• Journal of Information Processing Systems
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• 제14권6호
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1085-1086
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• 2011

This paper proposes a structure optimal design of 2-phase BLDC motor. In order to improve the characteristics of the BLDC motor such as the efficiency, average torque, the Kriging method and genetic algorithm are utilized. In addition, the result of the optimal model were compared with the initial model and verified by 2D FEM.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1999년도 추계공동학술대회 논문집:21세기지식경영과 정보기술
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• pp.759-768
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• 1999

In this paper, we developed a simulation model of a car parts assembly line to improve the system performance such as worker's utilization balancing, productivity. This simulation model has been developed using QUEST, a true 3D discrete event simulation pakcage that is designed for modeling and analysis of manufacturing systems. We have suggested the results obtained to improve the system performances of an existing production line.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.230-232
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• 2000

To improve the voltage profile of the load bus, it is important that the coordinated controls among the reactive power compensators at the distribution substation. However, the conventional control scheme of the Under Load Tap Changer (ULTC) is not proper for coordinate control with Static Var Compensator (SVC). This paper proposes a new control model for ULTC and a new coordinated control scheme between ULTC and SVC. The numerical simulation verifies that the proposed system could improve the voltage profile on the load bus and could decrease the number of ULTC tap operation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.178.5-178
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• 2001

We, Human beings, use both powers of reason and emotion simultaneously, which surely help us to obtain flexible adaptability against the dynamic environment. We assert that this principle can be applied into the general system. That is, it would be possible to improve the adaptability by covering a digital oriented information processing system with an analog oriented emotion layer. In this paper, we proposed a vertical slicing model with an emotion layer in It. And we showed that the emotion-based control allows us to improve the adaptability of a system at least under some conditions.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1996년도 제24회 춘계학술대회
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• pp.153-157
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• 1996

A restrict jetting air is applied against leakage flow to improve a sealing performance of conventional labyrinth seal. A CFD analysis and sealing experiment are introduced to evaluate a sealing performance of applied model. The base of enhanced sealing is explained as a reducing clearance effect by jetting air. As a result, the applied model can improve the sealing performance of labyrinth seal in spite of the wide leakage clearance.

• 한국IT서비스학회지
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• 제17권1호
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• pp.91-103
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• 2018

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

• 전기학회논문지
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• 제66권9호
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.454-458
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• 2007

Nonpoint source (NPS) pollution is a serious problem causing the degradation of soil and water quality. Concentrated overland flow is the primary transport mechanism for a large amount of NPS pollutants from hillslope areas to downslope areas in a watershed. In this study, a soil erosion model, nLS model, to identify transitional overland flow regions (i.e., ephemeral gully head areas) was developed using the kinematic wave overland flow theory. Spatial data, including digital elevation models (DEMs), soil, and landcover, were used in the GIS-based model algorithm. The model was calibrated and validated using gully head locations in a large agricultural watershed, which were identified using 1-m aerial photography. The model performance was better than two previous approaches; the overall accuracy of the nLS model was 72 % to 87 % in one calibration subwatershed and the mean overall accuracy was 75 to 89 % in four validation subwatersheds, showing that the model well predicted potential transitional erosion areas at different watersheds. However, the user accuracy in calibration and validation was still low. To improve the user accuracy and study the effects of DEM resolution, finer resolution DEMs may be preferred because DEM grid is strongly sensitive to estimating model parameters. Information gained from this study can improve assessing soil erosion process due to concentrated overland flow as well as analyze the effect of microtopographic landscapes, such as riparian buffer areas, in NPS control.

• International Journal of Aerospace System Engineering
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• 제2권2호
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• pp.67-72
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• 2015

The main concern of this study is to integrate the EARSM into an industrial RANS solver in conjunction with the $k-{\omega}$ model, as proposed by Hellsten (EARSMKO2005). In order to improve the robustness, particular limiters are introduced to turbulent conservative variables, and a suitable full-approximation storage (FAS) multi-grid (MG) strategy is designed to incorporate turbulence model equations. The present limiters and MG strategy improve both robustness and efficiency significantly but without degenerating accuracy. Two discretization approachs for velocity gradient on cell interfaces are implemented and compared with each other. Numerical results of a three-dimensional supersonic square duct flow show that the proper discretization of velocity gradient improves the accuracy essentially. To assess the capability of the resulting EARSM $k-{\omega}$ model to predict complex engineering flow, the case of Common Research Model (CRM, Wing-Body) is performed. All the numerical results demonstrate that the resulting model performs well and is comparable to the standard two-equation models such as SST $k-{\omega}$ model in terms of computational effort, thus it is suitable for industrial applications.