• Journal of Space Technology and Applications
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• v.1 no.1
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• pp.23-32
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• 2021

The high-level Space Situational Awareness (SSA) objective is to provide to the users dependable, accurate and timely information in order to support risk management on orbit and during re-entry and support safe and secure operation of space assets and related services. Therefore the risk assessment for the re-entry of space objects should be managed nationally. In this research, the Software for Re-Entry Prediction of space objects (SREP) was developed for national SSA system. In particular, the rate of change of the drag coefficient is estimated through a newly proposed Drag Scale Factor Estimation (DSFE), and is used for high-precision orbit propagator (HPOP) up to an altitude of 100 km to predict the re-entry time and position of the space object. The effectiveness of this re-entry prediction is shown through the re-entry time window and ground track of space objects falling in real events, Grace-1, Grace-2, Tiangong-1, and Chang Zheng-5B Rocket body. As a result, through analysis 12 hours before the final re-entry time, it is shown that the re-entry time window and crash time can be accurately predicted with an error of less than 20 minutes.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.458-467
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• 2010

In this paper, a new audio reproduction system was developed in which the cross-talk signals would be reasonably cancelled at an arbitrary listener position. To adaptively remove the cross-talk signals according to the listener's position, a method of tracking the listener position was employed. This was achieved using the two microphones, where the listener direction was estimated using the time-delay between the two signals from the two microphones, respectively. Moreover, room reverberation effects were taken into consideration where linear prediction analysis was involved. To remove the cross-talk signals at the left-and right-ears, the paths between the sources and the ears were represented using the KEMAR head-related transfer functions (HRTFs) which were measured from the artificial dummy head. To evaluate the usefulness of the proposed listener tracking system, the performance of cross-talk cancellation was evaluated at the estimated listener positions. The performance was evaluated in terms of the channel separation ration (CSR), a -10 dB of CSR was experimentally achieved although the listener positions were more or less deviated. A real-time system was implemented using a floating-point digital signal processor (DSP). It was confirmed that the average errors of the listener direction was 5 degree and the subjects indicated that 80 % of the stimuli was perceived as the correct directions.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of Korean Society of Forest Science
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• v.88 no.2
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• pp.240-248
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• 1999

On the basis of data obtained from five forest roads collapsed due to a heavy rainfall of 1995 in Chunchon, Kangwon-do, this study was carried out to evaluate and predict the fill slope failure of forest roads with four factors of forest road structure and those of location condition by using Quantification theory(II). The results were summarized as follows ; In the structure factors of forest road, the fill slope failure was mainly occurred in longitudinal gradients less than $2^{\circ}$ or more than $4^{\circ}$, distance of surface-flow longer than 80m, fill slope length greater than 6m, and fill slope gradients steeper than $35^{\circ}$. In the factors of location condition, the failure was mainly occurred in ridge portion of road position, weathered rock and soft rock of constituent material, slope gradients in the range from $35^{\circ}$ to $45^{\circ}$, and concave and convex of longitudinal slope forms. The priority order for factors influencing on fill slope failure was ranked by fill slope length, constituent material, road position, and so on. And the rate of correct discrimination by analysis of fill slope failure was estimated at the high prediction of 86.5%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.310-319
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• 2020

Korea estimates the traffic noise by measuring the total traffic noise when the traffic passes (SPB; Statistical Pass-By). Another method (CPX; Close Proximity) directly measures the tire/road noise by installing a microphone near the tire. The CPX method is not a formal test method in Korea. There has been little research between CPX and SPB. This study proposes a method for estimating SPB, using the CPX, which is easy to measure. This study used the results of a large-scale test conducted by Korea Expressway Corporation (KEC) and a research paper on CPX in this section. The results by the KEC showed that the low noise pavement has a noise reduction of 10.4dB. In CPX research, the noise reduction was 10.7dB and was similar to 10.4dB in SPB. This study shows why the noise reduction is the same regardless of the position, the reason that the amount of noise reduction is similar, the difference of the noise according to the position of the microphone using the concept of noise summation and distance reduction. This study shows that including the CPX as a variable in the traffic noise prediction program is very important to improve noise prediction reliability.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.3
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• pp.337-342
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• 2011

Medical lift column controlling the vertical position while supporting heavy eccentric load should have the high fatigue strength as well as the extremely low structural deflection and vibration in order to maintain the positioning accuracy. The lift column driven by a induction motor is generally in a three-step sliding boom structure and exhibits the time-varying stress distribution according to the up-and-down motion. This study is concerned with the numerical prediction of the fatigue strength of the lift column subject to the time-varying stress caused by the up-and-down motion. The stress variation during a motion cycle is obtained by finite element analysis and the fatigue life is predicted making use of Palmgren-miner's rule and S-N curves. In order to secure the numerical analysis reliability, a 3-D FEM, model in which the detailed lift column structure and the fitting parts are fully considered, is generated and the interfaces between lift column and pads are treated by the contact condition.

• Journal of Drive and Control
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• v.13 no.4
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• pp.14-22
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• 2016

This paper presents a stochastic and predictive working-risk-assessment algorithm for excavators based on a one-layer laser scanner. The one-layer laser scanner is employed to detect objects and to estimate an object's dynamic behaviors such as the position, velocity, heading angle, and heading rate. To estimate the state variables, extended and linear Kalman filters are applied in consideration of laser-scanner information as the measurements. The excavator's working area is derived based on a kinematic analysis of the excavator's working parts. With the estimated dynamic behaviors and the kinematic analysis of the excavator's working parts, an object's behavior and the excavator's working area such as the maximum, actual, and predicted areas are computed for a working risk assessment. The four working-risk levels are defined using the predicted behavior and the working area, and the intersection-area-based quantitative-risk level has been computed. An actual test-data-based performance evaluation of the designed stochastic and predictive risk-assessment algorithm is conducted using a typical working scenario. The results show that the algorithm can evaluate the working-risk levels of the excavator during its operation.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.541-548
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• 2004

In the maritime container terminal. LMCTS (Linear Motor-based Container Transfer System) is horizontal transfer system for the yard automation, which In., been proposed to take the place of AGV (Automated Guided Vehicle). The system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car (mover). Because of large variant of mover's weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's trouble etc. LMCTS is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the softcomputing method of a multi-step prediction control for LMCTS using DR- FNN (Dynamically-constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi step prediction Consequently, the system has an ability to adapt for external disturbance, detent force, force ripple, and sudden changes by loading and unloading the container.

• Atmosphere
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• v.17 no.4
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• pp.435-453
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• 2007

KEOP (Korea Enhanced Observing Period)-2004 intensive summer observation was carried out from 20 June to 5 July 2004 over the Southwestern part of the Korean peninsula. In this study, the effects of KEOP-2004 intensive observation data on the simulation of precipitation system are investigated using KLAPS (Korea Local Analysis and Prediction System) and PSU/NCAR MM5. Three precipitation cases during the intensive observation are selected for detailed analysis. In addition to the control experiments using the traditional data for its initial and boundary conditions, two sensitivity experiments using KEOP data with and without Jindo radar are performed. Although it is hard to find a clear and consistent improvement in the verification score (threat score), it is found that the KEOP data play a role in improving the position and intensity of the simulated precipitation system. The experiments started at 00 and 12 UTC show more positive effect than those of 06 and 18 UTC. The effect of Jindo radar is dependent on the case. It plays a significant role in the heavy rain cases related to a mesoscale low over Changma front and the landing of a Typhoon. KEOP data produce more strong difference in the 06/18 UTC experiments than in 00/12 UTC, but give more positive effects in 00/12 UTC experiments. One of the possible explanations for this is that : KEOP data could properly correct the atmosphere around them when there are certain amounts of data, while gives excessive effect to the atmospheric field when there are few data. CRA analysis supports this reasoning. According to the CRA (Contiguous Rain Area) analysis, KEOP data in 00/12 UTC experiments improve only the surrounding area, resulting in essentially same precipitation system so the effects remain only in each convective cell rather than the system itself. On the other hand, KEOP data modify the precipitation system itself in 06/18 UTC experiments. Therefore the effects become amplified with time integration.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.8
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• pp.625-631
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• 2013

Reliability of a pin puller was predicted by Monte Carlo simulation. The prediction method is based on the stress-strength interference model that failure occurs if the stress exceeds the strength. In this study, the strength is considered as the energy delivered by combustion of pyrotechnics to retreat the pin to a predetermined position, whereas the stress is regarded as the energy required to resist the pin movement. The former mainly depends on the amount of pyrotechnic charge and the latter is governed by several friction forces and the energy dissipation within locking mechanism. Both the variables of stress and strength were computed using an analytical performance model. The method presented here, not depending upon a large number of test item, can be applicable to predict the reliability of other kinds of pyrotechnic devices.