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

358 篇 会议
51 篇 期刊文献

馆藏范围

409 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

52 篇 工学
- 50 篇 电气工程
- 40 篇 信息与通信工程
- 38 篇 计算机科学与技术...
- 4 篇 交通运输工程
- 3 篇 仪器科学与技术
- 3 篇 材料科学与工程（可...
- 2 篇 电子科学与技术（可...
- 2 篇 土木工程
- 1 篇 冶金工程
- 1 篇 动力工程及工程热...
- 1 篇 控制科学与工程
- 1 篇 测绘科学与技术
- 1 篇 环境科学与工程（可...
- 1 篇 生物医学工程（可授...
7 篇 理学
- 5 篇 物理学
- 1 篇 化学
- 1 篇 地球物理学
2 篇 医学
- 1 篇 临床医学

主题

409 篇 whale optimizati...
134 篇 optimization
95 篇 accuracy
63 篇 heuristic algori...
54 篇 feature extracti...
49 篇 prediction algor...
45 篇 predictive model...
43 篇 convergence
42 篇 metaheuristics
38 篇 costs
37 篇 whales
35 篇 stability analys...
33 篇 particle swarm o...
31 篇 simulation
30 篇 resource managem...
26 篇 classification a...
26 篇 data models
25 篇 power system sta...
23 篇 internet of thin...
23 篇 renewable energy...

机构

5 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 karim baiche

语言

409 篇 英文

检索条件"主题词=Whale Optimization Algorithms"

共 409 条记录，以下是341-350 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：

iTHD Improvement for TCM Totem Pole PFC Based on WOA Compensated System Delay

iTHD Improvement for TCM Totem Pole PFC Based on WOA Compens...

引用

Power Energy Systems and Applications (ICoPESA), International Conference on

作者： Di Zhu Chihua Lu Zhien Liu Laifeng Shi Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan University of Technology Wuhan China Passenger Car Power Center Dongfeng Motor Corporation Wuhan China

ISBN: (数字)9798350351668

ISBN: (纸本)9798350351675

This paper presents a system delay compensation method based on the whale optimization Algorithm (WOA) for the triangular current mode(TCM) Totem Pole PFC to improve the input current total harmonic distortion (iTHD). The inherent system delay of the TCM Totem Pole PFC rectifier near the zero-crossing point of the input voltage can cause distortion of the input current. The traditional system delay compensation method is to correct the conduction time of the switch tube with an estimated delay value. However, inaccurate compensation for system delay may lead to poor iTHD improvement or failure to achieve Zero Voltage Switching (ZVS). The method presented in this paper can find the actual system delay value of the rectifier for compensation under different load conditions, to achieve the maximize improvement of iTHD, without affecting ZVS. A simulation model of a 1650W, 220V/450V Totem Pole PFC rectifier was built to test this method. The results show that this method results in an average error of only 0.1055% between the actual and ideal iTHD of PFC.

关键词： Total harmonic distortion Rectifiers Switches Zero voltage switching Distortion whale optimization algorithms Delays optimization Load modeling

来源：评论

学校读者我要写书评

暂无评论

An Efficient Sparrow Search Algorithm based Clustering and Energy Efficient Opportunistic Routing Protocol (EEOR) for Wireless Sensor Network

An Efficient Sparrow Search Algorithm based Clustering and E...

引用

Innovations in Communications, Electrical and Computer Engineering (ICICEC), International Conference on

作者： Ramkumar Natarajan S. Ramalingam M. A. Amarnath P. Tamil Selvi Boopathy. S S. Ravi Department of ECE SR University Warangal Telangana Department of ECE Sri Eshwar College of Engineering Coimbatore Department of CSE J.J College of Engineering Trichy Department of ECE Sri Sakthi Engineering College Coimbatore Kumaraguru College of Technology Coimbatore Tamilnadu India Department of ECE Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Wireless Sensor Networks (WSNs) have had a substantial influence on diverse industries such as education, agriculture, manufacturing, and environmental monitoring. However, due to their wireless characteristics, sensor nodes (SNs) in distant or unsupervised regions cannot substitute their batteries, prompting extensive research to extend their operational lifespan. Although cluster-based routing has proven effective in tackling this challenge, there is still room for improvement in optimizing the selection of cluster heads (CHs) by incorporating crucial parameters. Traditionally, the emphasis has been on either selecting CHs or transmitting data between nodes, but meta-heuristic methods present a promising strategy for attaining optimal network performance. This proposed research work presents a novel approach called the sparrow search optimization algorithm (SSO) for improving performance through the combination of clustering and the energy-efficient opportunistic routing (EEOR) protocol. The suggested hybrid methodology was implemented using MATLAB, and the obtained outcomes were compared to three commonly employed techniques: low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed (HEED), and a whale optimization algorithm (WOA)-based clustering approach. The QoS parameters are represented by the following performance results: packet delivery ratio (PDR) of 98%, throughput of 0.92 Mbps, energy consumption of 8.70 mJ, network lifetime (NLT) of 6000 rounds, and an end-to-end delay (E2ED) of 0.01843 ms for the proposed method.

关键词： Wireless communication Wireless sensor networks Technological innovation Clustering algorithms Quality of service Routing Throughput Energy efficiency whale optimization algorithms Routing protocols

来源：评论

学校读者我要写书评

暂无评论

An AGV Scheduling Problem Based on Partial Charging Policy

An AGV Scheduling Problem Based on Partial Charging Policy

引用

International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Wei Wang Hui Sun Mengxin Zhang Department of Mechanical Engineering Southeast University Nanjing China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Automated guided vehicles (AGVs) have become common devices in intelligent manufacturing environments. This paper studies a scheduling problem for AGVs, in which a fleet of homogeneous AGVs are used to deliver parts to workstations in a shop floor environment. Due to the limited battery capacity of the AGV, we have adopted a partial charging policy in this paper. The transportation tasks undertaken by each AGV and the charging operations on each AGV need to be scheduled. The objective is to minimize the makespan to complete all transportation tasks. A mixed integer programming model is presented to describe the AGV scheduling problem. A two-stage heuristic approach is proposed for problem-solving. For stage one, a hybrid genetic whale optimization algorithm (HGWOA) is developed to assign transportation tasks to each AGV. For stage two, a rule-based heuristic is proposed to schedule the assigned transportation tasks and charging operations for each AGV. Computational experiments show that the proposed solution procedures can solve the scheduling problem effectively and efficiently.

关键词： Sequential analysis Schedules Job shop scheduling Processor scheduling Heuristic algorithms Transportation whale optimization algorithms Manufacturing Workstations optimization

来源：评论

学校读者我要写书评

暂无评论

Survey of Brain Tumour Detection and Prediction Using Machine Learning, Deep Learning and Metaheuristic Techniques

Survey of Brain Tumour Detection and Prediction Using Machin...

引用

International Conference on Reliability, Infocom Technologies and optimization (ICRITO) (Trends and Future Directions)

作者： Tanisha Verma Aditya Kumar Singh Lakshya Mittal Sanjana Sumit Kumar Surbhi Vijh ASET Amity University UP India School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The intricate anatomy of the brain makes it difficult for researchers to identify and predict brain Tumours in their early stages. Manually analyzing many Magnetic Resonance Imaging (MRI) images can occasionally be exhausting and biased. This survey aims at many methods for early brain Tumour diagnosis and prediction using Machine Learning, Deep Learning and Metaheuristics. The survey depicts that Convolutional Neural Network (CNN) is the most preferred algorithm for detection and prediction of Brain Tumour as compared to K-Nearest Neighbor (KNN), Deep Neural Netwok (DNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Compared to previous metaheuristic algorithms such as Particle Swarm optimization (PSO), Ant Colony optimization (ACO), and whale optimization have demonstrated superior outcomes in Brain Tumour Detection.

关键词： Surveys Deep learning Support vector machines Magnetic resonance imaging Metaheuristics Prediction algorithms whale optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Research on the Traveling Wave Extraction Method Based on WOA-VMD and Cubic Spline Interpolation

Research on the Traveling Wave Extraction Method Based on WO...

引用

Frontiers of Electronic, Electrical and Information Engineering (ICFEEIE), International Conference on the

作者： Zhibin Lu Wei Chen School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331528300

ISBN: (纸本)9798331528317

The traveling wave fault distance measurement method is high accuracy for ranging and fast positioning speed. This method is mainly used in high-voltage transmission lines. The extraction of traveling wave signals and the identification and calibration of traveling wave heads are the basis and prerequisite for the time-domain reflectometry method. The accuracy of head recognition and extraction has a bearing on the precision of traveling wave ranging. In current research, due to the non-stationary nature and strong singularity of traveling wave signals, some head extraction methods may encounter issues such as susceptibility to interference, slow extraction speed, weak adaptability, modal aliasing, and insufficient local feature focusing ability during application. This leads to unsatisfactory results in traveling wave head extraction. For these reasons, this paper proposes to use the whale optimization Algorithm-based Variational Mode Decomposition method (WOA-VMD) to address the method adaptability issues in traveling wave head extraction. Additionally, in order to further improve head recognition accuracy and reduce calibration errors in head moments, this paper presents a traveling wave extraction method based on WOA-VMD combined with cubic spline interpolation. This will provide assistance for the widespread using of traveling wave fault distance measurement in power systems.

关键词： Interpolation Accuracy Reflectometry Feature extraction Transmission line measurements Distance measurement whale optimization algorithms Calibration Splines (mathematics) Time-domain analysis

来源：评论

学校读者我要写书评

暂无评论

MCWOA based Hybrid Deep Learning for Detecting the Attacks in Cybersecurity with IoT Network

MCWOA based Hybrid Deep Learning for Detecting the Attacks i...

引用

Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Sravanthi Dontu Rohith Vallabhaneni Santosh Reddy Addula Piyush Kumar Pareek Haider Mohammed Abbas Department of Information Technology University of the Cumberlands Williamsburg USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

All throughout the globe, machine learning is being used in a diversity of settings, from universities to businesses. Intuitive machine learning algorithms can assess dangers and react quickly to security breaches and problems. In the realm of cybersecurity, it is essential for providing a proactive security system. Cybersecurity safeguards data, systems, and networks against unauthorized access in real time. Multiple analyses of privacy and security metrics over the past decade have found that the frequency and severity of cybersecurity breaches have been steadily increasing. Criminals are penetrating data protection systems at a rapid clip. Top cyber-security problems that need efficient explanations include anomaly finding, software vulnerability diagnosis, phishing page proof of identity, malware identification. In addition, NSL-KDD or KDD-CUP99 datasets provide the basis of most extant research. None of these datasets contain assaults that occurred recently. Networkflow ToN-IoT, a realistic dataset obtained from a large-scale, heterogeneous IoT network, was therefore utilized in the study. The study used a hybrid deep learning model combining ResNet and AlexNet for classification purposes. This study employs a hybrid optimization method that takes cues from both the Chimp optimization Algorithm (COA) and the whale optimization Algorithm (WOA) to fine-tune AlexNet's parameters. In order to recover the accuracy of MCWOA's identification, the Sobol arrangement is employed during the initialization of the population to distribute it uniformly over the solution space. Additionally, the position update process of classic MCWOA is enhanced by combining the bubble-net hunting and random search processes of the whale optimization algorithm. The results have important consequences for the continuous creation of adaptable, efficient, and secure intrusion detection systems for the complicated environment of IIoT networks Hybrid (ResNet-AlexNet) model reached the accuracy a

关键词： Deep learning Machine learning algorithms Accuracy optimization methods Intrusion detection whale optimization algorithms Classification algorithms Reliability Computer security Computer crime

来源：评论

学校读者我要写书评

暂无评论

Routing Security in RPL-based Internet of Things (IoT) Networks using whale Optimisation Algorithm and Unsupervised Machine Learning

Routing Security in RPL-based Internet of Things (IoT) Netwo...

引用

Smart Electronics and Communication (ICOSEC), International Conference on

作者： P.S. Nandhini Saravanan Matheswaran Priyanka Lingala N.M. Indhumathi Department of CSE Kongu Engineering College Perundurai Erode Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Sci & Tech Department of CSE Malla Reddy Institute of Engineering & Technology Hyderabad Department of CSE Nandha Engineering College Erode

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Routing Protocol for Low-Power and Lossy Networks is a widely adopted routing protocol specifically designed for Low Power and Lossy Networks. It offers energy-efficient mechanisms and supports secure modes of operation, making it suitable for resource-constrained devices in IoT. So, RPL is extensively utilized in various IoT applications for efficient data communication. However, the issue of routing security in RPL-based IoT networks has emerged as a significant concern. To address this, whale optimization Algorithm is used to select the relevant features to reduce the resource consumption. The selected relevant features are given as input to various unsupervised Machine Learning models. Among the models, Local outlier factor yielded the inlier accuracy of $\mathbf{9 9. 3 0 \%}$ and outlier accuracy of $\mathbf{9 9. 6 8 \%}$. The results are also compared with and without feature selection algorithm.

关键词： Machine learning algorithms Accuracy Machine learning Routing Feature extraction whale optimization algorithms Routing protocols Internet of Things Security optimization

来源：评论

学校读者我要写书评

暂无评论

Wavelet-Based Multiresolution FOPID (MRPIλDµ) Controller for Automatic Generation Control

Wavelet-Based Multiresolution FOPID (MRPIλDµ) Controller f...

引用

International Conf on Electrical and Electronic Engineering (ICEEE)

作者： Ali Younis Al Dahhan Reem Al Najjar Ahmed H. Osman Department of Electrical Engineering American University of Sharjah Sharjah UAE

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

This paper presents a Wavelet-Based Multiresolution Fractional-Order Proportional-Integral-Derivative $(MRPI^{\lambda}D^{\mu})$ controller for Automatic Generation Control (AGC) in power systems. The proposed controller leverages the principles of multiresolution analysis and fractional calculus to achieve improved control performance and robustness. The Area Control Error (ACE) is decomposed into multiple scales using wavelet analysis, allowing for a detailed analysis of different frequency components. At each scale, fractional-order proportional, integral, and derivative actions are applied to the error signal, enabling enhanced control over complex system dynamics. The gains of the controller are optimized using Particle Swarm optimization (PSO), whale optimization Algorithm (WOA), Stochastic Fractal Search (SFS) to achieve optimal control performance. $\text{PSO}-MRPI^{\lambda}D^{\mu}$ was able to obtain the best overall performance and the minimum Integral Time Absolute Error (ITAE). Simulation results demonstrate the superior performance of the proposed controller in terms of frequency regulation, disturbance rejection, and system stability when compared to conventional controllers. The $MRPI^{\lambda}D^{\mu}$ controller offers a promising solution for enhancing AGC performance in power systems, leading to improved system reliability and efficiency.

关键词： Time-frequency analysis Simulation Automatic generation control Power system stability whale optimization algorithms Stability analysis Particle swarm optimization Frequency control Signal resolution Thermal stability

来源：评论

学校读者我要写书评

暂无评论

Research on fault diagnosis of KPCA-WOA-BP transformer

Research on fault diagnosis of KPCA-WOA-BP transformer

引用

International Conference on Computer Engineering and Applications (ICCEA)

作者： Hong Peng Chonghao Zhao School of Electrical and Control Engineering Liaoning Polytechnic University Huludao China School of Electronic and Information Engineering Liaoning Polytechnic University Huludao China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

In order to quickly identify transformer faults and process them, a transformer fault diagnosis model based on Kernel PCA (KPCA) and whale optimization Algorithm (WOA) optimized BP neural network is provided. Firstly, the traditional data of various miscellaneous gases used to solve the nonlinear fault of transformer are reduced by kernel principal component analysis (KPCA). According to the uncertainty of a single BP (Back Backpropagation) neural network, which is easy to fall into local optimization and has poor accuracy, the input data is normalized first, and then whale algorithm is used to solve its various weights and thresholds for each work, reducing the workload, The overall mean square error of the training set and the test set is used as the fitness function to optimize the parameters. The performance comparison with Grey Wolf Optimizer (GWO)and Particle Swarm optimization (PSO) proves its superiority. KPCA-WOA-BP transformer fault diagnosis model is built. The accuracy is 90%, Compared with KPCA-GWO-BP model, KPCA-PSO-BP model and KPCA-BP model, it is improved by 8%, 12% and 22% respectively.

关键词： Fault diagnosis Training Accuracy Neural networks whales Transformers whale optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

GEWO: An Efficient Prioritised Task Scheduling in Cloud Fog Computing Environment

GEWO: An Efficient Prioritised Task Scheduling in Cloud Fog ...

引用

International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Santhosh Kumar Medishetti G. Swapna K. Anusha Ganesh Reddy Karri Kuppusamy P VIT-AP University Amaravathi India Anurag University Hyderabad India Marri Laxman Reddy Institute of Technology India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

The rapid expansion of Cloud-Fog Computing (CFC) underscores the need for effective task scheduling (TS) strategies to optimize resource utilization and bolster system performance. This paper introduces a novel optimization algorithm, Golden Eagle whale optimization (GEWO), which combines the unique behaviors of Golden Eagles and whales by integrating Golden Eagle optimization (GEO) and whale optimization Algorithm (WOA). GEWO strives to achieve a balance between global exploration, emulating the soaring capabilities of Golden Eagles, and local exploitation, inspired by the strategic diving behaviors of whales. Specifically designed to tackle the challenges posed by the dynamic and heterogeneous nature of cloud and fog computing environments, GEWO takes into account factors such as task characteristics, resource availability, and network conditions. By incorporating these elements into the optimization process, GEWO enhances convergence speed and solution quality, presenting a promising solution for efficient TS in CFC. To evaluate GEWO's efficacy, comprehensive experiments were conducted using diverse benchmark tasks and real-world CFC scenarios. Comparative analyses against state-of-the-art optimization algorithms reveal the superior performance of GEWO, showcasing a 26% reduction in task completion time, a 32 % improvement in resource utilization, and a 29% increase in energy efficiency. GEWO contributes to ongoing efforts aimed at enhancing the efficiency and scalability of cloud-fog systems, marking advancements in resource management and overall system performance.

关键词： Wireless communication Processor scheduling System performance Signal processing algorithms whales whale optimization algorithms Resource management

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41页 << < 31 32 33 34 35 36 37 38 39 40 > >>

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：