• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1057-1068
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• 2005

This paper proposes a real-time face detection system which detects multiple faces from low resolution video such as web-camera video. First, It finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next, it generates reduced feature vector for each face region candidate by using principle component analysis. Finally, it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine) based binary classification. According to experiment results, the proposed method achieves real-time face detection from low resolution video. Also, it reduces the false detection rate than existing methods by using PCA and SVM based face classification step.

• Nuclear Engineering and Technology
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• v.45 no.5
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• pp.653-664
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• 2013

The design of computer-based instrumentation and control (I&C) systems is determined by the allocation of I&C functions to I&C systems and components. Due to the characteristics of computer-based technology, component failures can negatively affect several I&C functions, so that the reliability proof of the I&C systems requires the accomplishment of I&C system design analyses throughout the I&C life-cycle. On one hand, this paper proposes the restructuring of the sequential IEC 61513 I&C life-cycle according to the V-model, so as to adequately integrate the concept of verification and validation. On the other hand, based on a metamodel for the modeling of I&C systems, this paper introduces a method for the modeling and analysis of the effects with respect to the superposition of failure combinations and event sequences on the I&C system design, i.e. the temporal change of physical structure is analyzed. In the first step, the method is concerned with the modeling of the I&C systems. In the second step, the method considers the analysis of temporal change of physical structure, which integrates the concepts of the diversity and defense-in-depth analysis, fault tree analysis, event tree analysis, and failure mode and effects analysis.

• Journal of National Security and Military Science
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• s.2
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• pp.261-283
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• 2004

To build up the military strength based on information oriented armed forces, the Korean ministry of national defense (MND) promotes the defense CALS (Continuous Acquisition and Life cycle Support) initiative for the reductions of acquisition times, improvements of system qualities, and reductions of costs. These defense CALS activities are the major component of the underlying mid and long term defense digitization program and the ultimate goal of program is to bring a quick victory by providing real-time battlefield intelligence and the economical operations of the military. The concept of defense CALS is to automate the acquisition and disposition of defense systems throughout their life cycle. For implementing defense CALS, the technology for exchange and sharing CALS standard data that is created once and used many times should be considered. In order to develop an efficient CALS information exchange and sharing system, it is required to integrate distributed and heterogeneous data sources and provide systematic search tools for those data. In this study, we developed a content based search engine technology which is essential for the construction of integrated data environments. The developed technology provides the environment of sharing the CALS standard data such as SGML(Standard Generalized Markup Language) and STEP(Standard for The Exchange of Product model data). Utilizing this technology, users can find and access distributed and heterogeneous data sources without knowing its actual location.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.10
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• pp.34-42
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• 2009

HAO is a ORB engine to support the logic-based CORBA component developments in FPGA. In this papers, in order to give the interoperability between general CORBA and HAO, we propose newly the specialization of GIOP(General Inter-ORB Protocol) from consideration of FPGA. It compose of the two major capability. First, it can abstract the hardware structure from the various system board environments. Secondly, also it is possible to minimize the monopoly occupation for shared resource such as system bus, external memory.

• Journal of IKEEE
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• v.24 no.1
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• pp.254-259
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• 2020

This paper is the implementation of the conversion system from image to music based on intentional synesthesia. The input image based on color, texture, and shape was converted into melodies, harmonies and rhythms of music, respectively. Depending on the histogram of colors, the melody can be selected and obtained probabilistically to form the melody. The texture in the image expressed harmony and minor key with 7 characteristics of GLCM, a statistical texture feature extraction method. Finally, the shape of the image was extracted from the edge image, and using Hough Transform, a frequency component analysis, the line components were detected to produce music by selecting the rhythm according to the distribution of angles.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.55-61
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• 2010

In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of Power Electronics
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• v.14 no.5
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• pp.957-966
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• 2014

Some of DC actuators used in home automation, office automation, medical equipment and automotive systems require a position sensor. In low power applications, the introduction of such a transducer remarkably increases the whole system cost, which justifies the development of sensorless position estimation techniques. The well-known AC motor drive sensorless techniques exploiting the fundamental component of the back electromotive force cannot be used on DC motor drives. In addition, the sophisticated approaches based on current or voltage signal injection cannot be used. Therefore, an effective and inexpensive sensorless position estimation technique suitable for DC motors is presented in this paper. This technique exploits the periodic pulses of the armature current caused by commutation. It is based on a simple pulse counting algorithm, suitable for coping with the rather large variability of the pulse frequency and it leads to the realization of a sensorless position control system for low cost, medium performance systems, like those in the field of automotive applications.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.172-177
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• 2008

Biometrics is essential for person identification because of its uniqueness from each individuals. Face recognition technology has advantage over other biometrics because of its convenience and non-intrusive characteristics. In this paper, we will present a overview of face recognition technology including face detection, feature extraction, and face recognition system. For face detection, we will describe template based method and face component based approach. PCA and LDA approach will be discussed for feature extraction, and nearest neighbor classifiers -will be covered for matching. Large database and the standardized performance evaluation methodology is essential in order to support state-of-the-art face recognition system. Also, 3D based face recognition technology is the key solution for the pose, lighting and expression variations in many applications.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1999.05a
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• pp.171-176
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• 1999

Nondestructive inspection and accompanying life analysis based on fracture mechanics were the major conventional methods for evaluating remaining life of critical high temperature components in power plants. By using these conventional methods, it has been difficult to perform in-service inspection for life prediction. Also, quantitative damage evaluation due to unexpected abrupt changes in operating temperature was almost impossible. Thus, many efforts have been made for evaluating remaining life during operation of the plants and predicting real-time life usage values based on the shape of structures, operating history, and material properties. In this study, a core software for on-line life monitoring system which carries out real-time life evaluation of a critical component in power boiler(high temperature steam headers) is developed. The software is capable of evaluating creep and fatigue life usage from the real-time stress data calculated by using temperature/stress transfer Green functions derived for the specific headers and by counting transient cycles. The major benefits of the developed software lie in determining future operating schedule, inspection interval, and replacement plan by monitoring real-time life usage based on prior operating history.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).