• Proceedings of the KIPE Conference
• /
• 2002.07a
• /
• pp.498-501
• /
• 2002

In the conventional PWM converter, high-frequency switching techniques was used for high-density of energy, but occurred a lot of problems such as switching losses, switching voltage/current stresses, EMI(Electromgnetic Interference) and so on. To overcome these problems, various soft switching techniques have been presented. However these techniques are focused on reducing switching losses and voltage/current stresses . The simulation and experimental results are shown that the active clamp ZVS flyback converter with the proposed RPWM(Random Pulse Width Modulation) technique reduces the conducted noise.

• Proceedings of the KIEE Conference
• /
• 1987.11a
• /
• pp.311-315
• /
• 1987

This paper describes the automatic visual inspect ion system of fabric defects based on pattern recognition techniques. To extract features for detection of fabric defects, four different techniques such as SGLDM. GCM, decorrelation method, and Laws' texture measure were investigated. From results of computer simulation, it has been found that GCM and decorrelation techniques provide good features. By employing a simple statistical pattern recognition technique, theaccuracy of classification of defect and nondefect was more than 90%. Some experimental results arm also presented.

• International Journal of Knowledge Content Development & Technology
• /
• v.7 no.1
• /
• pp.101-120
• /
• 2017

Healthcare professionals have edited many health questions (HQs) and their answers for healthcare consumers on the Internet. The HQs provide both readable and reliable health information, and hence retrieval of those HQs that are relevant to a given question is essential for health education and promotion through the Internet. However, retrieval of relevant HQs needs to be based on the recognition of the intention of each HQ, which is difficult to be done by predefining syntactic and semantic rules. We thus model the intention recognition problem as a text classification problem, and develop two techniques to improve a learning-based text classifier for the problem. The two techniques improve the classifier by location-based and area-based feature weightings, respectively. Experimental results show that, the two techniques can work together to significantly improve a Support Vector Machine classifier in both the recognition of HQ intentions and the retrieval of relevant HQs.

• Journal of Korea Society of Industrial Information Systems
• /
• v.11 no.3
• /
• pp.10-14
• /
• 2006

To effectively manage a very large amount of music data, we need an indexing technique based on audio features. But the indexing techniques for audiofeatures have not been studied completely. In this paper, we describe a content-based music information retrieval technique for audio features using the TIP-indexing file. In addition, we develop and experiment the TIP-indexing files using various blocking factors to present performance comparisons for effective indexing. Experimental results show the effectiveness of the proposed techniques.

• Journal of Ship and Ocean Technology
• /
• v.9 no.1
• /
• pp.64-71
• /
• 2005

In probabilistic design, the challenge is to estimate the uncertainty propagation, since outputs of subsystems at lower levels could constitute inputs of other systems or at higher levels of the multilevel systems. Three uncertainty propagation estimation techniques are compared in this paper in terms of numerical efficiency and accuracy: root sum square (linearization), distribution-based moment approximation, and Taguchi-based integration. When applied to reliability-based design optimization (RBDO) under uncertainty, it is investigated which type of applications each method is best suitable for. Two nonlinear analytical examples and one vehicle crashworthiness for side-impact simulation example are employed to investigate the unique features of the presented techniques for uncertainty propagation. This study aims at helping potential users to identify appropriate techniques for their applications in the multilevel design.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2000.10b
• /
• pp.1297-1302
• /
• 2000

A literature review has been carried out to investigate why bridges have collapsed without warning. The reasons behind the collapses have been categorized into short and long term risks. It is thought that permanent monitoring systems which assess structural adequacy are more appropriate to long term risks. From the knowledge of the Korean bridge stock, its current problems and its likely future problems, it was considered that generally the most useful application for a permanent monitoring system is to monitor where chloride-induced corrosion either of the reinforcement or prestressing tendons is possible. A number of permanent monitoring systems currently in use on existing bridges which include some aspect of corrosion detection have been reviewed. The reasons as to why they are being used, what is being measured, what techniques are being used, and if they are deemed successful has been investigated. Based on these findings, and experimental programme has been constructed to investigate the accuracy, reliability and usefulness of various suitable techniques which could be included in a permanent monitoring system.

• Journal of Power Electronics
• /
• v.11 no.5
• /
• pp.767-777
• /
• 2011

Power electronic systems such as switching power supplies are accounted as noise sources for other sensitive circuits. EMI caused by power converters can disturb the normal operation of the converter and other adjacent systems. Major research is concentrated on EMI mitigation for power converters in which the main concern is compliance with EMC standards to ensure proper operation of converters and nearby systems. This paper reviews EMI reduction techniques related to switching power converters with emphasis on the conducted EMI. A comprehensive review of significant research works is performed and various methods are thoroughly discussed and compared. Also, a classification of methods is presented. Moreover, converter prototypes are realized which contain several EMI mitigation techniques and their effects are presented via experimental results.

• The Korean Journal of Financial Studies
• /
• v.3 no.2
• /
• pp.267-279
• /
• 1996

The prediction of future demand is a vital task in managing business operations. To this end, traditional approaches often focused on statistical techniques such as exponential smoothing and moving average. The need for better accuracy has led to nonlinear techniques such as neural networks and case based reasoning. In addition, experimental design techniques such as orthogonal arrays may be used to assist in the formulation of an effective methodology. This paper investigates a multistrategy approach involving neural nets, case based reasoning, and orthogonal arrays. Neural nets and case based reasoning are employed both separately and in combination, while orthoarrays are used to determine the best architecture for each approach. The comparative evaluation is performed in the context of an application relating to the prediction of Treasury notes.

• Electronics and Telecommunications Trends
• /
• v.34 no.5
• /
• pp.26-35
• /
• 2019

Modern imaging technologies utilizing electromagnetic waves are indispensable in our daily lives. Applications, such as television and smartphone screens, radar imaging for weather forecast, and medical imaging, can be attributed to technology developments in various electromagnetic regions. Terahertz (THz) waves, electromagnetic (EM) waves located between far infrared and microwave regions, had left unexplored EM waves. Recent advances in technology have led to various two-dimensional and three-dimensional THz imaging techniques. In this article, we explain THz imaging techniques as well as the experimental results from our laboratory. Additionally, we introduce commercial THz cameras developed worldwide. Finally, we present the applications of THz imaging techniques.

• Journal of Multimedia Information System
• /
• v.6 no.2
• /
• pp.87-90
• /
• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).