• 대한지역사회영양학회지
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• 제7권4호
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• pp.548-558
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• 2002

The purpose of this study was to develop a computer software program for nutritional assessment using a Semi-Quantitative Food Frequency Questionnaire (SQFFQs) and the 24-hour Recall Method. The software for the SQFFQ was divided into input, output, and database. For dietary analyses, recipe and food databases were used. The recipe database included 25 items and the food database was divided into 18 food groups. The food database was composed of 19 general nutrient items, 33 fatty acids, and 18 amino acids. The software developed in this study can be summarized as follows: 1) input items related to the individual s ages information, lifestyle, biological values, and dietary habits; 2) individualized data in percent of the Korean RDA, the energy ratios of carbohydrates, proteins and fats, the ratio of animal to plant source intakes, and the distribution of food group intakes; 3) Statistical data on the individual's information, lifestyle, biological values, and dietary intakes including the frequency of intake of cooked foods, the amounts of food, and the number of food groups, and nutrients. In the 24-hour Recall Method, the input and output consisted of the individual s information and cooked dish intakes. The individual s report included the amounts of nutrient intake according to number of meal and days, in comparison to the Korean RDA, the energy ratio for carbohydrates, proteins and fats, the ratio of animal to plant source intakes, and the distribution of food group intakes. The statistical report presented the number of food groups and foods, and the nutrient intakes. To evaluate the validity of the SQFFQ, the Spearman Rank Order Correlation and kappa values were used. As a result, correlation coefficients comparing the 24-hour Recall Method appeared to be more than 0.5, except for vitamin $B_1, B_2$, niacin, and vitamin E. The kappa values for energy and carbohydrate intakes were both 0.7, and protein, fat, vitamin C, folate, Ca, and iron intakes ranged from 0.3 to 0.7.

• 한국음향학회지
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• 제25권8호
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• pp.370-374
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• 2006

음성인식의 특징벡터로서 멜-주파수 켑스트럴 계수 (MFCC, mel-frequency cepstral coefficients)가 가장 널리 사용되고 있다. FMCC 추출과정은 입력되는 음성신호를 푸리에 변환한 후, 주파수 대역별로 필터를 취하여 에너지 값을 구하고 이산 코사인 변환을 하여 그 계수 값을 구한다. 본 논문에서는 멜-스케일 된 주파수 대역필터를 취할 때 가중함수에 의해서 구해진 각 대역필터별 가중치를 적용하여 필터의 출력 에너지를 계산한다. 여기서 가중치를 구하기 위해 사용된 가중함수는 포만트가 존재하는 대역을 중심으로 인접한 대역들이 가우시안 분포를 가지는 함수이다. 제안한 방법으로 실험한 결과, 잡음이 거의 없는 음성신호에 대해서는 기존의 MFCC를 사용했을 때와 비슷한 인식률을 보이고 잡음성분이 많을수록 가중치가 적용된 방법이 인식률에서 보다 높은 성능 향상을 가져온다.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권7호
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• 기술혁신학회지
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• 제21권4호
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• pp.1296-1312
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• 2018

문재인정부 들어 제4차 산업혁명의 키워드가 부각되며서 정부의 국가 연구개발예산에 대한 효율성이 주목받기 시작하여 국가과학기술지식정보서비스는 그 역할이 강조되고 있다. 국가과학기술지식정보서비스는 성과, 과제, 연구시설장비, 인력, 사업 등 국가연구개발사업에 대한 정보를 세계 최초로 한곳에서 서비스하는 국가 R&D 정보 지식포털이다. 국가과학기술지식정보서비스는 지난 2005년부터 주관기관으로 한국과학기술정보연구원이 선정되면서부터 서비스를 시작하게 되어 10년 이상 서비스를 지속하고 있다. 이에 본 논문에서는 지난 13년동안 국가과학기술지식정보서비스의 경제적 파급효과를 분석하기 위해 한국은행의 투입 산출표인 산업연관표에 의거하여 산업연관분석을 실시하여, 이론적 차원에서 생산유발효과, 부가가치유발효과, 취업자유발효과와 전방 후방 연쇄효과를 분석한다. 국가과학기술지식정보서비스에 대한 정부의 R&D예산(1,214억원, 2006~2018년)을 투입계수로 설정했을 때, 전문가의 의견을 반영한 생산유발효과는 2,113억원, 부가가치유발효과는 1,008억원, 취업자유발효과는 10억원당 1,822명으로 분석되었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.383-388
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• 2004

This paper presents the aerodynamic coefficient modeling with a new model structure explored by Least Squares using Modulating Function Technique (LS/MFT) for an F-16XL airplane using wind tunnel data supplied by NASA/LRC. A new model structure for aerodynamic coefficient was proposed, one that considered all possible combination terms of angle of attack ${\alpha}$(t) and ${\alpha}$(t) given number of harmonics K, and was compared with Pearson's model, which has the same number of parameters as the new model. Our new model harmonic results show better agreement with the physical data than Pearson's model. The number of harmonics in the model was extended to 6 and its parameters were estimated by LS/MFT. The model output of lift coefficient with K=6 correspond reasonably well with the physical data. In particular, the estimation performances of four aerodynamic coefficients were greatly improved at high frequency by considering all harmonics included in the input${\alpha}$(t), and by using the new model. In addition, the importance of each parameter in the model was analyzed by parameter reduction errors. Moreover, the estimation of three parameters, i.e., amplitude, phase and frequency, for a pure sinusoid and a finite sum of sinusoids- using LS/MFT is investigated.

• 전자공학회논문지B
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• 제30B권12호
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• pp.12-20
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• 1993

In a conventional adaptive echo canceller, an ADF(Adaptive Digital Filter) with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to compute the coefficients, and NET detector using energy comparison method prevents the ADF to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yields more accurate detection of the start point of the NET signal.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1992년도 학술논문발표회 논문집 제11권 1호
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• pp.23-28
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• 1992

The purpose of this paper is to develop a new adaptive echo canceller improving convergence speed and near-end-talker detection performance of the conventional echo canceller. In a conventional adaptive echo canceller, an adaptive digital filter with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to cote the coefficients, and NET detector using energy comparison method prevents the adaptive digital filter to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF(Adaptive Digital Filter) output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yield more accurate detection of the start point of the NET signal.

• 전기학회논문지
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• 제65권9호
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• pp.1541-1550
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• 2016

In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as "If" clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.

• 한국산업정보학회논문지
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• 제16권4호
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• pp.19-26
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• 2011

중계기를 활용한 다중 입출력 기술이 차세대 광대역 무선 이동 통선의 유력한 후보로 연구되고 있다. 본 논문에서는 AF (amplify-and-forward) 방식 중계기 네트워크에서 SC-FDE (single carrier-frequency domain equalizer)를 위한 MRC (maximum ratio combining) MMSE (minimum mean-square-error) 등화 기법을 제안한다. 송신국과 수신국 간의 전송 신호와, 중계기를 통한 송신국과 수신국 간의 전송 신호를 MRC 방식으로 합쳐 MMSE 등화 기법을 적용하면 다이버시티 이득과 MMSE 등화 이득을 모두 획득하여 SC-FDE 시스템의 수신 성능을 크게 향상시킬 수 있다. AF 방식 중계기 네트워크에서 SC-FDE를 위한 MRC 계수 및 MMSE 등화탭의 수식을 정확히 도출하여 제시하고, 실험 결과를 통해 성능 향상을 확인한다.

• 대한전자공학회논문지SP
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• 제42권4호
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• pp.39-48
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• 2005

본 논문에서는 Bayesian 추정법과 신경회로망을 이용한 새로운 결 분할 방법을 제안한다 신경회로망의 입력으로는 다중스케일을 가지는 웨이블릿 계수와 인접한 이웃 웨이블릿 계수들의 문맥정보를 사용하고, 신경회로망의 출력을 사후 확률로 모델링한다. 문맥정보는 HMT(Hidden Markov Tree) 모델을 이용하여 구한다. 제안 방법은 HMT를 이용한 ML(Maximum Likelihood) 분할 보다 더 우수한 결과를 보여준다. 또한 HMT를 이용한 결 분할 방법과 제안 방법을 이용한 결 분할 각각에 HMTseg라고 불리는 다중 스케일 Bayesian 영상 분할 기술을 이용하여 후처리를 행한 결 분할 또한 제안 방법이 우수함을 보여준다.