• Diabetes and Metabolism Journal
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• v.42 no.6
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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.

• Health Policy and Management
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• v.29 no.1
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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.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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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

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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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.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.7
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• pp.779-789
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• 2014

Utilizing smart phones based on android applications in the field of FA(Factory Automation) or PA(Production Automation) is being deployed actively. In general, MDM(Mobile Device Management) is a crucial infra-structure to build such a FA or PA environment. In this paper, we suggest an open mobile device management platform and implement its prototype. The developed prototype consists of three modules such as DMS(Device Management Server), FUMO(Firmware Update Management Object) and SCOMO(Software Component Management Object). In addition, we suggest a security module based on the concept of the EAP (Extensible Authentication Protocol) and the AES (Advanced Encryption Standard). The suggested security module's prototype is applied to guarantee the data integrity in the process of communicating among DMS, FUMO and SCOMO for the purpose of utilizing smart phones based on android applications in a FA field. We also evaluate the performance of the implemented security prototype. According to our simulation results, the implemented prototype has a good performance in a FA environment and can be utilized in the other FA, PA or OA(Office Automation) environment with guaranteeing the security.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2002.11a
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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)

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.137-148
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• 2010

In this paper, a mobile office support. system which consists of mobile devices and servers is proposed. An efficient mobile office support system is required to study since the existing mobile office support systems may face the serious problems such as an absence of interoperability, difficulties of data synchronization and device management in the near future, especially when a company has a large number of outdoor service employees. In this paper, a mobile office support system requirements for a company with a large number of outdoor service employees is proposed. And then, the methods to satisfy the requirements using a remote data synchronization and a remote device management technologies are proposed. The mobile office support system proposed in this paper synchronizes remotely a large number of various mobile devices of employees with centralized servers located in the company. In addition, it manages the mobile devices remotely for the employees. This paper proposes a mobile office support system based on OMA(Open Mobile Alliance) DS(Data Synchronization) and DM(Device Management) protocols. As OMA DS and DM protocols are de facto international standards, the interoperability between the mobile office support systems can be guaranteed.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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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.