检索结果-内蒙古大学图书馆

International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Xiao Meng Zhou Yunyao Wuhan Univ Technol Int Educ Coll Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Informat Engn Coll Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9783038351153

This passage is to put forward knapsack problem optimization algorithm based on complex network(KOABCN). Knapsack problem has extremely wide application in a great number of fields. For instance, knapsack problem can be applied in information coding, budget control, project choosing, material cutting, cargo loading and unloading as well as Internet information safety. Since 1950's, knapsack problem has been one of the most heated topics in algorithm and complexity research. Therefore, knapsack will still be largely focused in the next period of research.

关键词： Knapsack problem optimization algorithm complex network

来源：评论

学校读者我要写书评

暂无评论

A new multi-resolution optimization algorithm for PET - CT cardiac images registration

引用

11th International Congress of the IUPESM/World Congress on Medical Physics and Biomedical Engineering

作者： Marinelli, M. Tucci, F. Positano, V. Neglia, D. Landini, L. Scuola Super Sant Anna Piazza Martiri Liberta 33 Pisa Italy Univ Pisa Dept Informat Engn I-56100 Pisa Italy G Monasterio Fdn Pisa Italy

ISBN: (纸本)9783642038815

Cardiac image fusion is the combination of medical images of heart acquired by different modalities. A prerequisite for image fusion is the image registration process, that allows the spatial alignment of the volumetric data sets involved in the fusion. In this paper we present a registration algorithm able to align positron emission tomography (PET) and computed tomography (CT) cardiac images. The registration method was developed by hypotizing a rigid transformation between data sets and using the mutual information (MI) as registration metric. The core of the study is the development of a new and robust optimization algorithm that allowed to determine the global maximum of MI and to define the correct spatial transformation. The optimization algorithm exploits a multi-resolution method. In the first step, a global optimization method (i.e. genetic algorithm) explores the entire space of possible transformations to find a solution near the global maximum of the MI. In the second step, a local optimizer (i.e. simplex method) refines the search finding the global maximum. Different data resolutions and interpolation algorithms were adopted in the two steps to achieve reasonable processing times. The developed algorithm was tested on two volumetric PET and CT cardiac software models and on PET and CT real data sets. The developed optimization algorithm allows to reach the global maximum of the similarity function and to determine the correct spatial alignment in both the synthetic and real data sets.

关键词： Image fusion registration process optimization algorithm genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Short-term wind speed prediction based on deep learning and intelligent optimization algorithm 6

Short-term wind speed prediction based on deep learning and ...

引用

6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Guan, Peilong Wu, Zixi Guangdong Univ Technol Sch Management Guangzhou Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

In order to improve the accuracy of wind speed prediction, a wind speed prediction model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), long short-term memory (LSTM) and gray wolf optimization (GWO) algorithm was proposed from the perspective of reducing wind speed nonstationarity and optimizing combination weight. First, CEEMDAN was used to decompose the observed wind speed into a series of sub-sequences reflecting the characteristics of the original wind speed. Then the subsequence is predicted by LSTM, and the predicted value of the subsequence is output. Finally, the combined weight of the sub-sequences was optimized by GWO, and the sub-sequences were combined to obtain the wind speed prediction results. The experimental results show that CEEMDAN-LSTM-GWO wind speed prediction model proposed in this study has better performance than the comparison model.

关键词： wind speed prediction data decomposition deep learning optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant

引用

Iran Journal of Computer Science 2023年第3期6卷 233-243页

作者： Hasan, Nazir M. Rashid, Tarik A. Alsadoon, Abeer Qosaeri, Ahmed S. Abualigah, Laith Yaseen, Zaher Mundher Computer Science and Engineering Department University of Kurdistan Hawler KR Erbil Iraq Charles Sturt University Sydney Australia Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Throughout the last few decades, Nature-Inspired algorithms (NIA) have become very popular in solving real-world problems by getting inspiration from nature. This work suggests the Modified Donkey and Smuggler optimization (MDSO) algorithm for solving the selection problem to choose suitable job applicants for a specific position. The original Donkey and Smuggler optimization algorithm (DSO) has two modes: smuggler and donkey mode (non-adaptive and adaptive). In the smuggler mode, the algorithm tries to find the best solutions, which is the path to send the donkey to the destination;once the best path is found, the smuggler will send the donkey through the selected path. The donkey's actions will start when the smuggler mode's best solution is no longer the best one. Certain modifications have been made in the smuggler mode, including replacing the original fitness with a new fitness equation since that method can work more accurately. The Human Resource (HR) department in Korek-telecom has been used as a resource to achieve real-world data to test the original and Modified DSO. Using MDSO, not only will organizations be able to choose suitable job applicants more accurately but also will manage to do so at a faster pace as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Donkey and smuggler algorithms Human resource management MDSO Nature-inspired algorithms optimization algorithm Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Fault diagnosis optimization algorithm based on k nearest neighbor 31

Fault diagnosis optimization algorithm based on k nearest ne...

引用

31st Chinese Control And Decision Conference (CCDC)

作者： Yang Yinghua Wang Xiulong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781728101057

In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor reconstruction (FVR-kNN) has been proposed. However, dealing with fault problem caused by multiple variables, the algorithm needs to exhaustive the arrangement of all variables, resulting in high complex computation. And the multivariate estimation in FVR-KNN is not accurate. Thus, this paper proposes a variable recognition optimization algorithm based on FVR-kNN (OFVR-kNN). It optimizes the estimation steps of FVR-kNN in reconstructing multivariate, guaranteeing that the estimations of these potential fault variables have no mutual influence. According to the fault magnitude in corresponding direction, the fault variables are selected in turn. OFVR-kNN does not need to exhaustive all the combinations, greatly reducing the number of reconstructions in fault sample. In this paper, the validity of the optimization method is proved in Tennessee Eastman process.

关键词： Fault recognition KNN optimization algorithm Tennessee Eastman process (TEP)

来源：评论

学校读者我要写书评

暂无评论

Comparative study of microgrid optimal scheduling under multi-optimization algorithm fusion 10

Comparative study of microgrid optimal scheduling under mult...

引用

10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Duan, Hongyi Li, Qingyang Li, Yuchen Ji, Tianjiao Zhang, Jianan Xie, Yuming Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China Shanghai University of Finance and Economics University School of Mathematica Shanghai China National University of Defense Technology Faculty of Electronic Science Changsha China

ISBN: (纸本)9798350309379

As global attention on renewable and clean energy grows, the research and implementation of microgrids become paramount. This paper delves into the methodology of exploring the relationship between the operational and environmental costs of microgrids through multi-objective optimization models. By integrating various optimization algorithms like Genetic algorithm, Simulated Annealing, Ant Colony optimization, and Particle Swarm optimization, we propose an integrated approach for microgrid optimization. Simulation results depict that these algorithms provide different dispatch results under economic and environmental dispatch, revealing distinct roles of diesel generators and micro gas turbines in microgrids. Overall, this study offers in-depth insights and practical guidance for microgrid design and operation. © 2023 IEEE.

关键词： Microgrid Multi-objective Dispatchs multi-target optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

SSALEO optimization algorithm for Feature Selection for Diabetic Retinopathy 24

SSALEO Optimization Algorithm for Feature Selection for Diab...

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Salem, Mohammed A. Alijla, Basem O. Islamic University of Gaza Palestine

ISBN: (纸本)9798350384307

Diabetic Retinopathy is a severe health problem in the modern world that affects people of all ages and can result in blindness. Many of the affected persons won't lose their eyesight if Diabetic Retinopathy can be identified in its early stages. Therefore, several studies employ several classification algorithms in order to detect DR disease in its early stages. In this study, we use the SSALEO optimization algorithm as a Multi-Objective Feature Selection algorithm in order to obtain higher accuracy with the minimum number of most informative features (i.e., feature reduct). The APTOS-2019 Gaussian-Filtered dataset was used which contains 3662 images. In the proposed model, features were extracted from the input images using a novel feature extraction technique called 'FE-CM'. Feature Selection was performed using SSALEO optimization algorithms as a wrapper-based feature selection using the KNN classifier. the performance of SSALEO was compared using five well-known optimization algorithms: PSO, GWO, MFO, WOA, and BAT. The findings confirm the capability of the SSALEO algorithm as a Feature Selection algorithm in order to help ophthalmologists to detect the disease accurately. © 2023 IEEE.

关键词： Diabetic Retinopathy Feature Selection optimization algorithm SSALEO

来源：评论

学校读者我要写书评

暂无评论

Protective Project Decision Model Based on Critic-Topsis and optimization algorithm 11th

Protective Project Decision Model Based on Critic-Topsis and...

引用

11th International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Liang, Zhengrong Wu, Congcong Li, Jinkang College of Physics and Communication Electronics Jiangxi Normal University Nanchang China

This paper deals with one of the dire needs of wildlife conservation: effective decision-making frameworks. Growing global connectivity and economic development result in growing demand for certain wildlife products, causing enormous ecological damage and losses in biodiversity. This three-dimensional model includes evaluation based on power, resources, and interest for finding the best partners for conservation projects using the Critic-Topsis model combined with optimization algorithms. The model is then applied using a case study of the United States' terrestrial and inland water reserves. Data used in the study were sourced from 21 countries and organizations identifying key stakeholders like governments, specifically that of the US. The research showed that successful conservation would only be reached by cooperation between governments, NGOs, and other stakeholders that need to collaborate on the same platform. The findings show that strategic and data-driven approaches can make a huge difference in enhancing the impact of wildlife protection initiatives. Conclude that further research into and adaptation of these models must be continued to respond to the ever-changing challenges that surround wildlife conversation, sustainable development, and ecological balance. This forms a very strong foundation for future planning and implementation of the conservation plan. © 2024 The Authors. Published by Elsevier B.V.

关键词： Conservation Plan Critic-Topsis Data-Driven optimization algorithm Protective Project

来源：评论

学校读者我要写书评

暂无评论

Multi-source Heterogeneous Data Processing and optimization algorithm in Intelligent Distribution Network Planning System 3

Multi-source Heterogeneous Data Processing and Optimization ...

引用

3rd International Conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Hu, Xinyu Han, Wei Cao, Xinnan Zhou, Jinfei Jiang, Wangjie State Grid Nantong Power Supply Company Nantong Jiangsu226000 China

ISBN: (纸本)9798331519032

In the process of distribution network planning (DNP), massive heterogeneous data, such as power system operation data, enterprise operation management data, nonelectric energy data, etc., bring challenges to data processing because of their high dimension, large volume and strong coupling. Aiming at these problems, this paper proposes a DNP method combining multi-source heterogeneous data (MHD) processing technology and optimization algorithm. This method fuses multi-source data by Kalman filter (KF) to improve data accuracy, and extracts key features by principal component analysis (PCA) to provide a simplified data set for subsequent optimization. In the aspect of optimization algorithm, the multi-objective optimization algorithm NSGA-II is used to solve the DNP problem, taking into account the objectives of economy and reliability, and satisfying the physical constraints such as power flow balance, voltage limitation and line capacity. The experimental results show that this method is superior to the traditional planning method in terms of total investment cost, operation cost, average power outage time and power supply reliability, which effectively verifies its effectiveness and superiority. The research in this paper provides new ideas and methods for intelligent DNP, which is helpful to improve the safety, stability and economic operation level of power system. © 2024 IEEE.

关键词： distribution network planning system Kalman filtering Multi-source heterogeneous data optimization algorithm principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Research on the Construction of Smart Grid Digital System Based on Big Data Cloud Computing Model optimization algorithm 4

Research on the Construction of Smart Grid Digital System Ba...

引用

4th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Li, Zhipeng Liu, Shaobo Zhang, Jianfeng Wang, Jun Shen, Shuo State Grid Gansu Electric Power Company Lanzhou China State Grid Gansu Electric Power Company Digital Division Lanzhou China

ISBN: (纸本)9798350359589

An intelligent power business development method based on digital service information is proposed. At the same time, a complete data mining framework is constructed. Parameters such as distance of adjacent pixels, similarity degree of carrier components and average amplitude were determined by coding and parsing. Then the digital business information resources are extracted. The mining method of data cloud in power system based on time series is established. The correlation degree of each information characteristic of middle station is analyzed based on classification and characteristic identification of power system middle station. Experiments show that the algorithm can effectively improve the distribution of data in the middle station in every minute. The standard deviation of the algorithm is reduced and the mining effect is improved. © 2024 IEEE.

关键词： artificial intelligence digital system optimization algorithm smart grid

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：