• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.1
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• pp.77-83
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• 2001

To product the map by terrestrial photogrammetry method, a few rather nuisance stereo image acquiring processing and plot using expensive analytical instruments have to be performed. In this study, plot was made by acquiring and rectification image using simple method rather than above it. For this, geometry rectification system was constructed for the generation of single ortho-image analysis. and these ortho-images of architecture were made and analysed by appling various warping methods. As a result, the performance of single image analysis could be estimated, and it is expected that the application of this is possible to various non-topographic photogrammetry.

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.191-195
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• 2008

The present study contributes reviewing and suggesting various models for measurement system analysis (MSA). Measurement errors consist of accuracy, linearity, stability, part precision, repeatability and reproducibility (R&R). First, the major content presents split-plot design, and the combination method of crossed and nested design for obtaining gage R&R. Second, we propose $\bar{x}-s$ variable control chart for calculating the gage R&R and number of distinct category. Lastly, investigating the determination of gage performance curve which establishes the control specification propagating calibration uncertainties and measurement errors is described.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1361-1369
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• 2008

The analysis of software reliability model provides the means to analysts, software engineers, and systems analysts and developers who want to predict, estimate, and measure failure rate of occurrences in software. In this paper, reliability growth model, in which the operating time between successive failure is a continuous random variable, is proposed. This model is based on order statistics of two parameters Burr type XII distribution. We propose the measure based on U-plot. Also the performance of the suggested model is tested on real data set.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1840-1847
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• 2003

In this paper, we have developed the MHDA scheme for data association. This scheme is important in providing a computationally feasible alternative to complete enumeration of JPDA which is intractable. We have proved that given an artificial measurement and track's configuration, MHDA scheme converges to a proper plot in a finite number of iterations. Also, a proper plot which is not the global solution can be corrected by re­initializing one or more times. In this light, even if the performance is enhanced by using the MHDA, we also note that the difficulty in tuning the parameters of the MHDA is critical aspect of this scheme. The difficulty cat however, be overcome by developing suitable automatic instruments that will iteratively verify convergence as the network parameters vary.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.397-412
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• 2020

In this paper, a notion of data depth is used to propose nonparametric multivariate two sample tests for difference between scale parameters. Data depth can be used to measure the centrality or outlying-ness of the multivariate data point relative to data cloud. A difference in the scale parameters indicates the difference in the depth values of a multivariate data point. By observing this fact on a depth vs depth plot (DD-plot), we propose nonparametric multivariate two sample tests for scale parameters of multivariate distributions. The p-values of these proposed tests are obtained by using Fisher's permutation approach. The power performance of these proposed tests has been reported for few symmetric and skewed multivariate distributions with the existing tests. Illustration with real-life data is also provided.

• Bulletin of the Korean Chemical Society
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• v.20 no.1
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• pp.59-64
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• 1999

Based on the study of hydrolysis behaviour of U(Ⅵ) ion and major fission product metal ions such as Cs(Ⅰ), Ce(Ⅲ), Nd(Ⅲ), Mo(Ⅵ), Ru(Ⅱ), and ZR(Ⅳ) in the titration media, the performance of noncomplexing-alkalimetric titration method for the determination of free acid in the presence of these metal ions was investigated and its results were compared to those from the completing methods. The free acidities could be determined as low as 0.05 meq in uranium solutions in which the molar ratio of U(Ⅵ)/H+ was less than 5, when the end-point of titration was estimated by Gran plot. The biases in the determinations were less than 1% and about +3% respectively for 0.4 meq and 0.05 meq of free acid at the U(Vl)/H+ molar ratio of up to 5. Applicability of this method to the determination of free acid in spent fuel solutions was confirmed by the analysis of nitric acid content in simulated spent fuel solutions and in a real spent fuel solution.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1081-1087
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• 2015

This article aims to provide a design method of warning thresholds for active safety systems based on the performance-based approach considering driver behaviors. Both positive and negative consequences of warnings are considered, and the main idea is to choose a warning threshold where the positive consequence is maximized, whereas the negative consequence is minimized. The process of the performance-based approach involves: Defining the operating scenarios; setting the trajectory models, including human characteristics; estimating the alert and nominal trajectories; estimating the performance metrics; generating a performance-metric plot; and determining the alert thresholds. This paper chose a lane-departure warning system as an example to show the usefulness of the performance-based approach. Both human and sensor characteristics were considered in the system design, and this paper provided a quantitative method to include human factors in designing active safety systems.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.1
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• pp.64-70
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• 2010

This paper presents how to decide on tuning factor(${\delta}$) and quality factor(Q) values in design of single-tuned passive harmonic filters. Tuning factor(${\delta}$) and quality factor(Q) values have to consider before decision on circuit parameters of passive filters. A Study on these two value has not been scarcely performed and only experienced values has been used in passive harmonic filter design by far. As a experienced value, in cases of 5th and 7th filter, tuning factor(${\delta}$) is about 0.94 and 0.96 respectively and quality factor(Q) is, in all cases of, 50. If Single-tuned passive harmonic filter will be off-tuned, performance of filter will be decreased steeply and occur to parallel resonance between system reactance and filter capacitance. Therefore During the operation, In order not to off-tuning, Filter must be tuned at former order than actual tuning order. This is the same that total impedance of filter must have a reactive impedance. In this paper, Tuning factor(${\delta}$) is decided via example of real system and using the bode-plot and then performance of filters confirmed by filter current absorbtion rate. And Quality factor(Q) decided using the bode plot in example system and then performance of filters confirmed by filter current absorbtion rate also, which makes a calculated filter parameters to satisfy IEEE-519 distortion limits. Finally, Performance of the designed passive harmonic filter using the tuning factor(${\delta}$) and quality factor(Q) values, decided in this paper is verified by experiment and shows that 5th, 7th, 9th, 11th and 13th current harmonic distortions are decreased within IEEE-519 distortion limits, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.189-192
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• 2021

This study applies a deep learning-based long short-term memory(LSTM) model to track tracking technology. In the case of existing track tracking technology, the weight of constant velocity, constant acceleration, stiff turn, and circular(3D) flight is automatically changed when tracking track in real time using LMIPDA based on Kalman filter according to flight characteristics of an aircraft such as constant velocity, constant acceleration, stiff turn, and circular(3D) flight. In this process, it is necessary to improve performance of changing flight characteristic weight, because changing flight characteristics such as stiff turn flight during constant velocity flight could incur the loss of track and decreasing of the tracking performance. This study is for improving track tracking performance by predicting the change of flight characteristics in advance and changing flight characteristic weigh rapidly. To get this result, this study makes deep learning-based Long Short-Term Memory(LSTM) model study the plot and target of simulator applied with radar error model, and compares the flight tracking results of using Kalman filter with those of deep learning-based Long Short-Term memory(LSTM) model.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.