• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1841-1848
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• 1999

DELVAUX is a genetics-based inductive learning system that learns a rule-set, which consists of Bayesian classification rules, from sets of examples for classification tasks. One problem that DELVAUX faces in the rule-set learning process is that, occasionally, the learning process ends with a local optimum without finding the best rule-set. Another problem is that, occasionally, the learning process ends with a rule-set that performs well for the training examples but not for the unknown testing examples. This paper describes efforts to alleviate these two problems centering on the N-version learning approach, in which multiple rule-sets are learning and a classification system is constructed with those learned rule-sets to improve the overall performance of a classification system. For the implementation of the N-version learning approach, we propose a decision-making scheme that can draw a decision using multiple rule-sets and a genetic algorithm approach to find a good combination of rule-sets from a set of learned rule-sets. We also present empirical results that evaluate the effect of the N-version learning approach in the DELVAUX learning environment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.9
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• pp.55-61
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• 2008

Because of the high increasing rate of load demand these days the necessity of deciding what optimum reserve level is appropriate to most stably supply electricity is being emphasized. This research studies the downward tendency of reserve ratio by analyzing the trend of change of the network scale, reserve, and reserve ratio while optimum reserve has been increased as the network system scale grow up. This means, at this moment 6,000[MW] is optimum level for short term prospect of power supply and demand. And also, it has been analyzed that, as the annual peak load exceeded 50,000[MW], confirming the amount of optimum reserve level is more stable than keeping 10 to 12[%] reserve ratio.

• Journal of environmental and Sanitary engineering
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• v.12 no.3
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• pp.81-86
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• 1997

In case of Industrial Wastewater, It was various pollutions, high concentration and different physical, chemical properties each other in accordance with classification of wastewater. Therefore, after inquiring into the influence on the membrane of the dissolved pollutants, we should select the membrane of best efficient quality. As results of experiments on pilot plant test, optimum operating pressure for fouling removal was 34BAR, when continues operating was 34 BAR, recovery rate was 75% and permeate water flux was $32.9{\;}{\ell}/hr{\cdot}m^{2}$.

• Proceedings of the IEEK Conference
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• summer
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• pp.229-232
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• 2004

In this paper, we propose a robust music retrieval system based on the content analysis of music. New feature extraction method called Multi-Feature Clustering (MFC) is proposed for the robust and optimum performance of the music retrieval system. It is demonstrated that the use of MFC significantly improves the system stability of music retrieval with better classification accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.129-135
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• 1993

Quality is become not only the most critical component of manufacturing strategy but also the most critical measure of performance and justification of advanced manufacturing system. The objective of this paper is to offer classification & optimum concept of quality-cost and to illustrate a method of evaluation quality-cost then a case example is presented to illustrate the result of quantifying the suggested formula and these values are given to justify of management.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.10-21
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• 2008

A forest functions classification map is an essential element for the management planning of national forests. This study was intended to make out the map at the stand level by utilizing the Forest Functions Evaluation Program(FFEP), developed by Korea Forest Research Institute. In this program, the potential of each function was evaluated in each grid cell, and then a forest functions estimation map was generated based on the optimum grid cell values in each sub-compartment unit. Finally, the program produced a forest functions classification map with consideration of the priority of the functions. The final forest functions classification map required for the national forest management planning made out overlapping those results which the rest of the forest classified referring priority functions classification map to national forest manager and classified according to the local administrative guidance and sustainable forest resources management guidance. The results indicated that the forest function classification using the FFEP program could be an efficient tool for providing the data required for national forest management planning. Also this study made a meaningful progress in the forest function classification by considering the local forest administrative guidance and sustainable forest resources management guidance.

• Journal of Navigation and Port Research
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• v.27 no.4
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• pp.397-402
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• 2003

Generally, container arrangement has been carried out based on the Classification guidelines. However, guidelines provide only container securing forces for the given container arrangement and Classification requirements of the forces. In order to design container arrangement additional information is needed such as container securing forces and arrangement that accounts for lashing bridges, vertical lashing, vertical center of gravity (VCG), and maximum stack weight. Trial and error method using the existing guidelines requires excessive amount of calculation time and cannot provide accurate results of the calculations. In order to fulfill this need, a new container securing system has been established based on the equilibrium conditions that include lashing bridges and vertical lashing. An optimization algorithm has been developed for the new system since current optimization methods such as genetic algorithms and evolution strategies are unsuitable for the container securing problems, which involve equality constraint. Design variables are container weights of tier and objective function is either total container weight or VCG of a stack. The newly developed system provides optimum arrangement of containers for both maximum stack weight and maximum VCG. It also greatly reduces time for designing container arrangement.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.993-1002
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• 2016

Hepatitis is a major public health problem all around the world. This paper proposes an automatic disease diagnosis system for hepatitis based on Genetic Algorithm (GA) Wavelet Kernel (WK) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by ELM learning method. The hepatitis disease datasets are obtained from UCI machine learning database. In Wavelet Kernel Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. Therefore, values of these parameters and numbers of hidden neurons should be tuned carefully based on the solved problem. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using Genetic Algorithm (GA). The performance of proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specivity analysis and ROC curves. The results of the proposed GA-WK-ELM method are compared with the results of the previous hepatitis disease studies using same database as well as different database. When previous studies are investigated, it is clearly seen that the high classification accuracies have been obtained in case of reducing the feature vector to low dimension. However, proposed GA-WK-ELM method gives satisfactory results without reducing the feature vector. The calculated highest classification accuracy of proposed GA-WK-ELM method is found as 96.642 %.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.