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Feature extraction and track measurement for low resolution images: A cost effective method

RADIATION MEASUREMENTS 2013年 50卷 228-231页

作者： Khayat, O. Ghergherehchi, M. Afarideh, H. Durrani, S. A. Pouyan, Ali A. Amirkabir Univ Technol Nucl Engn & Phys Dept Tehran Iran Univ Birmingham Sch Phys & Astron Birmingham W Midlands England Shahrood Univ Technol Dept Comp Engn Shahrood Iran

In a track counting system a digital USB CCD camera is often used to make a communication between the visually seen etched tracks of a solid state nuclear track detector and the image analyzer software installed on the PC. The camera's characteristics determine the size of the image, and the number of pixels per track. The resolution and contrast of the images are the key features in how well an image analyzer can perform the task of track measurement. In this paper we present an approach to increase the resolution and contrast of the images, aiming to extract the features which cannot be simply attained from the original image. The presented algorithm as an image processing technique is used to replace the economical cost of high quality imaging system with the computational cost of image processing. The tracks' characteristics will remain intact and the contrast of the image will be enhanced through Shannon entropy reaching its maximum. Feature preservation is done through maximizing the similarity between the original low resolution image and the processed higher resolution image. Similarity is defined as the mutual information between two images. This resolution increment will result in contrast enhancement and increased information transfer for feature extraction purpose. Experimental images of alpha tracks taken by a 350KP USB digital camera are used to verify the efficiency of the proposed algorithm. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm Shannon entropy Mutual information Solid state nuclear track detector Feature preservation

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MOGA-based fuzzy data mining with taxonomy

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KNOWLEDGE-BASED SYSTEMS 2013年第Dec.期54卷 53-65页

作者： Chen, Chun-Hao He, Ji-Syuan Hong, Tzung-Pei Tamkang Univ Dept Comp Sci & Informat Engn Taipei 251 Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 804 Taiwan

Transactions in real-world applications usually consist of quantitative values. Some fuzzy data mining approaches have thus been proposed for deriving linguistic rules from such transactions. Since membership functions may have a critical influence on the final mining results, several genetic-fuzzy mining approaches have been proposed for mining appropriate membership functions and fuzzy association rules at the same time. Most of them, however, focus on a single level and consider only one objective function. This paper proposes a multi-objective multi-level genetic-fuzzy mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. The algorithm first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. Two objective functions are then considered. The first one is the knowledge amount mined out at different levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated using these two objective functions. After the evolutionary process terminates, various sets of membership functions can be used for deriving multi-level fuzzy association rules according to decision-makers. Experimental results on the simulated and real datasets show the effectiveness of the proposed algorithm. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Data mining Fuzzy sets Fuzzy rules multi-objective genetic algorithm Taxonomy

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Economic and environmental impacts of the energy source for the utility production system in the HDA process

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ENERGY CONVERSION AND MANAGEMENT 2013年 74卷 129-139页

作者： Ouattara, A. Pibouleau, L. Azzaro-Pantel, C. Domenech, S. Inst Natl Polytech Houphouet Boigny Dept Genie Chim & Agroalimentaire Yamoussoukro Cote Ivoire ENSIACET INPT CNRS LGCUMR 5503 F-31432 Toulouse 4 France

The well-known benchmark process for hydrodealkylation of toluene (HDA) to produce benzene is revisited in a multi-objective approach for identifying environmentally friendly and cost-effective operation solutions. The paper begins with the presentation of the numerical tools used in this work, i.e., a multi-objective genetic algorithm and a multiple Choice Decision Making procedure. Then, two studies related to the energy source involved in the utility production system (UPS), either fuel oil or natural gas, of the HDA process are carried out. In each case, a multi-objective optimization problem based on the minimization of the total annual cost of the process and of five environmental burdens, that are Global Warming Potential, Acidification Potential, Photochemical Ozone Creation Potential, Human Toxicity Potential and Eutrophication Potential, is solved and the best solution is identified by use of multiple Choice Decision Making procedures. An assessment of the respective contribution of the HDA process and the UPS towards environmental impacts on the one hand, and of the environmental impacts generated by the main equipment items of the HDA process on the other hand is then performed to compare both solutions. This "gate-to-gate" environmental study is then enlarged by implementing a "cradle-to-gate" Life Cycle Assessment (LCA), for accounting of emission inventory and extraction. The use of a natural gas turbine, less economically efficient, turns out to be a more attractive alternative to meet the societal expectations concerning environment preservation and sustainable development. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Hydrodealkylation of toluene multi-objective genetic algorithm Energy source Environmental burdens Life Cycle Assessment

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multi-objective optimization for actuator and sensor layouts of actively controlled 3D buildings

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JOURNAL OF VIBRATION AND CONTROL 2013年第6期19卷 942-960页

作者： Cha, Young-Jin Kim, Yeesock Raich, Anne M. Agrawal, Anil K. CUNY City Coll New York NY 10031 USA Worcester Polytech Inst Worcester MA 01609 USA Lafayette Coll Easton PA 18042 USA

This paper investigates the multi-objective optimization of active control systems for vibration control of three-dimensional (3D) high-rise buildings under a variety of earthquake excitations. To this end, a novel multi-objective genetic algorithm is developed through the integration of the best features of a non-dominated sorting II (NS2) genetic algorithm (GA) and an implicit redundant representation (IRR) GA. The proposed NS2-IRR GA finds not only minimum distributions of both actuators and sensors within structures, but also minimum dynamic responses of 3D structures. Linear quadratic Gaussian controllers, hydraulic actuators and accelerometers are used for implementation of active control systems within the 3D buildings. To demonstrate the effectiveness of the proposed NS2-IRR GA, two 3D building models are investigated using finite element methods, including low-and high-rise buildings. It is shown that the proposed NS2-IRR GA is effective in finding not only optimal locations and numbers of both actuators and sensors in 3D buildings, but also minimum responses of the 3D buildings. The simulation also shows that the control performances of the proposed approach significantly enhance those of the engineering judgment oriented benchmark layout, which is validated by comparisons of each performance using the same number of actuators.

关键词： Actuators multi-objective genetic algorithm optimal placement sensors

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Optimal capacity design of eccentrically braced steel frameworks using nonlinear response history analysis

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ENGINEERING STRUCTURES 2013年 48卷 28-36页

作者： Gong, Yanglin Xue, Yusong Xu, Lei Lakehead Univ Dept Civil Engn Thunder Bay ON P7B 5E1 Canada Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

Presented in this paper is an optimal capacity design method for steel building frameworks by adopting a nonlinear response history analysis procedure as the evolution tool for the structures under extreme earthquake loading. Minimum structural weight, minimum seismic input energy, and maximum hysteretic energy of fuse members are identified as design objectives. The design constraints include plastic deformation limit on every frame member and total deformation limit on each inter-story drift. The design optimization method employs a multi-objective genetic algorithm to search for optimal member section sizes from among commercially available steel section shapes. The design method is illustrated in details for a three-story eccentrically-braced steel frame. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Structural optimization Seismic design Capacity design Steel frames Eccentrically-braced frames Nonlinear response history analysis multi-objective genetic algorithm

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A two-stage optimal planning and design method for combined cooling, heat and power microgrid system

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ENERGY CONVERSION AND MANAGEMENT 2013年 74卷 433-445页

作者： Guo, Li Liu, Wenjian Cai, Jiejin Hong, Bowen Wang, Chengshan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Sun Yat Sen Univ Sch Phys & Engn Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Sino French Inst Nucl Engn & Technol Guangzhou 510275 Guangdong Peoples R China

In this paper, a two-stage optimal planning and design method for combined cooling, heat and power (CCHP) microgrid system was presented. The optimal objective was to simultaneously minimize the total net present cost and carbon dioxide emission in life circle. On the first stage, multi-objective genetic algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) was applied to solve the optimal design problem including the optimization of equipment type and capacity. On the second stage, mixed-integer linear programming (MILP) algorithm was used to solve the optimal dispatch problem. The approach was applied to a typical CCHP microgrid system in a hospital as a case study, and the effectiveness of the proposed method was verified. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Combined cooling Heat and power microgrid system Two-stage optimal design method multi-objective genetic algorithm Mixed integer linear programming

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Application of elitist multi-objective genetic algorithm for classification rule generation

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APPLIED SOFT COMPUTING 2008年第1期8卷 477-487页

作者： Dehuri, S. Patnaik, S. Ghosh, A. Mall, R. Fakir Mohan Univ PG Dept Informat & Commun Technol Balasore 756019 Orissa India Indian Stat Inst Machine Intelligence Unit Kolkota W Bengal India Indian Stat Inst Ctr Soft Comp Res Kolkota W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

We present an elitist multi-objective genetic algorithm (EMOGA) for mining classification rules from large databases. We emphasize on predictive accuracy, comprehensibility and interestingness of the rules. However, predictive accuracy, comprehensibility and interestingness of the rules often conflict with each other. This makes it a multi-objective optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective genetic algorithm with a hybrid crossover operator for optimizing these objectives simultaneously. We have compared our rule discovery procedure with simple genetic algorithm with a weighted sum of all these objectives. The experimental result confirms that our rule discovery algorithm has a clear edge over simple genetic algorithm. (C) 2007 Elsevier B. V. All rights reserved.

关键词： genetic algorithm multi-objective genetic algorithm rule mining data mining

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Development of an effective data-driven model for hourly typhoon rainfall forecasting

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JOURNAL OF HYDROLOGY 2013年 495卷 52-63页

作者： Lin, Gwo-Fong Jhong, Bing-Chen Chang, Chia-Chuan Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan

In this paper, we proposed a new typhoon rainfall forecasting model to improve hourly typhoon rainfall forecasting. The proposed model integrates multi-objective genetic algorithm with support vector machines. In addition to the rainfall data, the meteorological parameters are also considered. For each lead time forecasting, the proposed model can subjectively determine the optimal combination of input variables including rainfall and meteorological parameters. For 1- to 6-h ahead forecasts, an application to high- and low-altitude metrological stations has shown that the proposed model yields the best performance as compared to other models. It is found that meteorological parameters are useful. However, the use of the optimal combination of input variables determined by the proposed model yields more accurate forecasts than the use of all input variables. The proposed model can significantly improve hourly typhoon rainfall forecasting, especially for the long lead time forecasting. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Typhoon rainfall forecasting Support vector machine multi-objective genetic algorithm Meteorological parameters

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The optimization of the key components of the spinning machine conveyor system based on ISIGHT

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2nd International Conference on Textile Engineering and Materials (ICTEM2012)

作者： Lin, Bin Zhang, Xu Liu, Xiaogang Tianjin Univ State Educ Minist Key Lab Adv Ceram & Machining T Tianjin 300072 Peoples R China

ISBN: (纸本)9783037855751

A transfer system in roving-spinning unit that establishes a connection between spindles of roving frame and spinning frame achieves the manipulations automatically in spinning workshop. We study that ISIGHT drive ANSYS to conduct integrated optimization of the key conveyor system components in this paper, the ANSYS-ISIGHT integration is successful. Before the optimal design, using the Latin method to design experiments. The experimental design can show the degree that the state variables and the target variables are affected by each input factor. The factor that has little impact on the results can be removed, thus the efficiency of the optimization can be improved. After the experimental design, using the multi-objective neural network to optimize the beam. Giving the beam safety conditions, including intensity, toughness, modal, stability. We optimize the structure base on security conditions, the volume of the beam in roving and yarn reservoir area has been largely decreased. Achieving the objectives of the lightweight design.

关键词： ISIGHT-ANSYS integration Latin method Optimization iterations multi-objective genetic algorithm

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Surrogate-Based multi-objective Optimization and Data Mining of Vortex Generators on a Transonic Infinite-Wing

Surrogate-Based Multi-Objective Optimization and Data Mining...

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IEEE Congress on Evolutionary Computation

作者： Namura, Nobuo Obayashi, Shigeru Jeong, Shinkyu Tohoku Univ Inst Fluid Sci Sendai Miyagi 9808577 Japan Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9781479904549;9781479904532

multi-objective optimization and data mining of vortex generators (VGs) on a transonic infinite-wing was performed using computational fluid dynamics (CFD), surrogate models, and a multi-objective genetic algorithm (MOGA). VGs arrangements were defined by five design variables: height, length, incidence angle, spacing, and chord location. The objective functions which should be maximized were three: lift-drag ratio at low angle of attack, lift coefficient at high angle of attack, and chordwise separation location at high angle of attack. In order to evaluate these objective functions of each individual in MOGA, the response surface methodology with Kriging model and the modified version of it was employed because CFD analysis of the wing with VG requires a large computational time. Two types of data mining method: analysis of variance (ANOVA) and self-organizing map (SOM), were applied to the result of the optimization. It was revealed by ANOVA that the ratio of spacing to height and the incidence angle had significant influences to maximizing each objective function. By using SOM, VG designs were split into four types which have different aerodynamic characteristics respectively. The appropriate values of parameters were identified by SOM.

关键词： multi-objective genetic algorithm Kriging model radial basis function networks self-organizing map analysis of variance computational fluid dynamics

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