• Journal of the Korean Geosynthetics Society
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• v.8 no.2
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• pp.47-54
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• 2009

The ballast track, the most common type of conventional railroad track in Korea, is deteriorated by abrasion of ballast, it's penetration into roadbed, and rugged surface of roadbed caused by cyclic loading of train. Persistent occurrence of those phenomena lead to insufficient drain capacity, one of major factors in track design, and it increases pore water pressure and decreases of shear strength under rainfall condition leading to unstable roadbed. In this study, cylindrical model tests are executed for 3 types of geotextile applying cyclic loading in order to observe the characteristics of displacement and bearing capacity of geotextile, and undrained condition has been applied for 0 day, 3 days and 7 days to each geotextiles. The results showed that there was about 1% difference at the final displacement rates between reinforced soils and nature soils and the displacement of the ground surface increases along with the degrees of the saturation. And in case that water contents exceeds the threshold, it is also apparent that weight and tensile strength of geotextile influences displacement of the ground surface. And the larger weight of geotextile is, the smaller plastic displacement. It is evaluated that non-woven fabric comes into effect on reducing the bearing capacity but, the weight of geotextile has little influence on it.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.4
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• pp.204-211
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• 2011

A dynamic analysis procedure is developed to provide a better estimation of the dynamic responses of bridge during the passage of high speed railway vehicles. Particularly, a three dimensional numerical model including the structural interaction between high speed vehicles, bridges and railway endures to analyse accurately and evaluate with in-depth parametric studies for dynamic responses of various bridge span lengths running KTX railway locomotive up to increasing maximum speed(450km/h). Three dimensional frame element is used to model the simply supported pre-stressed concrete (PSC) box bridges for four span lengths(40~25m). Track irregularity employed as a stationary random process from the given spectral density functions and irregularities of both sides of the track are assumed to have high correlation. The high-speed railway vehicle (KTX) is used as 38-degree of freedom system. Three displacements (Vertical, lateral, and longitudinal) as well as three rotational components (Pitching, rolling, and yawing) are considered in the 38-degree of freedom model. The dynamic amplification factors are evaluated by the developed procedure under various traveling conditions, such as track irregularity camber, train speed and ballast. The dynamic analysis such as Newmark-${\beta}$ and Runge-Kutta methods which are able to analyse considering the dynamic impact factors are compared and contrasted.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Journal of the Korean Society for Railway
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• v.16 no.6
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• pp.466-472
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• 2013

The use of a concrete track is gradually growing in urban and high-speed railways in many part of the world. The resilient pad, which is essentially when concrete tracks are used, plays the important role of relieving the impact caused by train loads. The simple fatigue test[1] to estimate the variable stiffness of resilient pads is usually performed, but it differs depending on the practical conditions of different railways. In this study, the static stiffness levels of used resilient pads according to passing tonnages levels were measured in laboratory tests. Also, the simple fatigue test and the multi-stress accelerated degradation test for new resilient pads were performed in a laboratory. The static stiffness of the used pad was compared with the results of tests of usage times and cycles. The results of the comparison showed that the variable static stiffness levels of the used pad were similar to results of the multi-stress accelerated degradation test considering the fatigue and heat load. With a T-NT equation related to the degree of the multi-stress accelerated degradation, a model of multi-stress accelerated degradation for a resilient pad was devised. It was found through this effort that the total acceleration factor was approximately 2.62. Finally, this study proposes an equation for a multi-stress accelerated degradation model for polyurethane resilient pads.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.51-61
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• 2008

Most of Korea's 15 local airports, with the exception Jeju, Gimpo and Gimhae airports, have been several billion Won in the red each year. It has been reported that one of the causes of the poor financial performance is inaccurate air traffic demand predictions. Under the situation, the entry of low-cost carrier air service using turbo-prop airplanes into the domestic airlines market gets a wide range of support, which is expected to promote the convenience of consumers and help to activate local airports. In this study, the authors (1) suggest a high-speed transport demand model among existing airlines, Korea Train Express (KTX) and low-cost carrier air service; (2) try to make low-cost air carrier demand predictions for a route between Seoul and Daegu through a stated-preference survey; and (3), examine possible effectiveness of selected policy measures by establishing an estimation model. First, fare has a strong influence for mode choice between high-speed transport modes when considering the entry of low-cost carrier air service between Seoul and Daegu. Even low-cost carrier air service fare is set at 38,000 won, which is considerably low compared with that of KTX, in the regions where the total travel time is the same for both low-cost carrier air service and KTX, the probability of selecting low-cost carrier air service is 0.1, which shows little possibility of modal change between high speed transportation means. It is suggested that the fare of low-cost air service between Seoul and Daegu should be within the range of from of 38,000 to 44,000 Won; if it is higher, the demand is likely to be lower than expected.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.155-164
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• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1172-1179
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• 2020

With the establishment of the railway integrated radio network (LTE-R) environment, radio-based train control transmission and reception and various forms of service are provided. The smooth delivery of these services requires improved performance in a highly reliable and available wireless environment. This paper measured the LTE-R radio communication environment to improve radio communication performance of railway integrated wireless network reliability and availability, analyzed the results, and established the wireless environment model. Based on the built-up model, we also proposed an improved radio-access algorithm to control trains for improved reliability, suggesting a way to improve stability for handover that occur during open-air operation, and proposed an algorithm for frequency auto-heating to improve availability. For simulation, data were collected from the Korea Rail Network Authority (Daejeon), Manjong-Gangneung KTX route, which can measure the actual data of LTE-R wireless environment, and the results of the simulation show performance improvement through algorithm.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1709-1722
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• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.359-366
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• 2022

In this paper, we present the Korean menu-ordering Sentence Text-to-Speech (TTS) system using conformer-based FastSpeech2. Conformer is the convolution-augmented transformer, which was originally proposed in Speech Recognition. Combining two different structures, the Conformer extracts better local and global features. It comprises two half Feed Forward module at the front and the end, sandwiching the Multi-Head Self-Attention module and Convolution module. We introduce the Conformer in Korean TTS, as we know it works well in Korean Speech Recognition. For comparison between transformer-based TTS model and Conformer-based one, we train FastSpeech2 and Conformer-based FastSpeech2. We collected a phoneme-balanced data set and used this for training our models. This corpus comprises not only general conversation, but also menu-ordering conversation consisting mainly of loanwords. This data set is the solution to the current Korean TTS model's degradation in loanwords. As a result of generating a synthesized sound using ParallelWave Gan, the Conformer-based FastSpeech2 achieved superior performance of MOS 4.04. We confirm that the model performance improved when the same structure was changed from transformer to Conformer in the Korean TTS.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.102-111
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• 2023

Automatic Music Transcription (AMT) is a task that detects and recognizes musical note events from a given audio recording. In this paper, we focus on reducing the latency of real-time AMT systems on piano music. Although neural AMT models have been adapted for real-time piano transcription, they suffer from high latency, which hinders their usefulness in interactive scenarios. To tackle this issue, we explore several techniques for reducing the intrinsic latency of a neural network for piano transcription, including reducing window and hop sizes of Fast Fourier Transformation (FFT), modifying convolutional layer's kernel size, and shifting the label in the time-axis to train the model to predict onset earlier. Our experiments demonstrate that combining these approaches can lower latency while maintaining high transcription accuracy. Specifically, our modified model achieved note F1 scores of 92.67 % and 90.51 % with latencies of 96 ms and 64 ms, respectively, compared to the baseline model's note F1 score of 93.43 % with a latency of 160 ms. This methodology has potential for training AMT models for various interactive scenarios, including providing real-time feedback for piano education.