• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.4
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• pp.359-366
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• 2022

In this study, the load fluctuation of the main engine is considered to be a disturbance for the jacket coolant temperature control system of the low-speed two-stroke main diesel engine on the ships. A nonlinear PID temperature control system with satisfactory disturbance rejection performance was designed by rapidly transmitting the load change value to the controller for following the reference set value. The feed-forwarded load fluctuation is considered the set points of the dual loop control system to be changed. Real-coded genetic algorithms were used as an optimization tool to tune the gains for the nonlinear PID controller. ITAE was used as an evaluation function for optimization. For the evaluation function, the engine jacket coolant outlet temperature was considered. As a result of simulating the proposed cascade nonlinear PID control system, it was confirmed that the disturbance caused by the load fluctuation was eliminated with satisfactory performance and that the changed set value was followed.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.1-14
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• 2007

Case-based reasoning (CBR) is one of the most widely applied data mining techniques and has proven its effectiveness in various domains. Since CBR is basically based on k-Nearest Neighbors (NN) method, the value of k affects the performance of CBR model directly. Once the value of k is set, it is fixed for the lifetime of the CBR model. However, if the value is set greater or smaller than the optimal value, the performance of CBR model will be deteriorated. In this research, we propose a new method of composing the NN set using similarity scores as themselves, which we shall call s-NN method, rather than using the fixed value of k. In the s-NN method, the different number of nearest neighbors can be selected for each new case. Performance evaluation using the data from UCI Machine Learning Repository shows that the CBR model adopting the s-NN method outperforms the CBR model adopting the traditional k-NN method.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2711-2719
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• 2018

When an observable is described by a single value, the statistic significance may be estimated by construction of null distribution using permutation and counting the portion of it that exceeds the observed value by chance. Genome-wide association study usually focuses on the association measure between a single or interacting genotypes with a single phenotype. However investigation of common genotypes associated simultaneously on multiple phenotypes may involve the observables that should be described with multiple numbers. Statistical significance for such an observable would involve null distribution in multiple dimensions. In this study, extension of the p-value estimation process using null distribution in one dimension has been sought that may be applicable to two dimensional case. Comparison of the position of points within the set of points they form has been proposed to use a positioning parameter inspired by the extension of the Kolmogorov-Smirnov statistic to two dimensions.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.141-144
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• 2002

This paper describes the correction of the protective relay equipped in the dedicated line used for connecting distributed generators (DG) to power grid. The fault current measured in a relaying point might be changed according to the fault conditions. Generally, the fault current of the line to line fault or the line to ground fault at the dedicated line is much higher than the protective set value due to the large fault level. However. when the high impedance fault is occurred in the dedicated line, we may not detect it because its fault level can be lower than the generating capacity of DG. And, the protective relay with conventional set value may generate a trip signal for insertion of DG due to the large transient characteristics of generators. Through the various simulations such as the fault in the dedicated line and the insertion of DG, we show that it would be necessary to modify the protective relay set value for detecting the high impedance fault occurred in the dedicated line and for preventing the mis-operation of protective relay caused by the insertion of DG.

• Journal of Information Technology Services
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• v.20 no.2
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• pp.13-31
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• 2021

Recently, delivery app services based on the O2O platform are increasing rapidly. Accordingly, various studies on O2O service have been conducted. Most of the studies are on consumer behavior in O2O services, and few studies on platform vendors have been conducted. Therefore, this study empirically analyzed the factors affecting the vender's intention to continuous use in the O2O delivery app platform service. Based on prior researches, we set the quality characteristics and network characteristics of the O2O platform as independent variables. The quality characteristics of the O2O platform consisted of system quality, information quality, and service quality, and the O2O platform network characteristics consisted of network externality and platform reputation. Perceived value and switching cost were set as mediated variables, and vender's intention to continuous use was set as dependent variables. For empirical analysis, we conducted a survey targeting vendors of O2O delivery app platform service, and conducted frequency analysis, factor analysis, reliability analysis, and regression analysis. As a result of the analysis, the quality characteristics of the O2O platform, such as system quality, information quality, service quality, and O2O platform network characteristics, showed that network externality and platform reputation had a positive effect on perceived value. The perceived value was found to have a positive effect on the switching cost and the intention to continuous use, and the switching cost was found to mediate the perceived value and the intention to continuous use. This study can contribute to the establishment of platform operation strategy as an empirical analysis on the factors that influence the intention of O2O platform vendors to use the platform continuously.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.180-184
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• 1997

This paper presents the fuzzy neural network which utilizes a fuzzified Kohonen learning uses a fuzzy membership value, a function of the iteration, and a intra-membership value instead of a learning rate. The IRIS data set if used to test the fuzzy neural network. The test result shows the performance of the fuzzy neural network depends on k and the vigilance parameter T.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.491-501
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• 2002

Case-based reasoning is emerging as a leading methodology for the application of artificial intelligence. CBR is a reasoning methodology that exploits similar experienced solutions, in the form of past cases, to solve new problems. Hybrid model achieves some convergence of the wide proliferation of credit evaluation modeling. As a result, Hybrid model showed that proposed methodology classify more accurately than any of techniques individually do. It is confirmed that proposed methodology predicts significantly better than individual techniques and the other combining methodologies. The objective of the proposed approach is to determines a set of weighting values that can best formalize the match between the input case and the previously stored cases and integrates fuzzy sit concepts into the case indexing and retrieval process. The GA is used to search for the best set of weighting values that are able to promote the association consistency among the cases. The fitness value in this study is defined as the number of old cases whose solutions match the input cases solution. In order to obtain the fitness value, many procedures have to be executed beforehand. Also this study tries to transform financial values into category ones using fuzzy logic approach fur performance of credit evaluation. Fuzzy set theory allows numerical features to be converted into fuzzy terms to simplify the matching process, and allows greater flexibility in the retrieval of candidate cases. Our proposed model is to apply an intelligent system for bankruptcy prediction.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.413-422
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• 2019

The purpose of this study is to analyze the purchase intention of outdoor products for 40s - 50s consumers The factors influencing the purchase intention are various factors, but the service quality and the consumption value are analyzed from an exploratory viewpoint. The quality of service was set as detailed, tangibles, responsiveness, and reliability. Consumption value was set as functional value, rare value, and conditional value. As a result, tangibles, responsiveness, and reliability of the service quality, the functional value influences the purchase intention, but the rare value and the conditional value of the consumption value indicate the purchasing intention. The result of this analysis shows that service quality is a very important factor for consumers' purchase intention in 40's - 50's consumers, but consumption value is not very important factor in purchase intention.

• Asian Journal of Innovation and Policy
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• v.5 no.2
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• pp.116-128
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• 2016

Value chain analysis has been applied in various fields, from the time the concept of “value chain” was introduced by Porter in 1985. Several frameworks have emerged and have been used to study individual firms, entire industries, industry clusters, as well as global production networks. The purpose of this paper is to provide a brief review of these frameworks, identify factors that influence the performance of value chains, and suggest areas for future research. Since there is a wide range of value chain literature, this paper focuses on a selective set of earlier works within the value chain model as conceptualized by Porter. The study takes note of the many dimensions and applications of value chain analysis, and shows that value chain analysis is an effective way to examine the interaction among different players in a given industry. The study further points out the shortcomings of the traditional or Porter view of value chain analysis.