• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1728-1736
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• 2015

Several algorithms have been developed to classify arrhythmia which either rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose arrhythmia classification method using QRS Pattern of ECG signal according to personalized type. For this purpose, we detected R wave through the preprocessing method and define QRS pattern of ECG signal by QRS feature Also, we detect and modify by pattern classification, classified arrhythmia duplicated QRS pattern in realtime. Normal, PVC, PAC, LBBB, RBBB, Paced beat classification is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.98%, 97.22%, 95.14%, 91.47%, 94.85%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.450-460
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• 2017

T wave is cardiac parameters that represent ventricular repolarization, it is very important to diagnose arrhythmia. Several methods for detecting T wave have been proposed, such as frequency analysis and non-linear approach. However, detection accuracy is at the lower level. This is because of the overlap of the P wave and T wave depending on the heart condition. We propose T wave detection algorithm based on target area extraction through QRS cancellation and moving average. For this purpose, we detected Q, R, S wave from noise-free ECG(electrocardiogram) signal through the preprocessing method. And then we extracted P, T target area by applying decision rule for four PAC(premature atrial contraction) pattern another arrhythmia through moving average and detected T wave using RT interval and threshold of RR interval. The performance of T wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 95.32%.

• Applied Chemistry for Engineering
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• v.1 no.2
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• pp.116-123
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• 1990

Synthesis and characterization of photosensitive orthonaphthoquinonediazide-sulfonyl derivatives were studied. These photoactive compounds underwent a UV induced transformation to the base-soluble photoproduct. The photoresists were prepared using these photoactive compounds with low molecular weight m-cresol novolacs as matrix resin. And photosensitive characteristics of the photoresists were studied. 3, 4, 5-Trihydroxy-benzophenone with bulky resonance structure increased the sensitivity and the solubility rate of the exposed region. The mixture of PAC and matrix resin having 3:8 weight ratio had the moderate rate of dissolution in the developer. The photoresist using these conditions showed the best snsitivity and contrast under the fixed conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.669-672
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• 2017

최근 한국인의 주요 사망원인 중 하나로 부정맥이 부각되고 있다. 심방조기수축(PAC:Premature Atrial Contraction)은 심방이 동방결절의 명령이 있기 전에 수축해 버리는 것이다. 심방조기수축은 일시적으로 유발하였다 사라지곤 할 수 있기 때문에 심한 증상이 없다면 생명에 위협을 가하진 않지만 반대의 경우에는 위험할 수 있다. 따라서 비정상적인 심장 박동이 발생하면 이를 검출하여 조기에 부정맥을 진단할 수 있는 방법이 필요하다. 이를 위해 대상의 ECG 신호로부터 QRS패턴에 해당하는 특징들을 추출하였고 특징들을 이용하여 심방조기수축 파형을 분류한다. 오류 역전파 기반으로 특징들을 훈련하며 가중치와 바이어스값을 구한뒤 이를 이용하여 정상파형과 심방조기수축 파형을 분류한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.192-200
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• 2015

Several algorithms have been developed to classify arrhythmia which rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

• Journal of Korea Game Society
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• v.15 no.5
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• pp.89-98
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• 2015

The artificial intelligence (AI) of Non-Player Companions (NPC), especially opponents, is a key element to adjust the level of games in game design. Smart opponents can make games more challenging as well as allow players for diverse experiences, even in the same game environment. Since game users interact with more than one opponent in most of today's games, collaboration control of opponent characters becomes more important than ever before. In this paper, we introduce a cooperative strategy based on the A* algorithm for enemies' AI in the Pac-Man game. A survey from 17 human testers shows that the levels with our collaborative opponents are more difficult but interesting than those with either the original Pac-Man's personalities or the non-cooperative greedy opponents.

• Progress in Medical Physics
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• v.18 no.2
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• pp.98-106
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• 2007

In June 2005, Yonsei University Medical Center, Severance Hospital upgraded a full-PACS system by adding twenty (5 mega pixels) Totoku ME511L flat panel LCD display devices for diagnostic interpretation purposes. Here we report upon the quantitative (or visual) acceptance testing of the twenty Totoku ME511L display devices for reflection, luminance response, luminance spatial dependency, resolution, noise, veiling glare, and display chromaticity based on AAPM TG 18 report. The tools used in the tests included a telescopic photometer, which was used as a colorimeter, illuminance meter, light sources for reflection assessment, light-blocking devices, and digital TG18 test patterns. For selected 8 flat panel displays, mean diffuse reflection coefficient ($R_d$) was $0.019{\pm}0.02sr^{-1}$. In the luminance response test, luminance ratio (LR), maximum luminance difference ($L_{max}$), and deviation of contrast response were $550{\pm}100,\;2.0{\pm}1.9%\;and\;5.8{\pm}1.8%$, respectively. In the luminance uniformity test, maximum luminance deviation was $14.3{\pm}5.5%$ for the 10% luminance of the TG18-UNL10 test pattern. In the resolution test with luminance measurement method, percent luminance (${\Dalta}L$) at the center was $0.94{\pm}0.64%$. In all cases of noise testing, rectangular target In every square in the three quadrants was visible and all 15 targets except the smallest one in the every corner pattern and the center pattern. The glare ratio (GR) was $12,346{\pm}1,995$. The color uniformity, (u',v'), was $0.0025{\pm}0.0008$. Also, the research results of qualify control guideline of primary disply devices suitable for domestic circumstance are presented All test results are in-line with the criteria recommended by AAPM TG18 report and are thus fully acceptable for diagnostic image interpretation. As a result, the acceptance testing schedule described provides not only an acceptance standard but also guidelines for quality control, optimized viewing conditions, and a means for determining the upgrading time of LCD display devices for diagnostic interpretation.