• 로봇학회논문지
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• 제18권4호
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• pp.463-471
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• 2023

This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권6호
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• pp.303-308
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• 2017

생활 수준의 향상 및 소비자들의 건강에 대한 관심의 증가로 인해 자신의 건강에 대해서 스스로 결정하고자 하는 요구가 점차 증가하고 있다. 이로 인해 개인 맞춤형 의료에 대한 요구가 높아지고 있으며 각종 의료 정보를 기반으로 하는 질병 진단에 대한 연구가 많이 진행되고 있다. 하지만 기존의 연구들은 특정 질환과 관련된 데이터를 이용한 특정 질환 예측을 위한 것으로 진료과목을 예측한 연구는 없었다. 본 논문에서는 국민건강정보데이터를 이용하여 진료과목 예측에 관한 연구를 진행하였다. 실험 결과에서 보여주다시피 일반 건강검진 데이터를 이용하여 진료과목을 예측한 결과 평균 80% 이상의 정확도를 보여 주고 있으며 SVM은 다른 예측 알고리즘들보다 뛰어난 성능을 보여 주었다.

• 대한산업공학회지
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• 제41권4호
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• pp.359-367
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• 2015

Geographical Indication (GI) has been used with the expectation to influence customer buying behavior. In this research, we empirically investigate if such relationship exists using apple price changes in Korea along with web search traffic reflecting customers' interest in GI. The experimental results indicate that the apple price of the past, apple supply and web search traffic including GI name were significant on the prediction of price change of Chungju while web search traffic of regional name and that of product were significant for Cheongsong apples with GI. In Yeongcheon with no GI, the apple price of the past turns out to be significant only. The results indicated that interests in GI can help the price prediction but the regional name itself can play the same role, if the GI product is well known in association with the region.

• 경영과학
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• 제28권1호
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• pp.53-60
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• 2011

Time series analysis methods have been traditionally used in stock price prediction. However, most of the existing methods represent some methodological limitations in reflecting influence from external factors that affect the fluctuation of stock prices, such as oil prices, exchange rates, money interest rates, and the stock price indexes of other countries. To overcome the limitations, we propose a network based method incorporating the relations between the individual company stock prices and the external factors by using a graph-based semi-supervised learning algorithm. For verifying the significance of the proposed method, it was applied to the prediction problems of company stock prices listed in the KOSPI from January 2007 to August 2008.

• 전기학회논문지
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• 제59권8호
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• pp.1382-1387
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• 2010

Recently, there has been a growing interest in the environmental conservation. Accordingly, problems related to the audible noise of transformers have became more frequent. Therefore, it is urgent to find a fundamental solution about the audible noises in the substations. This paper described a sort of fundamental solution to solve the noise problem. As a fundamental solution, we suggested the proper audible noise level of transformers through noise prediction in the substation construction phase. And we applied the low noise transformers which have the predicted noise level. As the result, we are able to satisfy the noise regulation through measuring 43.6dBA at the boundary of substation. It is confirmed that the average error rate of prediction was within 3 percent.

• 한국측량학회지
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• 제33권6호
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• pp.589-594
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• 2015

In reflection of road expansion and increasing use rates, interest has blossomed in predicting driving environment. In addition, a gigantic scale of big data is applied to almost every area around the world. Recently, technology development is being promoted in the area of road traffic particularly for traffic information service and analysis system in utilization of big data. This study examines actual cases of road management systems and road information analysis technologies, home and abroad. Based on the result, the limitations of existing technologies and road management systems are analyzed. In this study, a development direction and expected effort of the prediction of road information are presented. This study also examines regression analysis about relationship between guide name and traffic volume. According to the development of driving environment prediction platform, it will be possible to serve more reliable road information and also it will make safe and smart road infrastructures.

• Journal of the Korean Statistical Society
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• 제35권1호
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• pp.37-47
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• 2006

Recently, as a result of the growing interest in modeling stationary processes with discrete marginal distributions, several models for integer valued time series have been proposed in the literature. One of these models is the integer-valued autoregressive (INAR) models. However, when modeling with integer-valued autoregressive processes, the distributional properties of forecasts have been not yet discovered due to the difficulty in handling the Steutal Van Ham thinning operator 'o' (Steutal and van Ham, 1979). In this study, we derive the mean squared error of h-step-ahead prediction from a Poisson INAR(1) process, reflecting the effect of the variability of parameter estimates in the prediction mean squared error.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• 한국멀티미디어학회논문지
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• 제19권9호
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• pp.1659-1668
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• 2016

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

• Computers and Concrete
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• 제24권4호
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• pp.295-302
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• 2019

Evolutionary algorithms based on conventional statistical methods such as regression and classification have been widely used in data mining applications. This work involves application of gene expression programming (GEP) for predicting compressive strength of fly ash geopolymer concrete, which is gaining increasing interest as an environmentally friendly alternative of Portland cement concrete. Based on 56 test results from the existing literature, a model was obtained relating the compressive strength of fly ash geopolymer concrete with the significantly influencing mix design parameters. The predictions of the model in training and validation were evaluated. The coefficient of determination ($R^2$), mean (${\mu}$) and standard deviation (${\sigma}$) were 0.89, 1.0 and 0.12 respectively, for the training set, and 0.89, 0.99 and 0.13 respectively, for the validation set. The error of prediction by the model was also evaluated and found to be very low. This indicates that the predictions of GEP model are in close agreement with the experimental results suggesting this as a promising method for compressive strength prediction of fly ash geopolymer concrete.