检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

331 篇 会议
47 篇 期刊文献

馆藏范围

378 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

46 篇 工学
- 44 篇 电气工程
- 36 篇 信息与通信工程
- 33 篇 计算机科学与技术...
- 4 篇 交通运输工程
- 2 篇 仪器科学与技术
- 2 篇 土木工程
- 1 篇 冶金工程
- 1 篇 测绘科学与技术
- 1 篇 生物医学工程（可授...
3 篇 理学
- 2 篇 物理学
- 1 篇 地球物理学
2 篇 医学
- 1 篇 临床医学

主题

378 篇 whale optimizati...
117 篇 optimization
84 篇 accuracy
53 篇 heuristic algori...
49 篇 feature extracti...
43 篇 prediction algor...
41 篇 metaheuristics
41 篇 predictive model...
37 篇 convergence
37 篇 costs
35 篇 whales
31 篇 simulation
31 篇 stability analys...
30 篇 particle swarm o...
28 篇 resource managem...
25 篇 power system sta...
23 篇 classification a...
22 篇 internet of thin...
22 篇 energy consumpti...
22 篇 data models

机构

4 篇 department of co...
3 篇 school of inform...
3 篇 college of elect...
3 篇 faculty of engin...
2 篇 department of co...
2 篇 school of automa...
2 篇 department of co...
2 篇 china electric p...
2 篇 graphic era deem...
2 篇 department of el...
2 篇 department of ai...
2 篇 faculty of infor...
2 篇 school of commun...
2 篇 department of el...
2 篇 department of ai...
2 篇 department of el...
2 篇 chitkara univers...
2 篇 faculty of infor...
2 篇 big data instuti...
2 篇 school of electr...

作者

5 篇 piyush kumar par...
3 篇 baohang zhang
3 篇 krittika kantawo...
3 篇 jie zhang
3 篇 sakkayaphop prav...
3 篇 haichuan yang
2 篇 md ashraful isla...
2 篇 sami abduljabbar...
2 篇 louay karadsheh
2 篇 duangjai jitkong...
2 篇 mustafa hamid ha...
2 篇 liangbo xie
2 篇 haitao yuan
2 篇 oguzhan ceylan
2 篇 renu sharma
2 篇 yang yang
2 篇 shangce gao
2 篇 sizhuo lv
2 篇 mohammed i. habe...
2 篇 xu zhang

语言

378 篇 英文

检索条件"主题词=whale optimization algorithms"

共 378 条记录，以下是71-80 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Optimal Configuration of Grid-Forming Energy Storage System Based on the whale optimization Algorithm

Optimal Configuration of Grid-Forming Energy Storage System ...

引用

Energy, Power and Electrical Technology (ICEPET), International Conference on

作者： Xin Ma Wenbo Gu Sizhuo Lv Chao Zheng Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd. Yinchuan Chian China Electric Power Research Institute Beijing China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

This paper investigates the optimal configuration of grid-forming energy storage systems (GFM-ESS) in a power grid with a high proportion of renewable energy using the whale optimization Algorithm (WOA). The model aims to minimize the GFM-ESS capacity while ensuring that transient overvoltage remain within safe limits in wind farms. The optimization model incorporates penalties for transient overvoltage exceeding a specific threshold. The algorithm iteratively determines the best capacity and location of GFM-ESS to stabilize the system and maintain secure voltage levels. The simulation results confirm that strategic GFM-ESS deployment can significantly improve the stability and resilience of power grids integrated with high shares of renewable energy.

关键词： Renewable energy sources Power system stability Wind farms whale optimization algorithms Stability analysis Power grids Voltage control

来源：评论

学校读者我要写书评

暂无评论

whale optimization Algorithm (WOA) for Task Scheduling Problem in Multi-Processors Environment

Whale Optimization Algorithm (WOA) for Task Scheduling Probl...

引用

IEEE Symposium on Industrial Electronics & Applications (ISIEA)

作者： Saiful Izwan Suliman Mohammad Nur Khalish Yuslinda Wati Mohamad Yusof Farah Yasmin Abdul Rahman School of Electrical Engineering College of Engineering Universiti Teknologi MARA (UiTM) Shah Alam Selangor

ISBN: (数字)9798350386868

ISBN: (纸本)9798350386875

Use of multiprocessors applications to solve complex problems in computer's system contributes to the increase in performance and speed of today's computer. In multiprocessors environment, it is very important to have a great strategy or method on scheduling the processors that are utilized to handle a specific task in the computer system. As each scheduling problem involves problem-specific variables, multiprocessors need different and appropriate algorithm for their problems. This study introduces the whale optimization Algorithm (WOA) for task scheduling. Comparisons with other existing methods were also conducted to highlight the efficiency of the proposed WOA in performing task scheduling.

关键词： Program processors Processor scheduling Instruments Scheduling whale optimization algorithms Computational efficiency Resource management

来源：评论

学校读者我要写书评

暂无评论

A whale optimization Algorithm Based on Simulated Annealing and Adaptive Mutation

A Whale Optimization Algorithm Based on Simulated Annealing ...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yumei Zhu Yushu Lai Chuo Mei Tianguo Li School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing China School of Mechanical Engineering Chongqing Three Gorges University Chongqing China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Aiming at the problems of the whale optimization algorithm, such as slow convergence speed and easy to fall into local optimization, the algorithm is proposed to be improved by integrating simulated annealing and adaptive mutation strategy. Chaotic inverse learning mechanism is used to initialize the population, and nonlinear convergence factor and dynamic inertia weights are introduced to timely improve the population's global optimization ability in the early stage and local accurate optimization ability in the late stage. Simulation experiments are carried out on 18 basic test functions and verified by statistical methods, comparing the Gray Wolf algorithm, whale optimization algorithm, Particle Swarm algorithm and Genetic algorithm, and the experimental results show that the improved whale optimization algorithm has a better convergence speed and stability.

关键词： Learning systems Statistical analysis Heuristic algorithms Perturbation methods Sociology Simulated annealing whale optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Fracture Detection in X-Ray Images Using whale optimization Algorithm-Enhanced Bi-Directional LSTM Networks

Fracture Detection in X-Ray Images Using Whale Optimization ...

引用

International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Raj Gaurang Tiwari Mahesh Manchanda Ambuj Kumar Agarwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Rapid and reliable fracture detection in X-ray images is essential for medical diagnosis. This research offers a hybrid deep learning strategy that leverages the whale optimization Algorithm (WOA) to choose the most significant features from AlexNet and ResNet50. WOA enhances feature selection by searching for relevant attributes. To identify fractures, a Bi-directional Long Short-Term Memory (Bi-LSTM) network is employed. The proposed hybrid fracture diagnosis method beats existing methods with 96.86% accuracy. Integration of optimization approaches with powerful deep-learning architectures may improve medical imaging diagnosis accuracy.

关键词： Deep learning Visualization Accuracy Soft sensors Feature extraction whale optimization algorithms optimization X-ray imaging Standards Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

ADM1-Guided optimization of anaerobic digestion Model Parameters via whale optimization Algorithm

ADM1-Guided Optimization of anaerobic digestion Model Parame...

引用

Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Messili Oualid Chaabna Abdelhani Semcheddine Samia Power Electronic and Industrial Control Laboratory (LEPCI) UFAS Setif1 Setif Algeria

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

Anaerobic Digestion (AD) technology, known for its waste recycling and environmental clean-up capabilities, also generates renewable energy in the form of biogas. The need to reduce dependence on fossil fuels has extensively stimulated interest in this process. The process is known for its complexity, high sensitivity, and potential for instability, which is inherently reflected in the models describing it. Models contain uncertain parameters and highly sensitive to process noise. Parameter identification plays a crucial role during modeling and is a delicate task preceding control law development. It is mandatory to have the best possible estimates of a model that guarantees efficient predictions. This paper focuses on the optimization of stoichiometric and kinetic coefficients of the Acidogenesis Methanogenesis 2 model (AM2). We present in this study the use of the whale optimization Algorithm (WOA) to optimize AM2 model parameters and validate the results using data acquired from a Anaerobic Digestion Model $\mathbf{N}^{\circ} 01$ (ADM1). The identification algorithm was implemented in MATLAB 2018. To validate the effectiveness of our proposed approach, we conduct a comparative analysis with Genetic Algorithm (GA). The results, despite certain limitations, underscore the robustness of WOA for parameter estimation.

关键词： Renewable energy sources Parameter estimation Sensitivity Biological system modeling whale optimization algorithms Data models Robustness Recycling optimization Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Multi objective non dominated sorting whale optimization genetic algorithm for convolutional neural network-based on-chip networks

Multi objective non dominated sorting whale optimization gen...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Zuda Liang Feng Hu Bangjian Xu Chunyuan Wei National Supercomputing Center in Zhengzhou Zhengzhou University Zhengzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In recent years, chip technology has experienced rapid development, achieving the integration of multiple processing units and driving the development of modern multi-core systems. Network on Chip (NoC) has been widely adopted as an interconnect communication architecture superior to traditional buses. In the realm of artificial intelligence, the widespread application of convolutional neural networks has brought enormous computational demands, while traditional computers have relatively slow processing speeds. In order to address this challenge, people have begun to develop convolutional neural network accelerators, namely artificial intelligence chips, which use on-chip networks to achieve efficient data transmission and communication. In order to effectively and efficiently map convolutional neural networks onto on-chip networks and achieve goals such as low power consumption and low latency, a multi-objective mapping method for non dominated sorting whale optimization genetic algorithm is proposed by utilizing the strong global search ability of whale optimization algorithm and combining it with non dominated sorting algorithm, aiming to solve the mapping problem of on-chip networks. The experimental results demonstrate that the proposed method ∊an achieve reduced power consumption and latency.

关键词： Seminars Power demand Search problems whale optimization algorithms System-on-chip Convolutional neural networks Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

SURAKSIT: A Secure Resource Allocation Scheme Using whale optimization for D2D Communication

SURAKSIT: A Secure Resource Allocation Scheme Using Whale Op...

引用

IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Nilesh Kumar Jadav Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

As the demand for mobile data traffic continues to rise, short-range communication, such as device-to-device (D2D) communication, will become a paramount research focus. D2D communication is severely strained by distinct challenges, such as interference, resource allocation, and security, that degrade its performance in terms of sum rate, resource allocation fairness, and system throughput. In this article, we aim to maximize the overall system throughput of the D2D communication by employing a bio-inspired algorithm - whale optimization algorithm (WOA). It uses whales’ social behaviour to detect the optimal D2D users based on their signal-to-noise-plus-interference ratio (SINR) values. Furthermore, we employed an isolation forest algorithm with the intent of identifying potential eavesdroppers who illicitly augment their resources (transmission power) to intercept ongoing D2D communication. The isolation forest algorithm offers anomaly scores that identify deviations in their resources, signalling the presence of entities that may be eavesdropping. The WOA-based access control restricts the eavesdropper and allows only the optimal D2D users to participate in the resource allocation process, thus improving the throughput of the D2D communication. Lastly, the proposed scheme is assessed using different assessment metrics, such as fairness (0.861), system throughput (28.67 Mbps), and convergence rate.

关键词： Access control Performance evaluation Process control Interference Forestry Throughput whale optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Multi-Strategy Improved Dung Beetle optimization Algorithm for Global optimization Problems

A Hybrid Multi-Strategy Improved Dung Beetle Optimization Al...

引用

Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Zheyi Wang Aosheng Xing Jie Zhang College of Computer and Information Science Chongqing Normal University Chongqing China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Dung Beetle Optimizer (DBO) is a meta-heuristic algorithm formed by modeling inspired by living creatures and is known for its fast convergence. However, it still suffers from poor global exploration abilities, a proclivity to settle for local optimums and a lack of population variety. Therefore, this paper proposes a hybrid beluga whale optimization (BWO) and dung beetle optimization algorithm with somersault foraging (SRBDBO) using three strategies to improve the DBO. First, the interaction behavior between individuals in the hybrid BWO exploration phase enhances the global exploration ability of the rolling dung beetle, and the diversity of the dung beetle population is improved by mixing the BWO whale-fall phase at the end of each iteration of the population. Second, a somersault foraging strategy is introduced to expand the foraging area of small dung beetles. An adaptive somersault factor is also incorporated to balance global exploration and local exploitation and prevent the population from stopping updating at a locally optimal solution. Third, refraction opposition-based learning (ROBL) is used to reduce the likelihood that a thief dung beetle will stagnate updating in a locally optimal region. Finally, the widely recognized CEC2017 benchmark function set is utilized to assess the performance of SRBDBO. The simulation experiments show that SRBDBO demonstrates strong optimization-seeking performance and robustness when compared with the selected algorithm.

关键词： Diversity reception Metaheuristics Learning (artificial intelligence) Benchmark testing whale optimization algorithms Stability analysis Robustness Internet of Things Thermal stability Convergence

来源：评论

学校读者我要写书评

暂无评论

Intelligent optimization design of antipodal Vivaldi antenna for partial discharge detection of cables

Intelligent optimization design of antipodal Vivaldi antenna...

引用

Energy, Power and Grid (ICEPG), International Conference on

作者： Zhenan Luo Chengyi Liang Huawei Xue Zhongjin Tan Yao Fu Yi Feng Chao Yuan Yangjiang Power Supply Bureau Guangdong Power Grid Limited Liability Company Yangjiang China College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In this study, a modified non-dominated sorting whale optimization algorithm (MNSWOA) is utilized for optimizing UHF antennas for partial discharge (PD) detection to satisfy the requirements of minimal return loss and maximal gain in the target frequency band. Based on an elite retention strategy, MNSWOA, significantly improving algorithm optimization and increasing population diversity, employs the one-point binary crossover and polynomial variation. Then, a Pareto optimal solution is selected as the proposed UHF antenna. Finally the experimental results demonstrate that the antenna achieves a reflection coefficient of less than -10 dB and an average gain of 4.4 dBi within the frequency range from 1 GHz to 3 GHz.

关键词： Partial discharges Vivaldi antennas UHF antennas Pareto optimization whale optimization algorithms Polynomials Gain optimization Standards Sorting

来源：评论

学校读者我要写书评

暂无评论

A New Belief Rule Base Considering Explainability optimization for Complex System Health State Assessment

A New Belief Rule Base Considering Explainability Optimizati...

引用

Chinese Automation Congress (CAC)

作者： Chao Chen You Cao Zhixiang Chen Ruiqiao Yao Hongbin Shu Junliang Han High-tech Institute Qingzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Health status assessment of complex systems is a critical method to ensure their safety. The belief rule base (BRB) is essentially a white-box expert system. However, experts cannot fully understand the mechanism of complex systems, the parameters of the BRB need to be adjusted through optimization algorithms. Due to the optimization algorithm has a random exploration mechanism, the optimized expert knowledge cannot be effectively retained, which reduces the explainability of BRB. Therefore, a new belief rule base considering explainability optimization (BRB-EO) for complex system health status assessment is proposed. In BRB-EO model, a modified whale optimization algorithm (WOA) for explainable BRB is proposed. Firstly, a new scattering method centered on expert knowledge is proposed to effectively utilize expert knowledge. Then, an explainability constraint is proposed to preserve the expert knowledge after optimization. Finally, the effectiveness of the BRB-EO is demonstrated through a NASA lithium battery case study.

关键词： Deep learning Representation learning Scattering Feature extraction Lithium batteries whale optimization algorithms Safety Data mining Complex systems optimization

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共38页 << < 4 5 6 7 8 9 10 11 12 13 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：