• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.16-24
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• 1997

This paper proposes a modified GA whose individuals have their own ages. Thus, a chromosome will die only when the age becomes zero, as a result, the population size of this method increases according to the generations. This helps a GA to preserve the good characteristics of a few chromosomes during several generations if the ages are evaluated with fitness values. As a result, the performance of the method is better than that of existing ones. A multi-modal function optimization problem is employed to simulate the performance of this method. To show the effective:~esso f ageing paradigm, three ageing evaluation methods are introduced. A paper whose itlea is similar to that of ours have been published in a conference. We also experimented a method that showed the best performance in the paper. Original simple GA was also experimented and the performance is compared with others. However, the perforniance of the previous method shows worse than that of our methods in some aspects because the previous method didn't take the fitness value into account in the selection process.

• Korean Journal of Construction Engineering and Management
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• v.14 no.5
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• pp.74-83
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• 2013

Most current BIM systems are focused on the visual information of work status in the design and construction stages. In a passive BIM system, 3D CAD tool can visualize the interference elements of design drawings, however, it cannot suggest a solution to solve the interference status. And 4D CAD tool in the construction stage can simulate the appearance of each activity by construction schedule, however, it cannot suggest an optimized schedule plan considering specified schedule condition of the project. Recently, many organizations need BIM solutions that can improve the work status beyond the level of simple visual information from BIM system. Active BIM system can provide the solutions to the project manager. This study suggests active BIM functions for the solutions and attempts to develop a 4D CAD engine to validate the usability of the functions.

• Journal of Korea Water Resources Association
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• v.46 no.4
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• pp.389-400
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• 2013

In this study, a conventional pipeline unsteady friction model has been integrated into Levenberg Marquardt method to calibrate friction coefficient in a pipeline system. The method of characteristics has been employed as the modeling platform for the frequency dependant model of unsteady friction. In order to obtain Hessian and Jacobian matrix for optimization, the direct differentiation of pressure to friction factor was calculated and sensitivities to friction for heads and discharges were formulated for implementation to the integration constant in the characteristic method. Using a hypothetical simple pipeline system, time series of pressure, introduced by a sudden valve closure, were obtained for various Reynolds numbers. Convergency in fiction factors were evaluated both in steady and unsteady friction models. The comparison of calibration performance between the proposed method and genetic algorithm indicates that faster and stabler behaviour of Levenberg Marquardt method than those of evolutionary calibration.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.16 no.2
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• pp.70-78
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• 2016

21-hydroxylase deficiency (21-OHD), most common form of congenial adrenal hyperplasia, is categorized into classical forms, including the salt-wasting (SW) and the simple virilizing (SV) types, and nonclassical (NC) forms based on the severity of the disease. Newborn screening for 21-OHD has been performed in Korea since 2006. $17{\alpha}$-hydroxyprogesterone (17-OHP) is a marker for 21-OHD and is measured using a radioimmunoassay or a fluoroimmunoassay. Premature and low birth weight infants are likely to give false positive 17-OHP findings, therefore, cutoff values for these infants should be determined based on gestational weeks or birth weight. ACTH simulation test is helpful when the 17-OHP shows equivocal increase, and it is gold standard for diagnosis of NC type. Recently, liquid chromatography linked with tandem mass spectrometry was developed for rapid, highly specific, and sensitive analysis of multiple analytes. Molecular analysis of CYP21A2 is useful for confirming diagnosis of mild SV or NC type, predicting prognoses, and genetic counseling. In order to make newborn screening for 21-OHD more efficient, early detection of boy with SW type, early determination of girl with ambiguous genitalia, detection of NC type, and overcoming of false positive in premature and low birth weight infants should be considered. Above all, early treatment should be started when the patient is suspected as having 21- OHD clinically before confirming the diagnosis to prevent adrenal crisis. Here, author reviewed recent articles of guideline and proposed guideline for 21-OHD.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.5
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• pp.544-551
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• 2008

QUAL2K enhanced QUAL2E and applied in real fields efficiently incorporates denitrification process, sediment-water interaction process, bottom algae and detritus. Also, the CBOD of QUAL2K is divided into two real parts, one is slow CBOD(sCBOD) and another is fast CBOD(fCBOD). The simulation results of QUAL2E and QUAL2K models in Anyang Stream were compared and analyzed in water quality constituents of DO, BOD, Org-N, NH$_3$-N, NO$_3$-N, Org-P, Dis-P and Chl-a respectively. The similar results were shown in Org-N, NH$_3$-N, Org-P and Chl-a both QUAL2K and QUAL2E models. But the different results were revealed in DO, BOD, Dis-P and NO$_3$-N by the influence of new incorporating processes. DO was shown relatively low values in the effect of bottom algae. BOD which is influenced by particulate organic matter was revealed high values. NO$_3$-N was closed to the real values by the two processes of denitrification and sediment-water interaction. To evaluate the running results of QUAL2K and QUAL2E models, a simple statistical analysis was conducted. According to the statistical analysis, QUAL2K represented less relative error and coefficient of variation than QUAL2E in almost all of constituents. It was found that QUAL2K, which simulates the water quality more realistically, can be applied to control and manage the water problems of river or river-run reservoir effectively.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.7-17
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• 2007

An analysis and design theory of an aperture-coupled cavity-fed back-to-back microstrip directional coupler is presented for the efficient and optimized design. For this purpose, an equivalent network is developed, and simple but accurate calculations of circuit element values are described. Design equations of the coupler are derived based on the equivalent circuit. In order to determine various structural design parameters, the evolutionary hybrid optimization method based on the genetic algorithm and Nelder-Mead method is invoked. As a validation check of the proposed theory and optimized design method, a 10 dB directional coupler was designed and fabricated. The measured coupling was 10.3 dB at 3 GHz, and the return loss and isolation were 31.8 dB and 30.5 dB, respectively. The directional coupler also showed very good quadrature phase characteristics. Good agreements between the measured and the design specifications fully validate the proposed network analysis and design procedure.