• 대한원격탐사학회지
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• 제35권6_1호
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• pp.895-905
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• 2019

현재 현업에서 사용되고 있는 상용 무인기 영상처리 소프트웨어는 카메라 캘리브레이션 정보나 영상 전체에 대한 블록 번들조정 정확도만 제공할 뿐 스테레오 페어의 실제 도화 가능여부에 대한 정확도는 거의 제공하지 않는다. 본 논문에서는 무인기 영상처리 소프트웨어에서 산출된 표정요소를 사용하여 도화품질을 산출하고 실제 도화기에 적용하여 도화품질의 신뢰성에 대해서 분석하였다. 도화품질은 Y시차 정확도, 상대모델 정확도, 절대모델 정확도의 3가지 정확도로 정의하였다. Y시차 정확도는 스테레오 페어간 입체시 여부를 판단할 수 있는 정확도이다. 상대모델 정확도는 모델 좌표계 상에서 스테레오 페어간 상대적인 번들조정 정확도이다. 절대모델 정확도는 절대 좌표계에서 번들조정 정확도이다. 실험데이터는 도심지를 대상으로 회전익에서 취득된 GSD 5 cm급의 영상 723장을 사용하여 도화품질을 분석하였다. 연구진이 개발한 기술을 사용해 예측한 상대모델 정확도와 실제 도화기에서 관측한 정확도의 최대오차는 0.11 m로 정밀한 결과를 보여 주었다. 절대모델 정확도도 마찬가지로, 도화기에서 관측한 정확도의 최대오차는 0.16 m로 정밀한 결과를 보여주었다.

• Imaging Science in Dentistry
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• 제34권1호
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• pp.13-18
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• 2004

Purpose : This study was to evaluate the influence of slice thickness of computed tomography (CT) and rapid protyping (RP) type on the accuracy of 3-dimensional medical model. Materials and Methods: Transaxial CT data of human dry skull were taken from multi-detector spiral CT. Slice thickness were 1, 2, 3 and 4 mm respectively. Three-dimensional image model reconstruction using 3-D visualization medical software (V-works /sup TM/ 3.0) and RP model fabrications were followed. 2-RP models were 3D printing (Z402, Z Corp., Burlington, USA) and Stereolithographic Apparatus model. Linear measurements of anatomical landmarks on dry skull, 3-D image model, and 2-RP models were done and compared according to slice thickness and RP model type. Results: There were relative error percentage in absolute value of 0.97, 1.98,3.83 between linear measurements of dry skull and image models of 1, 2, 3 mm slice thickness respectively. There was relative error percentage in absolute value of 0.79 between linear measurements of dry skull and SLA model. There was relative error difference in absolute value of 2.52 between linear measurements of dry skull and 3D printing model. Conclusion: These results indicated that 3-dimensional image model of thin slice thickness and stereolithographic RP model showed relative high accuracy.

• 한국농림기상학회지
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• 제14권4호
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• pp.269-276
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• 2012

작물모형 평가에 사용되거나 사용할 수 있는 9가지 지표를 소개하였으며 이들의 특징은 다음과 같다. efficiency of model (EF)와 index of agreement (d)은 dimension이 없고 관측수(n)에 의존적이지 않았으며, dimension에 대해서만 자유로운 것은 relative root mean square error (RRMSE), bias factor (Bf)와 accuracy factor (Af)이다. Root mean sqruar, mean error, mean absolute error들은 관측수와 dimension에 영향을 받기 때문에 판단 시 주의가 필요하다. 따라서 이들의 특징을 파악하여 목적에 맞게 모형의 성능을 파악하여야 한다.

• Imaging Science in Dentistry
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• 제32권1호
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• pp.27-33
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• 2002

Purpose: To evaluate the influence of threshold value of computed tomography on the accuracy of rapid prototyping (RP) medical model Material and Methods : CT datas of a human dry skull were transferred from CT scanner via compact disk to a personal computer (PC). 3-dimensional image reconstruction on PC by V-works/sup TM/ 3.0 (CyberMed. Inc.) software and RP models fabrication were followed. 2-RP models were produced by threshold value of 500 and 800 selected in surface rendering process. Linear measurements between arbitrary 12 anatomical landmarks on dry skull, 3-D image model, and 2-RP models were done and compared. Thus, the accuracy of 500 RP and 800RP models was respectively evaluated. Results: There was mean difference (% difference) in absolute value of 2.27 mm (2.73%) between linear measurements of dry skull and 500 RP model. There was mean difference (% difference) in absolute value of 1.94 mm (2.52%) between linear measurements of dry skull and 800 RP model. Conclusion: Slight difference of threshold value in rendering process of 3-D modelling made a influence on the accuracy of RP medical model.

• 대한원격탐사학회지
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• 제18권5호
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• pp.243-253
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• 2002

Time-mean and absolute geostrophic velocities of the Kuroshio current south of Japan are derived from TOPEX/Poseidon altimeter data using a Gaussian jet model. When compared with simultaneous measurements from a shipboard acoustic Doppler current profiler (ADCP) at two intersection points, the altimetric and ADCP absolute velocities correlate well with the correlation coefficient of 0.55 to 0.74. The accuracy of time-mean velocity ranges from 1 cm s$^{-1}$ to 5 cm s$^{-1}$. The errors in the absolute and the mean velocities are similar to those reported previously for other currents. The comparable performance suggests the Gaussian jet model is a promising methodology for determining absolute geostrophic velocities, noting that in this region the Kuroshio does not meander sufficiently and thus provides unfavorable environment for the performance of the Gaussian jet model.

• 한국정보시스템학회지:정보시스템연구
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• 제26권3호
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• pp.63-90
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• 2017

Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.

• 한국도로학회논문집
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• 제20권2호
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• 대한인간공학회지
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• 제19권2호
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• pp.87-99
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• 2000

This study aims to examine a fuzzy logic-based human expert EMG prediction model (FLHEPM) for predicting electromyographic responses of trunk muscles due to manual lifting based on two task (control) variables. The FLHEPM utilizes two variables as inputs and ten muscle activities as outputs. As the results, the lifting task variables could be represented with the fuzzy membership functions. This provides flexibility to combine different scales of model variables in order to design the EMG prediction system. In model development, it was possible to generate the initial fuzzy rules using the neural network, but not all the rules were appropriate (87% correct ratio). With regard to the model precision, the EMG signals could be predicted with reasonable accuracy that the model shows mean absolute error of 8.43% ranging from 4.97% to 13.16% and mean absolute difference of 6.4% ranging from 2.88% to 11.59%. However, the model prediction accuracy is limited by use of only two task variables which were available for this study (out of five proposed task variables). Ultimately, the neuro-fuzzy approach utilizing all five variables to predict either the EMG activities or the spinal loading due to dynamic lifting tasks should be developed.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2002년도 추계학술대회
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• pp.289-312
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• 2002

This study suggests the Link weight analysis approach to choose input variables and an integrated model to make more accurate bankruptcy prediction model. the Link weight analysis approach is a method to choose input variables to analyze each input node's link weight which is the absolute value of link weight between an input nodes and a hidden layer. There are the weak-linked neurons elimination method, the strong-linked neurons selection method in the link weight analysis approach. The Integrated Model is a combined type adapting Bagging method that uses the average value of the four models, the optimal weak-linked-neurons elimination method, optimal strong-linked neurons selection method, decision-making tree model, and MDA. As a result, the methods suggested in this study - the optimal strong-linked neurons selection method, the optimal weak-linked neurons elimination method, and the integrated model - show much higher accuracy than MDA and decision making tree model. Especially the integrated model shows much higher accuracy than MDA and decision making tree model and shows slightly higher accuracy than the optimal weak-linked neurons elimination method and the optimal strong-linked neurons selection method.

• 한국산림과학회지
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• 제97권2호
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• pp.140-143
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• 2008

This study compared the plywood demand prediction accuracy of econometric and vector autoregressive models using Korean data. The econometric model of plywood demand was specified with three explanatory variables; own price, construction permit area, dummy. The vector autoregressive model was specified with lagged endogenous variable, own price, construction permit area and dummy. The dummy variable reflected the abrupt decrease in plywood consumption in the late 1990's. The prediction accuracy was estimated on the basis of Residual Mean Squared Error, Mean Absolute Percentage Error and Theil's Inequality Coefficient. The results showed that the plywood demand prediction can be performed more accurately by econometric model than by vector autoregressive model.