• 농업과학연구
• /
• 제47권4호
• /
• pp.1109-1122
• /
• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• 한국해양공학회지
• /
• 제9권2호
• /
• pp.112-122
• /
• 1995

In this work, prediction of fatigue life and fatigue crack growth are studied. 4th order polynominal function is presented to describe the crack growth behaviors from artifical pit of SM45C steel. Crack growth curves obtained from 4th order polyminal growth equations are in good agreement with experimental data The crack growth behaviors at arbitrary stress levels and investigated by the concept of elastic-plastic fracture mechanics using ${\Delta}J$. Fatigue life prediction are carried out by numerical integral method. Prediction lives obtained by proposed method in this study, is in good agreement with the experimental ones. Life prediction results calculated by using of ${\Delta}J$ better than those of ${\Delta}K$.

• 기술사
• /
• 제18권2호
• /
• pp.1-5
• /
• 1985

In the flood prediction research, it is pointed out that the difficulty of flood prediction is the frequently experienced overestimation of flood peak. That is caused by the rainfall prediction difficulty and the nonlinearity of hydrological phenomena. Even though the former reason will remain still unsolved, but the latter one can be possibly resolved the method of the AMRA (Auto Regressive Moving Average) model for each runoff component as developed by Dr. Hino and Dr. Hasebe. The principle of the method consists of separating though the numerical filters the total runoff time series into long-term, intermediate and short-term components, or ground water flow, interflow, and surface flow components. As a total system, a hydrological system is a non-linear one. However, once it is separated into two or three subsystems, each subsystem may be treated as a linear system. Also the rainfall components into each subsystem a estimated inversely from the runoff component which is separated from the observed flood. That is why flood prediction can be done without rainfall data. In the prediction of surface flow, the Kalman filter will be applicable but this paper shows only impulse function method.

• ETRI Journal
• /
• 제28권6호
• /
• pp.807-810
• /
• 2006

This letter presents a prediction model for sentence-final intonations for Korean conversational-style text-to-speech systems in which we introduce the linguistic feature of 'modality' as a new parameter. Based on their function and meaning, we classify tonal forms in speech data into tone types meaningful for speech synthesis and use the result of this classification to build our prediction model using a tree structured classification algorithm. In order to show that modality is more effective for the prediction model than features such as sentence type or speech act, an experiment is performed on a test set of 970 utterances with a training set of 3,883 utterances. The results show that modality makes a higher contribution to the determination of sentence-final intonation than sentence type or speech act, and that prediction accuracy improves up to 25% when the feature of modality is introduced.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2003년도 춘계학술대회
• /
• pp.329-337
• /
• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• 한국지능시스템학회논문지
• /
• 제16권6호
• /
• pp.730-735
• /
• 2006

본 논문은 퍼지 시스템과 유전자 알고리즘을 이용하여 단기 전력 부하 예측 방법을 제안한다. 우선 유전자 알고리즘을 이용하여 최적의 퍼지 소속함수를 구한다. 최적의 퍼지 규칙과 시계열 입력 차이를 이용하여 보다 더 나은 예측 시스템을 구한다. 제안된 방법을 이용하여 단기 전력 부하 예측에서 좋은 결과를 얻었다. 또한 제안된 알고리즘에 대한 그래픽 사용자 인터페이스를 구현한다. 마지막으로, 전력부하에 대한 지역 예측 시스템을 구현한다.

• Journal of the Korean Data and Information Science Society
• /
• 제13권1호
• /
• pp.139-145
• /
• 2002

This paper deals with the problem of obtaining the Bayesian predictive density function and the prediction intervals for a future observation and the p-th order statistics of n future observations for the exponential model under the censored sampling with incomplete information.

• Journal of the Korean Data and Information Science Society
• /
• 제10권1호
• /
• pp.147-154
• /
• 1999

Using a noninformative prior and an inverted gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p - th order statistic of n' future observations from the censord Pareto model have been obtained. In additions, numerical examples are given in order to illustrate the proposed predictive procedure.

• Journal of the Korean Statistical Society
• /
• 제33권1호
• /
• pp.25-34
• /
• 2004

In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
• /
• 제9권1호
• /
• pp.17-24
• /
• 2020

본 논문에서는 합성곱 신경망 기반의 영화 평점 예측 구조를 제안한다. 제안하는 구조는 문장 분류을 위하 고안된 TextCNN를 세 가지 측면에서 확장하였다. 첫 번째로 문자 임베딩을 이용하여 단어의 다양한 변형들을 처리할 수 있다. 두 번째로 주목 메커니즘을 적용하여 중요한 특징을 더욱 부각하였다. 세 번째로 활성 함수의 출력을 1-10 사이의 평점으로 만드는 점수 함수를 제안하였다. 제안하는 영화 평점 예측 구조를 평가하기 위해서 영화 리뷰 데이터를 이용하여 평가해 본 결과 기존의 방법을 사용했을 때보다 더욱 낮은 MSE를 확인하였다. 이는 제안하는 영화 평점 예측 구조의 우수성을 보여 주었다.