• Journal of Power Electronics
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• 제14권4호
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• pp.778-787
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• 2014

This study presents an input filter resonance mitigation method for an AC-DC matrix converter. This method combines the advantages of the one-cycle control strategy and the active damping technique. Unnecessary sensors are removed, and system cost is reduced by employing the grid-side input currents as feedback to damp out LC resonance. A model that includes the proposed method and the input filter is established with consideration of the delay caused by the actual controller. A zero-pole map is employed to analyze model stability and to investigate virtual resistor parameter design principles. Based on a double closed-loop control scheme, the one-cycle control strategy does not require any complex modulation index control. Thus, this strategy can be more easily implemented than traditional space vector-based methods. Experimental results demonstrate the veracity of theoretical analysis and the feasibility of the proposed approach.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.324-328
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• 2005

This study is to figure out uncertainty relationship between input data and calibrated parameter on unsteady hydraulic routing model. The uncertainty would be present to model results as a variant water surface profile along the channel. Firstly, Manning's n is calibrated through the model with assumed uncertainty on input hydrograph. Then, spatially distributed n-values sets based on the calibrated n values are used to get water profile of each n-values set. The results show that ${\pm}0.002$ of error in Manning's n cause ${\pm}30cm$ of maximum water surface differences at the Sumjin river.

• 소음진동
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• 제8권4호
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• pp.690-699
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• 1998

This paper discusses the techniques, procedures and the results of the ground vibration test(GVT) performed on the development aircraft and the simple procedure of FE model updating technique from the GVT results. The GVT was carried out using random excitation technique with MIMO(Multi-Input-Multi-Output) data acquistion method, and taking full advantage of poly-reference global parameter estimation technique to identify the vibration modes. In dynamic FE modeling, the aircraft was represented by beam elements and all dynamic analysis was performed using MSC/NASTRAN for this model. In updating procedure, the stiffness of the beam model was adjusted iteratively so as to get the natural frequencies and mode shapes close to the GVT results.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 춘계종합학술대회
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• pp.404-407
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• 2001

본 논문은 수력터빈시스템에서 동력학 시스템 모델의 매개변수를 얻기 위해 유전알고리즘과 모델 조정기법을 적용하고, 수력터빈시스템으로부터 취득한 실제 운전데이터를 사용하여 계수간을 얻었다. 완성된 모델과 실제시스템의 출력 데이터와 비교하여 유효성을 검증한다. 완성된 모델에 유전알고리즘을 이용하여 제어기의 최적 PID 이득값을 찾을 수 있도륵 하였으며, 디지털 조속기의 적용시에 발생할 수 있는 시행착오를 줄이도록 하는데 목적이 있다고 하겠다.

• 전기의세계
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• 제26권1호
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• pp.87-90
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• 1977

Various simulation techniques were developed in the modeling of biological system during the last decades. Mostly analog and hybrid simulation techniques have been used. The authors chose the Digital Analog Simulation (DAS) technique by using the MIMIC language to simulate the saccadic eye movement system performances on the digital computer. There have been various models presented by many investigators after Young & Stark's sampled-data model. The eye movement model chosen by the authors is Robinson's model III which shows the parallel information processing characteristics clearly to the double-step input stimuli. In the process of simulation, the parameter and constants used were based on the neurophysiological data of the human and animals. The analog model blocks were converted to the corresponding MIMIC block diagrams and programmed into the MIMIC statements. The program was run on the CDC Cyber 72-14 computer. The essential input stimulus was double-step of 5 and 10 degrees, and target durations chosen were 50,100 and 150 msec. The results obtained by the DAS technqiue were in good agreement with analog simulation carried out by other investigators as well as with the experimental human saccadic eye movement responses to double-step target movements.

• Journal of Information Technology Applications and Management
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• 제9권2호
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• pp.1-16
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• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• 천문학회보
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• 제46권1호
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• pp.48.1-48.1
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• 2021

Gaussian process regression (GPR) is a powerful method used for model-independent analysis of cosmological observations. In GPR, it is important to decide an input mean function and hyperparameters that affect the reconstruction results. Depending on how the input mean function and hyperparameters are determined in the literature, I divide into four main applications for GPR and compare their results. In particular, a zero mean function is commonly used as an input mean function, which may be inappropriate for reconstructing cosmological observations such as the distance modulus. Using mock data based on Pantheon compilation of type Ia supernovae, I will point out the problem of using a zero input and suggest a new way to deal with the input mean function.

• 한국대기환경학회지
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• 제22권1호
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• pp.135-143
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• 2006

The objective of this study is to develop PSCF (potential source contribution function) program and determine the optimal values of control parameters to enhance the prediction of PSCF modeling. This study provides an important information and methodologies that can be used to get better results of locating influencing sources, especially unknown and fugitive sources. To determine proper values of control parameters in PSCF model, the diagnostic assessment on the results obtained by the various input conditions was carried out. PSCF model has created and improved from version 1.0 to version 7.0 since 200 I and the measured data (at least > 100) of receptor, and the values of control input parameters should be arranged and determined to obtain reliable results in PSCF modeling. The size of modeling domain must be determined to include enough trajectories to get reliable results. And the size of grid is recommended to be 2.5 $\sim$ 5 degrees for global scale, 0.2 $\sim$ 1 degrees for regional scale and 0.05 degree for local scale.

• 유통과학연구
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• 제18권9호
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• pp.31-43
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• 2020

Purpose: This study examined a comprehensive model for assessing the success probability of electric vehicle (EV) commercialization in the Korean market. The study identified three risks associated with successful commercialization which were technology, social, policy, environmental, and consumer risk. Research design, methodology: The assessment of the riskiness was represented by a Bayes belief network, where the probability of success at each stage is conditioned on the outcome of the preceding stage. Probability of success in each stage is either dependent on input (i.e., investment) or external factors (i.e., air quality). Initial input stages were defined as the levels of investment in product R&D, battery technology, production facilities and battery charging facilities. Results: Reasonable levels of investment were obtained by expert opinion from industry experts. Also, a survey was carried out with 78 experts consisting of automaker engineers, managers working at EV parts manufacturers, and automobile industry researchers in government think tanks to obtain the conditional probability distributions. Conclusion: The output of the model was the likelihood of success - expressed as the probability of market acceptance - that depended on the various input values. A model is a useful tool for understanding the EV industry as a whole and explaining the likely ramifications of different investment levels.

• 통합자연과학논문집
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• 제14권4호
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• pp.183-188
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• 2021

Collaborative filtering is one of the commonly used methods in the web recommendation system. Numerous researches on the collaborative filtering proposed the numbers of measures for enhancing the accuracy. This study suggests the movie recommendation system applied with Word2Vec and ensemble convolutional neural networks. First, user sentences and movie sentences are made from the user, movie, and rating information. Then, the user sentences and movie sentences are input into Word2Vec to figure out the user vector and movie vector. The user vector is input on the user convolutional model while the movie vector is input on the movie convolutional model. These user and movie convolutional models are connected to the fully-connected neural network model. Ultimately, the output layer of the fully-connected neural network model outputs the forecasts for user, movie, and rating. The test result showed that the system proposed in this study showed higher accuracy than the conventional cooperative filtering system and Word2Vec and deep neural network-based system suggested in the similar researches. The Word2Vec and deep neural network-based recommendation system is expected to help in enhancing the satisfaction while considering about the characteristics of users.