检索结果-内蒙古大学图书馆

Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 31卷 126-135页

作者： Krawczyk, Bartosz Filipczuk, Pawel Wroclaw Univ Technol Dept Syst & Comp Networks PL-50370 Wroclaw Poland Univ Zielona Gora Inst Control & Computat Engn Zielona Gora Poland

Recently a great increase of interest in digital pathology and cytology can be observed. Computer-aided diagnosis solutions, developed to assist physicians in the early detection of diseases, can improve accuracy and robustness of the diagnosis. In this paper we present a work in progress on a computer-aided breast cancer diagnosis. We propose an efficient medical decision support framework that allows distinguishing between benign, malignant and fibroadenoma cases. The nuclei detection procedure is based on the firefly algorithm. The procedure generates nuclei markers that are used in marker-controlled watershed segmentation. Image recognition is done by a novel classifier. Instead of using a multi-class approach we decided to implement one-class decomposition strategy, where each of the classes is represented by an ensemble of one-class classifiers. We propose to use a multi-objective memetic algorithm to select the pool of one-class predictors that display at the same time high diversity and consistency. Experiments conducted on a set of 675 real case medical images obtained from patients of the Regional Hospital in Zielona Gora showed that our framework returns highly satisfactory results, outperforming other state-of-the-art methods. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： One-class classification Multi-class decomposition Classifier ensemble Ensemble pruning firefly algorithm Image analysis

来源：评论

学校读者我要写书评

暂无评论

Assessing Performance of Genetic and firefly algorithms for Optimal Design of Heat Exchangers

Assessing Performance of Genetic and Firefly Algorithms for ...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Rihanna Khosravi Abbas Khosravi Saeid Nahavandi Centre for Intelligent Systems Research Deakin University

ISBN: (纸本)9781479938414

This paper aims to comprehensively investigate performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA) and firefly algorithm (FA) are implemented for finding the optimal values for seven key design variables of the STHX model. ∈-NTU method and Bell-Delaware procedure are used for thermal modelling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA always lead to maximum STHX efficiency. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behaviour of the majority of decision variables remain consistent in different runs of the FA optimization process.

关键词： Heat exchanger Optimization Genetic algorithm firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

firefly as a novel swarm intelligence variable selection method in spectroscopy

引用

ANALYTICA CHIMICA ACTA 2014年 852卷 20-27页

作者： Goodarzi, Mohammad Coelho, Leandro dos Santos Katholieke Univ Leuven Fac Biosci Engn Dept Biosyst B-3001 Heverlee Belgium Fed Univ Parana UFPR Dept Elect Engn BR-81531980 Curitiba PR Brazil Pontificial Univ Catholic Parana PUCPR Ind & Syst Engn Grad Program PPGEPS BR-80215901 Curitiba PR Brazil

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. (C) 2014 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Variable selection Chemometrics Spectroscopy

来源：评论

学校读者我要写书评

暂无评论

AGC of a Multi - area Interconnected System with FACTS and firefly Optimized 2DOF PID Controller

AGC of a Multi - area Interconnected System with FACTS and F...

引用

International Conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Dash, Puja Saikia, Lalit Chandra Sinha, Nidul Natl Inst Technol Silchar Dept Elect Engn Silchar India

ISBN: (纸本)9781479920440

This paper presents automatic generation control (AGC) of three unequal area thermal systems with single reheat turbine and appropriate generation rate constraints (GRC) in each area. The system dynamic responses for various 2DOF controllers such as 2DOF-PI, and 2DOF-PID are compared. Investigations reveal that responses with 2DOF-PID are better than other. The performance of a two degree of freedom (2DOF) PID controller in presence of several FACTS devices such as Static synchronous series compensator (SSSC), and Interline power flow controller (IPFC) is evaluated for the first time in AGC. Secondary controller gains and other parameters of 2DOF controllers are optimized simultaneously using Fire fly algorithm (FA). Comparison of dynamic responses of the system with 2DOF PID is done with that of with 2DOF PID with SSSC and IPFC reveals that dynamic responses with IPFC are better than others. Sensitivity analysis reveals that the FA optimized 2DOF - PID controller parameters in presence of IPFC at nominal condition of loading and size of step load perturbation (SLP) are robust and need not be reset with wide changes in system loading and SLP.

关键词： Automatic Generation Control firefly algorithm IPFC Sensitivity Analysis SSSC 2DOF controller

来源：评论

学校读者我要写书评

暂无评论

A comprehensive review of firefly algorithms

引用

SWARM AND EVOLUTIONARY COMPUTATION 2013年第0期13卷 34-46页

作者： Fister, Iztok Fister, Iztok, Jr. Yang, Xin-She Brest, Janez Univ Maribor Fac Elect Engn & Comp Sci Maribor 2000 Slovenia Middlesex Univ Sch Sci & Technol London NW4 4BT England

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations. (C) 2013 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Swarm intelligence Nature-inspired algorithm Optimization

来源：评论

学校读者我要写书评

暂无评论

firefly based Ridge Polynomial Neural Network for Classification

Firefly based Ridge Polynomial Neural Network for Classifica...

引用

International Conference on Advanced Communication Control and Computing Technologies*

作者： Behera, N. K. S. Behera, H. S. Veer Surendra Sai Univ Technol Dept Comp Sci & Engn Burla India

ISBN: (纸本)9781479939145

Classification using higher order neural network (HONN) such as pi-sigma and ridge polynomial neural network (RPNN) are the most salient and active research area and popularly used in several applications such as financial time series forecasting and for solving inverse problems in electromagnetic non-destructive evaluation. This paper intends to use RPNN for classification which overcomes certain limitations of MLP having slow learning properties and ability to get stuck in local minima. RPNN distinguish themselves from MLP due to their fast learning capability and powerful mapping of single layer trainable weights in networks. firefly algorithm (FFA) is used for training of the RPNN and then the proposed technique is tested with three different real world dataset such as, glass, iris and Haberman's survival datasets archived from UCI respiratory. The Simulation results shows that the classification accuracy and the convergence rate of FFA based RPNN is higher as compared with FFA based MLP

关键词： Higher order neural networks data classification ridge polynomial neural network pi-sigma neural networks firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

AGC of a Multi - area System Using firefly Optimized Two Degree of Freedom PID Controller

AGC of a Multi - area System Using Firefly Optimized Two Deg...

引用

Power and Energy Systems Conference - Towards Sustainable Energy (PESTSE)

作者： Dash, Puja Saikia, Lalit Chandra Sinha, Nidul Natl Inst Technol Silchar Dept Elect Engn Silchar India

ISBN: (纸本)9781479934218

This paper presents the automatic generation control (AGC) of an interconnected three unequal area thermal system with appropriate generation rate constraints and governor dead band. The areas are provided with single reheat turbine. The system dynamic performance is evaluated by considering 1% step load perturbation (SLP) in areal. The firefly algorithm (FA) is used for optimization of secondary controller gains and other parameters. The performance of the two degree of freedom PID (2DOF PID) controller is evaluated for the first time in AGC of three area system and compared with that of conventional single degree of freedom PID controller (simply PID controller). Additionally, the 2DOF PID and PID controllers are tested for different loadings and SLPs. Investigations reveal that the performance of 2DOF PID controller is much better than PID controller. Also, it is seen that the control performance of 2DOF PID is better and improves with wide change in loading and SLP from their nominal values.

关键词： Automatic generation control firefly algorithm governor dead band two degree of freedom PID controller

来源：评论

学校读者我要写书评

暂无评论

Automatic generation control using two degree of freedom fractional order PID controller

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年第0期58卷 120-129页

作者： Debbarma, Sanjoy Saikia, Lalit Chandra Sinha, Nidul Natl Inst Technol Meghalaya Dept Elect & Elect Engn Shillong Meghalaya India Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India

In this paper, Two-Degree-of-Freedom-Fractional Order PID (2-DOF-FOPID) controller is proposed for automatic generation control (AGC) of power systems. Proposed controller is tested for the first time on a three unequal area thermal systems considering reheat turbines and appropriate generation rate constraints (GRCs). The simultaneous optimization of several parameters of the controllers and speed regulation parameter (R) of the governors is done by a recently developed metaheuristic nature-inspired algorithm known as firefly algorithm (FA). Investigation clearly reveals the superiority of the 2-DOF-FOPID controller in terms of settling time and reduced oscillations. Present work also explores the effectiveness of the firefly algorithm based optimization technique in finding the optimal parameters of the controller and selection of R parameter. Further, the convergence characteristics of the FA are compared to justify its efficiency with other well established optimization technique such as PSO, BFO and ABC. Sensitivity analysis confirms the robustness of the 2-DOF-FOPID controller for different loading conditions and wide changes in inertia constant (H) parameter. Furthermore, the performance of proposed controller is tested against higher degree of perturbation and random load pattern. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Automatic generation control firefly algorithm Fractional order controller Integer order controller 2-DOF-FOPID controller

来源：评论

学校读者我要写书评

暂无评论

Coordinated Robust Control of DFIG Wind Turbine and PSS for Stabilization of Power Oscillations Considering System Uncertainties

引用

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2014年第3期5卷 823-833页

作者： Surinkaew, Tossaporn Ngamroo, Issarachai King Mongkuts Inst Technol Ladkrabang Fac Engn Sch Elect Engn Bangkok 10520 Thailand

Uncertainties in power systems, such as intermittent wind power, generating and loading conditions may cause the malfunction of power system stabilizing controllers, which are designed without considering such uncertainties. To enhance the robustness of stabilizing controllers against system uncertainties, this paper proposes a new coordinated robust control of doubly fed induction generator (DFIG) wind turbine equipped with power oscillation damper (POD) and synchronous generator installed with power system stabilizer (PSS) for stabilization of power system oscillations. Without the difficulty of mathematical representation, the inverse output multiplicative perturbation is used to model system uncertainties. The structure of POD and PSS is specified as a practical second-order lead/lag compensator with single input. The parameters optimization of POD and PSS is conducted so that the stabilizing performance and robustness of POD and PSS are augmented. The improved firefly algorithm is applied to solve the optimization problem and achieve the POD and PSS parameters automatically. Simulation study in the modified IEEE-39 bus New England system included with DFIG wind turbines ensures that the robustness and stabilizing performance of the proposed coordinated DFIG with POD and PSS are much superior to those of the conventional DFIG with POD and PSS under various severe disturbances and system uncertainties.

关键词： Doubly fed induction generator (DFIG) wind turbine firefly algorithm power system oscillation power system stabilizer (PSS) robust control system uncertainties

来源：评论

学校读者我要写书评

暂无评论

Multiple cross-docks scheduling using two meta-heuristic algorithms

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 129-138页

作者： Madani-Isfahani, Mansooreh Tavakkoli-Moghaddam, Reza Naderi, Bahman Islamic Azad Univ Sch Ind Engn South Tehran Branch Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran 14174 Iran Univ Tehran Coll Engn Ctr Excellence Intelligence Based Expt Mech Tehran 14174 Iran Univ Kharazmi Fac Engn Dept Ind Engn Karaj Iran

This paper considers a truck scheduling problem in a multiple cross docks while there is temporary storage in front of the shipping docks. Receiving and shipping trucks can intermittently move in and out of the docks during the time intervals between their task execution, in which trucks can enter to any of the cross docks. Thus, a mixed-integer programming (MIP) model for multiple cross docks scheduling is developed inspired by models in the body of the respective literature. Its objective is to minimize the total operation time or maximize the throughput of the cross-docking system. Moreover, additional concepts considered in the new method is multiple cross docks with a limited capacity. In this study, there are two types of delay times. The first type occurs when there is a shipping truck change and the second one occurs when the current shipping truck does not load any product from a certain receiving truck or temporary storage and waits until its needed products arrive at the shipping docks. To solve the developed model, two meta-heuristics, namely simulated annealing (SA) and firefly algorithms (FA), are proposed. In addition, a procedure for trucks scheduling in a state of a constant discrete firefly algorithm for the discrete adaptation has been proposed. The experimental design is carried out to tune the parameters of algorithms. Finally, the solutions obtained by the proposed SA and FA are compared. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Multiple cross-docks scheduling Simulated annealing firefly algorithm Experimental 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：