检索结果-内蒙古大学图书馆

Optimal Rotor Design and Analysis of Energy-Efficient Brushless DC Motor-Driven Centrifugal Monoset Pump for Agriculture Applications

引用

ENERGIES 2024年第10期17卷 2280页

作者： Antony, Richard Pravin Komarasamy, Pongiannan Rakkiya Goundar Rajamanickam, Narayanamoorthi Alroobaea, Roobaea Aboelmagd, Yasser SRM Inst Sci & Technol Dept Elect & Elect Engn Kattankulathur 603203 India SRM Inst Sci & Technol Dept Comp Technol Kattankulathur 603203 India Taif Univ Coll Comp & Informat Technol Dept Comp Sci POB 11099 Taif 21944 Saudi Arabia Univ Business & Technol Coll Engn Jeddah 23435 Saudi Arabia

The agricultural sector emphasizes sustainable development and energy efficiency, particularly in optimizing water pumping systems for irrigation. Brushless DC (BLDC) motors are the preferred prime mover over induction motors due to their high efficiency in such applications. This article details the rotor design and analysis of an energy-efficient BLDC motor with specifications of 1 hp, 3000 rpm, and 48 V, specifically tailored for a centrifugal monoset pump for irrigation. The focus lies in achieving optimal energy efficiency through grey wolf optimization (GWO) algorithm in the rotor design to determine optimal dimensions of the Neodymium Iron Boron (NdFeB) magnet as well as its grade. The finite element method analysis software, MagNet, is used to model and analyze the BLDC motor. The motor parameters, such as speed, torque, flux functions, temperature, and efficiency, are analyzed. For performance comparison, the same model with different magnet models is also analyzed. Validation via 3D finite element analysis highlights improvements in magnet flux linkage, stator tooth flux density, and rotor inertia with increased magnet thickness. Simulation results affirm the consistent performance of the designed BLDC motor, preferably when efficiency is increased. This efficiency and the constant speed lead to an improvement in the overall conversion efficiency of 7% within its operating range, affirming that the motor pump system is energy-efficient.

关键词： BLDC motor centrifugal monoset pump grey wolf algorithm 3D FEM energy-efficient motor NdFeB magnet

来源：评论

学校读者我要写书评

暂无评论

Optimal scheduling workflows in cloud computing environment using Pareto-based grey wolf Optimizer

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第11期29卷

作者： Khalili, Azade Babamir, Seyed Morteza Univ Kashan Dept Comp Kashan Iran

A workflow consists of dependent tasks, and scheduling of a workflow in a cloud environment means the arrangement of tasks of the workflow on virtual machines (VMs) of the cloud. By increasing VMs and the diversity of task size, we have a huge number of such arrangements. Finding an arrangement with minimum completion time among all of the arrangements is an Non-Polynomial-hard problem. Moreover, the problem becomes more complex when a scheduling should consider a couple of conflicting objectives. Therefore, the heuristic algorithms have been paid attention to figure out an optimal scheduling. This means that although the single-objective optimization, ie, minimizing completion time, proposes the workflow scheduling as an NP-complete problem, multiobjective optimization for the scheduling problem is confronted with a more permutation space because an optimal trade-off between the conflicting objectives is needed. To this end, we extended a recent heuristic algorithm called grey wolf Optimizer (GWO) and considered dependency graph of workflow tasks. Our experiment was carried out using the WorkflowSim simulator, and the results were compared with those of 2 other heuristic task scheduling algorithms.

关键词： evolutionary algorithms grey wolf algorithm pareto optimality strength pareto WorkflowSim

来源：评论

学校读者我要写书评

暂无评论

A quantum inspired hybrid SSA-GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computing

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第6期26卷 3587-3610页

作者： Jain, Richa Sharma, Neelam Banasthali Vidyapith Dept Comp Sci & Engn Niwai Allyabad India

Software as a service (SaaS) provider hires resources from an Infrastructure as a Service (IaaS) provider and provides these sharable resources to user's applications on lease. However, it is becoming a more challenging issue for SaaS providers to meet user's Quality of Service (QoS) Parameter and maximize profit from cloud infrastructure. This proposed work satisfies both the user and the service provider by fulfilling service level agreement (SLA), user's QoS requirement, and increasing profit with efficient resources utilization. This paper proposes an Improved Quantum Salp Swarm algorithm (IQSSA), which improves the Salp Swarm algorithm by incorporating the principles of Quantum computing to increase the convergence rate. Further, Quantum-inspired Salp Swarm grey wolf algorithm (QSSGWA) embeds SSA with grey wolf Optimizer (GWO) to improve the global optimum solution, and quantum operator is used to initializing population. Proposed algorithms execute tasks under the user-defined deadline and budget constraints. Furthermore, the penalty cost is formulated and applied in the case of a deadline violation. IQSSA and QSSGWA are tested on 19 global benchmark functions, and results prove their superior performance compared to SSA, GWO, BAT, and Particle Swarm Optimization (PSO) algorithm. Furthermore, these algorithms are simulated on CloudSim, and performance matrices such as service provider's profit, makespan, SLA violation rate, task rejection rate, throughput, resource utilization, and response time are compared. The comparison analysis demonstrates that the proposed algorithms offer better performance and more efficient scheduling than existing metaheuristics. Furthermore, simulation results clearly show that QSSGWA gives the best results for all performance matrices. This proposed approach can be applied in many scientific domains, where distributed processing of data or large scale data analysis is required such as distributed and federated machine learning, se

关键词： Salp swarm algorithm grey wolf algorithm Quantum-inspired computing Task scheduling Service level agreement Quality of Service

来源：评论

学校读者我要写书评

暂无评论

Research on lithology identification based on multi-sensor hybrid domain information fusion and support vector machine

引用

EARTH SCIENCE INFORMATICS 2022年第2期15卷 1101-1113页

作者： Gao, Kangping Jiao, Shengjie Changan Univ Natl Engn Lab Highway Maintenance Equipment Xian 710064 Peoples R China

To quickly and accurately identify lithology, this paper proposes a lithology identification method based on the combination of three-dimensional vibration signal hybrid domain feature and support vector machine (SVM). First, the original signal is divided into several segments by the window function, and the multi-domain features of each segment are extracted respectively to construct a hybrid domain vector;then, the hybrid domain vector is dimensionally reduced by the kernel principal component analysis method, and the principal components with a cumulative contribution rate greater than 90% are selected as the feature parameters, which are input into the SVM model optimized by the gray wolf algorithm for lithology identification. Experiments show that the lithology identification results of the proposed method are consistent with the test rock types, which verifies the feasibility of the proposed method. Compared with other methods, the advantages of the proposed method in classification accuracy and time complexity are verified again.

关键词： grey wolf algorithm Support vector machine Kernel principal component analysis Lithology identification Feature extractions Hybrid domain analysis

来源：评论

学校读者我要写书评

暂无评论

An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-24页

作者： Qiu, Yihui Yang, Xiaoxiao Chen, Shuixuan Xiamen Univ Technol Sch Econ & Management Xiamen 361024 Peoples R China Xiamen Univ Technol Sch Mech & Automot Engn Xiamen 361024 Peoples R China Fujian Prov Key Lab Green Intelligent Cleaning Tec Xiamen Peoples R China

As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms.

关键词： grey wolf algorithm Function optimization Engineering design optimization Exploration and exploitation

来源：评论

学校读者我要写书评

暂无评论

Study on Prediction Model of Soil Nutrient Content Based on Optimized BP Neural Network Model

引用

COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS 2023年第4期54卷 463-471页

作者： Wang, Guowei Wang, Jiaxin Wang, Jiawei Yu, Haiye Sui, Yuanyuan Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Jilin Univ Sch Biol & Agr Engn Key Lab Bion Engn Minist Educ Changchun Peoples R China

Soil nutrient content is an important index to determine the amount of fertilizer. Traditional soil nutrient content is obtained by manual sampling, which greatly increases the cost of agricultural production input. In order to solve this problem, this paper studies the relationship between soil nutrients, fertilizer amount, yield and the next year's soil nutrients, and establishes an optimized BP neural network model to realize the prediction of soil nutrient content. Aiming at the problem that the traditional BP neural network model will affect the prediction accuracy due to the different weights and thresholds, the grey wolf algorithm with reverse learning mechanism is introduced to obtain the optimal solution of BP neural network weights and thresholds. The soil nutrient content data collected in chenjiadian village, Nongan County, Jilin Province for five consecutive years were used to test the model. The data of the first four years were used as the training set and the data of the fifth year as the test set. The prediction results of BP neural network model, grey wolf algorithm optimized BP neural network model and grey wolf algorithm optimized BP neural network model with reverse learning mechanism were compared The prediction accuracy of BP neural network model optimized by grey wolf algorithm with reverse learning mechanism reached 88.7%, which was better than the first two models. It can provide basis for the fertilization decision of variable rate fertilization in the next year, so as to reduce the input of labor cost.

关键词： BP neural network grey wolf algorithm precise fertilization prediction of soil nutrient content

来源：评论

学校读者我要写书评

暂无评论

Optimum sizing design of steel frame structures through maximum energy dissipation of friction dampers under seismic excitations

引用

STRUCTURES 2022年 44卷 1928-1944页

作者： Artar, Musa Carbas, Serdar Bayburt Univ Fac Engn Dept Civil Engn Bayburt Turkey Karamanoglu Mehmetbey Univ Fac Engn Dept Civil Engn Karaman Turkey

This paper focuses on optimum sizing design of steel frame structures equipped with friction dampers (FDs) in order to prevent the vulnerable effect of an earthquake. The fundamental concept is to maximize the energy dissipation of implemented FDs under different seismic excitations. The FDs utilized as passive energy dissipation devices in a steel structure increase the energy dissipation capacity of the structure by decreasing the seismic demand during an earthquake. In this context, the Pall friction dampers (PFDs) are implemented as diagonal damped brace members in investigated structures. Three different earthquake records (Kobe, Kocaeli-Duzce, Erzincan) are utilized to acquire nonlinear dynamic responses of the steel frame structures through time -history analyses. The open application programming interface (OAPI) functions are used to integrate a finite element modelling based structural analysis program, SAP2000, with a design optimization algorithm encoded in MATLAB for achieving the exact structural behaviours by synchronously data transferring. As an optimizer, one of the recent nature-inspired metaheuristic techniques, so-called grey wolf (GW) algorithm is used. Initially, under effect of seismic excitations, the steel frame structures are optimally sized for attaining minimum design weights without implementing the PFD elements by subjecting only strength, displacement, drift, and geometric structural design constraints taken from AISC-LRFD practice code provisions. Afterward, in order for optimally modelling the PFD elements, the frames are equipped with PFDs to calculate optimum yield strengths that is defined as slip load of a PFD. To do so, the GW algorithm ensures the maximizing of the dissipated energy and controls the yield strengths between the stories since the shear force due to the earthquake increases towards the base of the structure. Eventually, the provisions-based sizing design optimizations of the steel frame structures equipped with op

关键词： Structural optimization grey wolf algorithm Pall friction damper Maximum energy dissipation Optimized slip load

来源：评论

学校读者我要写书评

暂无评论

Source Discrimination of Mine Water Inrush Using Multiple Combinations of an Improved Support Vector Machine Model

引用

MINE WATER AND THE ENVIRONMENT 2022年第4期41卷 1106-1117页

作者： Wei, Zhonglin Dong, Donglin Ji, Yuan Ding, Jia Yu, Lujia China Univ Min & Technol Beijing CUMTB Dept Geol Engn & Environm Beijing 100083 Peoples R China

Accurate and timely identification of water inrush sources is important in controlling mine water hazards. We combined various algorithms to offer an improved inrush source identification method. The algorithms include the Fisher identification method, self-organizing correlation method (SOM), improved principal component analysis method (PCSOM), and grey wolf algorithm (GWO) optimized support vector machine (GWOSVM). The model was used to identify the water inrush source of 47 groups of training samples and 20 groups of analysed samples from different water sources in the Zhaogezhuang coal mine. The results show that PCSOM can reduce the information overlap between discriminant indexes, simplify the model structure, and improve the running speed of the algorithm. The penalty factor c and kernel parameter g of the SVM optimized by the grey wolf algorithm are faster and more stable in parameter optimization, and the discrimination result is more accurate. The proposed model can accurately and quickly identify water inrush sources. Thus, it is helpful for rapidly predicting inrush disasters and can serve as a reference for inrush source identification technology.

关键词： Water hazards Water inrush source identification Zhaogezhuang coal mine grey wolf algorithm

来源：评论

学校读者我要写书评

暂无评论

Sustainable and optimal design of Chinese herbal medicine supply chain network based on risk dynamic regulation mechanism

引用

SN APPLIED SCIENCES 2023年第6期5卷 1-22页

作者： Wu, Yao Liu, Weiwei Shenyang Univ Technol Sch Mech Engn Shenyang Peoples R China

We propose a robust fuzzy design model for a sustainable closed-loop supply chain network. The model is based on a risk dynamic regulation mechanism. In this way, we can solve the problem of sudden disruptions and uncertain demand in the supply chain of Chinese herbal medicines. We also develop a hybrid algorithm solution to solve the model and design a resilient supply chain network. The specific steps are as follows: (1) The risk dynamic regulation mechanism is created with strong risk resistance by considering the information sharing platform, facility defense, drying station scheduling, safety stock, and shared inventory. (2) Based on the dynamic risk regulation mechanism, we establish a sustainable Chinese herbal medicine supply chain network design model. Then, we use the robust fuzzy method and the epsilon constraint to deal with the uncertainty and integrate the model. (3) We introduce opposition-based learning, cosine convergence factor, and levy flight to the original Whale and grey wolf algorithms to obtain the Hybrid algorithm, which is used to solve the processed model. The results show the model and algorithm proposed in this paper have strong applicability and advantages in designing closed-loop supply chain networks for Chinese herbal medicine and provide references for relevant decision-makers.

关键词： Chinese herbal closed-loop supply chain network Risk dynamic regulation mechanism Robust fuzzy approach Whale algorithm grey wolf algorithm Opposition-based learning

来源：评论

学校读者我要写书评

暂无评论

Effective phishing website detection based on improved BP neural network and dual feature evaluation 16

Effective phishing website detection based on improved BP ne...

引用

16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Zhu, Erzhou Liu, Dong Ye, Chengcheng Liu, Feng Li, Xuejun Sun, Hui Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781728111414

Nowadays, phishing poses a big threat to people's daily network environment. By phishing, attackers obtain the network users private information by inducing them to open illegal websites. Due to the active learning ability and preferable classifying ability for many datasets, BP neural network is an important heuristic machine learning method in phishing websites detection and prevention. However, improper selection of initial parameters, such as the initial weight and threshold, will induce the BP neural network into local minimum and slow learning convergence. Aiming at these problems, this paper proposes DF. GWO-BPNN, an effective phishing website detection model based on the improved BP neural network and dual feature evaluation mechanism. Under this model, the grey wolf algorithm is firstly used to optimize the BP neural network to reasonably select initial parameters. Then, the dual feature mechanism is used to evaluate the results of the improved BP neural network. By the dual feature evaluation mechanism, the accuracy of phishing website recognition is improved. Meanwhile, the black and white list is used to improve the efficiency of the proposed model. The DF. GWO-BPNN model is compared with some existing phishing website detection models. The experimental results have demonstrated that our model is accurate and strong adaptability.

关键词： Phishing website detection BP neural network grey wolf algorithm

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：