• 한국IT서비스학회:학술대회논문집
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• 한국IT서비스학회 2003년도 추계학술대회
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• pp.616-623
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• 2003

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However if we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1543-1561
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• 2020

Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.

• 지식경영연구
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• 제19권2호
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1998년도 Proceedings of International Workshop on Advanced Image Technology
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• pp.92-97
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• 1998

For the efficient searching and browsing of digital video, it is essential to extract the internal structure of the video contents. As an example, a news video consists of several sections such as politics, economics, sports and others, and also each section consists of individual topics. With this information in hand, users can ore easily access the required video frames. This paper addresses the problem of automatic shot boundary detection and selection of representative frames (R-frames), which are the essential step in recognizing the internal structure of video contents. In the shot boundary detection, a new algorithm that have dual detectors which are designed specifically for the abrupt boundaries (cuts) and gradually changing bounaries respectively is proposed. Compared to the existing 미algorithms that mostly have tried to detect both types by a single mechanism, the proposed algorithm is proved to be more robust and accurate. Also in the problem of R-frame selection, simple mechanical approaches such as selecting one frame every other second have been adopted. However this approach often selects too many R-frames in static short, while drops important frames in dynamic shots. To improve the selection mechanism, a new R-frame selection algorithm that uses motion information extracted from pixel difference is proposed.

• 응용통계연구
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• 제31권6호
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• pp.721-732
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• 2018

잠재집단 모형은 다변량 범주형 자료 안에 숨겨진 집단을 찾는 매우 중요한 도구종의 하나이다. 하지만 실제 자료분석에서 너무 많은 관찰변수들을 포함시킨 모형은 모형을 복잡하게 만들고 또한 모수추정의 정확도에 영향을 주기 때문에 정보가 손실되지 않는 내에서 유용한 변수를 찾는 것은 중요한 문제이다. Dean과 Raftery (2010)은 잠재집단 모형에서의 변수선택을 위해 BIC를 이용한 Headlong search 알고리즘을 제시하였는데 본 논문에서는 이 방법을 대체할 수 있는 방법으로 적합한 모형으로부터 계산된 잠재집단에 속할 사후확률을 이용하여 변수 선택을 하는 방법을 제안하고자 한다. 이를 위하여 잠재집단 모형의 적합성을 측정할 수 있는 새로운 통계량과 이를 이용한 변수선택 알고리즘을 제시할 것이다. 또한 제안된 방법의 효율성을 모의실험과 실증자료 분석을 통해 살펴보고자 한다.

• 한국IT서비스학회지
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• 제14권2호
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• pp.129-142
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• 2015

The purpose of this study is to suggest an effective regression testing method in order to minimize the scope of test resulting from the modification of software and to prevent mismatch of test case and test objects. As a way to improve the efficiency of regression testing which uses a change-centric testing technique, the method flow is analyzed and grasped through a static analysis based on source code in order to identify modified parts. After the order of priority is set according to the results of user action log-based dynamic analysis on identified regression testing objects, test effect can be raised by adjusting the order of priority using code complexity. Quality assurance coverage can be checked using the user action log suggested in this study, and the progress of test and whether or not each function has been verified can be checked, too. In addition, by minimizing test parts and adjusting the order of test, costs and time can be saved, making it possible to conduct regression testing effectively.

• 산업기술연구
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• 제22권A호
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• pp.127-135
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• 2002

In this study, the preliminary risk assessment for the underground storage tanks(UST) in Chunchon city was implemented using the geographical information system(GIS). The estimation variables, such as the installation year, storage capacity, the distances from streams, and from groundwater pumping wells, were selected to estimate the relative risk levels. The weighting factors were given to all the estimation variables. Cumulative scores were induced by the combination of all the scores of the corresponding variables using the buffering technique and the overlay analysis in ArcView. Using the these process, the relative risk level of each UST was estimated. Some sites in this study are simplified and reduced because the number of useable data are limited or too enormous. Thus the selection of the comprehensive estimation variables and the proper weighting values are required for the future study. The methodology in this study could be served not only for the preliminary risk assessment of UST but also for the selection of the proper location of new and old UST. And, it can be used for the effective management system of UST.

• 대한기계학회논문집
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• 제16권12호
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• pp.2216-2225
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• 1992

본 연구에서는 제약조건중 절삭조건 뿐만 아니라 작업 가공 목표, 즉 현장에 서 중요시하는 표면조도를 제약조건에 첨가하여 가공조건의 최적화를 꾀하였다. 또 한 앞의 논문들에서 적응제어나 R-T-F의 개념으로 경제성으로 고려한 최적 절삭 조건 을 구하였으나 본 논문에서는 국내 업체의 노무비 및 간접비로 최소 가공비를 구하고, 이에 대응하는 최적 절삭 속도 및 최대 생산율을 검토하였다. 그리고 이송변화에 따 른 최적 절삭 속도의 영향을 검토하여 생산 가공의 경제성을 제고하였다. 또한 각 업체의 선삭작업에 해당하는 입력 데이터만으로 경제성을 고려한 최적 절삭 조건의 자 동 설정을 하기 위한 프로그램을 구축하였다. 그러므로 이와같은 방법으로 현재 업 체의 장비와 인원만으로 기계 가공의 최적 조건 선정을 자동화 하므로써 생산성 향상 과 원가 절감의 효과를 극대화 할 수 있을 것으로 기대된다.

• 조명전기설비학회논문지
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• 제26권12호
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• pp.28-36
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• 2012

Augmentation of existing EHV substations located in hilly terrain and at high altitude require many different considerations as compared to substations located in plain areas and at lower altitude. Owing to high altitude and steep terrain, meticulous engineering and preparations are required, considering the actual available space at the existing substation. Historical fault events too must be considered to enhance reliability and performance of the substation equipment. This paper proposes ways to augment the existing 2 banks of 20MVA, 220/66kV power transformers ($3{\times}6.67MVA$, single-phase) to $2{\times}50$/63MVA, 220/66kV power transformers to meet continuously increasing demand of the capital city over the next 20 years. Upgrading and augmentation of existing substations, especially the main transformers and associated equipment, require replacement with minimum or no disturbance to the existing power supply system. Considerations should also be made during engineering and design, the operational flexibilities, maintenance aspects, future expandability and value addition, in terms of reliability and space usage of the existing substation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4271-4294
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• 2018

Communication cost is the most important factor in Wireless Sensor Networks (WSNs), as exchanging control keying messages consumes a large amount of energy from the constituent sensor nodes. Time-based Dynamic Keying and En-Route Filtering (TICK) can reduce the communication costs by utilizing local time values of the en-route nodes to generate one-time dynamic keys that are used to encrypt reports in a manner that further avoids the regular keying or re-keying of messages. Although TICK is more energy efficient, it employs no re-encryption operation strategy that cannot determine whether a healthy report might be considered as malicious if the clock drift between the source node and the forwarding node is too large. Secure SOurce-BAsed Loose Synchronization (SOBAS) employs a selective encryption en-route in which fixed nodes are selected to re-encrypt the data. Therefore, the selection of encryption nodes is non-adaptive, and the dynamic network conditions (i.e., The residual energy of en-route nodes, hop count, and false positive rate) are also not focused in SOBAS. We propose an energy efficient selection of re-encryption nodes based on fuzzy logic. Simulation results indicate that the proposed method achieves better energy conservation at the en-route nodes along the path when compared to TICK and SOBAS.