• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.485-502
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• 2021

In order to evaluate the impact of ground subsidence and superstructures that are inevitably caused by tunnel excavation, a total of seven major influencing factors of surface subsidence and structural soundness reduction were set, and a Parameter Study using numerical analysis was conducted. Stability analysis was performed using scheme of Boscardin and Cording method and the maximum subsidence amount and the angular displacement, and correlation analysis was performed for each major influencing factor. In addition, it was applied that used the mutual behavior of the ground and the structure by parameter analysis in the site of the 𐩒𐩒𐩒 tunnel located in Hwaseong-si, Gyeonggi-do, and the applicability of the site was analyzed. As a result, the error was found to be 1.0%, and it could be used as a basic material for determining the appropriate tunnel route under various conditions when evaluating the stability of the structure according to tunnel excavating at the design stage.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.43.1-43.1
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• 2021

In this study, we suggest a new method to estimate the mass of a halo coronal mass ejection (CME) using synthetic CMEs. For this, we generate synthetic CMEs based on two assumptions: (1) the CME structure is a full ice-cream cone, (2) the CME electron density follows a power-law distribution (ρ_cme=ρ₀r^-n). The power-law exponent n is obtained by minimizing the root mean square error between the electron number density distributions of an observed CME and the corresponding synthetic CME at a position angle of the CME leading edge. By applying this methodology to 57 halo CMEs, we estimate two kinds of synthetic CME mass. One is a synthetic CME mass which considers only the observed CME region (M_cme1), the other is a synthetic CME mass which includes both the observed CME region and the occulted area larger than 4 solar radii (M_cme2). From these two cases, we derive conversion factors which are the ratio of a synthetic CME mass to an observed CME mass. The conversion factor for M_cme1 ranges from 1.4 to 3.0 and its average is 2.0. For M_cme2, the factor ranges from 1.8 to 5.0 with the average of 3.0. These results imply that the observed halo CME mass can be underestimated by about 2 times when we consider the observed CME region, and about 3 times when we consider the region including the occulted area. Interestingly these conversion factors have a very strong negative correlation with angular widths of halo CMEs.We also compare the results with the CME mass estimated from STEREO observations.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.262-271
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• 2024

In this paper, by applying deep learning, one of the A.I. techniques, through angle information, which is optical observation data generated when observing satellites at observatories, distance information from observatories is learned to predict range data, thereby increasing the precision of satellite's orbit determination. To this end, we generated observational data from GMAT, reduced the learning data error of deep learning through preprocessing of the generated observational data, and conducted deep learning through MATLAB. Based on the predicted distance information from learning, trajectory determination was performed using an extended Kalman filter, one of the filtering techniques for trajectory determination, through GMAT. The reliability of the model was verified by comparing and analyzing the orbital determination with angular information without distance information and the orbital determination result with predicted distance information from the model.

• Journal of the Korean earth science society
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• v.37 no.7
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• pp.398-407
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• 2016

We cooperated with four domestic educational astronomical observatories to construct a baseline and perform simultaneous observations to determine the geocentric parallax, distance, and motion of 1036 Ganymed, an Amor asteroid near the Earth. Observations were made on the day when simultaneous observations were possible from September to November 2011. Measured distances of 1036 Ganymed were 0.394 AU on Sept. 26, 0.365 AU on Oct. 11, and 0.340 AU on Oct. 25, respectively, which were within the error range as compared with the measured distances proposed by the US Jet Propulsion Laboratory. The 1036 Ganymed showed a tilting motion during the observation period, and the tangential angular velocities were measured at $0.037-0.052^{{\prime {\prime}}\;sec^{-1}$. Through this study, it was shown that the simultaneous observations among educational astronomical observations can obtain distance measurements with an error range of about 5% for asteroids near 0.4 AU. And it expected to be used as a research & education program emphasizing collaborative observation activities based on a network between observatories.

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.411-419
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• 2003

This paper aims at developing 'gyro-mouse' which provides decent and comfortable human-computer interface that supports the usage of such software as an internet-browser in PC for the people paralyzed in upper limbs. This interface operates on information collected from head movement to get the cursor control. The interface is composed of two modules. One is hardware module in which the head horizontal and vertical angular velocities are detected and transmitted into PC. The other is a PC software that translates the received data into movement and click signals of the mouse. The ANN (artificial neural network) learns the quick nodding pattern of each user as click input so that it can provide user-friendly interface. The performance of the system was evaluated by three indices that are click recognition rate. error in cursor position control. and click rate of the moving target box. The performance result of the gyro-mouse was compared with that of the optical-mouse to assess the efficiency of the gyro-mouse. The average click recognition rate was 93%, average error in cursor position control was 1.4∼5 times of optical mouse. and the click rate with 50 pixels target box was 40%(30 clicks/min) to that of optical mouse. The click rate increased monotonously with the number of trial from 35% to 44%. The suggested system is expected to provide a new possibility to communicate with the society.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1193-1203
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• 1999

본 논문에서는 한글과 영문 문서 영상들에 대한 기울어짐 추정(skew estimation) 알고리즘을 제안한다. 제안 방법은 전체 문서 영상에서 텍스트 요소들이 밀집되어 있는 영역을 선별하고, 선별된 영역에 대해 허프 변환을 적용하는 선택적 주의집중(selective attention) 방식을 채택한다. 제안 방법의 기울기 추정 과정은 2단계로 구성되는데, coarse 단계에서는 전체 영상을 몇 개의 영역으로 나누고 동일한 영역에 속하는 데이타들간의 연결 각도를 계산하여 각 영역별 accumulator에 저장한다. accumulator에 저장된 빈도치를 기준으로 $\pm$45$^{\circ}$범위 내에서 최대 $\pm$1$^{\circ}$의 오차를 가진 각 영역별 기울기를 계산한 후, 이들 중 최대 빈도값을 갖는 영역을 선정하고 그 영역의 기울기 각도를 문서 영상의 대략적인 기울기 각도로 결정한다. Refine 단계에서는 coarse 단계에서 선정된 영역에 허프 변환을 적용하여 정확한 기울기를 계산하는데, coarse 단계에서 추정한 기울기의 $\pm$1$^{\circ}$범위 내에서 0.1$^{\circ}$간격으로 측정한다. 이와 같은 선택적 주의집중 방식을 통해 기울기 추정에 소요되는 시간 비용은 최소화하고, 추정의 정확도는 최대화 할 수 있다.제안 방법의 성능 평가를 위한 실험은 다양한 형태의 영문과 한글 문서 영상 2,016개에 적용되었다. 제안 방법의 평균 수행 시간은 Pentium 200MHz PC에서 0.19초이고 평균 오차는 $\pm$0.08$^{\circ}$이다. 또한 기존의 기울기 추정 방법과 제안 방법의 성능을 비교하여 제안 방법의 우수성을 입증하였다.Abstract In this paper we propose a skew estimation algorithm for English and Korean document images. The proposed method adopts a selective attention strategy, in which we choose a region of interest which contains a cluster of text components and then apply a Hough transform to this region. The skew estimation process consists of two steps. In the coarse step, we divide the entire image into several regions, and compute the skew angle of each region by accumulating the slopes of lines connecting any two components in the region. The skew angle is estimated within the range of $\pm$45 degree with a maximum error of $\pm$1 degree. Next we select a region which has the most frequent slope in the accumulators and determine the skew angle of the image roughly as the angle corresponding to the most frequent slope. In the refine step, a Hough transform is applied for the selected region within the range of $\pm$1 degree along the angle computed from the coarse step, with an angular resolution of 0.1 degree. Based on this selective attention strategy, we can minimize the time cost and maximize the accuracy of the skew estimation.We have measured the performance of the proposed method by an experiment with 2,016 images of various English and Korean documents. The average run time is 0.19 second on a Pentium 200MHz PC, and the average error is $\pm$0.08 degree. We also have proven the superiority of our algorithm by comparing the performance with that of other well-known methods in the literature.

• Progress in Medical Physics
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• v.13 no.1
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• pp.21-26
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• 2002

A practical calculation algorithm which calculates the relative output factor(ROF) for irregular shaped electron field has been developed and evaluated the accuracy of the algorithm. The algorithm adapted two-source model, which assumes that the electron dose can be express as sum of the primary source component and the scattered component from the shielding block. Original two-source model has been modified in order to make the algorithm simpler and to reduce the number of parameters needed in the calculation, while the calculation error remains within clinical tolerance range. The primary source is assumed to have Gaussian distribution, while the scattered component follows the inverse square law. Depth and angular dependency of the primary and the scattered are ignored ROF can be calculated with three parameters such as, the effective source distance, the variance of primary source, and the scattering power of the block. The coefficients are obtained from the square shaped-block measurements and the algorithm is confirmed from the rectangular or irregular shaped-fields used in the clinic. The results showed less than 1.0 ％ difference between the calculation and measurements for most cases. None of cases which have bigger than 2.1 ％ have been found. By improving the algorithm for the aperture region which shows the largest error, the algorithm could be practically used in the clinic, since one can acquire the 1011 parameter's with minimum measurements(5∼6 measurements per cones) and generates accurate results within the clinically acceptable range.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.3
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• pp.261-270
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• 2021

Purpose. The aim of this study was (1) to compare the reverse engineering technique with other existing measurement methods and (2) to analyze the effect of implant angulations and impression coping types on implant impression accuracy with reverse engineering technique. Materials and methods. Three different master models were fabricated and the distance between the two implant center points in parallel master model was measured with different three methods; digital caliper measurement (Group DC), optical measuring (Group OM), and reverse engineering technique (Group RE). The 90 experimental models were fabricated with three types of impression copings for the three different implant angulation and the angular and distance error rate were calculated. One-way ANOVA was used for comparison among the evaluation methods (P < .05). The error rates of experimental groups were analyzed by two-way ANOVA (P < .05). Results. While there was significant difference between Group DC and RE (P < .05), Group OM had no significant difference compared with other groups (P > .05). The standard deviations in reverse engineering were much lower than those of digital caliper and optical measurement. Hybrid groups had no significant difference from the pick-up groups in distance error rates (P > .05). Conclusion. The reverse engineering technique demonstrated its potential as an evaluation technique of 3D accuracy of impression techniques.