• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.175-182
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• 2006

Radars are used to detect the motion of the low flying enemy planes in the military. Radar-detected raw data are first processed and then inserted into the ground tactical C4I system. Next, these data we analyzed and broadcasted to the Shooter system in real time. But the accuracy of information and time spent on the displaying and graphical computation are dependent on the operator's capability. In this paper, we propose the Flying Object Tracking Management System that allows the displaying of the objects' trails in real time by using data received from the radars. We apply the coordinate system translation algorithm, existing communication protocol improvements with communication equipment, and signal and information computation process. Especially, radar signal duplication computation and synchronization algorithm is developed to display the objects' coordinates and thus we can improve the Tactical Air control system's reliability, efficiency, and easy-of-usage.

• Journal of Information Management
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• v.39 no.4
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• pp.47-66
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• 2008

This study introduced the machine learning algorithms to overcome the many different limitations involved with manual classification and to provide the users with faster and more accurate classification service. The experiments objects of the study were consisted of 100 literature titles for each of the eight subject categories in MeSH. The algorithms used to the experiments included Neural network, C5.0, CHAID and KNN. As results, the combination of the neural network and C5.0 technique recorded classification accuracy of 83.75%, which was 2.5% and 3.75% higher than that of the neural network alone and C5.0 alone, respectively. The number represented the highest accuracy rates among the four classification experiments. Thus the use of the neural network and C5.0 technique together will result in higher accuracy rates than the techniques individually.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12B
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• pp.1219-1226
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• 2010

It is necessary to implementation of system contain intelligent decision making algorithm because discriminant and prediction system for Red Tide is insufficient development and the study of red tide are focused for the investigation of chemical and biological causing. In this paper, we designed inference system using case based reasoning method and implemented knowledge base that case for Red Tide. We used K-Nearest Neighbor algorithm for recommend best similar case and input 375 EA by case for Red Tide case base. As a result, conducted 10-fold cross verification for minimal impact from learning data and acquired confidence, we obtained about 84.2% average accuracy for Red Tide case and the best performance results in case by number of similarity classification k is 5. And, we implemented Red Tide monitoring system using inference result.

• Journal of Digital Convergence
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• v.13 no.3
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• pp.155-165
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• 2015

Bankruptcy prediction has been one of the important research topics in finance since 1960s. In Korea, it has gotten attention from researchers since IMF crisis in 1998. This study aims at proposing a novel model for better bankruptcy prediction by converging three techniques - support vector machine(SVM), fuzzy theory, and genetic algorithm(GA). Our convergence model is basically based on SVM, a classification algorithm enables to predict accurately and to avoid overfitting. It also incorporates fuzzy theory to extend the dimensions of the input variables, and GA to optimize the controlling parameters and feature subset selection. To validate the usefulness of the proposed model, we applied it to H Bank's non-external auditing companies' data. We also experimented six comparative models to validate the superiority of the proposed model. As a result, our model was found to show the best prediction accuracy among the models. Our study is expected to contribute to the relevant literature and practitioners on bankruptcy prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.613-614
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• 2017

Recently, research on the automation of home IoT has been carried out in which IoT (Internet of Things) is applied inside the home. However, the conventional IoT automation system has a problem that the operation of the device is limited only by the threshold value of the sensor, so that the device may collide and interfere with each other and the efficiency of the Task is low due to the malfunction of the device. In this paper, we propose a Situation-specific task classification algorithm to solve these problems. Using the sensor threshold and the current date as classification values in the decision tree, the task according to the internal situation of the home is classified and the corresponding device is selected and proceeded. Therefore, it is expected that the users will be provided with a service that changes flexibly according to changes in the internal situation of the home, and the accuracy of the operation will be increased by reducing the malfunction of the device and the collision between the devices.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.55-61
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• 2013

TSexual violence is physical and mental violence that violates the sexual self-determination contrary to the intention of the other party such as rape, forced molestation, sexual harassment, caught hidden camera. It is one of the many criminal acts that male is perpetrators and female is victims. Sexual harassment, one of the sexual violence is forced sexual harassment. It is considered a color frame where each pixel has 3 color components such that RGB. The averaging the absolute difference between the current frame and te next frame is divided by the absolute difference between the current frame and the previous frame. If there was a difference between the frame pair before a scene change the discontinuity value indicating a scene change could be relatively small. Therefore, Thus, the use of the redefined equation and redefined algorithm can be seen as it is much more good via experiment.

• Journal of Service Research and Studies
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• v.3 no.1
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• pp.87-93
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• 2013

Breast cancer is known as a disease that occurs in a lot of developed countries. However, in recent years, the incidence of Korea's modern woman is increased steadily. As well known, breast cancer usually occurs in women over 50. In the case of Korea, however, the incidence of 40s with young women is increased steadily than the West. Therefore, it is a very urgent task to build a manual to the accurate diagnosis of breast cancer in adult women in Korea. In this paper, we show how using data mining techniques to predict breast cancer. Data mining refers to the process of finding regular patterns or relationships among variables within the database. To this, sophisticated analysis using the model, you will find useful information that is easily revealed. In this paper, through experiments Deicion Tree Naive Bayes analysis techniques were compared using analysis techniques to diagnose breast cancer. Two algorithms was analyzed by applying C4.5 algorithm. Deicison Tree classification accuracy was fairly good. Naive Bayes classification method showed better accuracy compared to the Decision Tree method.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.121-135
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• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.736-741
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• 2006

The design of a classification system generally involves data acquisition module, learning module and decision module, considering their functions and it is often an important component of intelligent systems. The learning module provides a priori information and it has been playing a key role for the classification. The conventional learning techniques for classification are based on a winner take all fashion which does not reflect the description of real data where boundarues might be fuzzy Moreover they need all data for the learning of its problem domain. Generally, in many practical applications, it is not possible to prepare them at a time. In this paper, we design an adaptive classification model using incremental training fuzzy neural networks, FNN-I. To have a more useful information, it introduces the representation and membership degree by fuzzy theory. And it provides an incremental learning algorithm for continuously gathered data. We present tie experimental results on computer virus data. They show that the proposed system can learn incrementally and classify new viruses effectively.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.