• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.107-108
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• 2011

Since the development of railway technology, the current urban Railway the first train line in the country for safe operation control automatic/unattended operation, automatic train operation equipment available (ATO) on time and reliable operation has introduced. ATO Automatic operation controlled by the value (Target velocity) and the feedback value (Actual velocity) by the error between the backing and braking of the train by repeated low energy efficiency. In this paper, given a fixed distance stations between time operation with minimal energy in the driving characteristics and driving trains are modeled. Therefore, in line 5 real route time sectional drive straight sections for experimental data analysis / draft Section / curved and section of the train on that line is selected according to the changing driving patterns to minimize the energy optimal driving patterns were presented.

• Geophysics and Geophysical Exploration
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• v.16 no.3
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• pp.171-179
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• 2013

An automated ultrasonic velocity measurement system adopting pulse-echo-overlap (PEO) method has been constructed, which is known to be a precise and versatile method. It has been applied to velocity measurements for 5 kinds of engineering plastic cores and compared to first arrival picking (FAP) method. Because it needs multiple reflected waves and waves travel at least 4 times longer than FAP, PEO has basic restriction on sample length measurable. Velocities measured by PEO showed slightly lower than that by FAP, which comes from damping and diffusive characteristics of the samples as the wave travels longer distance in PEO. PEO, however, can measure velocities automatically by cross-correlating the first echo to the second or third echo, so that it can exclude the operator-oriented errors. Once measurable, PEO shows essentially higher repeatability and reproducibility than FAP. PEO system can diminish random noises by stacking multiple measurements. If it changes the experimental conditions such as temperature, saturation and so forth, the automated PEO system in this study can be applied to monitoring the velocity changes with respect to the parameter changes.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.80-85
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• 2008

최근 도심지역이 급변하고 고해상도 위성영상의 보급이 증가함에 따라 고해상도 위성영상을 이용한 수치표고모델과 정사영상 생성에 관한 연구가 활발해 지고 있다. 본 연구에서는 IKONOS, SPOT5, QUICKBIRD, KOMPSAT2 위성영상을 이용하여 DEM 과 정사영상을 생성하였으며 USGS DTED 와 기준점을 이용하여 결과의 정확도를 비교 분석하였다. 보다 정확한 DEM 생성을 위해 자동 피라미드 알고리즘을 적용하고 영상 정합시 에피폴라 기하학을 적용하였다. 정사 영상 생성시 DTED 높이값을 이용하여 보정을 수행하였으며 생성 속도를 높이기 위하여 리샘플링 그리드를 적용하였다. 본 연구에서 DEM 과 정사영상 생성시 QUICKBIRD 와 SPOT5 의 경우 영상의 용량이 매우 커 메모리 부족문제와 알고리즘 수행 속도 저하가 발생함을 확인하였다. 이를 개선하기 위하여 DEM 생성시 정합 후보점의 개수를 줄이는 알고리즘을 고안하여 기존에 메모리 문제로 생성하지 못했던 QUICKBIRD와 SPOT5 의 DEM 을 생성하였으며 정사 영상 생성시 리샘플링 그리드를 적용하여 고해상도 정상영상 생성 속도 개선에 상당한 효과를 가져왔다. 그러나 고해상도 위성 영상의 용량이 점점 커져감에 따라 이러한 메모리 문제와 처리 속도 저하에 관한 문제는 추후 계속적으로 연구되어야 할 부분이라고 할 수 있다. 본 연구에서 생성한 IKONOS, SPOT5, QUICKBIRD DEM 의 정확도를 USGS DTED 와 비교한 결과 13${\sim}$15 m 정도의 RMS 높이 오차가 산출되었으며 생성된 IKONOS, QUICKBIRD, KOMPSAT2 정사영상을 기준점과 비교한 결과 3 m 정도의 거리오차가 산출되었음을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1339-1346
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• 2015

The traffic identification is a preliminary and essential step for stable network service provision and efficient network resource management. While a number of identification methods have been introduced in literature, the payload signature-based identification method shows the highest performance in terms of accuracy, completeness, and practicality. However, the payload signature-based method's processing speed is much slower than other identification method such as header-based and statistical methods. In this paper, we first classifies signatures by matching type based on range, order, and direction of packet in a flow which was automatically extracted. By using this classification, we suggest a novel method to improve processing speed of payload signature-based identification by reducing searching space.

• Journal of The Korean Ophthalmological Society
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• v.59 no.11
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• pp.1017-1023
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• 2018

Purpose: A recently introduced phacoemulsification system, the WhiteStar $Signature^{(R)}$ PRO, has demonstrated improved nucleus followability and cutting efficiency via an improved pump regulator with a higher reaction response and an automatic occlusion sensing system. In this study, we compared various phacoemulsification parameters between the new system and an older version of the device. Methods: A total of 80 eyes of 68 patients with cataracts who had undergone phacoemulsification by a single surgeon were included in this study. Forty eyes of patients underwent phacoemulsification using the older $Signature^{(R)}$ system (WhiteStar); these patients were classified as the control group. Another 40 eyes of patients underwent phacoemulsification with the newer enhanced system, the WhiteStar $Signature^{(R)}$ PRO; these patients were assigned to the experimental group. During the operation, operative parameters, including the effective phaco time (parameter of effective phaco time with a specific coefficient for the transversal movement expressed in seconds, EFX), ultrasound time (seconds [s]), effective phacoemulsification time (EPT, s), average phacoemulsification power (AVG, %), and balanced salt solution usage, were measured to determine the performance enhancement offered by the updated system. Central corneal thickness was measured before and after surgery to compare corneal edema. The relationships between the two groups were analyzed using an independent t-test. Results: The Signature $PRO^{(R)}$ system showed a lower EFX (p < 0.001), a shorter EPT (p < 0.001), and a smaller AVG (p < 0.001). Postoperative corneal thickness did not differ significantly between the two groups. Conclusions: Comparing the efficacy of the improved reaction speed of the device and automatic occlusion sensing system in performing phacoemulsification, the updated Signature $PRO^{(R)}$ system demonstrated superior followability and cutting efficiency regardless of nuclear cataract hardness.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Journal of the Korean Geotechnical Society
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• v.21 no.9
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• pp.5-12
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• 2005

SPT hammer energy and its delivery are hon to influence the N value. The SPT hammer energy is classified into theoretical energy, velocity energy, rod energy and dynamic efficiency. In this study, the rod energy and the velocity energy are measured directly by PDA and Digital Line-Scan Camera which are most widely used type of SPT apparatus in Korea. The Dynamic efficiency is calculated through measured data. As the results of this study, the averages of rod energy ratio of donut, safety and automatic hammer are measured at 49.57, 61.60, and at $87.04\%$ by FV method. The averages of hammer velocity of donut, safety and automatic hammer are measured at $3.177{\pm}0.872$, $3.385{\pm}0.681$, and at $3.651{\pm}0.550$ m/s by Digital Line-Scan Camera, with the dynamic efficiencies at 0.732, 0.801, and 0.973 respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.441-444
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• 2012

오늘날 스마트폰의 보급으로 SNS는 급속도로 성장하였고, 매일 엄청난 분량의 텍스트 데이터가 생성되고 있다. 본 연구에서는 다른 매체에 비해 개인의 의견이 좀 더 거침없이 올라오는 SNS의 특징에 주목해 SNS의 텍스트 데이터를 대상으로 하는 평판 분석 기법을 제안한다. 제안 방법은 분석하고자 하는 대상에 대한 SNS 데이터를 수집하여 DB에 저장한 다음, 광고 제거 과정과 자동 띄어쓰기 과정 및 형태소 분석을 거친 후 감성 포함 여부 확인 과정과 극성 분류 과정으로 구성된다. 평판 분석을 위해 본 연구에서는 감성 단어 사전의 쾌-불쾌 수치와 활성화 수치를 사용한다. 분석 결과 모든 문서에 대한 극성 분류 정확도는 55%였고, 감성 포함 여부 확인 과정이 올바르게 수행된 문서에 대한 극성 분류 정확도는 82%였다.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.265-273
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• 2008

Video Image Processing (VIP) for traffic surveillance has been used not only to gather traffic information, but also to detect traffic conflicts and incident conditions. This paper presents a system development of gathering traffic information and conflict detection based on automatic calculation of pixel length within the detection zone on a Video Detection System (VDS). This algorithm improves the accuracy of traffic information using the automatic detailed line segmentsin the detection zone. This system also can be applied for all types of intersections. The experiments have been conducted with CCTV images, installed at a Bundang intersection, and verified through comparison with a commercial VDS product.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.227-232
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• 2004

Vehicle dynamics control systems are complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed supplies good results in normal conditions. But the estimation error in severe braking is discontented In this paper, we estimate the absolute vehicle speed of UCT(Unmanned Container Transporter) by using the wheel speed data from standard anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used 10 algorithms are verified experimentally to estimate the absolute vehicle speed and one of them is perfectly shown to estimate the vehicle speed within 4％ error during a braking maneuver.