检索结果-内蒙古大学图书馆

A novel estimation method for capillary pressure curves based on routine core analysis data using artificial neural networks optimized by cuckoo algorithm - A case study

引用

FUEL 2018年 220卷 363-378页

作者： Jamshidian, Majid Zadeh, Mostafa Mansouri Hadian, Mohsen Moghadasi, Ramin Mohammadzadeh, Omid Islamic Azad Univ Omidiyeh Branch Dept Petr Engn Omidiyeh Iran Petr Univ Technol Dept Automat & Instrumentat Engn Ahvaz Iran Petr Univ Technol Dept Petr Engn Ahvaz Iran DBR Technol Ctr Schlumberger Edmonton AB Canada Schlumberger Doll Res Ctr Cambridge MA 02139 USA

Capillary pressure is one of the most important parameters affecting fluids distribution in a reservoir rock and is an essential input parameter for reservoir simulation. Measurement of capillary pressure data, in the context of special core analysis (SCAL) is a time and cost consuming process that does not often lead to accurate and reliable results. Routine core analysis (RCAL) data, on the other hand, can be obtained by simple, accurate, and cost-effective procedures. In this paper, the idea of using RCAL measurements to predict SCAL data (more specifically capillary pressure data) using Artificial Neural Network (ANN) approach is presented. An ANN with Multi-Linear Perceptron structure and feed-forward propagation was used to predict capillary pressure curves for a target reservoir under study. The ANN model was then optimized by cuckoo optimization algorithm (COA). The ANN-COA model was used for 30 measurements, composed of both drainage and imbibition data points obtained from 15 core samples using centrifugation method. Out of this databank, 16 measurements were used for training and the remaining 14 measurements were used as the testing dataset. It was obtained that the optimized model shows a profound predicting performance based on the excellent value of coefficient of determination for predicted versus measured capillary pressure data.

关键词： Capillary pressure Artificial Neural Network cuckoo algorithm Routine core analysis Special core analysis

来源：评论

学校读者我要写书评

暂无评论

MODCSA-CA: A multi objective diversity controlled self adaptive cuckoo algorithm for protein structure prediction

引用

GENE REPORTS 2017年 8卷 100-106页

作者： Ramyachitra, D. Ajeeth, A. Bharathiar Univ Dept Comp Sci Coimbatore Tamil Nadu India

Finding the native structure of a protein initially from its amino acid sequence remains one of the most challenging open problems in Bioinformatics and molecular biology. Protein structure prediction plays a critical part in protein construction and mapping analysis, drug design and any other biological applications. Extracting good representation from protein sequence is fundamental to this prediction task. The multi-objective optimization tends to be more effective than other techniques in complex problems. The protein structure prediction problem is solved using Diversity Controlled Self-Adaptive cuckoo algorithm. MODCSA-CA, an improved version of Self-Adaptive Differential Evolution (SaDE), to check the diversity of individuals and local search to sustain a balance between exploration and exploitation. This subject looks into the application of cuckoo search (CS) algorithm on the protein structure prediction problem. Results point MODCSA-CA as a competitive approach for total energy and conformation similarity metrics. Comparability of the statistical results demonstrates that CS outperforms other algorithms in a meaningful manner.

关键词： Protein structure prediction cuckoo algorithm Levy flight Angle Optimization

来源：评论

学校读者我要写书评

暂无评论

Speech classification based on cuckoo algorithm and support vector machines 2

Speech classification based on cuckoo algorithm and support ...

引用

2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)

作者： Shi, Wenlei Fan, Xinhai Acad Armored Forces Engn Dept Mech Engn Beijing Peoples R China

ISBN: (纸本)9781538620304

Speech classification is an important part of speech signal processing. It is significant to classify speech accurately and quickly in speech coding and speech synthesis. Because of the diversity and uncertainty of the speech signals, the traditional classification method is slow and not so accurate in the large-scale application of real speech classification. In order to improve the accuracy and precision of speech classification, a speech classification method based on support vector machine optimized by cuckoo algorithm(CS-SVM) is proposed. Firstly, choose four types of music: folk songs, Guzheng, rock and pop. And adopt the cepstral coefficient to extract speech feature, then use the support vector machine optimized by the cuckoo algorithm to train the characteristic signals, and establish the optimal classifier model, and finally classify the tested speech. The results of the simulation experiment show that the support vector machine based on the cuckoo algorithm(CS-SVM) is better than the traditional SVM and BP neural network in speech recognition.

关键词： support vector machine cuckoo algorithm speech classification classification model BP neural network

来源：评论

学校读者我要写书评

暂无评论

Optimal Overcurrent Relays Coordination in Microgrid Using cuckoo algorithm 3

Optimal Overcurrent Relays Coordination in Microgrid Using C...

引用

3rd International Conference on Power and Energy Systems Engineering, (CPESE)

作者： Ahmarinejad, Amir Hasanpour, Seyed Mohsen Babaei, Mojtaba Tabrizian, Mohammad Islamic Azad Univ Dept Elect Engn Yadegar E Imam Khomeini RAH Shahr E Rey Branch Tehran Iran

Microgrid(MG) is a controlled branch of Distributed Generations(DGs) and loads in distribution network and offers more potential in DGs operation and control. MG can operate with and without utility grid (Island mode). The operation of this system in islanded mode requires sophisticated control and protection schemes. MG protection should be capable of fault detection with appropriate sensitivity and selectivity in both rigid-connected and islanded modes. This paper presents solving the issue of short circuit level difference in both modes using fault current limiter (FCL). Overcurrent(O/C) relays coordination and fault current limiter impedance are selected in such a way that protection system has a suitable operation for both modes. For this purpose, artificial intelligence methods of Particle Swarm Optimization (PSO) algorithm and cuckoo algorithm have been used to achieve optimal relays coordination and fault current limiter impedance. The under study grid is a small MG related to Tehran Oil Refinery and the simulations have been running using MATLAB. The results indicate that fault current limiter can be used to solve the problem of short current level difference in both modes and cuckoo algorithm is a more effective method for optimization applications. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Microgrid Overcurrent Relays Fault Current Limiters cuckoo algorithm Particle Swann Optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Minimizing Excessive Handover Using Optimized cuckoo algorithm in Heterogeneous Wireless Networks 2nd

Minimizing Excessive Handover Using Optimized Cuckoo Algorit...

引用

2nd International Conference on Computer and Communication Technologies

作者： Kumar, Salavadi Ananda Murthy, K. E. Sreenivasa Jawaharlal Nehru Technol Univ Hyderabad Andhra Pradesh India Adishankara Coll Engn & Technol Nellore India

ISBN: (纸本)9788132225263;9788132225256

In this paper we worked out to find the best solution for the two constraints in heterogeneous wireless environments: finding the best available network in a reasonable amount of time with optimized parameter values (such as cost of network should be minimum) and try to minimize the number of handovers, when mobile node is moving by overlapping of different wireless networks provided by different network providers. Here the decision problem is formulated as multiple objective optimization problems and simulated using cuckoo optimizing algorithm. In this paper our simulation result shows that the number of handovers can be minimized if we take optimized network parameter values.

关键词： Vertical Handoff cuckoo algorithm Number of handovers Heterogeneous network Best network Parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Portfolio selection using hybrid algorithm of data envelopment analysis based on Kohonen neural network and cuckoo algorithm

引用

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2016年第4期37卷 549-568页

作者： Razi, Farshad Faezy Shadloo, Naeimeh Islamic Azad Univ Dept Ind Management Semnan Iran

In stock markets, one of the main issues in which investors are involved is how to select stocks. If the investors select stocks rationally, he can achieve efficiencies greater than the market average. It is of great importance to find techniques that help investors in selecting stocks in particular at stock exchange. Investors are always looking for a set of stocks in the financial markets which is more profitable and less risky. The objective of this study is to presents a new model for portfolio selection. For portfolio selection, this model uses a hybrid algorithm of Data Envelopment Analysis (DEA) based Kohonen neural network and cuckoo search algorithm. The data are clustered using Kohonen neural network and then the efficiency is calculated separately for each cluster using DEA model based on the nature of the output. Pareto-optimal combination of risk and efficiency is analyzed via cuckoo algorithm.

关键词： Multiple-criteria decision-making Data envelopment analysis (DEA) Kohonen neural network cuckoo algorithm Portfolio optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal Overcurrent Relays Coordination in Microgrid Using cuckoo algorithm

引用

Energy Procedia 2016年 100卷 280-286页

作者： Amir Ahmarinejad Seyed Mohsen Hasanpour Mojtaba Babaei Mohammad Tabrizian Department of Electrical Engineering Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch Islamic Azad University Tehran Iran

关键词： Microgrid Overcurrent Relays Fault Current Limiters cuckoo algorithm Particle Swarm Optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Task processing optimization using cuckoo particle swarm (CPS) algorithm in cloud computing infrastructure

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第1期26卷 745-769页

作者： Zavieh, Hadi Javadpour, Amir Li, Yuan Ja'fari, Forough Nasseri, Seyed Hadi Rostami, Ali Shokouhi Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Harbin Inst Technol Dept Comp Sci & Technol Cyberspace Secur Shenzhen Peoples R China Inst Politecn Viana do Castelo Electrotech & Telecommun Dept ADiT Lab Porto Portugal Sharif Univ Technol Dept Comp Engn Tehran Iran Univ Mazandaran Dept Math Babolsar 48175866 Iran Foshan Univ Foshan Peoples R China Sharif Univ Technol Tehran Iran Univ Sistan & Baluchestan Dept Commun Engn Zahedan Iran

Recently, cloud computing infrastructure (CCI) models have received much attention for their exceptional scalability, dependability, Data Information Sharing (DIS), and low cost rate. There are many hardware and software elements that are accessed over the internet by cloud data centers. Modern data centers utilize Virtualization Techniques (VT) to offer a dispersed CI that employs Virtual Machines (VM) based on Physical Hosts (PH). With the increasing number of centers, optimizing energy consumption has become vital to saving costs due to DCC's high energy consumption. In our CPS algorithm, we combine the cuckoo algorithm and the particle swarm optimization (PSO). It is determined which virtual machine can be assigned to each host, thus choosing the best virtual machine. As a result, if the selected host is overloaded, it is determined which virtual machines are generating high loads and migrated to another host, which is determined based on the cuckoo algorithm and PSO. In testing each algorithm separately, the combination method proved to consume less energy and execute faster than the other methods in the CloudSim simulation environment. Fault tolerance for our network and evaluation of VMs have also been emphasized in vSphereTM.

关键词： cuckoo algorithm PSO Virtual machine migration Cloud computing Fault tolerance Task processing

来源：评论

学校读者我要写书评

暂无评论

PREDICTION MODEL OF ROUGH ROLLING FORCE BASED ON cuckoo-RANDOM FOREST algorithm

引用

METALURGIJA 2024年第2期63卷 212-214页

作者： Wang, B. Univ Sci & Technol Liaoning Sch Appl Technol Anshan 114051 Peoples R China

In order to improve the prediction accuracy of the roughing force prediction model, a cuckoo-random forest algorithm is proposed in this paper to quickly solve the set value of rolling force based on the standard random forest algorithm. The experimental results show that the prediction accuracy of the cuckoo-random forest algorithm is higher than that of traditional models, and it can effectively predict the set value of rough rolling force.

关键词： roughing mill rolling force random forest model cuckoo algorithm prediction model

来源：评论

学校读者我要写书评

暂无评论

Optimal Design of a Brushless DC Motor, by cuckoo Optimization algorithm

引用

INTERNATIONAL JOURNAL OF ENGINEERING 2017年第5期30卷 668-677页

作者： Azari, M. Niaz Samami, M. Pahnehkolaei, S. M. Abedi Univ Sci & Technol Mazandaran Dept Elect Engn Behshahr Iran Islamic Azad Univ Dept Elect Engn Sari Branch Sari Iran

This contribution deals with an optimal design of a brushless DC motor, using optimization algorithms, based on collective intelligence. For this purpose, the case study motor is perfectly explained and its significant specifications are obtained as functions of the motor geometric parameters. In fact, the geometric parameters of the motor are considered as optimization variables. Then, the objective function has been defined. This function consists of three terms i.e. losses, construction cost and the volume of the motor which should be minimized simultaneously. Three algorithms i.e. cuckoo, genetic and particle swarm have been studied in this paper. It is noteworthy that, cuckoo optimization algorithm has been used for the first time for brushless DC motor design optimization. A comparative study between the mentioned optimization approaches shows that, cuckoo optimization algorithm has been converged to optimal response in less than 250 iterations and its standard deviation is. 0.03, while the convergence rate of the genetic and particle swarm algorithms are about 400 and 450 iterations with standard deviations of. 0.07 and. 0.06, respectively for the case study motor. The obtained results show the best performance for cuckoo optimization algorithm among all mentioned algorithms in brushless DC motor design optimization.

关键词： Brushless DC Motor cuckoo algorithm Objective Function Optimal Motor Design

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：