based on co...

引用

2021 International Conference on Signal Image processing and Communication, ICSIPC 2021

作者： Zhongyu, Li Huajun, Wang Yuhang, Cao Yuting, Dai College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China College of Computer Science Chengdu Normal University Chengdu611130 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu Sichuan611130 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

To effectively realize the reasonable obstacle avoidance of the detection robot, VGG based obstacle discrimination method is proposed. Above all, the image captured by the robot is input into the multi-layer convolutional neural network to obtain the high-level image features, which are used to construct the more accurate neural network model parameters and to train the softmax classifier with these parameters. Then the distance between the imported images and the data images is calculated by using the softmax classifier, and the similarity between the obstacles and non-obstacles is estimated. The experimental results show that the discrimination accuracy increase to above 94%. And the proposed method is more effectively compared with traditional ultrasonic and radar methods. © 2021 SPIE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Cooperative coevolution with dependency identification grouping for large scale global optimization

Cooperative coevolution with dependency identification group...

引用

Congress on Evolutionary Computation

作者： Guangming Dai Xiaoyu Chen Liang Chen Maocai Wang Lei Peng School of Computer China University of Geosciences Wuhan China School of Computer China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan China Group of Discrete and Combinatorial Optimization University of Heidelberg Heidelberg Germany

ISBN: (纸本)9781509006243

Large scale optimization is a very challenging task in optimization area. The variable interaction in non-separable problems is a primary source of performance loss, especially for large scale problems. Cooperative Coevolution framework is a popular approach to deal with large scale optimization. It is based on a divide-and-conquer manner. This paper proposes a novel algorithm called DISCC to tackle large-scale optimization problems. It adopts a function-based grouping decomposition strategy called Dependency Identification Grouping to distinguish the interactive variables in the decision space. The grouping strategy aims to find the most suitable arrangement for the variables in order to minimize the limitation that occurs when they are grouped into different groups. The experimental results show this new algorithm is more effective than the existing Cooperative-Coevolution-based algorithms.

关键词： optimization Algorithm design and analysis Systematics Geology Correlation coefficient Computers Electronic mail

来源：评论

学校读者我要写书评

暂无评论

A Semantic Segmentation Method of Buildings in Remote Sensing Image Based on Improved UNet 2

A Semantic Segmentation Method of Buildings in Remote Sensin...

引用

2nd International Conference on Signal Image processing and Communication, ICSIPC 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Computer Science Chengdu Normal University Chengdu611130 China College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Sichuan Chengdu611130 China College of Movie and Media Sichuan Normal University Chengdu610066 China

ISBN: (纸本)9781510657694

Aiming at the problem of model instability and overfitting of deep neural networks with the deepening of the number of network layers, the current mainstream method is to use batch normalization (BN) to alleviate them. However, since the BN method is more sensitive to batch size when the batch size is small, the model performance will be poor. For a relatively large model, due to the limitation of video memory, the batch size cannot take a large value, limiting the model's performance. Because of the dependence of BN on batch size, this paper adopts group normalization (GN) instead of batch normalization (BN) in the UNet network to alleviate the impact of the model on batch size. Then experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet-GN) improves the mean intersection over union (MIoU) and mean pixel accuracy (MPA) by 10.66% and 1.65% respectively compared with the original model (UNet-BN). © 2022 SPIE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

TS-ENAS:Two-Stage Evolution for Cell-based Network Architecture Search

arXiv

引用

arXiv 2023年

作者： Zou, Juan Wu, Shenghong Xia, Yizhang Jiang, Weiwei Wu, Zeping Zheng, Jinhua Engineering Research Center of Hunan Province for Optimization and Security of Intelligent System Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China The College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China

Neural network architecture search provides a solution to the automatic design of network structures. However, it is difficult to search the whole network architecture directly. Although using stacked cells to search neural network architectures is an effective way to reduce the complexity of searching, these methods do not able find the global optimal neural network structure since the number of layers, cells and connection methods is fixed. In this paper, we propose a Two-Stage Evolution for cell-based Network Architecture Search(TS-ENAS), including one-stage searching based on stacked cells and second-stage adjusting these cells. In our algorithm, a new cell-based search space and an effective two-stage encoding method are designed to represent cells and neural network structures. In addition, a cell-based weight inheritance strategy is designed to initialize the weight of the network, which significantly reduces the running time of the algorithm. The proposed methods are extensively tested and compared on four image classification dataset, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet and compared with 22 state-of-the-art algorithms including hand-designed networks and NAS networks. The experimental results show that TS-ENAS can more effectively find the neural network architecture with comparative performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Combining Kernelized Autoencoding and Centroid Prediction for Dynamic Multi-objective optimization

arXiv

引用

arXiv 2023年

作者： Hou, Zhanglu Zou, Juan Ruan, Gan Liu, Yuan Xia, Yizhang Hunan Engineering Research Center of Intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China CERCIA School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

Evolutionary algorithms face significant challenges when dealing with dynamic multi-objective optimization because Pareto optimal solutions and/or Pareto optimal fronts change. This paper proposes a unified paradigm, which combines the kernelized autoncoding evolutionary search and the centriod-based prediction (denoted by KAEP), for solving dynamic multi-objective optimization problems (DMOPs). Specifically, whenever a change is detected, KAEP reacts effectively to it by generating two subpopulations. The first subpoulation is generated by a simple centriod-based prediction strategy. For the second initial subpopulation, the kernel autoencoder is derived to predict the moving of the Pareto-optimal solutions based on the historical elite solutions. In this way, an initial population is predicted by the proposed combination strategies with good convergence and diversity, which can be effective for solving DMOPs. The performance of our proposed method is compared with five state-of-the-art algorithms on a number of complex benchmark problems. Empirical results fully demonstrate the superiority of our proposed method on most test instances. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Analysis of the Research Status of information Technology Education Literature in China and Abroad

Analysis of the Research Status of Information Technology Ed...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yulin Zhao Kai Liu Junke Li School of Computer and Information Qiannan Normal University for Nationalities Duyun China Key Laboratory of Machine learning and Unstructured Data Processing of Qiannan Duyun China School of Information Engineering Suqian University Suqian Jiangsu China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu China

information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad.

关键词： Statistical analysis Databases Education Bibliometrics Educational technology Market research Internet

来源：评论

学校读者我要写书评

暂无评论

The Spatial-Temporal Evolution and Influencing Factors of Public Attention to 5G: Empirical Analysis Based on Baidu Index from 2011 to 2021

引用

Mathematical Problems in Engineering 2023年第1期2023卷

作者： Liu, Pan Wang, Guanyu Liu, Kai Li, Junke Chen, Yanlin School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Guizhou Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

The development of 5G (fifth-generation wireless systems) determines the future direction of technology and economy and has received extensive public attention. Studying the changing rules of public attention to 5G can provide an important guiding significance for the sustainable development of 5G. This paper takes Baidu Index as the measurement index of 5G public attention and analyzes the spatial and temporal evolution characteristics and influencing factors of public attention to 5G from 2011 to 2021 by using the elasticity coefficient, Gini coefficient, geographical concentration index, and panel data model. The results of the study show the following. (1) The public concern to 5G is generally on the rise, but the heat has declined in the past two years. (2) The public’s 5G attention shows a seasonal effect, with the highest attention in March and June. (3) The spatial difference of 5G public attention is obvious. The eastern region has a high degree of attention, the internal differences between the eastern and western regions are obvious, and the central region is relatively balanced. (4) The factors such as local economic level, education level, Internet development, and media attention have significantly affected the public focus on 5G. Also, some suggestions are made for the sustainable development of 5G and the planning of 6G (sixth-generation wireless systems). Copyright © 2023 Pan Liu et al.

关键词： Sustainable development

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：