• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.939-948
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• 2012

The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between items in a massive database based on the interestingness measures such as support, confidence, lift, etc. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control.In general, association rule technique generates the rule, 'If A, then B.', whereas negative association rule technique generates the rule, 'If A, then not B.', or 'If not A, then B.'. We can determine whether we promote other products in addition to promote its products only if we add negative association rules to existing association rules. In this paper, we proposed the negatively attributable and pure confidence to overcome the problems faced by negative association rule technique, and then we checked three conditions for interestingness measure. The comparative studies with negative confidence, negatively pure confidence, and negatively attributable and pure confidence are shown by numerical examples. The results show that the negatively attributable and pure confidence is better than negative confidence and negatively pure confidence.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.179-188
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• 2011

Association rule represents the relationship between items in a massive database by quantifying their relationship, and is used most frequently in data mining techniques. In general, association rule technique generates the rule, 'If A, then B.', whereas negative association rule technique generates the rule, 'If A, then not B.', or 'If not A, then B.'. We can determine whether we promote other products in addition to promote its products only if we add negative association rules to existing association rules. In this paper, we proposed the negatively pure association rules by negatively pure support, negatively pure confidence, and negatively pure lift to overcome the problems faced by negative association rule technique. In checking the usefulness of this technique through numerical examples, we could find the direction of association by the sign of the negatively pure association rule measure.

• Proceedings of the Acoustical Society of Korea Conference
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• 1995.06a
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• pp.55-60
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• 1995

고차 통계 방법을 이용하여 잡음이 섞인 음성 신호에서 피치를 구하는 방법과 이를 이용하여 유성음 및 무성음 구간을 구분하는 방법을 구현하고 그 결과를 기술하였다. 고차 통계의 일종인 3차 cumulant 함수의 경우 Gaussian 또는 대칭적인 분포를 갖는 잡음 신호를 음성신호로부터 효과적으로 분리하여 제거시키는 특징을 가지고 있으며, 이러한 특징을 이용하면 잡음 환경에서 여러 가지 음성 특징 파라메터들을 보다 신뢰도 높게 추정할 수 있다. 본 논문에서는 dam성 신호의 3차 cumulant 함수의 자기상관함수로부터 음성의 피치 주기를 추정하였으며, 피치 위치에서의 normalized peak 크기에 의해 유성음과 무성음을 구분하였다. 또한 성능 비교를 위해 음성 신호 자체의 자기 상관 함수로부터 역시 피치 주기 및 유성음/무성음 구분을 수행하였다. 백색 및 유색 Gaussian 잡음 환경에서의 음성의 피치 주기 추정 실험 결과 SNR가 낮은 경우에 3차 cumulant를 이용한 방법이 2차 통계에 비해 우수한 성능을 나타내었다. 또한 동일한 잡음 환경에서의 유성음/무성음 판별 시험에서도 3차 cumulant를 이용한 방법이 기존의 2차 통계를 이용한 방법에 비해 성능이 크게 향상된 결과를 얻었다.

• Journal of Applied Reliability
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• v.12 no.4
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• pp.265-274
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• 2012

We in this paper discuss the strong law of large numbers for weighted sums of arrays of rowwise LNQD random variables by using a new exponential inequality of LNQD r.v.'s under suitable conditions and we obtain one of corollary.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.170-170
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• 2012

최근 주목받고 있는 amorphous InGaZnO (a-IGZO) thin film transistors (TFTs)는 수소가 첨가된 비정질 실리콘 TFT (a-Si;H)에 비해 비정질 상태에서도 높은 이동도와 뛰어난 전기적, 광학적 특성에 의해 큰 주목을 받고 있다. 또한 넓은 밴드갭에 의해 가시광 영역에서 투명한 특성을 보이고, 플라스틱 기판 위에서 구부러지는 성질에 의해 플랫 패널 디스플레이나 능동 유기 발광 소자 (AM-OLED), 투명 디스플레이에 응용되고 있다. 하지만, 실제 디스플레이가 동작하는 동안 스위칭 TFT는 백라이트 또는 외부에서 들어오는 빛에 지속적으로 노출되게 되고, 이 빛에 의해서 TFT 소자의 신뢰성에 악영향을 끼친다. 또한, 디스플레이가 장시간 동안 동작 하면 내부 온도가 상승하게 되고 이에 따른 온도에 의한 신뢰성 문제도 동시에 고려되어야 한다. 특히, 실제 AM-LCD에서 스위칭 TFT는 양의 게이트 전압보다 음의 게이트 전압에 의해서 약 500 배 가량 더 긴 시간의 스트레스를 받기 때문에 음의 게이트 전압에 대한 신뢰성 평가는 대단히 중요한 이슈이다. 스트레스에 의한 문턱 전압의 변화는 게이트 절연막과 반도체 채널 사이의 계면 또는 게이트 절연막의 벌크 트랩에 의한 것으로 게이트 절연막의 선택에 따라서 신뢰성을 효과적으로 개선시킬 수 있다. 본 연구에서는 적층된 $Si_3N_4/SiO_2$ (NO 구조) 이중층 구조를 게이트 절연막으로 사용하고, 완충층의 역할을 하는 $SiO_2$막의 두께에 따른 소자의 전기적 특성 및 신뢰성을 평가하였다. a-IGZO TFT 소자의 전기적 특성과 신뢰성 평가를 위하여 간단한 구조의 pseudo-MOS field effect transistor (${\Psi}$-MOSFET) 방법을 이용하였다. 제작된 소자의 최적화된 $SiO_2$ 완충층의 두께는 20 nm이고 $12.3cm^2/V{\cdot}s$의 유효 전계 이동도, 148 mV/dec의 subthreshold swing, $4.52{\times}10^{11}cm^{-2}$의 계면 트랩, negative bias illumination stress에서 1.23 V의 문턱 전압 변화율, negative bias temperature illumination stress에서 2.06 V의 문턱 전압 변화율을 보여 뛰어난 전기적, 신뢰성 특성을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.611-618
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• 2015

By Wikipedia, big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Data mining is the computational process of discovering patterns in huge data sets involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. Association rule is a well researched method for discovering interesting relationships between itemsets in huge databases and has been applied in various fields. There are positive, negative, and inverse association rules according to the direction of association. If you want to set the evaluation criteria of association rule, it may be desirable to consider three types of association rules at the same time. To this end, we proposed a balanced comparative confidence considering sensitivity, specificity, false positive, and false negative, checked the conditions for association threshold by Piatetsky-Shapiro, and compared it with comparative confidence and inversely comparative confidence through a few experiments.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.194-201
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• 2003

In speech recognition, confidence measuring is to decide whether it can be accepted as the recognized results or not. The confidence is measured by integrating frames into phone and word level. In case of word recognition, the confidence measuring verifies the results of recognition and Out-Of-Vocabulary (OOV). Therefore, the post-processing could improve the performance of recognizer without accepting it as a recognition error. In this paper, we measure the confidence modifying log likelihood ratio (LLR) which was the previous confidence measuring. It accumulates only those which the log likelihood ratio is negative when integrating the confidence to phone level from frame level. When comparing the verification performance for the results of word recognizer with the previous method, the FAR (False Acceptance Ratio) is decreased about 3.49% for the OOV and 15.25% for the recognition error when CAR (Correct Acceptance Ratio) is about 90%.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.217-226
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• 2008

The term, travel-time reliability, refers to variations in journey time that travelers cannot predict. The purpose of this paper is to suggest a standard way to measure travel time reliability. A modified buffer time indicator is proposed. The index is represented by the difference between planned and actual travel times based on lognormal type travel time distribution. Using this framework, a constant function for railways and a negative parabola function for roads are discussed. The model developed is applied to the real data of Korean road and rail usages to empirically verify the methodology proposed. In this process, the unit value of travel time reliability for each group is estimated. The result of this research is expected to be helpful of conducting more cautious economic feasibility studies of transport.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.235-243
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• 2011

The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between each set of items based on the association thresholds such as support, confidence, lift, etc. There are many interestingness measures as the criteria for evaluating association rules. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The net confidence measure was developed to compensate for this drawback, but it is useless in the case that the value of positive confidence is the same as that of negative confidence. This paper propose a attributably pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence, net confidence, and attributably pure confidence are shown by numerical example. The results show that the attributably pure confidence is better than confidence or net confidence.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.365-371
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• 2014

Association rule of the well-studied techniques in data mining is the exploratory data analysis for understanding the relevance among the items in a huge database. This method has been used to find the relationship between each set of items based on the interestingness measures such as support, confidence, lift, similarity measures, etc. By typical association rule technique, we generate association rule that satisfy minimum support and confidence values. Support and confidence are the most frequently used, but they have the drawback that they can not determine the direction of the association because they have always positive values. In this paper, we compared support, basic confidence, and three kinds of confidence measures useful for classification model building to overcome this problem. The result confirmed that the causal confirmed confidence was the best confidence in view of the association mining because it showed more precisely the direction of association.