• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.40-45
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• 2014

This paper suggests the optimal generation dispatch algorithm for ensuring voltage stability margin considering high wind energy injection. Generally, with wind generation being installed into the power system, we would have to consider several factors such as the voltage stability margin because wind turbine generators are mostly induction machines. If the proportion of wind generation increases in the power system increases this would affect the overall stability of the system including the voltage stability. This paper considers a specific system that is composed of two areas: area 1 and area 2. It is assumed that generation cost in area 1 is relatively higher than that in area 2. From an economic point of view generation in area 1 should be decreased, however, in the stability point of view the generation in area 2 should be decreased. Since the power system is a nonlinear system, it is very difficult to find the optimal solution and the genetic algorithm is adopted to solve the objective function that is composed of a cost function and a function concerned with voltage stability constraints. For the simulations, the New England system was selected. The algorithm is implemented and Python 2.5.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2762-2767
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• 2011

With increasing use of wireless mobile devices like smartphone and tablet PC, telecommunication companies have been providing the internet service by installing a multitude of access points (AP) in subway stations. Most of these APs use frequency of 2.4 GHz band range and the three telecom providers (SKT, KT, LGT) are using the limited channels within this range without any regulations. The channels within 2.4GHz band are already saturated as the companies are setting up wireless AP even within the subway trains for better service. This can affect other 2.4GHz wireless devices used for other purposes with channel interference, etc. This study has tested and analyzed the effects of the wireless APs installed within the subway stations and trains for the internet service on ZigBee-based sensor network for the intelligent surveillance system of urban transit, which is currently being developed and installed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.202-210
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• 2018

Recently, the subway running in metro environments has a problem in securing the safety of passengers due to the failure of announcement in emergency situations such as breakdown, train accidents and power outage in the underground tunnels. Thus, there is a need to develop an emergency broadcasting system that can provide the announcement to all passenger cars in any emergency situations on the railway route. In this paper, the applicability of various wireless communication technologies for the emergency broadcasting system through the measurement campaign was examined in Seoul metropolitan subway. A WAVE(Wireless Access in Vehicular Environments) is communication technology that can use 5.9GHz dedicated frequency band without charge and it is possible to directly communicate between terminals over 200m without the help of additional relay. Especially, it confirms robust communication performance in the various metro environments, and therefore, it is considered to be suitable as a communication method of a radio-connected emergency broadcasting system for urban subway.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.1-20
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• 2022

The basic way to charge vehicles for using road and public transport networks is the entry-exit toll system. This system works by reading Hi-Pass and public transportation cards of the vehicles using card readers. However, the problems of navigating a route in consideration of entry-exit toll systems include the non-additive costs of enumerating routes. This problem is known as an NP-complete task that enumerates all paths and derives the optimal path. So far, the solution to the entry-exit toll system charging has been proposed in the form of transforming the road network. However, unlike in the public transport network where the cards are generalized, this solution has not been found in situations where network expansion is required with a transfer, multi-modes and multiple card readers. Hence, this study introduced the Link Label for a public transportation network composed of card readers in which network expansion is bypassed in selecting the optimal path by enumerating the paths through a one-to-one k-path search. Since the method proposed in this study constructs a relatively small set of paths, finding the optimal path is not burdensome in terms of computing power. In addition, the ease of comparison of sensitivity between paths indicates the possibility of using this method as a generalized means of deriving an optimal path.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of the Korean Society for Railway
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• v.13 no.1
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• pp.71-77
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• 2010

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.128-139
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• 2017

Railway is one of the public transportation systems along with shipping and aviation. With the recent introduction of high speed train, its proportion is increasing rapidly, which results in the higher risk of catastrophic failures. The wheel bearing to support the train is one of the important components requiring higher reliability and safety in this aspect. Recently, many studies have been made under the name of prognostics and health management (PHM), for the purpose of fault diagnosis and failure prognosis of the bearing under operation. Among them, the most important step is to extract a feature that represents the fault status properly and is useful for accurate remaining life prediction. However, the conventional features have shown some limitations that make them less useful since they fluctuate over time even after the signal de-noising or do not show a distinct pattern of degradation which lack the monotonic trend over the cycles. In this study, a new method for feature extraction is proposed based on the observation of relative frequency energy shifting over the cycles, which is then converted into the feature using the information entropy. In order to demonstrate the method, traditional and new features are generated and compared using the bearing data named FEMTO which was provided by the FEMTO-ST institute for IEEE 2012 PHM Data Challenge competition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.1
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• pp.43-55
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• 2019

Smart phone based navigation applications are very useful in everyday life. Cost-effective and user friendly navigation can be provided to the user by many applications available in market. Using the Smart phone these navigation applications provide accurate navigation for outdoor locations. But providing an accurate navigation underground space such as subway station is still a challenge. It is hence more convenient and appropriate for mobility services if the visitors could simply view the guidance of the subway station on their mobile phone, wherever and whenever it is needed. This study develops a algorithm for indoor navigation with the help of Augmented Reality(AR) and QR marker code from the entrance to the train platform for users. This indoor navigation uses AR and QR maker codes for two purposes: to provide the user link to the subway station location and to provide the current guidance details to the user. This Smart phone algorithm that uses a smart phone optical tool to decode the QR marker to determine the location information and provide guidance to the AR without indoor Maps. This algorithm also provides a module to guide mobility vulnerable to the Barrier Free route to destination.

• 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.