• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전기물성,응용부문
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• pp.264-267
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• 2004

The conventional testing as IEC-60587 is widely used in surface aging measurement of outside insulator those testing can carry out very short time in Lab testing. Also IEC-60587 testing is able to offer the standard judgement of relative degradation level of out side HV machine. Therefore it is very useful method compare to previous conventional tracking testing method and effective Lab testing method, But surface discharges(SD) have very complex characteristics of discharge pattern so it is required estimation research to development of precise analysis method. In recent, the study of IRR Camera is carrying out discover of temperature of power equipment through condition diagnosis and system development of degradation diagnosis.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.32-36
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• 2002

This paper presents the structural characteristic analysis of a centerless grinding machine with concrete bed. The centerless grinding machine is composed of grinding wheel head, regulating wheel head, concrete bed, wheel dresser and so on. Especially, the concrete bed is introduced to improve the static, dynamic and thermal characteristics of the centerless grinding machine. The structural analysis model of centerless grinding machine is constructed by the finite element method, and the structural characteristics in the design stage are estimated based on the structural deformation and harmonic response under the various testing conditions related to gravity force and directional farces

• 한국정밀공학회지
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• 제18권2호
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• pp.126-131
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• 2001

One of the major obstacles in testing or evaluating precisely the thermal behavior of a machine tool is the difficulty in measuring the heat transfer coefficients on the surfaces by a conventional method. This paper presents a new approach based on the inverse method to identify the values of heat transfer coefficients by using temperature changes measured on the surfaces of a machine tool during a short period in its operating. In the present method, a machine tool structure is modeled by the finite element method and the characteristic curves of the temperature change at several points on machine tool surfaces are theoretically derived in the form that they contain the heat transfer coefficient as an unfixed heat source are approximated so that the theoretical characteristic curves of temperature change fit the measured ones as closely as possible.

• 한국언어정보학회지:언어와정보
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• 제2권2호
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• pp.185-220
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• 1998

The purpose of this research is to propose a set of basic guidelines for the construction of English test suites, a set of basic guidelines for the construction of Korean test suites to objectively evaluate the performance of machine translation systems. For this end, we constructed 650 English test sentences, 650 Korean test sentences, and developed the statistical methods and tools for the comparative evaluation of the English-Korean machine translation systems. It also evaluates the existing commercial English-Korean machine translation systems. The importance of this research lies in that it will promote an awareness of the importance and need of testing machine translation systems within the Natural Language Community. This research will also make a big contribution to the development of evaluation methods and techniques for appropriate test suites for Korean information processing systems. The results of this research can be used by the natural language community to test the performance and development of their information processing systems or machine translation systems.

• 한국컴퓨터정보학회논문지
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• 제25권7호
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• pp.113-123
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• 2020

수면 장애 중 폐쇄성수면무호흡증은 비교적 흔한 질병 중 하나이다. 환자들은 수면다원검사를 통해 해당 질환의 여부를 알아볼 수 있다. 그러나 수면다원검사를 이용한 폐쇄성수면무호흡증 진단에 관한 한, 늘어나는 환자 수, 비싼 검사 비용, 검사 중 불편함, 수용 인원 제한 등 현실적인 문제점들이 지적됐다. 이에 따라, 수면다원검사를 대체할 목적으로 연구자들은 생체 신호를 활용한 기계학습 기반 폐쇄성수면무호흡증 진단 연구들을 활발히 진행해 왔다. 이 시점에서, 우리는 생체 신호 데이터를 기반으로 기계학습 기법을 적용하는 폐쇄성수면무호흡증 진단 연구를 복기한다. 그 결과, 본 논문은 복기 된 연구들에 대한 최신 분류 체계를 제시하고 그 연구들의 종합적인 비교 분석 결과를 제공한다. 또한, 본 논문은 생체 신호를 활용한 연구들의 다양한 한계점을 밝히고 사용된 기계학습 기법의 활용성에 대한 여러 개선점을 제안한다. 끝으로, 본 논문은 생체 신호를 활용한 기계학습 기법 적용과 관련한 향후 연구 주제를 제시한다.

• Computers and Concrete
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• 제19권3호
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• 감성과학
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• 제8권1호
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• pp.37-45
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• 2005

본 논문에서는 H사와 공동으로 개발한 가정용 비만치료기의 특징과 치료효과를 측정, 분석한 것이다. 이 가정용 비만치료기는 중주파 활용, 온열벨트 사용과 함께 부작용을 최소화하도록 설계되었는데 성능평가를 위해 20대 여성 8명을 대상으로 1개월 간의 비만치료 실험을 실시하였다. 실험은 심폐기능운동검사를 통하여 피실험자들의 비만치료 실험 전후의 가스교환 반응도($VCO_2$와 $VCO_2$양의 변화)에 초점을 두었다. 실험결과 체중(${\cal}kg$), 체지방율($\%$), 체지방량(${\cal}kg$), 비만도($\%$), 기초대사량(kcal) 등에서 비만감소 효과가 나타났으며 비만치료 전보다 산소($VCO_2$) 섭취량은 증가한 반면 이산화탄소($VCO_2$) 배출량은 감소한 것으로 나타났다. 이는 인체의 생리학적측면과 운동 역학적인 측면이 상관관계가 높다는 것을 보여주고 있으며 여기서 개발된 비만치료기가 의학적으로 비만치료에 도움이 될 수 있음을 입증하는 결과라 할 수 있다.

• Geomechanics and Engineering
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• 제37권4호
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2005년도 춘계 학술발표회 논문집
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• pp.1233-1240
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• 2005

As the geotechnical technologies have grown, the size of civil structures has become bigger than before, thereby requiring large-scale geotechnical testing equipments which can evaluate the mechanical behavior of large size testing materials such as gravel, crushed rock and so on. These kind of large testing equipments are usually used to evaluate the mechanical characteristics of large size material which are applied in the large infra structures like dam, seashore structure, coastal landfill, soil-structure interaction and seismic response of large-scale structure. In this research, state-of-the-art information in the field of geotechnical engineering was collected and summarized for such large-scale experimental equipments as large-scale geo-centrifuge, large-scale triaxial testing machine, large-scale direct shear testing apparatus and large-scale oedometer.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권7_8호
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• pp.744-751
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• 2003

본 논문에서는 최근 뛰어난 예측력으로 각광받는 최소제곱 Support Vector Machine(Least Square Support Vector Machine: LS-SVM)과 First Principle(FP)을 결합한 하이브리드 최소제곱ㆍSupport Vector Machine 모델, HLS-SVM(Hybrid Least Square-Super Vector Machine)을 제안한다. 제안한 모델인 하이브리드 최소제곱 Support Vector Machine을 기존의 방법인 하이브리드 신경망(Hybrid Neural Network:HNN), 비선형 칼만필터와 하이브리드 신경망을 결합한 HNN-EKF (Hybrid Neural Network with Extended Kalman Filter) 모델과 비교해 보았다. HLS-SVM 모델은 학습 및 validation 과정에서는 HNN-EKF와 근사한 성능을 보였고, HNN 보다는 우수한 결과를 보였고, 일반화 성능에서는 HNN-EKF에 비해 3배, HNN보다 100배정도 우수한 결과를 보였다.