• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.325-332
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• 2017

In this paper, a survey of test sequence generation methods for testing the conformance of a protocol implementation to its specification is presented. The best known methods proposed in the literature are called transition tour, distinguishing sequence, characterizing sequence, and unique input/output sequence. Also, several variants of the above methods are introduced. Applications of these methods to the finite state machine model are discussed. Then, comparative analysis of the methods is made in terms of test sequence length. Finally, conclusions are given as follows. The T-method produces the shortest test sequence, but it has the worst fault coverage. The W-method tends to produce excessively long test sequences even though its fault coverage is complete. The problem with the DS-method is that a distinguishing sequence may not exist. The UIO-method is more widely applicable, but it does not provide the same fault coverage as the DS-method.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.93-100
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• 2022

In the era of information overload on the Internet, the recommendation system, which is an indispensable function, is a service that recommends products that a user may prefer, and has been successfully provided in various commercial sites. Recently, studies to reflect the rating time of items to improve the performance of collaborative filtering, a representative recommendation technique, are active. The core idea of these studies is to generate the recommendation list by giving an exponentially lower weight to the items rated in the past. However, this has a disadvantage in that a time function is uniformly applied to all items without considering changes in users' preferences according to the characteristics of the items. In this study, we propose a time-aware collaborative filtering technique from a completely different point of view by developing a new similarity measure that integrates the change in similarity values between items over time into a weighted sum. As a result of the experiment, the prediction performance and recommendation performance of the proposed method were significantly superior to the existing representative time aware methods and traditional methods.

• Journal of Korea Multimedia Society
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• v.4 no.5
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• pp.465-475
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• 2001

Recent1y, there is growing attention on the researches of inducing association rules from large volume of database. One of them is the method that can be applied to quantitative attribute data. This paper presents a new method for generating large-interval itemsets, which uses locality for partitioning the range of data. This method can minimize the loss of data-inherent characteristics by generating denser large-interval items than other methods. Performance evaluation results show that our new approach is more efficient than previously proposed techniques.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.36 no.1
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• pp.34-38
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• 2007

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.389-395
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• 2007

Collaborative filtering is a popular technique for information filtering to reduce information overload and widely used in application such as recommender system in the E-commerce domain. Collaborative filtering systems collect human ratings and provide Predictions based on the ratings of other people who share the same tastes. The quality of predictions depends on the number of items which are commonly rated by people. Therefore, it is difficult to apply pure collaborative filtering algorithm directly to dynamic collections where items are constantly added or removed. In this paper we suggest a method for managing dynamic collections. It creates taste space for items using a technique called Singular Vector Decomposition (SVD) and maintains clusters of core items on the space to estimate relevance of past and future items. To evaluate the proposed method, we divide database of user ratings into those of old and new items and analyze predicted ratings of the latter. And we experimentally show our method is efficiently applied to dynamic collections.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.457-466
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• 2004

Recommendation is to offer information which fits user's interests and tastes to provide better services and to reduce information overload. It recently draws attention upon Internet users and information providers. The collaborative filtering is one of the widely used methods for recommendation. It recommends an item to a user based on the reference users' preferences for the target item or the target user's preferences for the reference items. In this paper, we propose a neural network based collaborative filtering method. Our method builds a model by learning correlation between users or items using a multi-layer perceptron. We also investigate integration of diverse information to solve the sparsity problem and selecting the reference users or items based on similarity to improve performance. We finally demonstrate that our method outperforms the existing methods through experiments using the EachMovie data.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.5
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• pp.477-488
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• 2024

The Korean government amended the Facilities Safety Act in 2018 to establish a performance-based maintenance system. This system is designed to comprehensively evaluate the safety, durability, and usage performance of facilities required to maintain their function in use, and to establish corresponding maintenance plans. However, the current maintenance system of utility tunnels is managed by a safety-oriented evaluation method, which has limitations in conducting performance evaluations that consider durability and usage performance. Therefore, in this study, safety, durability, and usage performance items for utility tunnels were selected using the Delphi method, and the weight of each item was calculated using the entropy weighting method. The results of this study are expected to be used in future performance evaluations of utility tunnels to support rational decision making when establishing maintenance plans.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2011.11a
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• pp.35-39
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• 2011

일반 건물과 달리 특별한 화재특성을 가지는 단독주택, 주차장, 지하대공간, 다중이용시설 등의 화재 시 유효하게 진압할 수 있는 용도별 맞춤식 스프링클러헤드를 개발하기 위한 성능검증방법을 마련하고자 시험방법 및 성능요건을 연구하였다. 먼저 용도별 장소의 화재특성을 고찰하였으며, 아울러 기존의 관련 국내외 시험기준을 면밀히 분석하여 시험항목 선정 및 적용방법 수립방안을 검토하였다. 시험항목은 환경시험과 기능시험, 화재시험으로 대별할 수 있는데 이 중 용도별 헤드의 성능을 좌우하는 살수분포와 화재시험에 대해 시험방법과 성능요건을 제시하였다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.327-332
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• 2023

데이터의 품질과 다양성은 모델 성능에 지대한 영향을 끼친다. 본 연구에서는 Topic을 활용한 데이터 전처리와 BERT 기반 MLM, T5, Random Masking을 이용한 증강으로 데이터의 품질과 다양성을 높이고자 했으며, 이를 KoBERT 기반 에세이 자동 평가 모델에 적용했다. 데이터 전처리만 진행했을 때, Quadratic Weighted Kappa Score(QWK)를 기준으로 모델이 에세이의 모든 평가 항목에 대해 베이스라인보다 더욱 높은 일치도를 보였으며 평가항목별 일치도의 평균을 기준으로 0.5368029에서 0.5483064(+0.0115035)로 상승했다. 여기에 제안하는 증강 방식을 추가 할 경우 MLM, T5, Random Masking 모두 성능 향상 효과를 보였다. 특히, MLM 데이터 증강 방식을 추가로 적용하였을 때 최종적으로 0.5483064에서 0.55151645(+0.00321005)으로 상승해 가장 높은 일치도를 보였으며, 에세이 총점으로 QWK를 기준으로 성능을 평가하면 베이스라인 대비 0.4110809에서 0.4380132(+0.0269323)로의 성능 개선이 있었다.

• Proceedings of the KAIS Fall Conference
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• 2010.05b
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• pp.653-656
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• 2010

개착식 구조물의 내진성능 예비평가는 설계자료나 현장조사를 토대로 지진도 그룹, 취약도 지수, 영향도 지수를 산정하여 내진성능 상세평가의 시행여부를 판단하는 근거가 되어왔다. 내진성능 예비평가를 적용했던 사례를 분석한 결과 평가항목 산정 시 근거가 미약하고, 내진성능 상세평가 대상으로 분류되지 않은 그룹에 대해 구조적 내진성능 확보가 불확실하여 내진성능 예비평가를 개정하였다. 개정한 내진성능 예비평가는 지진도 그룹, 취약도 지수, 영향도 지수를 적용하는 기존의 틀은 유지하면서 평가항목을 산정하는 근거를 명확하게 하였으며, 내진성능 상세평가를 실시하지 않는 그룹에 대해서도 간략하게 구조적 내진성능 확보여부를 검토할 수 있는 전단변형각을 도입하였다. 본 연구에서는 개정한 내진성능 예비평가를 기존 사례에 적용하여 결과를 도출하였으며, 기존의 적용 사례와 비교 분석하였다.