• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.79-90
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• 2004

In this study, we applied the Bayesian Networks for the case of the trip generation models using the Seoul metropolitan area's house trip survey Data. The household income was used for the independent variable for the explanation of household size and the number of cars in a household, and the relationships between the trip generation and the households' social characteristics were identified by the Bayesian Networks. Furthermore, trip generation's characteristics such as the household income, household size and the number of cars in a household were also used for explanatory variables and the trip generation model was developed. It was found that the Bayesian Networks were useful tool to overcome the problems which were in the traditional trip generation models. In particular the various transport policies could be evaluated in the very short time by the established relationships. It is expected that the Bayesian Networks will be utilized as the important tools for the analysis of trip patterns.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.415-429
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• 2011

Quantitative evaluation indexes for flood control effect of a multi-purpose reservoir used widely in Korea are the discharge control rate, reservoir release rate, reservoir storage rate, and flood control storage utilization rate. Because these indexes usually use and compare inflow, release, and storage data directly, the uncertainties included in these data are not considered in evaluation process, and the downstream flood control effects are not assessed properly. Also, since the acceptable partial failure in a design of water resources system is not considered, the development of a new flood control effect evaluation index is required. Fuzzy set theory is therefore applied to the development of the index in order to consider the data uncertainty, the downstream flood control effect, and the acceptable partial failure. In this study, the flood control effect of a multi-purpose reservoir is evaluated using the flood control effect index developed by applying fuzzy set theory. The Chungju reservoir basin was selected as a study basin and the storm events of July, 2006 are used to study the applicability of the developed index. The related factors for flood control effect are fuzzified, the acceptable failure region is divided from the system state to evaluate the flood control effect using developed flood control effect index. The flood control effect index were calculated by applying to the study basin and storm events. The results show that the developed index can represent the flood control effect of a reservoir more realistically and objectively than the existing index.

• Information Systems Review
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• v.11 no.1
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• pp.85-106
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• 2009

Current trend of audit is to check the physical aspects of developed information system, such as checking the budget constraints, time constraints or functional fluency etc. However, ultimate goal of information system is to help the organization to achieve the competency over their competitors. Also, there are three different interest groups in system auditing, like audit requesting group, audited group and audit group, who may have different points of interests in auditing. Current auditing process, however, ignores this point, and so does not check the differences between three groups. This study tries to develop new auditing method to cure these two problems. Contributions of this study may be summarized as follows. First, Redefine Information Systems Audit from a service point of view. Second, Divide the audit related person into three groups, and their different needs toward the information system was analyzed. Third, Analyze and compare the main interests of three groups, and weights of each groups to each indexes were calculated. Fourth, Fuzzy theory was applied to quantify the qualitative answers, which may minimize the ambiguity of questionnaire replies.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.173-178
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• 2014

In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.218-225
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• 2020

This study analyzed the priorities of the cross-compliance items of public-benefit direct payment using an Analytic Hierarchy Process and Fuzzy Decision Making Analysis. The valuation criteria are policy efficiency, farm acceptability, and feasibility, and the valuation targets are the basic and additional cross-compliance items. The survey was performed by targeting 50 experts from each class, and conducted for about a month starting from the beginning of July 2019. The results show that the weight of the valuation criteria is higher in the order of farm acceptability, feasibility, and policy efficiency. Compliance with PLS standards, compliance with disposal standards of waste vinyl and pesticides, soil testing, compliance with toxic substance standards, education, etc. are comparatively evaluated to be higher cross-compliance items in basic cross-compliance. Disposing of an abandoned well, jointly collecting and disposing of agricultural by-products, common area care and cleaning, maintenance of empty houses and poor facilities, growing green manure crops during the fallow period, etc. are comparatively evaluated to be higher cross-compliance items for the additional cross-compliance. The results of this study are expected to contribute to the government's policy related to the cross-compliance of public-benefit direct payment.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.388-397
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• 2009

There are many tasks which require robotic manipulators interaction with environment. It consists of three control problems, i.e., position control, impact control and force control. The position control means the way of reaching to the environment. The moment of touching to the environment yields the impact control problem and the force control is to maintain the desired force trajectory after the impact with the environment. These three control problems occur in sequence, so each control algorithm can be developed independently. Especially for redundant manipulators, each of these three control problems has been important independent research topic. For example, joint torque minimization and impulse minimization are typical techniques for such control problems. The three control problems are considered as a single task in this paper. The position control strategy is developed to improve the performance of the task, i.e., minimization of the individual joint torques and impulse. Therefore, initial conditions of the impact control problem are optimized at the previous position control algorithm. Such a control strategy yields improved result of the impact control. Similarly, the initial conditions for the force control problem are indirectly optimized by the previous position control and impact control strategies. The force control algorithm uses the individual joint torque minimization concept. It also minimizes the force disturbances. The simulation results show the proposed control strategy works well.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.533-538
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• 2013

The damage caused by Recent frequently occurring locality torrential rains is increasing rapidly. In case of densely populated metropolitan area, casualties and property damage is a serious due to landslides and debris flows and floods. Therefore, the importance of predictions about the torrential is increasing. Precipitation characteristic of the bad weather in Korea is divided into typhoons and torrential rains. This seems to vary depending on the duration and area. Rainfall is difficult to predict because regional precipitation is large volatility and nonlinear. In this paper, Very short-term precipitation forecasting pattern model is implemented using KLAPS data used by Korea Meteorological Administration. we designed very short term precipitation forecasting pattern model using GA-based RBFNNs. the structural and parametric values such as the number of Inputs, polynomial type,number of fcm cluster, and fuzzification coefficient are optimized by GA optimization algorithm.

• Journal of the Korean Society of Food Science and Nutrition
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• v.32 no.7
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• pp.965-970
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• 2003

This study has been performed to develope a method for the simultaneous determination of 1,4-dioxane (DOX), epichlorohydrin (EPC), propylene chlorohydrin (PCH), ethylene chlorohydrin (ECH) and 1,3-dichloro-2-pro-panol (DCP) in polysorbates, chloline chloride, choline bitartrate, modified starch and spices by purge and trapgas chromatography. Experimental design was used to select a suitable trap by measuring the limit of detection (LOD) and to investigate the effect of temperature and salt of extraction, and the percentage of recovery in various matrix. The LOD of DOX, EPC, PCH, ECH and DCP were 1.38$\mu\textrm{g}$, 0.23$\mu\textrm{g}$, 3.30$\mu\textrm{g}$, 3.97$\mu\textrm{g}$, 20.43$\mu\textrm{g}$ respectively, by means of using Vorcarb 3000 trap with 5$0^{\circ}C$ sample sparger. Excluding EPC, the recoveries of target compounds were above 90% in all matrix. Target compounds in polysorbates (17), choline chloride (5), choline bitartrate (5), modified starch (8) and spices (25) were not detected. But 2.5 ppm of DOX was detected in Tween 80.