• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.89-99
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• 2000

The Quality and Accuracy of digital image is important factor for decision of accuracy in digital photogrammetry because all the inside works in digital photogrammetry are based on digital image. But it is still difficult to ensure quality assurance and appication of data because there is no distinct criterion about quality and accuracy of digital image when the works in digital photogrammetry is accomplished. This study presents optimal resolution of aerial photo through error analysis of image coordinate using auto inner orientation in digital photograrnrnetry workstation. In second step, we are valified to optimum resolution of aerial photo image with orientation analysis. Finally, we are established to validity optimal resolution of aerial photo image with production of ortho image and mosaic image using optimal resolution aerial photo image.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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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.

• ETRI Journal
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• v.37 no.6
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• pp.1199-1210
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• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

• Journal of Industrial Technology
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• v.23 no.A
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• pp.83-92
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• 2003

The previous control point surveying, being standardized by trigonometric point which hasn't been unified in the whole country and producing put into operation through complex calculation process, has many problems about accurate results and economic side. Because most of trigonometric points that standardize a present surveying are in situation in top of the mountain, there are many difficulties in solving sight problems. Since trigonometric points are far away from one another, Differences are created because of limitation of point distance, observatory network construction and distribution of error. In the information age, the study about acquiring three dimension surveying information that uses GPS has been processed as fast as acquiring topography information is getting important gradually. For utilizing GPS in surveying work, deciding transformation 7-Parameters that changes data about location information which is received by GPS receiver is important. In this study, it is decided transformation 7-Parameters that can be used in ka-pyoung area by using GPS surveying production that had put into operation.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.403-431
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• 2016

The objective of this study is to integrate the business, maintenance and production processes of a manufacturing system by incorporating errors. First, the required functions are estimated according to the historical data. The system activities are simulated by Visual SLAM software and the required outputs are obtained. Several outputs including lead times in different dimensions, total cost and production rates are computed through simulation. Finally, data envelopment analysis (DEA) is utilized in order to select the best option between the defined scenarios due to the multi-criteria feature of the problem. This is the first study in which the lead times, cost and production rates are simultaneously considered in the integrated system imposed of business, maintenance and production processes by incorporating errors. In the current study, the major bottlenecks of the system being studied are identified and suggested different strategies to improve the system and make the best decision.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.11
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• pp.525-532
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• 2005

This paper presents a new two-terminal numerical algorithm for fault location estimation and for faults recognition using the synchronized phaser in time-domain. The proposed algorithm is also based on the synchronized voltage and current phasor measured from the assumed PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent or transient. In this paper the algorithm is given and estimated using DFT(discrete Fourier Transform) and the LES(Least Error Squares Method). The algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm, the Electro-Magnetic Transient Program(EMTP/ATP) is used.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.523-531
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• 2005

We derive decision regions of the maximum likelihood(ML) and suboptimum ML(S-ML) detectors in the first order moving average(FOMA) of an impulsive process. The ML and S-ML detectors are compared in terms of the bit-error-rate in the antipodal signaling system. Numerical results show that the S-ML detector, despite its reduced complexity and simpler structure, exhibits practically the same performance as the optimum ML detector. It is also shown that the performance gap between detectors for FOMA and independent and identically distributed noise becomes larger as the degree of noise impulsiveness increases.

• Steel and Composite Structures
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• v.37 no.2
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• pp.193-209
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• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.295-300
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• 1998

In this paper, a real time diagnostic algorithm fur estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. When the decision from ADM is concluded as the impact signal, the beginning time of burst-type signal, which the impact signal has usually such a form in time domain, provides the necessary data fur IEM. IEM by use of the arrival time method estimates the impact location of loose parts. The overall results of the estimated impact location are displayed on a computer monitor by the graphical mode and numerical data composed of the impact point, and thereby a plant operator can recognize easily the status of the impact event. This algorithm can perform the diagnosis process automatically and hence the operator's burden and the possible operator's error due to lack of expert knowledge of impact signals can be reduced remarkably. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.333-336
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• 2000

본 논문에서는 Monte-Carlo simulation과 deterministic simulation을 합성한 방법으로 특성허용 패턴을 만족하는 개별소자의 오차범위를 가중치에 따라 차별적으로 규정을 하였다. 일반적으로 사용되는 통계적인 방법은 불규칙한 특성을 갖는 랜덤오차를 정규분포를 갖는 랜덤변수로 모델링을 하여 허용 패턴으로부터 오차의 범위를 규정하는데, 이렇게 구해진 범위는 개별소자의 가중치의 영향을 고려하지 않고 일률적인 특성을 나타낸다는 단점이 있다. 이에 반해 deterministic simulation을 통해서 얻어진 오차의 범위는 가중치에 따라서 상대적인 범위를 결정할 수 있지만 해석 하고자하는 배열소자의 개수에 따라서 계산량이 지수승으로 증가하는 단점이 있어 10개 이상의 소자를 갖는 배열에는 적합하지 않다. 이러한 단점을 보완하기 위해서는 본 논문에서는 Monte-Carlo simulation과 deterministic simulation의 합성적 방법을 사용해서 배열소자의 증가에 따른 계산량의 증가를 줄이면서 각 가충치에 따라 상대적인 개별오차의 허용범위를 결정하였다. 그리고 이렇게 규정된 오차의 범위를 간단한 모의 실험을 통해서 검증하였다.