• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.631-639
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• 2008

This paper reports the mode shapes of Seohae cable-stayed bridge extracted by TDD technique. In order to record total 72 acceleration points in the vertical direction of the bridge deck, a custom made data acquisition system with LAN communication has been especially developed and a set of ambient vibration tests has been conducted. For the measured acceleration responses, total twenty four mode shapes up to 2Hz has been extracted by TDD technique. The extracted mode shapes include many new modes that have not been identified in the current on-line health monitoring system installed in the bridge. It is confirmed that TDD technique is the most effective in extracting the high resolution mode shapes on a particularly long span bridge.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.105-115
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• 2011

In Vision-based Intelligent Transportation System(ITS) the segmentation of road region is a very basic functionality. Accordingly, in this paper, we propose a region segmentation method using adaptive pattern extraction technique to segment road regions and sky regions from original images. The proposed method consists of three steps; firstly we perform the initial segmentation using Mean Shift algorithm, the second step is the candidate region selection based on a static-pattern matching technique and the third is the region growing step based on a dynamic-pattern matching technique. The proposed method is able to get more reliable results than the classic region segmentation methods which are based on existing split and merge strategy. The reason for the better results is because we use adaptive patterns extracted from neighboring regions of the current segmented regions to measure the region homogeneity. To evaluate advantages of the proposed method, we compared our method with the classical pattern matching method using static-patterns. In the experiments, the proposed method was proved that the better performance of 8.12% was achieved when we used adaptive patterns instead of static-patterns. We expect that the proposed method can segment road and sky areas in the various road condition in stable, and take an important role in the vision-based ITS applications.

• Journal of KIISE
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• v.42 no.2
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• pp.203-212
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• 2015

This paper proposes a traffic sign recognition method in real road environments. The video stream in driving environments has two different characteristics compared to a general object video stream. First, the number of traffic sign types is limited and their shapes are mostly simple. Second, the camera cannot take clear pictures in the road scenes since there are many illumination changes and weather conditions are continuously changing. In this paper, we improve a modified census transform(MCT) to extract features effectively from the road scenes that have many illumination changes. The extracted features are collected by histograms and are transformed by the dense descriptors into very high dimensional vectors. Then, the high dimensional descriptors are encoded into a low dimensional feature vector by Fisher-vector coding and Gaussian Mixture Model. The proposed method shows illumination invariant detection and recognition, and the performance is sufficient to detect and recognize traffic signs in real-time with high accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.3
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• pp.164-169
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• 2009

This paper proposes a road detection method using BP(Back-Propagation) neural network based on texture information of the each candidate road region segmented for satellite images. To segment the candidate road regions, the histogram-based binarization method proposed by N.Otsu is firstly performed and the neighboring regions surrounding road regions are then removed. And after extracting the principal color using the histogram of the segmented foreground, the candidate road regions are classified into the regions within ${\pm}25$ of the principal color. Finally, the road regions are segmented using BP neural network based on texture information of the candidate regions. The texture information in this paper is calculated using co-occurrence matrix and is used as an input data of the BP neural network. The proposed method is based on the fact that the road has the constant intensity and shape. The experiment demonstrated the validity of the proposed method and showed 90% detection accuracy for the various images.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.3
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• pp.43-49
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• 2012

Information obtained from DFD(Data Flow Diagram) are very important in system maintenance, because most legacy systems are analyzed using DFD in structured analysis. In our thesis, we design and implement an extractor for design sequence of database tables using DFD. Our extractor gets DFDs as input data, transform them into a directed graph, and extract design sequence of DB tables. We show practicality of our extractor by applying it to a s/w system in operation.

• Journal of KIISE
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• v.43 no.1
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• pp.40-46
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• 2016

Digital road map generation is primarily based on artificial satellite photographing or in-site manual survey work. Therefore, these map generation procedures require a lot of time and a large budget to create and update road maps. Consequently, people have tried to develop automated map generation systems using GPS trajectory data sets obtained by public vehicles. A fundamental problem in this road generation procedure involves the extraction of representative trajectory such as main roads. Extracting a representative trajectory requires the base data set of piecewise line segments(GPS-trajectories), which have close starting and ending points. So, geometrically similar trajectories are selected for clustering before extracting one representative trajectory from among them. This paper proposes a new divide- and-conquer approach by partitioning the whole map region into regular grid sub-spaces. We then try to find similar trajectories by sweeping. Also, we applied the $Fr{\acute{e}}chet$ distance measure to compute the similarity between a pair of trajectories. We conducted experiments using a set of real GPS data with more than 500 vehicle trajectories obtained from Gangnam-gu, Seoul. The experiment shows that our grid partitioning approach is fast and stable and can be used in real applications for vehicle trajectory clustering.

• 한국도로학회:학술대회논문집
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• 2006.10a
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• pp.429-438
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• 2006

• 한국도로학회:학술대회논문집
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• 2009.11a
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• pp.81-87
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• 2009

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.443-446
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• 2002

인터넷과 컴퓨터 기술이 발달함에 따라 가상공간에 대한 관심은 커져가고 있다. 그러나 가상공간을 생성하는 작업은 많은 시간과 노력이 필요하다. 그래서 가상공간 모델링에 관련된 연구들이 많이 이루어지고 있다. 본 논문에서는 2차원 CAD 데이터로부터 가상공간을 모델링하는 방법을 제시한다. CAD 파일에서 2차원 지형정보를 추출하여 웹에서 볼 수 있는 3차원 가상공간을 생성한다. 가상공간생성 과정은 전처리, 데이터 추출, 모델생성, 렌더링으로 이루어진다. 전처리는 CAD 파일에서 도로경계선을 분리하며 데이터 추출은 등고선, 도로경계선, 건물 정보를 CAD 파일로부터 추출하는 과정이다. 모델 생성은 추출한 지형정보들을 이용해서 3차원 공간모형 데이터를 생성하는 과정이다. 본 논문에서 제시한 방법은 실세계에 근접한 가상공간을 생성하며 가상공간을 생성하는데 드는 시간과 노력을 줄일 수 있다.