• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.605-610
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• 2007

사례기반추론(CBR)은 많은 장점으로 인해, 생산, 재무, 마케팅 등의 분야의 다양한 경영의사결정문제 해결에 적용되어 왔다. 그러나, 효과적인 CBR 시스템을 설계, 구축하기 위해서는 연구자가 직관적으로 설정해야 할 많은 변수들이 존재한다. 본 연구에서는 이러한 CBR의 여러 설계요소들 중, '결합할 유사사례의 선택' 과 관련해, CBR이 보다 개선된 형태로 경영문제 해결에 응용될 수 있는 모형을 제시하고 있다. 본 연구의 제안모형은 결합할 유사사례를 선택하는 기준으로 특정 사례수(k-NN)나 유사도의 상대적 비율을 사용하는 기존의 CBR과 달리 0에서 1사이의 값을 갖는 절대적 유사 임계치를 적용하고 있다. 다만, 절대적 유사 임계치를 사용할 때, 그 값이 작아질 경우 예측결과의 생성이 과도하게 이루어지지 않을 수 있는 문제를 해결하기 위해, 커버리지를 모형에 함께 반영하여 사용자가 원하는 수준의 커버리지는 유지한 상태에서 가장 효과적인 유사 사례를 찾아, 추론을 수행할 수 있도록 설계하였다. 제안모형을 검증하기 위해, 본 연구에서는 이 모형을 실제 인터넷 쇼핑몰의 고객 발굴 사례에 적용해 보았다. 이를 통해, 제안모형의 적용가능성을 확인하고, 향후 추가연구가 요구되는 개선방향을 고찰해 보았다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.943-947
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• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. In order to apply defect prediction models, we need to determine a threshold value. Because we cannot know actually where defects are, it is difficult to determine threshold. Therefore, we performed a series of experiments to explore the issue of determining a threshold. In the experiments, we applied defect prediction models to other systems different from the system used in building the prediction model. Specifically, we have applied three models - Olague model, Zhou model, and Gyimothy model - to four different systems. As a result, we found that the prediction capabilities varied considerably with a chosen threshold value. Therefore, we need to perform a study on the determination of an appropriate threshold value to improve the applicably of defect prediction models.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.89-95
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• 2015

Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.175-185
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• 2011

The threshold level of a receiver is analyzed for the simplification of system and the improvement of upstream data ratios in optical subscriber network of which the upstream date rate and the optical transmitted power are changed to meet the requested BER (Bit Error Rate) defined per interactive multimedia services. In asynchronous optical subscriber network of which the upstream to downstream data ratios are 1:1/2, 1:1/4, 1:1/8 and 1:1/16 with manchester coded downstream and NRZ (Non Return to Zero) downstream re-modulation, the BER performance is theoretically analyzed and it is performed by simulation with MATLAB according to the four types of downstream data for four models. The results have shown that in the cases which the upstream to downstream data ratios are 1:1/4, 1:1/8 and 1:1/16 the conventional receiver with threshold level of 1/2 can be applied regardless of average received optical powers and the BER is not much deteriorated compared with using the optimal threshold level. In the case that the upstream to downstream data ratio is 1:1/2 the threshold level in an optical receiver could be fixed at 1/3 and the BER is not much deteriorated compared with using the optimal threshold level as the average received optical power increases.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.739-742
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• 2005

본 논문은 디지털 영상 데이터를 대상으로 웨이블릿 변환을 이용하여 주파수 영역에서 워터마크를 삽입하는 블라인드 워터마킹 알고리즘을 제안한다. 실험을 통하여 다양한 임계치에 따른 워터마크 정보의 수용력과 영상의 손실 정도(PSNR), 저작권 인증 여부와 검출 값(Correlation response) 사이의 관계(Trade-off)들을 고려하여 최적의 임계치에 관하여 연구한다. 또한 인간의 시각적인 특성을 고려한 HVS(Human visual system) 기법을 적용하여 영상의 비가시도를 유지하면서 시각적으로 중요한 영역에 워터마크를 삽입하여 일반적인 공격에 강인성을 가지는 워터마킹 방법을 연구한다. 워터마크로서 가우시안 랜덤 수열(Gaussian Random sequence)을 삽입하여 최적의 임계값을 적용한 제안된 알고리즘의 성능 평가를 위해 여러 영상에 대하여 실험해 본 결과 워터마크가 삽입된 영상의 화질은 비가시도 측면에서 시각적으로 인지할 수 없을 만큼 측정되었으며, JPEG 손실압축, 선형 필터링, 잡음첨가 그리고 크로핑 등의 공격에 대하여 향상된 상관도와 강인함을 알 수가 있었다.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.105-108
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• 2007

차량에 부착된 회전식 레이저 스캐너는 360도로 회전하면서 데이터를 취득하기 때문에 고정식 레이저 스캐너에 비해 더 광범위하고 정확한 3차원 데이터를 획득하고 생성할 수 있다. 그러나 레이저 데이터 자료는 표적까지의 거리와 취득 당시의 스캐너의 각도로만 구성되어있기 때문에 이를 사용하기 위해서 이 데이터들을 일련의 좌표변환과정을 거쳐서 3차원 직교좌표계로 변환시킨다. 이 논문의 목적은 회전식 레이저에서 획득된 데이터를 DEM화하고，DEM영상의 밝기값， 즉 높이값을 이용하여 도로변을 주위의 사물과 분리하여 추출하는 것에 있다. 도로면은 일반적으로 주위의 사물에 비해 그 높이가 낮고 고르게 분포되어 있다고 가정한다. 그렇기 때문에 이 도로면의 높이를 대표할 수 있는 적절한 임계값을 찾을 수 있다면 도로면의 분리 또한 가능하다. 도로면의 추출을 위해 제안된 방법은 취득된 레이저 데이터를 일정 간격의 높이로 나누고 그에 대한 히스토그램을 구한 후, 가장 많은 빈도수를 나타낸 지역의 값을 염계치로 설정하는 방법과，레이저 스캐너가 지표면을 향할 때의 각도，즉 270도 일 때 취득된 거리의 값들을 수집한 후, 그 평균값을 임계치로 설정하는 방법이다. 이렇게 구해진 임계치를 이용 그 값보다 작은 지역을 도로로 인식하였으며，실험 결과 레이저 스캐너의 각도를 이용한 방법이 더욱 효과적으로 도로를 추출할 수 있음을 확인할 수 있었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.398-403
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• 2006

In this paper, we describe a fuzzy threshold selection technique for feature extraction in digital color images. this is achieved by the formulation a fuzzy inference system that evaluates threshold for feature configurations. The system uses two fuzzy measures. They capture desirable characteristics of features such as dependency of local intensity and continuity in an image. We give a graphical description of a nonlinear sketch feature extraction filter and design the fuzzy inference system in terms of the characteristics of the feature. Through the design, we provide selection method on the choice of a threshold to achieve certain characteristics of the extracted features. Experimental results show the usefulness of our fuzzy threshold inference approach which is able to extract features without human intervention.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.313-315
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• 2005

본 논문은 이동 애드혹 네트워크에서 이동 에이전트를 이용하여 동적으로 클러스터링을 구성하는 기법에 관한 것이다. 기존에 제안된 이동 애드혹 네트워크에서의 클러스터링 기법은 클러스터의 크기가 고정되어 있기 때문에 네트워크의 상태나 노드들의 이동성에 따라 클러스터 재구성의 오버헤드가 발생하였다. 본 제안 기법에서는 네트워크의 상태에 따라 클러스터 크기의 최대 임계치와 최소 임계치를 설정하고 이에 따라 이동 에이전트를 이용하여 클러스터를 병합 흑은 분할하면서 클러스터의 크기를 임계치 내에서 일정하게 유지시킴으로써, 클러스터 재구성의 오버헤드라 클러스터 내부의 경로 탐색의 오버헤드를 줄일 수 있다.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.11-19
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• 2022

This paper is a study to evaluate the performance when classifying explosive components by capacity using a convolutional neural network (CNN). Among the existing explosive classification methods, the IMS steam detector method determines the presence or absence of an explosive only when the explosive concentration exceeds the threshold set by the user. The IMS steam detector has a problem of determining that even if an explosive exists, the explosive does not exist in an amount that does not exceed the threshold. Therefore, it is necessary to detect the explosive component even when the concentration of the explosive component does not exceed the threshold. Accordingly, in this paper, after imaging explosive time series data with the Gramian Angular Field (GAF) algorithm, it is possible to determine whether there are explosive components and the amount of explosive components even when the concentration of explosive components does not exceed a threshold.