• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.2
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• pp.40-48
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• 2016

To observe the rail-slab interaction in continuous welded railway(CWR) bridge when earthquake occurs, additional axial rail stresses and relative longitudinal displacements between rail and bridge deck were calculated with input of various load combinations and 3 different types of seismic loads to an analytical model. As results of analysis, it can be found that standard response spectrum proposed by Korea Rail(KR) network authority for earthquake design showed less additional axial rail stresses than allowable levels, but greater relative longitudinal displacement between rail and bridge deck, which means that adjustment of relative longitudinal displacement within a standard level is much more difficult than axial train stress. Additionally, if a large-scaled earthquake as occurred at Kobe, Japan comes up, then both of additional axial rail stress and relative displacement in rail-bridge deck may exceed allowable levels, which indicates to make proper design guides against sudden earthquake occurrence.

• Journal of the Korean Institute of Navigation
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• v.20 no.2
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• pp.1-13
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• 1996

Most of the ShipHandling Simulators of full-mission-bridge system need vast area to install and even PC-based maneuvering simulators are often equipped with Steering Wheel or Engine Telegraphe etc. of data input interface, which necessarily makes the user face with excessive financial burden. These have been one of the obstacles for the officers, captains, pilots and students in access to maneuvering simulation whenever they want to try it in advance prior to actual ship maneuvering. Subsequently, all the officers and captains come to have little chances to train themselves until they arualified as a pilot after a long period of time of realship maneuvering practice on board, which means they have to control they have to control their own ship at sea without clear understanding on her maneuverability when they are forced to do it on the way. And these lack of capability for maneuvering have used so often to result in marine casualties of collision with other ships or pier facilities while maneuvering in harbor. To prevent those accidents by means of enhancing their maneuvering ability, PC-based DeskTop Simulator that allows anyong to access readily at anytime is needed and in conformation to such demand this simulator has been developed. The Software this simulator written in Turbo Pascal Ver. 5.0 has adopted MMG mathmatical model theoretically in part and also it was designed to make it possible that all numeric data inputs and outputs with graphic presentation for maneuvering operation be carried out just only with keyboard and monitor console. With the Simulation software, all the officers, captains, pilots and even students who has a proper computer at hand are expected to be able to make an attempt to simulate the maneuvering of their ownship or any other types of them at any port in which they want to do it.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.3-16
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• 1998

The goal of the transportation policy of Seoul is to increase the ridership of the subway system by constructing the public transportation network, the subway system. To accomplish this goal, the city of Seoul has been constructing the Metropolitan Subway System. Currently, seven subway lines which connect major areas in Seoul are operating. However, the ridership of subway system was not increased as much as we expected, even though more subway lines have been implemented. It seems that although the length of the subway line was extended, the current way of the subway operation that trains stop at every station cannot satisfy the passenger's need. Thus, we should try to increase the demand by providing quicker services and diversifying the subway operations; changing the point of view is required. This paper introduces the distinctive features of the express subway system and the model for analysing the effects of that system. This paper also presents the results for the feasibility study of the express subway system on the 5th Subway Line and Kyong-Eue Railway Line. Based on the results of the case studies, We can conclude as : First, the express system reduces a total travel time by about $13\%$; in particular, the Kyong-Eue Line is more effective than the subway Line ${\sharp}5$. Second, the shorter headway of express trains increases the time saving effects on subway system although it requests more waiting time to low-speed train passengers. When the service frequency is increased from 5 to 7.5 times/hour, total saved time ratio is about $10\%$ in the Subway Line ${\sharp}5$ and about $18\%$ in the Kyong-Eue Line.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.39-47
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• 2020

This paper introduces a novel system-level framework that derives energy efficient electric vehicle (EV) driving speed profile to extend EV driving range without additional cost. This paper first implements an EV power train model considering forces acting on a driving vehicle and motor efficiency. Then, it derivate the minimum-energy driving speed profile for a given driving mission defined by the route. This framework first formulates an optimization problem and uses the dynamic programming algorithm with a weighting factor to derive a speed profile minimizing both of energy consumption and driving time. This paper introduces various weighting factor tracking methods to satisfy the driving time constraint. Simulation results show that runtime of the proposed scaling algorithm is 34% and 50% smaller than those of the binary search algorithm and greedy algorithm, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.613-621
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• 2019

In general, transfer in subway stations is defined as transfer between lines and station transfer. In transfer between lines, passengers change from one subway line to another by utilizing horizontal pedestrian facilities such as transfer passages and pedestrian way. Station transfer appears in the situation that subway lines of enter and exit gate terminals differs from those of boarding and alighting trains and passenger trips utilize both vertical pedestrian facilities such as stair and escalator and horizontal facilities. The hypothesis on these two transfers presupposes that all subway lines are operated by either local train or express in subway network. This means that in a transfer case both local and express trains are operated in the same subway line, as a case of Seoul Metro Line 9, has not been studied. This research proposes a methodology of finding the same line transfer in the Seoul metropolitan subway network built based on the smart card network data by suggesting expanded network concept and a model that passengers choose a theirs minimum time routes.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.321-326
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• 2019

As the use of public transportation increases, many researches are being carried out to increase the fuel economy of car. The goal of this study is to design a front body in order to increase the fuel economy of car through three models of cars. All models were designed with CATIA program and the flow analyses on the air outside car by model were carried out with ANSYS program. At the driving speed of 90km/h, the longer the body, the less air resistance it received. So, it is thought that there is the effect to increase fuel economy. Through this study, it can be helpful to design the front car body that can maximize train efficiency. By utilizing the design data on flow analysis according to the position of radiator inside car in this study, the esthetic sense can be given by being grafted onto the real automotive part.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.44-54
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• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.495-507
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• 2019

Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.

• Computers and Concrete
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• v.27 no.4
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• pp.319-332
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• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.313-330
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• 2021

In an autonomous driving system equipped with a camera and lidar, depth completion techniques enable dense depth estimation. In particular, using self-supervised learning it is possible to train the depth completion network even without ground truth. In actual autonomous driving, such depth completion should have very short latency as it is the input of other algorithms. So, rather than complicate the network structure to increase the accuracy like previous studies, this paper focuses on network latency. We design a U-Net type network with RegNet encoders optimized for GPU computation. Instead, this paper presents several techniques that can increase accuracy during the process of self-supervised learning. The proposed techniques increase the robustness to unreliable lidar inputs. Also, they improve the depth quality for edge and sky regions based on the semantic information extracted in advance. Our experiments confirm that our model is very lightweight (2.42 ms at 1280x480) but resistant to noise and has qualities close to the latest studies.