• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.643-647
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• 2021

The use of data analysis on large volumes of data constitutes a challenge for deducting knowledge and new information. Data can be heterogeneous and complex: Semi-structured data (Example: XML), Data from social networks (Example: Tweets) and Factual data (Example: Spreading of Covid-19). In this paper, we propose a generic multidimensional model in order to analyze complex data, according to several dimensions.

• 한국생산제조학회지
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• 제8권4호
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• pp.19-28
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• 1999

In order to control thermal deformation of the machine origin of machine tools a empirical model and a compensation system have been developed, Prior to empirical modeling the volumetric error considering shape errors and joint errors of slides is formulated through the homogeneous transformation matrix (HTM) and kinematic chain. Simulation results of the HTM method show that the thermal error of the machine origin is more critical than position-dependent errors. In order to make a stable and effective software error compensation system the GMDH (Group Method of Data Handling) models are constructed to estimate the thermal deformation of the machine origin by measuring deformation data and temperature data. A test bar and gap sensors are used to measure the deformation data. In order to compensate the estimated error the work origin shift method is developed by implementing a digital I/O interface board between a CNC controller and an IBM PC. The method shifts the work origin as much as the amounts which are calculated by the pre-established thermal error model. The experiment results for a vertical machining center show that the thermal deformation of the machine origin is reduced within $\pm$5$mu extrm{m}$.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.161-164
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• 2005

Mobile device users are sensitive to pay telecommunication charge for downloading Internet data, because the cost is proportional to the amount of data received. If there are device users who want to download the same content, they may cooperate each other to form an ad hoc network and share the partially downloaded content in order to reduce the amount of data downloaded. Each mobile device, called a peer, downloads a specific portion of the whole content using fee-based telecommunication channel, and exchanges the portion with other peers with free ad hoc channel in order that all participating peers are able to reconstruct the whole content in this situation, al1 participating peers become senders and receivers at the same time. In order to distribute the partial content to other peers, the ad hoc network requires a control led distribution mechanism. This paper introduces the per-peer-based distribution method in which one designated peer can transmit partial data to its neighbors at a time. Simulation results show that 90$\%$ of the telecommunication cost is saved with as few as 10 peers .

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권4호
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• pp.342-356
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• 2000

주어진 프로그램에서 예외상황(exception)을 발생시키는 테스트 데이타를 자동으로 생성해주는 분석 방법을 제안한다. 분석 결과로 얻은 테스트 데이타를 사용하여, 프로그램 내에서 발생한 예외상황들이 프로그래머의 의도대로 처리되는지를 검사할 수 있다. 본 논문에서 제안하는 분석 방법은 입력으로 받은 프로그램에서 특정 예외상황이 발생한다는 조건을 시작으로 하여, 프로그램의 입력 값에 대한 제약식(constraints)을 만들어간다. 이 분석 방법이 옳다는 증명에 의해서, 분석 결과로 얻은 테스트 데이타를 입력으로 하여 프로그램을 수행시키면 지정한 예외상황이 항상 발생한다는 것을 보장할 수 있다.함수를 인수나 결과값으로 전달하지 않고(first-order) ML 스타일의 예외상황 관리 방법을 제공하는 언어를 대상으로 하여 테스트 데이타 생성 방법을 제안하고, 이 분석 방법이 옳다는 것을 증명한 후 몇 가지 예를 사용하여 분석 과정을 설명한다.

• Nuclear Engineering and Technology
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• 제44권4호
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.369-384
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• 2021

In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low- and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.96-96
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• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.

• 한국CDE학회논문집
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• 제13권6호
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• pp.459-468
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• 2008

A web-based Computer-Aided Engineering (CAE) supporting system is connected with a Product Data Management (PDM) system for Small and Medium Enterprises (SMEs) suffering from the lack of building hardware, software and related experts. An analysis of current business models and worksite requirements provides an improved process model and data to be shared between the PDM system and the CAE supporting system. Since all engineering tasks such as geometric modeling, mesh generation, static stress and modal analysis, and fatigue durability analysis are automated in the CAE supporting system, the user in charge of the CAE have only to configure the concerned values of design variables and result data through the web page. The existing Change Management module of the PDM system is modified for seamless data exchange, i.e. sending the Engineering Change Order (ECO) data to the CAE supporting system and receiving the CAE result data bark. The hi-directional data transfers between the PDM system and the CAE supporting system is made possible by adaptors bused on the Simple Object Access Protocol (SOAP). The current approach will be very helpful for SMEs that only have the PDM system and have no adequate infrastructure for CAE.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권8호
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• pp.235-242
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• 2020

4차 산업혁명 시대를 맞아 우리 농업·농촌의 디지털 전환을 추진하고 다가오는 인공지능 시대를 대비하기 위하여, 필요한 양질의 데이터를 수집하고 분석해서 활용할 수 있는 체계와 시스템 구축이 필요하다. 이를 위해 농업인이나 농정담당자 등 다양한 이해 관계자들이 느끼는 문제점이나 이슈들을 조사·분석하여, 공동 활용을 위한 빅데이터 플랫폼 확충, 지속 가능한 빅데이터 거버넌스 구축 그리고 수요자 기반의 빅데이터 활용 기반 활성화 등 우리 농업·농촌의 디지털 전환을 추진하기 위해서 반드시 선결되어야 할 빅데이터 활성화를 위한 전략적 방안들을 제시하고자 한다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권9호
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• pp.411-418
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• 2016

교수-학습 지원 시스템에서 교수자와 학습자의 참여 활동 데이터를 활용하여 성공적으로 과정을 이수할 수 있도록 지원하기 위해 학습 분석이 활용되고 있다. 즉, 학습 분석은 학습자의 학습활동을 이해하기 위한 방법이다. 교수-학습 활동 데이터를 보다 유용하게 활용하기 위해서는 데이터 모델이 필요하다. 이에 본 연구에서는 사용자 중심의 학습양식과 학습객체 데이터모델(LSLODM)을 제안한다. 이는 사용자, 학습양식, 학습객체, 학습활동을 결합하여 표현한 것이다. LSLODM은 이를 기반으로 교수-학습 데이터를 수집하고, 교수-학습 활동 요소의 속성들을 최근성, 빈도성, 지속성을 정량적으로 파악할 수 있도록 한 것이다. 즉, 단위 과목에서 학습자의 교수-학습 활동을 분석할 수 있는 토대를 마련한 것이다.