• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.500-506
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• 2005

본 논문에서는 자가 생성 지도 학습 알고리즘을 이용한 운송 컨테이너 식별자 인식 시스템을 제안한다. 일반적으로 운송 컨테이너의 식별자들은 글자의 색이 검정색 또는 흰색으로 이루어져 있는 특정이 있다. 이러한 특성을 고려하여 원 컨테이너 영상에 대해 검은색과 흰색을 제외하고는 모든 부분을 잡음으로 처리하기 위해 퍼지 추론 방법을 이용하여 식별자 영역과 바탕영역을 구별한다. 식별자 영역으로 구분 된 영역은 그대로 두고, 바탕 영역으로 구분된 영역 은 전체 영상의 평균 픽셀 값으로 대체시킨다. 그리고 Sobel 마스크를 이용하여 에지를 검출하고, 추출된 에지를 이용하여 수직 블록과 수평 블록을 검출 하여 컨테이너의 식별자 영역을 추출하고 이진화한다. 이진화 된 식별자 영역에 대해 검정색의 빈도수를 이용하여 흰바탕과 민바탕을 구분하고 4 방향 윤곽선 추적 알고리즘을 적용하여 개별 식별자를 추출 한다. 개별 식별자 인식을 위해 자가 생성 지도 학습 알고리즘을 제안하여 개별 식별자 인식에 적용한다. 제안된 자가 생성 지도 학습 알고리즘은 입력층과 은닉층 사이의 구조를 ART-l을 개선하여 적용하고 은닉층과 출력층 사이에는 일반화된 델타 학습 방법과 Delta-bar-Delta 알고리즘을 적용하여 학습 및 인식 성능을 개선한다. 실제 80 개의 컨테이너 영상을 대상으로 실험한 결과, 제안된 식별자 추출 방법이 이전의 개별 추출 방법보다 추출률이 개선되었고 FCM 기반 자가 생성 지도 학습 알고리즘보다 제안된 자가 생성 지도 학습 알고리즘이 컨테이너 식별자의 학습 및 인식에 있어서 개선된 것을 확인하였다.색 문제를 해결하고자 하는 것이 연구의 목적이다. 정보추출은 사용자의 관심사에 적합한 문서들로부터 어떤 구체적인 사실이나 관계를 정확히 추출하는 작업을 가리킨다.앞으로 e-메일, 매신저, 전자결재, 지식관리시스템, 인터넷 방송 시스템의 기반 구조 역할을 할 수 있다. 현재 오픈웨어에 적용하기 위한 P2P 기반의 지능형 BPM(Business Process Management)에 관한 연구와 X인터넷 기술을 이용한 RIA (Rich Internet Application) 기반 웹인터페이스 연구를 진행하고 있다.태도와 유아의 창의성간에는 상관이 없는 것으로 나타났고, 일반 유아의 아버지 양육태도와 유아의 창의성간의 상관에서는 아버지 양육태도의 성취-비성취 요인에서와 창의성제목의 추상성요인에서 상관이 있는 것으로 나타났다. 따라서 창의성이 높은 아동의 아버지의 양육태도는 일반 유아의 아버지와 보다 더 애정적이며 자율성이 높지만 창의성이 높은 아동의 집단내에서 창의성에 특별한 영향을 더 미치는 아버지의 양육방식은 발견되지 않았다. 반면 일반 유아의 경우 아버지의 성취지향성이 낮을 때 자녀의 창의성을 향상시킬 수 있는 것으로 나타났다. 이상에서 자녀의 창의성을 향상시키는 중요한 양육차원은 애정성이나 비성취지향성으로 나타나고 있어 정서적인 측면의 지원인 것으로 밝혀졌다.징에서 나타나는 AD-SR맥락의 반성적 탐구가 자주 나타났다. 반성적 탐구 척도 두 그룹을 비교 했을 때 CON 상호작용의 특징이 낮게 나타나는 N그룹이 양적으로 그리고 내용적으로 더 의미 있는 반성적 탐구를 했다용을 지원하는 홈페이지를 만들어 자료

• Journal of Korea Water Resources Association
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• v.56 no.1
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• pp.63-74
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• 2023

High-reliability prediction of dam inflow is necessary for efficient dam operation. Recently, studies were conducted to predict the inflow of dams using Multi Layer Perceptron (MLP). Existing studies used the Gradient Descent (GD)-based optimizer as the optimizer among MLP operators to find the optimal correlation between data. However, the GD-based optimizers have disadvantages in that the prediction performance is deteriorated due to the possibility of convergence to the local optimal value and the absence of storage space. This study improved the shortcomings of the GD-based optimizer by developing Adaptive moments combined with Improved Harmony Search (AdamIHS), which combines Adaptive moments among GD-based optimizers and Improved Harmony Search (IHS). In order to evaluate the learning and prediction performance of MLP using AdamIHS, Daecheong Dam inflow was learned and predicted and compared with the learning and prediction performance of MLP using GD-based optimizer. Comparing the learning results, the Mean Squared Error (MSE) of MLP, which is 5 hidden layers using AdamIHS, was the lowest at 11,577. Comparing the prediction results, the average MSE of MLP, which is one hidden layer using AdamIHS, was the lowest at 413,262. Using AdamIHS developed in this study, it will be possible to show improved prediction performance in various fields.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.51-61
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• 2002

In this dissertation, We demonstrated the Travel Time forecasting model in the freeway of multi-section with regard of drives' attitude. Recently, the forecasted travel time that is furnished based on expected travel time data and advanced experiment isn't being able to reflect the time-lag phenomenon specially in case of long distance trip, so drivers don't believe any more forecasted travel time. And that's why the effects of ATIS(Advanced Traveler Information System) are reduced. Therefore, in this dissertation to forecast the travel time of the freeway of multi-section reflecting the time-lag phenomenon & the delay of tollgate, we used traffic volume data & TCS data that are collected by Korea Highway Cooperation. Also keep the data of mixed unusual to applicate real system. The applied model for forecasting is consisted of feed-forward structure which has three input units & two output units and the back-propagation is utilized as studying method. Furthermore, the optimal alternative was chosen through the twelve alternative ideas which is composed of the unit number of hidden-layer & repeating number which affect studying speed & forecasting capability. In order to compare the forecasting capability of developed ANN model. the algorithm which are currently used as an information source for freeway travel time. During the comparison with reference model, MSE, MARE, MAE & T-test were executed, as the result, the model which utilized the artificial neural network performed more superior forecasting capability among the comparison index. Moreover, the calculated through the particularity of data structure which was used in this experiment.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.537-550
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• 2000

In this study, Neural Networks models were used to forecast daily streamflow at Jindong station of the Nakdong River basin. Neural Networks models consist of CASE 1(5-5-1) and CASE 2(5-5-5-1). The criteria which separates two models is the number of hidden layers. Each model has Fletcher-Reeves Conjugate Gradient BackPropagation(FR-CGBP) and Scaled Conjugate Gradient BackPropagation(SCGBP) algorithms, which are better than original BackPropagation(BP) in convergence of global error and training tolerance. The data which are available for model training and validation were composed of wet, average, dry, wet+average, wet+dry, average+dry and wet+average+dry year respectively. During model training, the optimal connection weights and biases were determined using each data set and the daily streamflow was calculated at the same time. Except for wet+dry year, the results of training were good conditions by statistical analysis of forecast errors. And, model validation was carried out using the connection weights and biases which were calculated from model training. The results of validation were satisfactory like those of training. Daily streamflow forecasting using Neural Networks models were compared with those forecasted by Multiple Regression Analysis Mode(MRAM). Neural Networks models were displayed slightly better results than MRAM in this study. Thus, Neural Networks models have much advantage to provide a more sysmatic approach, reduce model parameters, and shorten the time spent in the model development.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.681-690
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• 2017

This study estimated the daily maximum snow depth using the Artificial Neural Network (ANN) model in Korean Peninsula. First, the optimal ANN model structure was determined through the trial-and-error approach. As a result, daily precipitation, daily mean temperature, and daily minimum temperature were chosen as the input data of the ANN. The number of hidden layer was set to 1 and the number of nodes in the hidden layer was set to 10. In case of using the observed value as the input data of the ANN model, the cross validation correlation coefficient was 0.87, which is higher than that of the case in which the daily maximum snow depth was spatially interpolated using the Ordinary Kriging method (0.40). In order to investigate the performance of the ANN model for estimating the daily maximum snow depth of the ungauged area, the input data of the ANN model was spatially interpolated using Ordinary Kriging. In this case, the correlation coefficient of 0.49 was obtained. The performance of the ANN model in mountainous areas above 200m above sea level was found to be somewhat lower than that in the rest of the study area. This result of this study implies that the ANN model can be used effectively for the accurate and immediate estimation of the maximum snow depth over the whole country.

• Journal of the Korean Geotechnical Society
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• v.21 no.8
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• pp.73-84
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• 2005

A three layered neural network model was developed using back propagation algorithm to estimate the UU undrained shear strength of Korean soft soil based on the database of actual undrained shear strengths and piezocone measurements compiled from 8 sites over the Korea. The developed model was validated by comparing model predictions with measured values about new piezocone data, which were not previously employed during development of model. Performance of the neural network model was also compared with conventional empirical methods. It was found that the number of neuron in hidden layer is different for the different combination of transfer functions of neural network models. However, all piezocone neural network models are successful in inferring a complex relationship between piezocone measurements and the undrained shear strength of Korean soft soils, which give relatively high coefficients of determination ranging from 0.69 to 0.72. Since neural network model has been generalized by self-learning from database of piezocone measurements and undrained shear strength over the various sites, the developed neural network models give more precise and generally reliable undrained shear strengths than empirical approaches which still need site specific calibration.

• Korean Security Journal
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• no.61
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• pp.333-352
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• 2019

In January and August 2019, there were amendments to the Unfair Competition and Trade Secrets Protection Act (UCPA) and the Industrial Technology Protection Act(ITPA). These amendments will contribute to technology protection. But these amendments need to be supplemented further. In the area of civil remedies, despite the introduction of treble damages in the case of the UCPA and ITPA, the provisions related to the submission of supporting data have not been maintained. Therefore, it is necessary to recognize the claim of the other party as true if it is maintained at the level of the revised Patent Act and the scope of submission of supporting data. And the enforcement of the case of compulsory submission for the calculation of damages, and the order of filing documents are not followed. ITPA, on the other hand, has introduced the compensation for damages, but there is no provision for estimating the amount of damages. Therefore, it is necessary to estimate the amount of lost profits, profits, and royalties. In the area of criminal remedies, both the UCPA and ITPA have raised the penalty, but the sentencing regulations are not maintained. In addition, although the recent outflow of technology has expanded beyond organizational deviations to organizational outflows, amendments need to be made in relation to the serious consequence for the punishment of related juristic persons, such as companies involved in it. It should be noted that Japan and the United States have corporate regulations and regulations. In addition, in relation to the confiscation system, Act on Regulation and Punishment of criminal proceeds concealment require that domestic defenses be confiscated by defense industry technology, while trade secrets and industrial technologies are confiscated only by "foreign" outflows, and an amendment is necessary.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.244-249
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• 2017

Steganalysis is to detect information hidden by steganography inside general data such as images. There are stegoanalysis techniques that use machine learning (ML). Existing ML approaches to steganalysis are based on extracting features from stego images and modeling them. Recently deep learning-based methodologies have shown significant improvements in detection accuracy. However, all the existing methods, including deep learning-based ones, have a critical limitation in that they can only detect stego images that are created by a specific steganography method. In this paper, we propose a generalized steganalysis method that can model multiple types of stego images using deep learning. Through various experiments, we confirm the effectiveness of our approach and envision directions for future research. In particular, we show that our method can detect each type of steganography with the same level of accuracy as that of a steganalysis method dedicated to that type of steganography, thereby demonstrating the general applicability of our approach to multiple types of stego images.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.505-515
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• 2000

THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.1-10
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• 2001

Neural networks we increasingly being seen as an addition to the statistics toolkit which should be considered alongside both classical and modern statistical methods. Neural networks are usually useful for classification and function estimation. In this paper we concentrate on function estimation using neural networks with weight decay factor The use of weight decay seems both to help the optimization process and to avoid overfitting. In this type of neural networks, the problem to decide the number of hidden nodes, weight decay parameter and iteration number of learning is very important. It is called the optimization of weight decay neural networks. In this paper we propose a automatic optimization based on genetic algorithms. Moreover, we compare the weight decay neural network automatically learned according to automatic optimization with ordinary neural network, projection pursuit regression and support vector machines.