• 한국IT서비스학회지
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• 제18권1호
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• The Korean Journal of Physiology and Pharmacology
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• 제23권5호
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• pp.305-310
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• 2019

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2004년도 전력전자학술대회 논문집(2)
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• pp.997-1000
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• 2004

The mathematical models of the Switched Reluctance machine system integrating the state equation of the phase current with the two dimensions finite element model of the machine are advanced, no matter what the topologies of the main circuit of the power converter and the control strategies are adopted in the system. Based on the mathematical models, the comparison of the simulated and the tested results of a three-phase Switched Reluctance motor drive prototype system are given. The simulated results of the prototype tally with the tested results of the prototype. It is shown that the mathematical models have the advantage in high precision.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 춘계학술대회 논문집
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• pp.71-72
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• 2008

• 한국국방경영분석학회지
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• 제35권2호
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• pp.63-76
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• 2009

IT와 과학 기술의 비약적인 발전으로 미래전의 양상도 바뀌고 있으며 이에 따라 효과적인 군수지원의 중요성이 부각되고 있다. 본 연구에서는 최근 부각되고 있는 통합 물류창고 중 자동창고를 중심으로 S/R machine의 운영문제를 Travel Time에 기반을 둔 수리적 분석과 AutoMod를 이용한 시뮬레이션으로 수리적 분석의 타당성을 검증하였다. 이에 따라 자동창고의 S/R machine의 개선된 정주위치 고려와 재고회전 빈도에 따른 운용성능을 분석할 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1576-1579
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• 1991

This paper discusses on the machine loading and tool allocation (MLTA) problem. Mathematical formulation of the problem is given first. Then a heuristic approach based on Group Technology (GT) is presented to deal with the MLTA problem effectively. By using this approach, part-tool group generation and their assignment to adequate machines can easily be obtained in consideration of the work load on each machine, the number of tool-set replacement, and the total number of cutting tools required through the interactive setting of the desired machine utilization rate.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.102-105
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• 1995

In power system stability analysis, modelling of the synchronous machine is necessary and vary important. In this paper, a synchronous machine is modeled and simulated by using Object-Oriented method. The mathematical equations describing the dynamic behavior of the synchronous machine is represented by block diagram and Objected-Oriented Digital Computer Simulater(ODCS). The developed method is tested for a one-machine-to-infinite-bus system, which is accurate and very useful for a multi-machine system simulation.

• ETRI Journal
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• 제10권3호
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• pp.140-156
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• 1988

This paper presents methodological and theoretical principles for constructing a machine thanslation system between Korean and Japanese. We focus our discussion on the real time computing problem of the machine translation system. This problem is characterized in the time and space complexity during the machine translation. The NARA system has the real time computing algorithm which is based on a mathematical model integrating the linguistic competence and the linguistic performance of both languages, with consequence that the system NARA has also the functional characteristic : the two-way translation mechanism.

• 품질경영학회지
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• 제21권2호
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• pp.224-231
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• 1993

This paper propose a procedure to form the cell and part family using both of similarity coefficient method and mathematical programming in order to slove the exceptional part and bottleneck machine problems. In the procedure proposed by this paper, the machine duplication and subcontracting are considered to slove the bottleneck machine and intercellular moving part problems An example is given to show how this procedure works.

• Tuberculosis and Respiratory Diseases
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• 제86권4호
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• pp.251-263
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• 2023

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.