• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2017년도 춘계공동학술대회
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• pp.135-135
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• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• 조명전기설비학회논문지
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• 제15권6호
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• pp.65-72
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• 2001

본 논문에서는 UPS용 인버터의 강인한 디지털제어를 위하여 인버터 출력측 LC필터의 커패시터 전압과 전류의 2중 제어루프로 구성된 새로운 제어기법을 제안한다. 제안된 전압·전류의 2중 제어루프는 전압 제어루프의 커패시터 전압을 전류 제어루프의 커패시터 전류의 위상중심으로 두고, 2중 데드비트 제어를 수행함으로써 커패시터 전류의 위상지연이 보상된 완전한 진상전류 제어가 가능하게 된다. 전류 제어루프는 디지털 제어기의 시간 지연요소를 시스템의 고유한 파라미터로 가정한 2차 데드비트 제어기로 설계하여 디지털 제어기의 고유한 연산 지연시간에 의한 성능저하를 개선한다. 또한, 외란에 의한 데드비트 제어의 영향을 제거하기 위하여 부하전류 예측기법을 전류 제어루프에 부가하여 외란을 피드포워드 보상함으로써 외란에 강인한 전류제어를 수행한다.

• 대한기계학회논문집B
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• 제29권6호
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• pp.671-686
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• 2005

The transient nature and complex geometries of two-phase gas-liquid flows cause fundamental difficulties when measuring flow velocity using an electromagnetic flowmeter. Recently, a current-sensing flowmeter was introduced to obtain measurements with high temporal resolution (Ahn et al.). In this study, current-sensing flowmeter theory was applied to measure the fast velocity transients in slug flows. The velocity fields of axisymmetric gas-liquid slug flow in a vertical pipe were obtained using Volume-of-Fluid (VOF) method, and the virtual potential distributions for the electrodes of finite size were also computed using the finite volume method for simulating slug flow. The output signal prediction for slug flow was carried out from the velocity and virtual potential (or weight function) fields. The flowmeter was numerically calibrated to obtain the cross-sectional liquid mean velocity at an electrode plane from the predicted output signal. Two calibration parameters are proposed for this procedure: a flow pattern coefficient and a localization parameter. The flow pattern coefficient was defined by the ratio of the liquid resistance between the electrodes for two-phase flow with respect to that for single-phase flow, and the localization parameter was introduced to avoid errors in the flowmeter readings caused by liquid acceleration or deceleration around the electrodes. These parameters were also calculated from the computed velocity and virtual potential fields. The results can be used to obtain the liquid mean velocity from the slug flow signal measured by a current-sensing flowmeter.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2011년도 추계학술대회 초록집
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• pp.56.1-56.1
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• 2011

The solar and meteorological resources map is calculated using by one-layer solar radiation model (GWNU model), satellites data and numerical model output on the Korean peninsula. The Meteorological input data to perform the GWNU model are retrieved aerosol optical thickness from MODIS (TERA/AQUA), total ozone amount from OMI (AURA), cloud fraction from geostationary satellites (MTSAT-1R) and temperature, pressure and total precipitable water from output of RDAPS (Regional Data Assimilation and Prediction System) and KLAPS (Korea Local Analysis and Prediction System) model operated by KMA (Korea Meteorological Administration). The model is carried out every hour using by the meteorological data (total ozone amount, aerosol optical thickness, temperature, pressure and cloud amount) and the basic data (surface albedo and DEM). And the result is analyzed the distribution in time and space and validated with 22 meteorological solar observations. The solar resources map is used to the solar energy-related industries and assessment of the potential resources for solar plant. The National Institute of Meteorological Research in KMA released $4km{\times}4km$ solar map in 2008 and updated solar map with $1km{\times}1km$ resolution and topological effect in 2010. The meteorological resources map homepage (http://www.greenmap.go.kr) is provided the various information and result for the meteorological-solar resources map.

• Design & Manufacturing
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• 제15권4호
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• 전력전자학회논문지
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• 제5권5호
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• pp.419-426
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• 2000

일반적으로 아크용접기의 정류기로 다이오드정류기가 사용되고 있다. 이것의 문제점은 높은 고조파전류가 발생된다는 것이고 아울러 전원측의 오염문제를 야기한다. 본 논문에서는 PWM 컨버터를 사용함으로써 아크용접기의 입력특성이 개선됨을 보인다. 회로구조상 다이오드 정류기와 PWM컨버터로 대체되고 부하단에 안정적인 전력을 공급하기 위하여 직류단전압이 PI제어기에 의해 제어된다. PI 제어기가 직류단 전압제어를 위해 사용되는 경우 예측불가능한 용접부하의 특성상 높은 전압맥동성분이 발생된다. 따라서 본 논문에서는 전압제어기의 속응성을 개선하기 위하여 전향보상기가 도입되었고 이의 분석을 통하여 전압제어기의 외란으로 간주되는 부하전류의 변동이 전압의 맥동성분에 영향을 줄이는 이론적 예측을 시뮬레이션과 실험을 통해 검증한다.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.648-658
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• 2018

Regarding the fact that wind power forecast accuracy is gradually improved as time is approaching, this paper proposes a two-stage rolling dispatch approach based on model predictive control (MPC), which contains an intra-day rolling optimal scheme and a real-time rolling base point tracing scheme. The scheduled output of the intra-day rolling scheme is set as the reference output, and the real-time rolling scheme is based on MPC which includes the leading rolling optimization and lagging feedback correction strategy. On the basis of the latest measured thermal unit output feedback, the closed-loop optimization is formed to correct the power deviation timely, making the unit output smoother, thus reducing the costs of power adjustment and promoting wind power accommodation. We adopt chance constraint to describe forecasts uncertainty. Then for reflecting the increasing prediction precision as well as the power dispatcher's rising expected satisfaction degree with reliable system operation, we set the confidence level of reserve constraints at different timescales as the incremental vector. The expectation of up/down reserve shortage is proposed to assess the adequacy of the upward/downward reserve. The studies executed on the modified IEEE RTS system demonstrate the effectiveness of the proposed approach.

• 한국멀티미디어학회논문지
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• 제13권5호
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• pp.722-729
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• 2010

예측 문제를 해결하기 위한 데이타마이닝 기법은 다양한 분야에서 주목받고 있다. 이것에 대한 한 예로 컴퓨터-기반의 질병의 예측 혹은 진단은 CDSS(Clinical Decision support System)에서 가장 중요한 요소이기도 하다. 이러한 예측 문제를 해결하기 위해서 RBF커널 같은 비선형 커널을 사용한 SVM이 가장 널리 사용되고 있는데, 이는 비선형 SVM이 어떠한 다른 분류기법보다 정확한 성능을 보이기 때문이다. 하지만 비선형 SVM을 사용한 경우에는 모델내부를 시각화하는 일이 어려워서 예측결과에 대한 직관적인 이해가 힘들고, 의학 전문가들은 이러한 비선형 SVM의 사용을 기피하고 있는 실정이다. Nomogram은 SVM을 시각화하기 위해 제안된 기법이다. 하지만 이는 선형 SVM의 경우에만 사용이 가능하고. 이 문제를 해결하기 위해서 LRBF 커널이 제안된 바 있다. LRBF 커널은 기존의 RBF 커널을 사용한 SVM과 대등한 결과를 보이면서도 예측결과의 선형적 분석도 가능하게 한다. 본 논문에서는 노모그램(Nomogram)과 LRBF 커널을 사용한 SVM이 통합되어 있는 예측 툴 VRIFA를 제안한다. 이 툴은 사용자와 상호작용하며 비선형 SVM 모델의 내부구조를 데이타의 각 속성별로 보여주는 방법으로 사용자가 예측결과를 직관적으로 이해하도록 도와준다. VRIFA는 Nomogram기반의 피쳐선택(feature selection) 기능도 포함하고 있는데, 이 기능은 예측결과에 부정적인 영향을 끼치거나 중복된 연관성을 보이는 속성을 제거함으로써 모델의 정확도를 높이는 데 기여한다. 그리고 데이터에 포함된 클래스의 비율이 한 쪽으로 치우쳐져 있는 경우에는 ROC 곡선 넓이(AUC)를 예측결과를 평가하기 위한 측도로 사용할 수 있다. 이 툴은 컴퓨터-기반의 질병 예측 혹은 질병의 위험 요소 분석에 대해 연구하는 연구자들에게 유용하게 사용될 것으로 전망하는 바이다.

• 한국공간구조학회논문집
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• 제20권2호
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• 디지털융복합연구
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• 제18권11호
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• pp.241-246
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• 2020

본 논문에서는 해양 전기추진 시스템과 딥러닝 알고리즘을 융합하여 전기추진 리제너레이션 시스템에서 DC/DC 컨버터 출력 전류 예측 및 리제너레이션 수행 시 배터리 충전량을 예측하기 위해 신경망 모델을 제안한다. 제안 된 신경망을 실험하기 위해 PCM의 입력 전압과 전류를 측정하고 시제품 PCM 보드의 출력 결과를 통해 데이터 세트를 구성하였다. 또한 불충분 한 데이터 세트에서 학습 결과를 향상시키기 위해 기존 데이터 세트를 데이터 피팅하여 학습을 진행하였다. 학습 후 신경망 모델의 데이터 예측 결과와 실제 측정 데이터의 차이를 그래프를 통해 확인하였다. 제안한 신경망 모델은 입력 전압과 전류 변화에 따른 배터리 충전량 예측을 효율적으로 보여주었다. 또한, DC/DC 컨버터를 구성하는 아날로그 회로의 특성변화를 신경망을 통하여 예측함으로써, 리제너레이션 시스템의 설계 시, 아날로그 회로의 특성을 고려해야 할 것으로 판단된다.