• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.71-78
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• 2016

This paper proposes the new electric power demand forecast model which is based on an artificial neural network and considers time and weather factors. Time factors are selected by measuring the autocorrelation coefficients of load demand in summer and winter seasons. Weather factors are selected by using Pearson correlation coefficient The important weather factors are temperature and dew point because the correlation coefficients between these factors and load demand are much higher than those of the other factors such as humidities, air pressures and wind speeds. The experimental results show that the proposed model using time and seasonal weather factors improves the load demand forecasts to a great extent.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1237-1245
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• 2018

We developed the assessment method for weekly groundwater resources management vulnerability. To consider the effect of precipitation on groundwater level, we calculated correlation coefficient between groundwater level and precipitation applying critical infiltration concept. We developed the vulnerability assessment criteria and calculated weights for criteria using the entropy method. Weekly groundwater resources management vulnerability of small administrative districts were estimated using developed method in this research. The developed method can be a basis for the establishment of the spatio-temporal groundwater resources management plan.

• Phonetics and Speech Sciences
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• v.13 no.4
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• pp.9-21
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• 2021

This study investigated whether Korean learners improve acoustic cue weights to identify Korean lenis and aspirated stops in the direction of native values through perception training that focused on contrasting the stops in various phonetic contexts. Nineteen native Chinese learners of Korean and two native Korean instructors for the perception training participated in the experiment. A training group and a non-training group were divided according to pretest results, and only the training group participated in the training for 5 days. To estimate the perceptual weights of the stop cues, a pretest and a posttest were conducted with stimuli whose stop cues (F0 and VOT) were systematically manipulated. Binary logistic regression analyses were performed on each learner's test results to calculate perceptual β coefficients, which estimate the perceptual weights of the acoustic cues used in identifying the stop contrast. The training group showed a statistically significant increase of 0.451 on average in the posttest for the coefficient values of the F0, which is the primary cue for the stop contrast, whereas the non-training group showed an insignificant increase of 0.246. The patterns of change in the F0 use after training varied considerably among individual learners.

• Journal of Korean Tunnelling and Underground Space Association
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• v.9 no.1
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• pp.63-74
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• 2007

Unexpected ground conditions have always been a major problem for the construction of tunnel. Therefore, it is necessary to evaluate the risk capacity before and/or during construction of new tunnel. This paper presents the simplified risk assessment system using modified stability number (N), namely Underground Risk Index (URI) system, to evaluate the tunnel risk possibility in the design stage. URI is a scoring system for risk possibility by rating the each appraisal elements. The modified stability number (N) which is one of risk factor in the Interaction Matrix parameters such as RQD, UCS, weathering, overburden, stability number, ground water-table, RMR, permeability and so on, is used in the system. In addition, the case study is performed in order to verify the applicability of URI-system in practice.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.63-74
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• 2005

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.15-23
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• 2022

Among the learning methods for Continuous Learning environments, "Learning without Forgetting" has fixed regularization strengths, which can lead to poor performance in environments where various data are received. We suggest a way to set weights variable by identifying the features of the data we want to learn. We applied weights adaptively using correlation and complexity. Scenarios with various data are used for evaluation and experiments showed accuracy increases by up to 5% in the new task and up to 11% in the previous task. In addition, it was found that the adaptive weight value obtained by the algorithm proposed in this paper, approached the optimal weight value calculated manually by repeated experiments for each experimental scenario. The correlation coefficient value is 0.739, and overall average task accuracy increased. It can be seen that the method of this paper sets an appropriate lambda value every time a new task is learned, and derives the optimal result value in various scenarios.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.287-290
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• 2002

본 논문에서는 디지털 컨텐츠 저작권 보호를 위해 강인성과 비가시성의 유지를 위한 보다 효과적인 방법으로 웨이브릿 변환에서 적응적 워터마크 삽입강도를 갖는 지각적 데이터 은닉 모델을 제안한다. 먼저 영상을 9/7 쌍직교 웨이브릿 필터를 사용해 4레벨로 다해상도 분해한다. 다음으로 연속부대역 양자화(successive subband quantization)를 통한 시각적 중요계수(perceptually significant coefficient: PSC)들을 선정하여 선택된 계수들에 대해서만 워터마크 정보를 삽입한다. 지각 모델은 정상상태의 일반화 가우시안 모델(generalized gaussian model)로 추정된 NVF(noise visibility function)로 에지와 텍스쳐영역 그리고 평탄영역에 따라 각각 적응적으로 삽입되게 한다. 이는 각 서브밴드 내의 분산과 형상계수(shape parameter)에 의해 결정된다. 적응적 워터마크의 삽입강도를 갖기 위해 에지와 텍스쳐영역의 삽입강도는 각 서브밴드의 주파수 감도(frequency sensitivity)로 결정되고, 평탄영역의 삽입강도는 영상의 국부적 특성에 근거한 통계적 가중치를 사용한다. 삽입되는 워터마크는 랜덤시퀀스로 N(0,1)이다. 여러 가지 공격에 대한 실험으로 제안한 방법의 비가시성과 강인성을 확인한다.

• The Journal of the Korea Contents Association
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• v.3 no.3
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• pp.98-102
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• 2003

For elucidating the key role of hidden neurons in information processing of Multilayer perceptrons(MLPs), we prove that the correlation coefficient between weighted sums to hidden neurons decreases under element-wise nonlinear transformations. This is verified through training of MLPs for an isolated word recognition problem. From this result, we can say that the element-wise nonlinear functions reduces redundancy in the information contents of hidden neurons.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.5B
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• pp.669-676
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• 2001

본 논문은 DCT 기반의 블라인드 워터마킹 방법에 관한 연구 결과이다. 본 논문에서 사용한 4096비트의 워터마크는 512x512 영상이 수용할 수 있는 최대 용량의 절반에 해당한다. 각 워터마크 비트는 8x8 DCT 블록의 12개의 계수에 확산 패턴을 이용하여 삽입하였다. 워터마크 삽입 과정에서, 워터마크 이득 계수는 비가시성과 견고성을 고려하여 최적화되었다. 워터마크 검출 과정에서는, 상관검출을 통하여 워터마크에 대한 예비판정을 하며, 이진가설 검증의 검증 과정을 통해서 예비판정에서의 검출 오류를 수정하였다. 검증 과정에서는 복원된 DCT계수를 이용한 가중치가 사용되었으며, 대부분의 예비 판정의 오류는 검증 과정에서 수정이 되었다. 실험 결과, 영상에 공격이 가해지지 않은 경우는 최종 검증 후에 BER이 0.5% 미만으로 낮아졌으며, 20% JPEG의 고압축에서도 BER이 9% 미만으로 산출되었다. 기존의 방법들과의 비교에서, 제안한 방법은 워터마크 검출 성능 및 워터마크 용량 측면에서 우수하였다.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.1
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• pp.33-39
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• 1998

This paper discusses approximation modelling of discrete-time linear time-varying system(LTVS). The wavelet transform is considered as a tool for representing and approximating a LTVS. The joint time-frequency properties of wave analysis are appropriate for describing the LTVS. Simulation results is included to illustrate the potential application of the technique.