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
46 篇 期刊文献

馆藏范围

377 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

45 篇 工学
- 43 篇 电气工程
- 35 篇 信息与通信工程
- 32 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

377 篇 whale optimizati...
116 篇 optimization
84 篇 accuracy
52 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...
27 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

377 篇 英文

检索条件"主题词=whale optimization algorithms"

共 377 条记录，以下是21-30 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：

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

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

Optimized PV Panel Control in Hybrid DC Microgrid Systems Using whale optimization & Reinforcement Learning

Optimized PV Panel Control in Hybrid DC Microgrid Systems Us...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Kathir D Umesh Prabhu H Maathavan R Department of EEE St. Joseph’s College of Engineering OMR Chennai

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

As the demand for reliable and sustainable energy grows, DC microgrids powered by renewable sources are becoming an efficient alternative for modern power systems. This research introduces an optimized control strategy for a DC microgrid that combines solar, wind, and batteries, ensuring efficient power management and uninterrupted energy supply to connected loads. The system leverages the whale optimization Algorithm combined with Reinforcement Learning to enhance Maximum Power Point Tracking for solar panels, enabling real-time adaptation to changing environmental conditions. A dedicated power converter controls the DC bus voltage, and the battery circuit maximizes charging and discharging to maintain system stability. Additionally, a power flow management algorithm is developed to ensure optimal energy distribution among the three sources. The proposed approach is implemented and tested in SIMULINK under varying solar and wind conditions. Simulation results demonstrate improved energy harvesting, faster convergence, enhanced voltage regulation, and higher system efficiency. This research contributes to the advancement of smart and self-sustaining DC microgrids, providing an intelligent solution for renewable energy management.

关键词： Maximum power point trackers Renewable energy sources Lighting Microgrids Reinforcement learning Power system stability whale optimization algorithms Stability analysis Batteries Voltage control

来源：评论

学校读者我要写书评

暂无评论

Solving optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm optimization and Invasive Weed optimization Algorithm

Solving Optimization Problems by a Hybrid Algorithm Based on...

引用

International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakkayaphop Pravesjit Krittika Kantawong Duangjai Jitkongchuen Arit Thammano Panchit Longpradit School of Information and Communication Technology University of Phayao Phayao Thailand Big Data Instutite Bangkok Thailand Facluty of Information Technology King Mongkut’s Institute of Technology Ladkrabang Ladkrabang Thailand Faculty of Social Sciences and Humanities Mahidol University Nakhon Pathom Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper addresses an optimization problem using hybrid algorithms of the Sand Cat Swarm optimization (SCSO) and Invasive Weed optimization (IWO). In this study, the reproduction step in the IWO algorithm was incorporated after the initial population step of the SCSO. The proposed algorithm was compared against the following: Intersection Mutation Differential Evolution (IMDE), Differential Evolution (DE), SCSO, and whale optimization Algorithm (WOA), whereby the performance was tested on six benchmark functions using a 10-fold cross validation. The results indicate that the proposed algorithm yielded the optimal solution for two out of the six benchmark functions. Additionally, when compared with the other four chosen algorithms, it yielded the best overall results. The findings suggest that the proposed algorithm is able to generate solutions similar to those obtained from the previous methods, essentially for the continuous step function, the multimodal function, and the discontinuous step function.

关键词： Benchmark testing whale optimization algorithms Particle swarm optimization optimization

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic Approaches for Energy Efficiency and Network Lifetime optimization in Wireless Sensor Networks

Metaheuristic Approaches for Energy Efficiency and Network L...

引用

Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： A.S. Sai Puneeth Theja S. Swarnalatha B. Shoban Babu Department of ECE Sri Venkateswara University College of Engineering Tirupati A.P. India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This paper focuses on traditional and natureinspired metaheuristic algorithms, such as Low-Energy Adaptive Clustering Hierarchy (LEACH), Particle Swarm optimization (PSO) in Wireless Sensor Networks (WSNs). The evaluation focuses on key parameters like network lifetime, node clustering, and energy consumption levels. Results indicate that PSO consistently outperform the LEACH algorithm in these metrics However, the Adaptive whale optimization Algorithm (AWOA) is competitive with the PSO algorithm with its results leaning towards on the better side. The study complements related research on the application of swarm intelligence in WSN by focusing on routing optimization, energy aware protocols and centralized clustering of nodes.

关键词： Measurement Wireless sensor networks Protocols Visual analytics Metaheuristics Clustering algorithms Routing Energy efficiency whale optimization algorithms Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Beluga whale optimization for Effective Energy Management in Microgrids with Electric Vehicle Charging in Smart Distribution Networks

Beluga Whale Optimization for Effective Energy Management in...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Narendra Pothula Nagasrisaihari Sunkara Vanitha R Thiyagesan M H. Mickle Aancy Balasubbareddy Mallala Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad Telangana India Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering R.M.K Engineering College Chennai Tamil Nadu India Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telanagana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

EM in MGs with EV charging refers to the special mainstream of providing electricity and continuously managing and balancing energy supply, load demand and integrating prosumer elements to maintain proper grid stability and efficiency. There are several disadvantages in the incorporation of EVs in to MGs and smart distribution networks. They are the high initial investment costs which accompany infrastructure improvements like updating charging stations and smart grid technologies may hinder implementation. In this paper, we present a new approach of Beluga whale optimization (BWO) incorporating electric vehicle charging stations (EVCS) for EM in MGs. The primary task that the given approach addresses is the optimization of system utilization and LCOE reduction. The BWO is applied for the performance enhancement of load scheduling of MGs with incorporated EV charging in smart distribution grids. The structure of BWO approach is also established on MATLAB platform, and their performance is compared with other algorithms including Artificial Neural Network (ANN), Shell Game optimization (SGO), Multi-Objective Improved arithmetic optimization Algorithm (MOIAOA) and Young's double Slit-Experiment Optimizer (YDSEO). The presented BWO method obtains LCOE equal to 22,173$/kWh to prove its efficiency over existing methodologies for planning and designing energy systems.

关键词： Energy consumption Renewable energy sources Electricity Artificial neural networks Distribution networks Power system stability Electric vehicle charging whale optimization algorithms MATLAB Energy management

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Puma Optimizer Algorithm for optimization Problems

Enhancing the Puma Optimizer Algorithm for Optimization Prob...

引用

International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakkayaphop Pravesjit Krittika Kantawong School of Information and Communication Technology University of Phayao Phayao Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper proposes an enhanced Puma Optimizer Algorithm for solving optimization problems. The Rao Algorithm is incorporated and refined within the exploration phase of the Puma Optimizer, incorporating novel procedures to improve position updates. The proposed algorithm is evaluated on six benchmark functions. An evaluation results show in comparison to Differential Evolution (DE), Intersection Mutation Differential Evolution (IMDE), the whale optimization Algorithm (WOA), and the Sand Cat Swarm optimization Algorithm (SCSO). Computational results demonstrate that the proposed algorithm achieves optimal solutions for two of the six benchmark functions. Furthermore, it consistently outperforms the other four algorithms across the test suite. These findings highlight the potential for further advancements in the Puma Optimizer and demonstrate its effectiveness in addressing continuous step functions, multimodal functions, and discontinuous step functions, achieving performance that is competitive with or surpasses existing state-of-the-art methods.

关键词： Benchmark testing whale optimization algorithms Particle swarm optimization optimization

来源：评论

学校读者我要写书评

暂无评论

Fopid Controllers Based Forward DC to DC Converter With Modified Osprey optimization Algorithm for MPPT of PV System Under Partial Shading Conditions

Fopid Controllers Based Forward DC to DC Converter With Modi...

引用

Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Naveen Kumar Bind K. Krishnaveni Burada Sankara Rao Vikrant Singh Sadu Munisekhar Neel Kamal Ch. Lenin Babu Department of Electrical Engineering JSS Academy of Technical Education Noida Uttar Pradesh India Department of EEE Chaitanya Bharathi Institute of Technology Hyderabad Telangana India CPGC Pvt. Ltd and ERG Foundation Anakapalli Visakhapatnam Andhra Pradesh India Department of Electrical Engineering O.P. Jindal University Raigarh Chhattisgarh India Electrical and Electronics Engineering Sri Venkatesa Perumal College of Engineering and Technology (SVPCET) Puttur India JNTU Ananthapuramu India Electrical & Electronics Engineering School of Engineering & Technology IIMT University Meerut U.P. India Department of EEE RSR Engineering College Kavali India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Everywhere, electric power is needed for a variety of functions and tasks. Creating electric power through renewable resources, like solar energy, is a practical way to solve a lot of issues. Typically, photovoltaic modules use solar radiation to generate electricity. To guarantee maximum usage or optimal efficiency, a photovoltaic system must be equipped with a maximum power point tracking converter (MPPTC). In this paper, a DC to DC forward converter is chosen to function as MPPTC. For MPPTC under uniform sun irradiances, a variety of traditional methods are available, such as incremental conductance, perturb, and observe. However, trees, shade, dust, birds, clouds, and other factors will cause photovoltaic modules - of which many are coupled to make a power production unit - to receive non-uniform irradiances. The system enters a partial shading condition (PSC) as a result of this event. Conventional algorithms may be failed to approach the maximum utilization point to increase energy extraction under PSC. Therefore, to operate the power unit at its optimal usage under the situation of PSC, an optimization method is needed. In this study on MPPTC, an optimization method called the “Modified Osprey optimization Algorithm (MOOA)” is designed to gather more energy during PSCs. This research also compares MOOA, Modified Invasive Weed optimization (MIWO), Modified Grey Wolf optimization (MGWO), and whale optimization Algorithm (WOA). OPALRT technology is used to create the Hardware-in-the-Loop, which gathers diverse outcomes for analysis under different operations.

关键词： Maximum power point trackers Renewable energy sources optimization methods Solar energy whale optimization algorithms Power systems Solar panels Solar radiation Voltage control Sun

来源：评论

学校读者我要写书评

暂无评论

An Improved IIR Filter Design using Artificial Rabbits optimization Algorithm and its FPGA Implementation

An Improved IIR Filter Design using Artificial Rabbits Optim...

引用

Devices for Integrated Circuit (DevIC)

作者： Subhadeep Mondal Sajal Sarkar Raina Modak Aich Supriya Dhabal Palaniandavar Venkateswaran Dept. of ETCE Jadavpur University Kolkata West Bengal India Dept. of ECE Netaji Subhash Engineering College Kolkata West Bengal India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

This paper presents a meta-heuristic optimization algorithm known as Artificial Rabbits optimization (ARO) to get the optimal set of infinite impulse response (IIR) filter coefficients. Application of the ARO algorithm to design two real-life examples is used for performance testing. The proposed algorithm is simulated to get the coefficients of different types of linear and non-linear (with noise and without noise) problems with similar order and lower order, and the simulated results are compared with few recent optimization algorithms such as Salp Swarm Algorithm (SSA), whale optimization Algorithm (WOA), and Dragonfly Algorithm (DA). The proposed algorithm is much faster and efficient than the numerous standard algorithms. The Proposed ARO algorithm demonstrates a lower Mean Square Error (MSE) and superior convergence speed as compared to the other optimization techniques examined herein. FPGA implementation is performed to design IIR filters where ARO shows better performance.

关键词： Rabbits Noise Metaheuristics IIR filters Skin whale optimization algorithms Internet of Things Field programmable gate arrays Convergence Testing

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 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：