• Diabetes and Metabolism Journal
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• 제42권6호
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• pp.451-464
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• 2018

Latent autoimmune diabetes in adults (LADA) is a heterogeneous disease characterized by a less intensive autoimmune process and a broad clinical phenotype compared to classical type 1 diabetes mellitus (T1DM), sharing features with both type 2 diabetes mellitus (T2DM) and T1DM. Since patients affected by LADA are initially insulin independent and recognizable only by testing for islet-cell autoantibodies, it could be difficult to identify LADA in clinical setting and a high misdiagnosis rate still remains among patients with T2DM. Ideally, islet-cell autoantibodies screening should be performed in subjects with newly diagnosed T2DM, ensuring a closer monitoring of those resulted positive and avoiding treatment of hyperglycaemia which might increase the rate of ${\beta}-cells$ loss. Thus, since the autoimmune process in LADA seems to be slower than in classical T1DM, there is a wider window for new therapeutic interventions that may slow down ${\beta}-cell$ failure. This review summarizes the current understanding of LADA, by evaluating data from most recent studies, the actual gaps in diagnosis and management. Finally, we critically highlight and discuss novel findings and future perspectives on the therapeutic approach in LADA.

• 보건행정학회지
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• 제29권1호
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• pp.68-76
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• 2019

Background: This study investigates the association between sleep duration and diabetes mellitus (DM) in a large representative population-based survey in South Korea. Methods: The fourth (2007-2009), fifth (2010-2012), and sixth (2013) Korea National Health and Nutrition Examination Survey data sets were used. A total of 37,989 individuals were selected for the study. Chi-square tests and multivariate logistic regression analyses were used to analyze whether general characteristics, health status, and health risk behaviors were associated with DM. Results: After adjusting for confounders, the odds of DM in short sleepers (${\leq}5hr/day$) and long sleepers (${\geq}9hr/day$) were 1.033-times higher (95% confidence interval [CI], 0.913-1.169) and 1.334-times higher (95% CI, 1.140-1.562), respectively, compared with individuals who slept 7 hr/day. Subgroup analysis according to gender showed a U-shaped association for both genders, although it appeared stronger in men. Conclusion: This study identified a U-shaped association between sleep duration and the risk for DM. Additional studies should help clarify the important information in this study.

• 품질경영학회지
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• 제34권4호
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• pp.33-39
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• 2006

These days manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. In order to win international competition, it is important for companies how fast get the useful information from vast data. Statistical process control(SPC) techniques have been used as a problem solution tool at manufacturing process until present. However, these statistical methods are not applied more extensively because it has much restrictions in realistic problems. These statistical techniques have lots of problems when much data and factors are analyzed. In this paper, we proposed more practical and efficient a new statistical design technique which integrated data mining (DM) and statistical methods as alternative of problems. First step is selecting significant factor using DM feature selection algorithm from data of manufacturing process including many factors. Second step is finding optimum of process after estimating response function through response surface methodology(RSM) that is a statistical techniques

• 한국지능시스템학회논문지
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• 제23권5호
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• pp.400-405
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• 2013

The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based on Data Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extracts logical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refines and manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using the DT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, in Inference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• 한국전자통신학회논문지
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• 제9권7호
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• pp.779-789
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• 2014

안드로이드 기반의 스마트폰을 공장자동화 및 생산자동화 분야에서의 활용이 중요하게 여겨지고 있다. 일반적으로 OMA DM 표준은 공장자동화 및 생산자동화 환경을 위한 중요한 인프라 구조 기술이다. 본 논문에서는 OMA DM 플랫폼 설계 및 구현에 대해 연구한다. 개발된 프로토타입은 세 가지 모듈 즉 DMS, FUMO 그리고 SCOMO로 구성된다. 그리고 EAP 및 AES 개념을 응용한 보안 모듈도 제시되었다. 제안된 보안 모듈은 공장자동화를 위해 안드로이드 기반의 스마트폰을 활용한 DMS, FUMO 및 SCOMO 모듈 간 통신에서 안전한 통신을 보장하기 위해 적용된다. 시뮬레이션 결과에 의하면 구현된 해당 프로토타입은 공장자동화 환경에서 좋은 성능을 보이며 보안성을 보장하면서 여러 공장자동화, 생산자동화 및 사무자동화 환경에서 활용이 가능하다.

• 한국에너지공학회:학술대회논문집
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• 한국에너지공학회 2002년도 추계 학술발표회 논문집
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• pp.9-15
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• 2002

PDM(Product Data Management)은 제품정보 및 개발 프로세스를 관리하는데 많은 이점이 있는 도구로써 인식되어 왔으며, 제품 설계, 제작, 건설, 유지, 보수하는데 필요한 많은 양의 데이터 및 정보를 효율적으로 관리할 수 있다. PDM은 일반적으로 EDM(engineering data management), DM(document management), PIM(product information management), TDM(technical data management), TIM(technical information management) 등으로 알려진 기술의 보다 더 일반화된 개념이다.(중략)

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 추계 학술대회
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• pp.205-210
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• 2006

Nowaday manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. It is measured automatically do much quality characteristics thereby and great many data happen in a day. corporations is important if have gotten fast information that are useful from wide data to go first in international competition according to these change. Statistical process control(SPC) techniques are used as a problem solution tool at manufacturing process until present. However, this statistical methods is not applied more extensively because have much restrictions in realistic problem. In this paper, wish to develop more realistic and scientific new statistical design techniques doing to integrate data mining(DM) and statistical methods by the alternative to cope these problem. First step selects significant factor using DM techniques from datas of manufacturing process including much factors and second step wish to find optimum of process after get the estimated response function through response surf ace methodology(RSM) that is statistical techniques.

• 한국산업정보학회논문지
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• 제15권5호
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• pp.137-148
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• 2010

본 논문에서는 대규모의 다양한 모바일 단말기와 서버로 구성된 모바일 오피스 지원 시스템을 제안한다. 나날이 다양한 모바일 단말기들이 출시되고 있으므로, 현재 적용되고 있는 모바일 오피스 지원시스템은 추후 단말기들 간의 상호호환성 결여, 자료동기화 및 단말기 장비관리의 어려움과 같은 문제에 봉착할 수 있으며 효율적인 모바일오피스 지원시스템에 관한 연구가 필요하다. 대규모의 외근직원을 두는 기업에서는 이런 현상이 더욱 심화될 것이다. 이에, 본 논문에서는 우선 대규모 외근직원을 두는 기업을 위한 모바일 오피스 지원시스템의 요구사항을 도출하였다. 또한, 원격 자료동기화 기술과 장비관리 기술을 적용하여 도출된 요구사항을 만족시키기 위한 방안용 제시한다. 본 논문의 모바일 오피스 지원시스템은 원격 자료동기화 기술을 통해 기업 구성원의 대규모의 다양한 모바일 단말기와 기업의 중앙 서버와의 자료를 일치시키며, 원격 장비관리 기술을 통해 관리자를 대신하여 모바일 단말기를 원격 관리할 수 있다. 본 논문에서는, OMA(Open Mobile Alliance)의 DS(Data Synchronization)와 DM(Device Management) 적용한 모바일 오피스 지원시스템을 제안한다. OMA DS와 DM은 현재 산업계 표준으로 받아들여지고 있기 때문에, 적용 시스템 간의 상호호환성을 보장할 수 있기 때문이다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.