• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.403-412
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• 1993

This paper presents the progress of the development of a nondestructive technique for the classification of normal, septicemic , and cadaver poultry carcasses by the Instrumentation and Sensing Laboratory at Beltsville, Maryland, U.S.A. The Sensing technique is based on the diffuse reflectance spectroscopy of poultry carcasses.

• Journal of Positioning, Navigation, and Timing
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• 제4권2호
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• pp.79-85
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• 2015

Existing Differential GPS (DGPS) pseudorange correction (PRC) generation techniques based on a virtual reference station cannot effectively assign a weighting factor if the baseline distance between a user and a reference station is not long enough. In this study, a virtual reference station DGPS PRC generation technique was developed based on an enhanced inverse distance weighting method using an exponential function that can maximize a small baseline distance difference due to the dense arrangement of DGPS reference stations in South Korea, and its positioning performance was validated. For the performance verification, the performance of the model developed in this study (EIDW) was compared with those of typical inverse distance weighting (IDW), first- and second-order multiple linear regression analyses (Planar 1 and 2), the model of Abousalem (1996) (Ab_EXP), and the model of Kim (2013) (Kim_EXP). The model developed in the present study had a horizontal accuracy of 53 cm, and the positioning based on the second-order multiple linear regression analysis that showed the highest positioning accuracy among the existing models had a horizontal accuracy of 51 cm, indicating that they have similar levels of performance. Also, when positioning was performed using five reference stations, the horizontal accuracy of the developed model improved by 8 ~ 42% compared to those of the existing models. In particular, the bias was improved by up to 27 cm.

• 산학경영연구
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• 제4권
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• pp.281-295
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• 1991

Nothing is more incorrect than forecasting. Nevertheless, forecasting is one of the most important business activities for the effective management. There has been rapid changes of the growth rate in every respect of the Korean hospitaity industry, especially the hotel industry, before and after the 88 Olympic Games. Therefore, the hoteliers shall be in need of more-than-ever accourate demand forecasting for the more systematic management and control. Under the above circumstances, this study suggested the best forecasting technique and method for the better sales and operations of the hotel rooms. The number of rooms sold is selected as a dependent variable of this study which is regarded as the best representative factor of measuring the growth rate of the rooms division performance of the hotels. The first step was to select the most verifiable independent variable diferently from the other countries or other areas of Korea. As a result, the number of foreign visitors was chosen. Empirical research, i.e. correlation and multiple regression analysis, shows that this independent variable has a strong relationship with the dependent variable told above. The second procedure was to estimate the number of rooms will be sold in 1991 on the basis of the formula calculated through the multiple regression analysis. Time series technique was conducted using the data of the number of foreign visitors by purpose of travel from 1987 to 1990. For the more correct forecasting, however, it would be desirable to adopt the data from 1989 considering the product or the industry life cycle. In addition, deeper analysis for the monthly or seasonal forecasting method is needed as a future research.

• 한국분무공학회지
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• 제20권4호
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• pp.247-253
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• 2015

Recently, the engine calibration technique has been much more complicated than that of the past engine case in order to satisfy the strict emission regulations. The current calibration method for the diesel engine which has an increasing market is both costly and time-consuming. New engine calibration method is required to develop for high-quality diesel engines with low cost and release it at the appropriate time. This study provides the optimal calibrating technique for complex engine systems using statistical modeling and numerical optimization. Firstly, it design a test plan based on Design of Experiments, a V-optimality methodology which is suitable looking for set-points, and determine the shape of test engine response. Secondly, it uses functions to make linear regression model for data analysis and optimization to fit the models of engines behavior. Finally, it generates the optimal calibrations obtained directly from empirical engine models using Grey Relational Analysis and compares the calibrations with data. This method can develop a process for systematically identifying the optimal balance of engine emissions.

• 산업경영시스템학회지
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• 제41권3호
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• 한국지반공학회논문집
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• 제33권6호
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• pp.27-36
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• 2017

자갈다짐말뚝(Gravel Compaction Pile) 공법은 연약지반 개량공법 중의 하나로 육상 및 해상에서 연약 지반을 개량하기 위해 많이 사용되어 왔다. 자갈다짐말뚝으로 보강된 지반의 극한 지지력은 자갈다짐말뚝 및 지반의 강도, 치환율, 시공조건 등에 영향을 받으며 이를 예측하기 위한 다양한 예측식이 제안되었다. 하지만 기존 예측식을 활용한 극한지지력 예측은 오차율 및 변동성이 매우 크며, 실제 설계에 활용하기에는 부적합한 것으로 나타났다. 본 연구에서는 자갈다짐말뚝으로 보강된 지반의 극한 지지력을 예측하기 위하여 현장 재하시험결과를 활용한 다중회귀분석을 수행하였으며, 단일잔류 교차검증에 따른 예측오차평가를 통하여 가장 효율적인 입력변수를 선정하고 이에 대한 극한 지지력 예측식을 제안하였다. 또한 선정된 입력변수를 활용하여 인공신경망 적용에 따른 극한 지지력 예측오차를 평가하고 이를 기존 예측식에 따른 결과와 비교 분석하였다.

• 한국정보과학회논문지:데이타베이스
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• 제34권6호
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• pp.483-492
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• 2007

최근 센서 네트워크의 발달로 실세계의 많은 데이타가 시간 속성을 갖고 실시간으로 수집되고 있다. 기존의 시계열 데이타 예측 기법은 모델 갱신 없이 예측을 수행하였다. 그러나 스트림 데이타는 매우 빠르게 수집이 되고 시간이 지남에 따라 데이타의 특성이 변경될 수 있으므로 기존의 시계열 예측 기법을 적용하는 것은 적절하지 않다. 따라서 이 논문에서는 슬라이딩 윈도우와 점진적인 회귀분석을 이용한 스트림 데이타 예측 기법을 제안한다. 이 기법은 스트림 데이타를 다중 회귀 모델에 입력하기 위해 차원 분열을 통해 여러 개의 속성으로 분열(Fractal)하고, 변화되는 데이타의 분포를 반영하기 위해 슬라이딩 윈도우 기법을 사용하여 점진적으로 회귀 모델을 갱신한다. 또한 고정 크기 큐를 이용하여 최근의 데이타로만 모델을 유지한다. 이전 데이타의 유지 없이 최소 정보를 갖는 행렬을 통해 모델을 갱신하므로 낮은 공간 복잡도를 갖고 점진적으로 모델을 갱신함으로써 에러율의 증가를 방지한다. 제안된 기법의 타당성은 RME(Relative Mean Error)와 RMSE(Root Mean Square Error)를 이용하여 측정하였고, 실험 결과 다른 기법에 비해 우수하였다.

• Journal of Biosystems Engineering
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• 제23권1호
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• pp.49-56
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• 1998

The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.

• 신재생에너지
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• 제2권2호
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• pp.37-43
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• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

• 산업경영시스템학회지
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• 제38권1호
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• pp.1-7
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• 2015

The traffic accidents in Korea have been increasing every year due to various reasons and simultaneously causing socioeconomic cost at the national level. This study has analyzed the correlation between meteorological factors and the traffic accidents in Seoul during 2013. Especially, we have selected season, rain and temperature among the meteorological factors to identify their significance with the traffic accidents. In addition, analysis of variance, t-test and a multiple regression technique is applied. Major findings from the analyses are discussed at the district point of view, including the different effect of weather condition and the interaction effect of rain and temperature in winter. The results of this study would be useful for developing management strategies to reduce car crashes and injury severity in Seoul.