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

339 篇 会议
47 篇 期刊文献

馆藏范围

386 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

386 篇 whale optimizati...
121 篇 optimization
88 篇 accuracy
56 篇 heuristic algori...
49 篇 feature extracti...
45 篇 prediction algor...
43 篇 predictive model...
42 篇 metaheuristics
39 篇 convergence
37 篇 costs
35 篇 whales
32 篇 stability analys...
31 篇 simulation
31 篇 particle swarm o...
28 篇 resource managem...
25 篇 data models
25 篇 power system sta...
24 篇 classification a...
22 篇 internet of thin...
22 篇 energy consumpti...

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

386 篇 英文

检索条件"主题词=whale optimization algorithms"

共 386 条记录，以下是61-70 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：

Hybrid WOA-GA Algorithm for Coverage optimization and Energy Efficiency in Wireless Sensor Networks

Hybrid WOA-GA Algorithm for Coverage Optimization and Energy...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Momar Ousmane BA Cheikhou Thiam Mamadou Mbaye Department of Science and Technology Université Iba Der Thiam Thiès Senegal Department of Economic and Social Sciences Université Iba Der Thia Thiès Senegal

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

This article presents a comparative study of the hybrid WOA-GA algorithm (whale optimization Algorithm - Genetic Algorithm) applied to the optimization of wireless sensor networks, in comparison with three other algorithms: GA (Genetic Algorithm), PSO (Particle Swarm optimization), and ACO (Ant Colony optimization). The performance of the algorithms is evaluated based on three key criteria: network lifetime, residual energy, and the amount of data transmitted to the base station. The results show that WOA-GA outperforms the other algorithms in each category, significantly extending node lifetime through better energy management while maintaining more efficient data transmission. WOA-GA demonstrates particularly marked superiority in the network's later phases by retaining higher residual energy and delaying node death. These results highlight WOA-GA's capabilities in optimizing sensor network resources in energy-constrained environments. Finally, the article proposes avenues for future research, including the evaluation of WOA-GA in more complex and dynamic networks and the integration of new optimization approaches, such as artificial intelligence, to enhance the robustness and efficiency of sensor networks in real-world contexts.

关键词： Wireless sensor networks Machine learning algorithms Heuristic algorithms Conferences Prediction algorithms whale optimization algorithms Robustness Particle swarm optimization optimization Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Adaptive Energy and Latency optimization in Multi-Tier Networks via DDPG-Based Multi-Objective Control

Adaptive Energy and Latency Optimization in Multi-Tier Netwo...

引用

International Conference on Information Networking

作者： Vitou That Vanda Yorn Jung-Ryun Lee Department of Intelligent Energy Industry Convergence Chung-Ang University Seoul South Korea Department of Intelligent Energy and Industry School of Electrical and Electronics Engineering Chung-Ang University Seoul South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the whale optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.

关键词： Performance evaluation Energy consumption Heuristic algorithms Simulation Autonomous aerial vehicles Deep reinforcement learning whale optimization algorithms Air to ground communication Internet of Things Vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

Financial Distress Prediction by using Long Short-Term Memory based Adaptive whale optimization Algorithm

Financial Distress Prediction by using Long Short-Term Memor...

引用

Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Qiuyu Yang Yantai Vocational College Yantai China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent days, financial distress had been attributed to both internal and external factors, the internal causes included poor financial management while external influences economic downturns, heightened competition, and regulatory shifts. Traditional approaches for predicting financial distress had faced several challenges which include high sensitivity to outliers and inability to process sequential data. Therefore, this research proposes Long Short-Term Memory based Adaptive whale optimization Algorithm (LSTM-AWOA) for predicting financial distress, as it is effective at handling sequential data. Initially, data is collected from polish company dataset which consists of 10,503 samples with 64 financial attributes. Then, this dataset is preprocessed for handling missing values, and removing redundant rows which improves data quality, leading to better model performance and also resolves overfitting issues. After that, prediction is done by using LSTM and to improve its performance AWOA optimization is done. The proposed LSTM-AWOA achieved better results in terms of accuracy (0.9765), precision (1.0000), recall (0.9678) and F1-score (0.9769) when compared to existing AWOA-Deep Learning (AWOADL).

关键词： Accuracy Data integrity Companies Prediction algorithms whale optimization algorithms Data models Long short term memory optimization Streams Overfitting

来源：评论

学校读者我要写书评

暂无评论

Structural optimization Design of the Rotary Blowout Preventer Shell Based on an Adaptive Sand Cat and whale optimization Algorithm

Structural Optimization Design of the Rotary Blowout Prevent...

引用

IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Tian Haijiang Houpin Yoong Bih Lii Chua Ismail Saad Institute of Intelligent Manufacturing Fujian Polytechnic of Information Technology Fuzhou China Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

A critical component of the Rotary Blowout Preventer (RBOP) is the rotating assembly, essential for its “drill-in circulation” functionality. The bottom of the outer cylinder of the rotating assembly endures significant hydrostatic pressure, leading to substantial deformation. This study establishes a finite element analysis model of the outer cylinder of the rotating assembly in ANSYS Workbench and validates the strength of the assembly shell by analyzing the stress distribution. The radii of curvature at the interface between the rotating assembly and the clamp, along with five shell thicknesses at different locations along the outer cylinder, are designated as independent variables. To minimize the mass of the shell, a mathematical model is formulated using Von Mises stress as the state variable. optimization computations are conducted using the Adaptive Sand Cat and whale optimization Algorithm (SCWOA). By adjusting the radii of curvature, the concentration of maximum stress is reduced, optimizing the stress distribution, thinning the wall thickness, and reducing the outer cylinder's weight. Compared to optimization results using the hybrid penalty function in ANSYS Workbench, SCWOA demonstrates faster convergence, achieving near-optimal solutions in fewer iterations, making it suitable for addressing challenging engineering optimization problems. Furthermore, the optimized results provide a theoretical foundation for subsequent repair and remanufacturing studies.

关键词： Deformation Maintenance engineering whale optimization algorithms Mathematical models Finite element analysis Clamps optimization Assembly Stress Convergence

来源：评论

学校读者我要写书评

暂无评论

Parallel whale optimization Algorithm-Kmeans for Image Segmentation using Multiprocessing

Parallel Whale Optimization Algorithm-Kmeans for Image Segme...

引用

iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Ali Mezaghrani Mohammed Debakla Khalifa Djemal Departement of computer science LISYS Laboratory Faculty of exact science University of Mustapha Stambouli Mascara Algeria IBISC Laboratory Evry Val d'Essone University Evry Every France

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

Image segmentation is an important step in inter-preting and analyzing images, allowing computers and algorithms to process the content of an image accuratly and more effectively. Image segmentation is a crucial step in image preprocessing, while Kmeans is a widely-used clustering algorithm for im-age segmentation. However, the whale optimization Algorithm (WOA) is a nature-inspired metaheuristic optimization tech-nique known for its simplicity and global search capability. In this study, a hybrid approach was proposed, which combined WOA with Kmeans algorithm (WOA-Kmeans) to optimize image segmentation process, in the other hand, a parallel strategy was implemented for enhancing both segmentation quality and execution time. Traditional Kmeans suffers from sensitivity to initial cluster centroids, often leading to suboptimal results. By integrating WOA, the centroids initialization is improved, guiding Kmeans towards better convergence. Additionally, the parallelization of WOA-Kmeans using multiprocessing enables faster computation by distributing the work among multiple processes, making it suitable for high-resolution image processing tasks. This combination of WOA-Kmeans in a multiprocess environment ensures efficient and accurate image segmentation. The results demonstrate that the Parallel WOA-Kmeans outperform the sequentiel WOA-Kmeans in term of several performance measures, such as, accuracy, Root Mean Squar Error (RMSE), Peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

关键词： Image segmentation PSNR Accuracy Sensitivity Metaheuristics Measurement uncertainty Clustering algorithms whale optimization algorithms Noise measurement Informatics

来源：评论

学校读者我要写书评

暂无评论

Reducing Task Scheduling Time in Cloud Computing using Novel Improved whale optimization Algorithm over Ant Colony Algorithm

Reducing Task Scheduling Time in Cloud Computing using Novel...

引用

Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： P. Sushma Priya S. John Justin Thangaraj Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

To decrease Cloud Computing Task Scheduling Time utilising Novel Improved whale Optimisation Algorithm over Ant Colony Algorithm. A Novel Improved whale Optimisation Algorithm schedules tasks in cloud computing. Cloud computing involves internet-based distribution of data storage, servers, databases, networking, and applications. Task scheduling data is obtained using cloudsim. We analyse, create, and implement cloud computing application framework and cloud computing infrastructures. After data import, Improved whale Optimisation and Ant Colony Algorithm are tried. Two algorithms have two groups. Each group has 120 samples with 80% G power, 0.05% threshold, and 95% CI. Task Scheduling Time (ms) is returned for inputs. The findings come from IBM SPSS. Results indicate a significant independent sample t test (p= 0.000, p<0.05). The algorithms vary statistically. Ant Colony takes longer to schedule tasks than Improved whale Optimisation Algorithm. According to study, the Improved whale Optimisation Algorithm (53.6000 MS) has a shorter Task Scheduling Time than the Ant Colony Algorithm (93.1000 MS).

关键词： Cloud computing Schedules Quantum computing Processor scheduling whales Scheduling whale optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Multiagent Adaptive Clustering Algorithm Using whale optimization in VANETs Network

A Hybrid Multiagent Adaptive Clustering Algorithm Using Whal...

引用

Communication and Networks (ASIANComNet), Asian Conference on

作者： Zahraa Saad Abdulali Hussein Al-Aboudy S. Sri Nandhini Kowsalya Fatima H. Alsalamy Maysam Reyad Hadi Mohammed Ihsan National University of Science and Technology Dhi Qar Iraq Department of Computer Techniques Engineering Mazaya University College DhiQar Iraq Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Medical Laboratories Techniques Department Al-Mustaqbal University Hillah Iraq Department of Computer Science Altoosi University College Najaf Iraq Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

Mainly to improve the efficiency to reduce the power utilization in earlier researches the clustering model is concentrated. It is the formation of clusters in the network which increases the connection degree fundamentally and with the leaders the network stability is also increased. With the presence of a huge number of devices even the earlier clustering models consists of certain drawbacks like data loss and delay. Overcome such drawbacks in this article a hybrid multiagent adaptive algorithm with whale optimization (HMACWO) is developed so that an optimal clustering is introduced which is able to increase the efficiency of the vehicular network. The core modules which are present in this article are an efficient clustering process and whale optimization with an improved clustering model. Experimental demonstration of this model is done in NS3 software and using certain parameters such as the cluster efficiency, CH lifetime, packet delivery ratio, network throughput, and average delay the performance of the network is analyzed. From the results, it is shown that the HMACWO attains a maximum cluster efficiency and $\mathbf{C H}$ lifetime when compared with the earlier methods.

关键词： Adaptation models Adaptive systems Clustering algorithms Throughput whale optimization algorithms Stability analysis Software Hybrid power systems Delays optimization

来源：评论

学校读者我要写书评

暂无评论

An Effectual Strategy to Augment the Hello Interval of Each Node Using Enhanced Humpback whales optimization

An Effectual Strategy to Augment the Hello Interval of Each ...

引用

Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Richa Purohit T. Saravanan Rashmi Dharwadkar Y Vahidhabanu S.P. Sasirekha N. Krishnaveni School of Computer Studies Sri Balaji University Pune India Dept. of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Dept. of Computer Science Sri Balaji University Pune India Department of Computer Science and Engineering Kongunadu college of Engineering and Technology Trichy India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Wireless networks are highly utilized, especially Mobile Ad-hoc Networks (MANETs) for real-time data transmission. However, current routing protocols for MANETs face challenges that limit their effectiveness. Design issues include minimizing energy usage and ensuring node security. A commonly used routing protocol, AODV, involves periodic broadcasting of hello messages to monitor link connectivity and maintain routing tables. When link failure occurs due to node mobility, hello messages are broadcasted more frequently, which consumes more node energy and increases network overhead. To address this issue, this research proposes an Enhanced Humpback whales Optimized Routing Approach (EHWORA) that calculates an optimal hello interval based on a node's energy and mobility using an adaptive fuzzy inference system. This approach involves optimizing the hello interval of AODV routing through the use of an adaptive fuzzy inference system. Each node can dynamically adjust the transmission of hello messages based on the fuzzy-based hello interval. Simulation results demonstrate that ORA reduces network overhead, end-to-end delay, hello message fraction, average energy consumption, packet delivery ratio, and improves throughput compared to a recent method. The proposed protocol is evaluated using MATLAB software. To assess the validity of the research, we utilized MATLAB software to compare the proposed method with a recent approach. During the simulation, the proposed protocol depleted the energy of 3 nodes after 10 seconds, 5 nodes after 20 seconds, 9 nodes after 30 seconds, 11 nodes after 40 seconds, and 13 nodes after 50 seconds, respectively.

关键词： Fuzzy logic Adaptive systems Routing whale optimization algorithms Ad hoc networks Software Routing protocols MATLAB Artificial intelligence Mobile computing

来源：评论

学校读者我要写书评

暂无评论

Feasibility and optimization of Solar Systems for Academic Institutions: A Multi-Algorithm Approach

Feasibility and Optimization of Solar Systems for Academic I...

引用

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Ashaduzzaman Niloy M M Naushad Ali Md Shahadat Hossen Bibekananda Nath Md Ashraful Islam Shahela Akter Ahmed Al Mansur Department of Electrical and Electronic Engineering Green University of Bangladesh Bangladesh Department of ECE Gannon University Pennsylvania

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Renewable energy systems (RES) present a sustainable and resilient alternative to traditional single-source energy systems, offering improved safety and cost-effectiveness in power generation. This study investigates the efficiency and economic viability of a photovoltaic (PV)-based energy system with integrated energy storage, tailored for a university campus setting. To optimize the utilization of available space, advanced optimization algorithms- Grey Wolf Optimizer (GWO), whale optimization Algorithm (WOA), and Artificial Hummingbird Al-gorithm (AHA) were employed to identify the optimal system size and associated costs. The findings highlight the strong potential of RES in urban environments. Specifically, the case study on the Green University of Bangladesh campus demonstrates the feasibility of the proposed system, achieving a cost of energy (COE) of $0.018SlkWh and a payback period of 8 years and 10 months. This research illustrates the capacity of PV-based systems to provide reliable and sustainable energy to urban campuses, delivering significant economic and environmental advantages that advocate for their wider adoption in similar settings.

关键词： Photovoltaic systems Renewable energy sources Costs Urban areas Green products whale optimization algorithms Solar system Safety Reliability optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal Trajectory Planning for Robotic Arm Based on Improved whale optimization Algorithm

Optimal Trajectory Planning for Robotic Arm Based on Improve...

引用

Service Robotics (ICoSR), International Conference on

作者： Zhiqiang Ni Guirong Wang Hengchu Ye College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798350367119

ISBN: (纸本)9798350367126

An integrated optimal trajectory planning approach is suggested to increase the shock resilience and operational efficiency of industrial robots. First, the refractive reverse learning mechanism, Lévy flights, and nonlinear convergence factor are incorporated to the basic whale optimization algorithm to create the improved whale optimization algorithm (IWOA). Then the superiority and efficacy of the improved algorithm are confirmed using the CEC2017 test function set. Subsequently, trajectory planning in the robot joint space utilizing 3-5-3 polynomial functions determines the whole optimization objective function in terms of time, impact, and energy consumption. When the joint motion curves derived from the improved algorithm are continuous and devoid of sudden shifts, the efficacy of the trajectory planning approach has finally been verified.

关键词： Energy consumption Trajectory planning Service robots Electric shock Manipulators whale optimization algorithms Polynomials Robots optimization Resilience

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39页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：