• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.1
• /
• pp.19-25
• /
• 1998

Remote fault diagnosis and maintenance system using general telecommunication network is necessary for an effective fault diagnosis and higher productivity of NC machine tools. In order to monitor machine tool condition and diagnose alarm states due to electrical and mechanical faults, a remote data communication system for monitoring of NC machine fault diagnosis and status is developed. The developed system consists of (1) remote communication module among NC's and host PC using PSTN. (2) 8 channels analog data sensing module, (3) digital I/O module for control or NC machine, (4) communication module between NC machine and remote data communication system via RS-232C, and (5) software man-machine interface. Diagnostic monitoring results generated through a successive type inference engine are displayed in user-friendly graphics. The validity and reliability of the developed system is verified to be a powerful commercial version on a vertical machining center through a series of experiments.

• International journal of advanced smart convergence
• /
• v.7 no.2
• /
• pp.67-72
• /
• 2018

On the internal based search, the big data inference, which is failed in the president's election in the United States of America in 2016, is failed, because the prediction method is used on the base of the searching numerical value of a candidate for the presidency. Also the Flu Trend service is opened by the Google in 2008. But the Google was embarrassed for the fame's failure for the killing flu prediction system in 2011 and the prediction of presidential election in 2016. In this paper, using the virtual vote algorithm for virtual election and data mining method, the election prediction algorithm is proposed and unpacked. And also the WEKA DB is unpacked. Especially in this paper, using the K means algorithm and XEDOS tools, the prediction of election results is unpacked efficiently. Also using the analysis of the WEKA DB, the smart election prediction system is proposed in this paper.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10b
• /
• pp.223-227
• /
• 2006

시맨틱 웹 기술을 이용하는 추론 엔진들이 사용하는 지식은 기본적으로 주어(subject)와 술어(predicate), 목적어(object)로 구성된 트리플(triple)들의 집합이며, 이를 저장하기 위한 구조로 관계형 데이터베이스(R-DB)가 주로 이용된다. 본 논문은 DBMS 기반의 추론 서비스인 OntoThink-K$^{(R)}$트리플 저장 구조와 함께 SPARQL 질의 언어를 지원하기 위한 SPARQL-SQL 매핑에 대해 설명한다. OntoThink-K$^{(R)}$스키마 무관과 스키마 인지의 두 가지 방식의 트리플 저장 구조를 지원하며, 본 논문에서는 각 저장 구조에 따른 SPARQL-SQL 매핑 방법을 설명하고 실험을 통해 두 방식에서의 추론 속도의 차이를 비교한다. 이 실험 결과로부터 우리는 스키마 인지 방식을 사용함으로써 스키마 무관 방식에 비해 적어도 2배 이상의 속도 향상을 꾀할 수 있음을 알았다.

• Communications for Statistical Applications and Methods
• /
• v.25 no.5
• /
• pp.545-557
• /
• 2018

The inclusion of covariates in the model often affects not only the estimates of meaningful variables of interest but also its statistical significance. Such gap between statistical and subject-matter significance is a critical issue in huge sample studies. A popular huge sample study, the sample cohort data from Korean National Health Insurance Service, showed such gap of significance in the inference for the effect of obesity on cause of mortality, requiring careful consideration. In this regard, this paper proposes a sample size calibration method based on a Monte Carlo t (or z)-test approach without Monte Carlo simulation, and also proposes a test procedure for subject-matter significance using this calibration method in order to complement the deflated p-value in the huge sample size. Our calibration method shows no subject-matter significance of the obesity paradox regardless of race, sex, and age groups, unlike traditional statistical suggestions based on p-values.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1423-1431
• /
• 2016

When the sample size in a certain domain is too small to produce adequate information, small area model with random effects is usually used. Also, if we do not consider an inherent pattern which data possess, it considerably affects inference. In this paper, we mainly focus on modeling to handle increased variation of the Current Population Survey (CPS) median income as the Internal Revenue Service (IRS) mean income increases. In a hierarchical Bayesian framework, most estimations are carried out through the Gibbs sampler while the grid method is used to generate parameters from non-standard form. Numerical study indicates that the performance of proposed model is better than that of CPS method in terms of four comparison measurements.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.4
• /
• pp.107-114
• /
• 2018

Recently, various wearable personal devices such as a smart watch have been developed and these personal devices are being miniaturized. The user desires to receive new services from personal devices as well as services that have been received from personal computers, anytime and anywhere. However, miniaturization of devices involves constraints on resources such as limited input and output and insufficient power. In order to solve these resource constraints, this paper proposes a resource collaboration system which provides a service by composing sharable resources in the resource sharing environment like IoT. the paper also propose a method to infer and recommend user-customized resources among various sharable resources. For this purpose, the paper defines an ontology for resource inference. This paper also classifies users behavior types based on a user model and then uses them for resource recommendation. The paper implements the proposed method as a prototype system on a personal device with limited resources developed for resource collaboration and shows the effectiveness of the proposed method by evaluating user satisfaction.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06b
• /
• pp.136-138
• /
• 2006

최근 이동통신 기술의 급격한 발전과 PPC(Pocket PC), 노트북 등의 휴대단말기의 보급 확산에 따라 위치기반 서비스(Location Based Service: LBS)가 주요한 응용분야고 부상하고 있다. 위치 정보에 대한 정확한 위치 추적 및 활용 방안에 대한 활발한 연구가 진행되고 있지만, 대부분 제공되는 서비스는 현재 사용자의 위치에 기반한 정적인 서비스를 제공하는 초보적인 단계에 있다. 이동경로는 사용자의 성향이나 상태를 반영하기 때문에 사용자의 이동패턴을 예측하거나, 사용자의 현재 상태를 추론하는데 도움을 줄 수 있다. 본 논문에서는 이동패턴에 따른 사용자의 의도를 예측하여 개별화 된 서비스 제공을 위해, RSOM(Recurrent Self Organizing Map)과 마르코프 모델을 단계적으로 구성하여 사용자의 이동패턴을 모델링하는 방법을 제안한다. 실제 연세대학교 캠퍼스 내에서 실제 대학원생의 생활을 모델로 GPS(Global Positioning System) 데이터를 수집하여. 이동패턴을 모델링하고 개별화된 서비스를 제공함으로써 제안하는 방법의 유용성을 검증하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.11a
• /
• pp.239-242
• /
• 2006

현재 우리생활은 언제 어디서나 네트워크에 접속하여 통신할 수 있는 유비쿼터스 컴퓨팅 환경화 되고 있다. 이러한 환경에서 상황인식 서비스는 의료, 여행, 가정, 교육 등 사회 전 분야에 걸쳐 응용될 수 있어 사회 전반에 걸쳐 영향을 주고 있다. 기존의 대부분의 상황인식 시스템의 연구들은 센서로부터 입력된 주변 환경 정보를 기반으로 사용자에게 적합한 서비스 제공에 중점을 두고 있다. 이로써 환경정보와 별개로 사용자가 궁극적으로 원하는 분야에 상황인식 시스템을 적용하기 위해서는 서비스 부합되지 않은 여러 요소가 존재하였다. 본 논문에서는 이러한 요소를 착안하여 사용자의 의도를 포함한 상황인식 시스템을 제안한다. 제안된 시스템은 지능형 홈 도메인 환경에서 시간에 따라 변화하는 사용자의 행위 정보를 기반하여 사용자가 향후 궁극적으로 원하는 의도를 예측 할 수 있는 시스템으로 되어있다. 또한 여러개의 작은 행위에 따른 사용자의 의도가 모여 보다 큰 사용자의 의도를 파악하는 기법을 정의하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.234-236
• /
• 2022

딥러닝 원격 컴퓨팅 서비스(Deep Learning as a Service, DLaaS)가 널리 활용되면서 클라우드에서의 개인 정보 보호에 대한 우려가 커졌다. 신뢰실행환경(Trusted Execution Environment, TEE)는 운영체제의 접근까지 차단한 메인 프로세서의 보안 영역으로 DLaaS 환경에서의 개인 정보 보호 기법으로 채택되고 있다. 사용자의 데이터를 보호하면서 고성능 클라우드 환경을 활용하기 위해 신뢰실행환경을 활용한 딥러닝 모델 추론 연구들을 살펴보고자 한다.

• Communications of Mathematical Education
• /
• v.36 no.4
• /
• pp.535-558
• /
• 2022

In the current society, where statistical literacy is recognized as an important ability, statistical education utilizing the statistical problem solving, a series of processes for performing statistics, is required. The result interpretation stage is especially important because many forms of statistics we encounter in our daily lives are the information from the analysis results. In this study, data on private education were provided to pre-service mathematics teachers, and a project was carried out in which they could experience a statistical problem solving process using the population mean estimation. Therefore, this study analyzed the characteristics shown by pre-service mathematics teachers during the result interpretation stage. First, many pre-service mathematics teachers interpreted results based on the data, but the inference was found to be a level of 2 which is not reasonable. Second, pre-service mathematics teachers in this study made various kinds of decisions related to public education, such as improving classes and after-school classes. In addition, the pre-service mathematics teachers in this study seem to have made decisions based on statistical analysis results, but they made general decisions that teachers could make, rather than specifically. Third, the pre-service mathematics teachers of this study were reflective about the question formulation stage, organizing & reducing data stage, and the result interpretation stage, but no one was reflective about the result interpretation stage.