• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.560-565
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• 2002

In this paper, we implemented the sign board system that displays user's image, user's sentence, the information from DARC an[1 information based position by GPS module for vehicle. The existing sign board is displaying only user's image and sentence. Or other existing sign board is displaying the information via CDMA network. However, our system is also able to display the user's message like other system and gain the information more cheap by DARC. This system consists of 6 parts. The DARC control part classes the DARC information - news, weather, stock and time. The GPS control part gains moment and item to display with calculating the information of global position, direction, speed and satellite. The LED control part has two buffers to store and handle the image. The buffers help the system display various effected images on LED board. An external memory card includes the location based data, the option file and the displayed data files. The data files are stored by FAT 16 with the folder structure on external memory card. The USB controls the communication with PC. PC programs can control and monitor this system. This system is using G72l voice file format, for casting the information. This system was established at the vehicle and we monitored this system. The system displayed the DARC data , user's data and the location based data on the LED board, successfully.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.172-180
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• 2013

In this paper, we propose an energy-efficient camera operating scheme to save energy which can be used for mass surveillance cameras. This technique determines how many cameras should be turned on in the consideration of the velocity vector of monitoring targets, which is acquired by DSRC object tracking, the model of the specification of installed cameras, and the road model of installed sites. Also, we address other techniques used to save energy for the surveillance system as well. Throughout performance evaluation, we demonstrate the excellence of our proposed scheme compared with previous approaches.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.995-999
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• 2010

Every year, traffic accidents and traffic congestion have been rapidly increasing, Although the roadway design and signal system have been improved to relieve traffic accidents, traffic casualties and property damage do not decrease. This paper develops a real-time traffic accident detection and analysis system (RTADAS): In the proposed system, we aim to precisely detect traffic accidents at different design and flow of intersections, However, because the data collected from intersections have uncertainty and complicated causal dependency between them, we construct probability-based networks for correct accident detection.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.44-55
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• 2013

Intelligent transportation systems allow us to have valuable opportunities for collecting reliable wide-area coverage traffic and weather data. Significant efforts have been made in many countries to apply these data. This study identifies the critical points for classifying rain intensity by analyzing the relationship between rainfall and the amount of speed reduction. Then, traffic prediction performance by rain intensity level is evaluated using relative errors. The results show that critical points are 0.4mm/5min and 0.8mm/5min for classifying rain intensity (slight, moderate, and heavy rain). The best prediction performance is observable when previous five-block speed data is used as inputs under normal weather conditions. On the other hand, previous two or three-block speed data is used as inputs under rainy weather conditions. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.74-83
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• 2011

The high-pass transportation information system directly collects section information by using probe cars and therefore can offer more reliable information to drivers. However, because the running condition and features of probe cars and statistical processing methods affect the reliability of the information and particularly because the section travel time is greatly influenced by whether there has been delay by signals on urban roads or not, there can be much deviation among the collected individual probe data. Accordingly, researches in multilateral directions are necessary in order to enhance the credibility of the section information. Yet, the precedent studies related to high-pass information provision have been conducted on the highway sections with the feature of continuous flow, which has a limit to be applied to the urban roads with the transportational feature of an interrupted flow. Therefore, this research aims at analyzing the features of high-pass transportation data on urban roads and finding a proper processing method. When the characteristics of the high-pass data on urban roads collected from RSE were analyzed by using a time-space diagram, the collected data was proved to have a certain pattern according to the arriving cars' waiting for signals with the period of the signaling cycle of the finish node. Moreover, the number of waiting for signals and the time of waiting caused the deviation in the collected data, and it was bigger in traffic jam. The analysis result showed that it was because the increased number of waiting for signals in traffic jam caused the deviation to be offset partially. The analysis result shows that it is appropriate to use the mean of this collected data of high-pass on urban roads as its representative value to reflect the transportational features by waiting for signals, and the standard of judgment of delay and congestion needs to be changed depending on the features of signals and roads. The results of this research are expected to be the foundation stone to improve the reliability of high-pass information on urban roads.

• Journal of Platform Technology
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• v.11 no.3
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• pp.3-11
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• 2023

Real-time control systems are growing rapidly as infrastructure technologies such as IoT and mobile communication develop and services that value real-time such as factory management and vehicle operation checks increase. Various solutions have been proposed to increase the time sensitivity of this system, but most real-time control systems are currently composed of local servers and multiple clients located in control stations, which are transmitted to local servers where control systems are located. In this paper, we proposed an edge computing-based real-time control model that can reduce the time it takes for the bus information system, one of the real-time control systems, to provide the information to the user at the time it collects the information. Simulating the existing model and the edge computing model, the edge computing model confirmed that the cost for users to receive data is reduced from at least 10% to up to 80% compared to the existing model.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.8-19
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• 2016

Potholes are caused by the presence of water in the underlying soil structure, which weakens the road pavement by expansion and contraction of water at freezing and thawing temperatures. Recently, automatic pothole detection systems have been studied, such as vibration-based methods and laser scanning methods. However, the vibration-based methods have low detection accuracy and limited detection area. Moreover, the costs for laser scanning-based methods are significantly high. Thus, in this paper, we propose a new pothole detection system using a commercial black-box camera. Normally, the computing power of a commercial black-box camera is limited. Thus, the pothole detection algorithm should be designed to work with the embedded computing environment of a black-box camera. The designed pothole detection algorithm has been tested by implementing in a black-box camera. The experimental results are analyzed with specific evaluation metrics, such as sensitivity and precision. Our studies confirm that the proposed pothole detection system can be utilized to gather pothole information in real-time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.747-761
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• 2024

Recently, modern vehicles have been controlled by Electronic Control Units (ECUs), by which the safety and convenience of drivers are highly improved. It is known that a luxury vehicle has more than 100 ECUs to electronically control its function. However, the modern vehicles are getting targeted by cyber attacks because of this computer-based automotive system. To address the cyber attacks, automotive manufacturers have been developing some methods for securing their vehicles, such as automotive Intrusion Detection System (IDS). This development is only allowed to the automotive manufacturers because they have databases for their in-vehicle network (i.e., DBC Format File) which are highly confidential. This confidentiality poses a significant challenge to external researchers who attempt to conduct automotive security researches. To handle this restricted information, in this paper, we propose a method to partially understand the DBC Format File by analyzing in-vehicle network traffics. Our method is designed to analyze Controller Area Network (CAN) traffics so that checksum signals are identified in CAN Frame Data Field. Also, our method creates a Lookup Set by which a checksum signal is correctly estimated for a given message. We validate our method with the publicly accessible dataset as well as one from a real vehicle.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1232-1235
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• 2022

최근 자동차, 철도차량 등 사용자가 있는 기계 시스템에서의 고장 발생 시 사용자의 안전과 관련된 사고로 이어질 수 있어 부품에 대한 모니터링 및 고장 여부 판단은 매우 중요하다. 이러한 부품 중에서 베어링은 회전체와 회전하지 않는 물체 사이에서 회전이 원활하게 이루어질 수 있도록 하는 부품인데, 베어링에 결함이 발생하게 될 경우, 기계 시스템이 정지하거나, 마찰 열에 의해 화재 등의 치명적인 위험이 발생한다. 본 논문에서는 Resnet과 오토인코더를 활용하여 진동 신호 기반의 베어링의 고장을 감지하고 분류할 수 있는 모델을 제안한다. 제안 방법은 raw data를 이미지로 변환하여 입력으로 사용하는데, 이러한 접근을 통해 수집된 데이터의 손실을 최소화하고 데이터가 가지는 정보를 최대한 분석에 활용할 수 있다. 제안 모델의 검증을 위하여 공개된 데이터셋으로 학습/검증 하였고, 제안 방법이 기존 방법과 비교하여 더 높은 F1 Score와 정확도를 보임을 확인하였다.