• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.994-1002
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• 2013

This study suggests a generalized availability and safety evaluation model to evaluate the influences to the system's fault tolerant capabilities depending on automatic fault detection function such as the automatic periodic testings. The conventional evaluation model of automatic fault detection function deals only with the self diagnostics, and supposes that the fault detection coverage of self diagnostics is always constant. But all of the fault detection methods could be degraded. For example, the periodic surveillance test has the potential human errors or test equipment errors, the self diagnostics has the potential degradation of built-in logics, and the automatic periodic testing has the potential degradation of automatic test facilities. The suggested evaluation models have incorporated the loss or erroneous behaviors of the automatic fault detection methods. The availability and the safety of each module of the safety grade platform have been evaluated as they were applied the automatic periodic test methodology and the fault tolerant evaluation models. The availability and safety of the safety grade platform were improved when applied the automatic periodic testing. Especially the fault tolerant capability of the processor module with a weak self-diagnostics and the process parameter input modules were dramatically improved compared to the conventional cases. In addition, as a result of the safety evaluation of the digital reactor protection system, the system safety of the digital parts was improved about 4 times compared to the conventional cases.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.10
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• pp.856-867
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• 2018

This paper presents the applicability of MIMO joint decoding to dual-contact satellite systems in which two LEO satellites using X-band frequency band are transmitting each image data to two ground station antennas, simultaneously. When two satellites are closely positioned within the looking angle of the two antennas, each satellite interferes with each other by the relative antenna gain corresponding to an offset angle and this might cause the performance degradation without interference mitigation. To mitigate the performance degradation, SM MIMO techniques for joint decoding are applied. Especially, the relative antenna gain of ground station depending on the angle difference between two satellites in ground station antenna plays an important role in modelling the dual-contact satellite systems. The condition number of MIMO channel including the antenna gain calculated from the mathematical gain pattern model was primarily analyzed. Simulation results showed that the SM MIMO techniques using detection schemes such as ZF-SIC, MMSE-SIC, and ML can be applicable to dual-contact satellite systems.

• Journal of the Korean Society of Radiology
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• v.12 no.3
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• pp.289-295
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• 2018

Most diagnostic devices in the medical field use X-ray sources, which emit energy spectra. In radiological diagnosis, the quantitative and qualitative analyses of X-rays are essential for maintaining the image quality and minimizing the radiation dose to patients. This work aims to obtain the X-ray energy spectra used in diagnostic imaging by Monte Carlo simulation. Various X-ray spectra are simulated using a Monte Carlo simulation tool. These spectra are then compared to the reference data obtained with a tungsten anode spectral model using the interpolating polynomial (TASMIP) code. The X-ray tube voltages used are 50, 60, 80, 100, and 110 kV, respectively. CdTe and a-Se detector are used as the detectors for obtaining the X-ray spectra. Simulation results demonstrate that the various X-ray spectra are well matched with the reference data. Based on the simulation results, an appropriate X-ray spectrum, in accordance with the tube voltage, can be selected when generating an image for diagnostic imaging. The dose to be delivered to the patient can be predicted prior to examination in the diagnostic field.

• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.466-476
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• 2008

The more people use ambulatory electrocardiogram(ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies don't consider the un-balanced data distribution. Even in patients, there are much more normal beats than abnormal beats among the data from 24 hours. To solve this problem, the hierarchical classification using 21 features was adopted for arrhythmia abnormal beat detection. The features include R-R intervals and data to describe the morphology of the wave. To validate the algorithm, 44 non-pacemaker recordings from physionet were used. The hierarchical classification model with 2 stages on domain knowledge was constructed. Using our suggested method, we could improve the performance in abnormal beat classification from the conventional multi-class classification method. In conclusion, the domain knowledge based hierarchical classification is useful to the ECG beat classification with unbalanced data distribution.

• Journal of the Korean Institute of Gas
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• v.12 no.4
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• pp.14-20
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• 2008

In this numerical study, the leakage safety of the LNG tank in which is constructed by membrane inner tank-plywood-polyurethane form-plywood-prestressed concrete structures has been presented for four leakage analysis models. The LNG leak criterion of the tank wall with a storage capacity of $200,000\;m^3$ is analyzed based on the thermal resistance technique. This means that if the cryogenic temperature of a leaked LNG is detected at the outer side of the PC wall, it may be leaked through the wall thickness of the tank. The calculated results based on the thermal resistance method between two walls show that the plywood, PUF, and another plywood walls may block the leakage of the leaked LNG even though the strength of these walls is already collapsed by a leaked LNG pressure. But, the leaked LNG may pass the thickness of the prestressed concrete wall for a period of elapsed time even though the PC outer tank supports the leaked LNG pressure. Thus, the PC outer tank may extend the leakage time of a leaked LNG.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.12
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• pp.665-682
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• 2007

Line drawing is a widely used style in non-photorealistic rendering because it generates expressive descriptions of object shapes with a set of strokes. Although various techniques for line drawing of 3D objects have been developed, line drawing of 2D images has attracted little attention despite interesting applications, such as image stylization. This paper presents a robust and effective technique for generating line drawings from 2D images. The algorithm consists of three parts; filtering, linking, and stylization. In the filtering process, it constructs a likelihood function that estimates possible positions of lines in an image. In the linking process, line strokes are extracted from the likelihood function using clustering and graph search algorithms. In the stylization process, it generates various kinds of line drawings by applying curve fitting and texture mapping to the extracted line strokes. Experimental results demonstrate that the proposed technique can be applied to the various kinds of line drawings from 2D images with detail control.

• Journal of KIISE
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• v.43 no.11
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• pp.1210-1215
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• 2016

Readership of online newspapers has grown with the proliferation of smart devices. However, fierce competition between Internet newspaper companies has resulted in a large increase in the number of hoax articles. Hoax articles are those where the title does not convey the content of the main story, and this gives readers the wrong information about the contents. We note that the hoax articles have certain characteristics, such as unnecessary celebrity quotations, mismatch in the title and content, or incomplete sentences. Based on these, we extract and validate features to identify hoax articles. We build a large-scale training dataset by analyzing text keywords in replies to articles and thus extracted five effective features. We evaluate the performance of the support vector machine classifier on the extracted features, and a 92% accuracy is observed in our validation set. In addition, we also present a selective bigram model to measure the consistency between the title and content, which can be effectively used to analyze short texts in general.

• Journal of the Korean Applied Science and Technology
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• v.29 no.3
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• pp.473-479
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• 2012

The influences of scatterer and absorber in turbid material by light scattering were interpreted for the scattered intensity and wavelength. The molecular properties have been studied by Monte Carlo simulation in resin of New Austria Tunnel Method. It has been found that the effects of optical properties in scattering media could be investigated by the optical parameters(${\mu}_s$, ${\mu}_a$, ${\mu}_t$). Monte Carlo Simulation method for modelling of light transport in the civil engineering and construction field was applied. The results using a phantom were discussed that the distance from source to detector is closer, and scattering intensity is stronger with those obtained through Monte Carlo Simulation. It may also aid in designing the best model for coatings and corrosion for the durability of metal constructions.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.706-711
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• 2010

In the applications of a human speech interface, reducing the error rate in recognition is the one of the main research issues. Many previous studies attempted to correct errors using post-processing, which is dependent on a manually constructed corpus and correction patterns. We propose an automatically learnable post-processing method that is independent of the characteristics of both the domain and the speech recognizer. We divide the entire post-processing task into two steps: error detection and error correction. We consider the error detection step as a classification problem for which we apply the conditional random fields (CRFs) classifier. Furthermore, we apply transformation-based learning (TBL) to the error correction step. Our experimental results indicate that the proposed method corrects a speech recognizer's insertion, deletion, and substitution errors by 25.85%, 3.57%, and 7.42%, respectively.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1373-1382
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• 2011

In this study, we propose an automated smile analysis system for self smile training. The proposed system detects the face area from the input image with the AdaBoost algorithm, followed by identifying facial features based on the face shape model generated by using an ASM(active shpae model). Once facial features are identified, the lip line and teeth area necessary for smile analysis are detected. It is necessary to judge the relationship between the lip line and teeth for smiling degree analysis, and to this end, the second differentiation of the teeth image is carried out, and then individual the teeth areas are identified by means of histogram projection on the vertical axis and horizontal axis. An analysis of the lip line and individual the teeth areas allows for an automated analysis of smiling degree of users, enabling users to check their smiling degree on a real time basis. The developed system in this study exhibited an error of 8.6% or below, compared to previous smile analysis results released by dental clinics for smile training, and it is expected to be used directly by users for smile training.