• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.1-24
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• 2016

Northeast Asia is changing rapidly, including becoming an increasingly important part of the world economy. Various logistical systems and networks are being established in this area, including South and North Korea, China, Japan, Russia, and Mongolia. Thus, a new systematic logistical network and development strategy is required. The division of the Korean peninsula has for a long time made the region ineffective in terms of land logistics. South Korea's connection to Eurasia is blocked by North Korea, which means it is essentially treated as an island. Furthermore, South Korea does not have an efficient logistics system or effective vision and strategy. Northeast Asia has the potential to be one of the largest economic communities in the world, similar to the EU and North America. Thus, a united Korea can play an important role as the land bridge of Northeast Asia and the logistical hub of the region. This study reviews the logistical position, tasks, and problems of the Korean peninsula and suggests a desirable development strategy and vision. Specifically, after examining the conditions for a land logistical system, focusing on railways, this study proposes several future-oriented development strategies and tactics that will position the peninsula as a logistical hub in Northeast Asia. We anticipate that this study will serve as the basis for future research that will investigate the topics presented here in more detail.

• Journal of the Korean Geographical Society
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• v.31 no.3
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• pp.593-612
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• 1996

This study concern with the changes of street from the Choseon Dynasty to present days around Old-Boundary in Taegu, analyzing the backgrounds of change factors and development trends of the Taegu City. The basis element of a city structure is the street. Therefore, in this study, a chage of street space of a city was investigted. Historically, Taegu was a walled city and had a Mono-nucleus which was restricted by the castle, and served as a starting point of formation of spatial structuure. The form of the artery street took a "T" pattern, othe streets were formed in irregular shapes. As the city grew gradually, the castle was removed on account of diversification in traffic network, change of socio-economic organization in traffic network, change of socio-economic organization, formation of industrial bases and functional distribution. CBD of this city has been located within the area surrounded by these streets. This is a kind of general pattern of traditional walled cities through the world in both Western and Oriental societies. A s the begining of this centry, a 'Dark Ages' descended upon Korea because the country was under the Japanese-Korean Annexation, and, throughout this period, the urban planning was planned exclusively for Japanese. The street pattern within residential areas of Korea took the maze type, in contrast with Japanese residential areas which showed grid pattern of streets. This is another general pattern of almost of all colonial cities especially in Asia. High class residential areas were planned and built by Japanese, and they were located within 5-10 minutes' on-foot distance from the CBD hard core. This high prestige has continued until the 1980s when it occurred land use succession which commerical functions invaded into residential areas. Back in the colonial period, there was a between two hetrogeneous groups due to the fact that the Japanese lived mainly oriented the new railway system but that Koreans still lived along the old highway system which ran through the Korea Peninsula. Street netwook formed in the above process has maintained its shape without great changes after the liberation form the Japanese Colony. Taegu has, accordingly, developed ring-radial network system which has been a combination of radial and ring facilities. The present conditions of street patterns in Taegy mainly depend on 4 rings and 8 radius, with grid pattern street able to be found in Old Boundary.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1701-1708
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• 2011

A typical day in the subway transportation is used by hundreds of thousands are also concerned about the safety of the various workrooms with high underground fire or other less than in the subway users could be damaging even to be raised and there. In 2010, in fact, room air through vents in the fire because smoke and toxic gas accident victims, and train service suspended until such cases are often reported. In response to these incidents in subway stations, even if the latest IT technology, wireless sensor network technology and intelligent video surveillance technology by integrating fire and structural integrity, such as a comprehensive integrated surveillance system to monitor the development of intelligent urban transit system and are under study. In this study, prior to the application of the monitoring system into the field stations, authors carried out the ZigBee-based wireless sensor networks performance analyzation in the Chungmuro station. The test results at a communications room and ventilation room of the station are summarized and analyzed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.502-502
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• 2007

A catenary system should be designed to have an uniform elasticity over a span in order to maintain the lowest possible loss of contact between a pantograph and a contact wire. A elasticity uniformity of a catenary can be regarded as a important design factor used for predicting the current collection performance for a catenary. There are a couple of formulas to calculate the degree of elasticity uniformity of a catenary according to the literature survey. The effectiveness of these formulas is reviewed by performing catenary elasticity and loss of contact analysis for various different configurations of catenary systems using a beam element based FEM program. The results reveals that these formulas are not suitable to predict the current collection performance for a catenary. Therefore, a new formula based on the standard deviation of the elasticity over a span is proposed in this study. The analysis results show that the new formula for an elasticity uniformity of a catenary is very effective in predicting the current collection performance for a catenary.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.9-18
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• 2021

Constructing the Metropolitan Railway Express (the GTX) may have an impact on consumer confidence and housing sales price located near the planned route. This study looked at how consumers' psychology and housing prices change as the large-scale transport infrastructure plane was planned. Also, it looked at the relationship between consumer sentiment and housing prices to analyze the impact of new transportation facilities inflows. Using a correlation analysis, the relationship between the consumer sentiment index and the actual transaction price of apartments was identified. The impact of GTX on the consumer sentiment index and the actual transaction price of apartments was looked at using the Difference-in-Differences methodology. Our finding shows that the construction plan of a large-scale transportation infrastructure in the metropolitan area affects the sentiment of housing consumption and actual transactions. In a situation where the government is speeding up the construction of a wide-area transportation network such as GTX with the goal of becoming a city where people can commute to downtown Seoul within 30 minutes, policies that can stabilize the housing market in transportation hubs should be suggested.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.55-63
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• 2022

In recent years, with the rapid development of large and medium-sized urban rail transit in China, the total operating mileage of high-speed railway and the total number of EMUs(Electric Multiple Units) are rising. The system complexity of high-speed EMU is constantly increasing, which puts forward higher requirements for the safety of equipment and the efficiency of maintenance.At present, the maintenance mode of high-speed EMU in China still adopts the post maintenance method based on planned maintenance and fault maintenance, which leads to insufficient or excessive maintenance, reduces the efficiency of equipment fault handling, and increases the maintenance cost. Based on the intelligent operation and maintenance technology of PHM(prognostics and health management). This thesis builds an integrated PHM platform of "vehicle system-communication system-ground system" by integrating multi-source heterogeneous data of different scenarios of high-speed EMU, and combines the equipment fault mechanism with artificial intelligence algorithms to build a fault prediction model for traction motors of high-speed EMU.Reliable fault prediction and accurate maintenance shall be carried out in advance to ensure safe and efficient operation of high-speed EMU.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.41-48
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• 2021

As the rural population continues to decline and aging, the improvement of agricultural productivity is becoming more important. Early prediction of crop quality can play an important role in improving agricultural productivity and profitability. Although many researches have been conducted recently to classify diseases and predict crop yield using CNN based deep learning and transfer learning technology, there are few studies which predict postharvest crop quality early in the planting stage. In this study, a early quality prediction model is proposed for sprout ginseng, which is drawing attention as a healthy functional foods. For this end, we took pictures of ginseng seedlings in the planting stage and cultivated them through hydroponic cultivation. After harvest, quality data were labeled by classifying the quality of ginseng sprout. With this data, we build early quality prediction models using several pre-trained CNN models through transfer learning technology. And we compare the prediction performance such as learning period and accuracy between each model. The results show more than 80% prediction accuracy in all proposed models, especially ResNet152V2 based model shows the highest accuracy. Through this study, it is expected that it will be able to contribute to production and profitability by automating the existing seedling screening works, which primarily rely on manpower.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.679-686
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• 2006

Recently, the city railway in the Seoul Metropolitan Area (SMA) has offered a low quality of service as a passage time, because it was operated slowly. So, the people who live in modern society are not satisfied about passage time, therefore, this study tried to make that the subway in the SMA becomes a more functional and effective wide-area-transportation-network through an express train introduction's method which examined cases from abroad and current system. and then presented how express train could be applied to current system. In a case study, We used the An-San Line and Su-In Line as a examples and developed a schedule which can minimize the delaying time of subway by using Branch & Bound Algorithm. The train operational plan was loaded to consider a railroad siding, Obtained site, and the dispatch interval(three to ten minutes) for the express and local lines and finally, We presented an alternative operational plan which made by those factors.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.91-98
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• 2023

Recently, according to development of artificial intelligence, a wide range of industry being automatic and optimized. Also we can find out some research of using supervised learning for deteceting defect of railway in domestic rail industry. However, there are structures other than rails on the track, and the fastener is a device that binds the rail to other structures, and periodic inspections are required to prevent safety accidents. In this paper, we present a method of reducing cost for labeling using semi-supervised and transfer model trained on rail fastener data. We use Resnet50 as the backbone network pretrained on ImageNet. At first we randomly take training data from unlabeled data and then labeled that data to train model. After predict unlabeled data by trained model, we adopted a method of adding the data with the highest probability for each class to the training data by a predetermined size. Futhermore, we also conducted some experiments to investigate the influence of the number of initially labeled data. As a result of the experiment, model reaches 92% accuracy which has a performance difference of around 5% compared to supervised learning. This is expected to improve the performance of the classifier by using relatively few labels without additional labeling processes through the proposed method.