• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.163-166
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• 2005

The latest wireless communications technology bring about the rapid developments of Global Position System and Location-Based Service. It is very important for the moving object database to deal with database queries related to the trajectories of a moving objects and the valid time of the query results as well. In this paper, we propose how to get not only the current result of query but also the valid time and the result after the time when a query point and objects are moving at the same time. We would like to predict the valid time by formula because the current results will be incorrect due to the characteristic of the continuous movements of the moving objects and the future results can not be calculated by iterative computations.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1179-1186
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• 2013

In this paper, an efficient vehicle detection and tracking algorithm for detection incident in tunnel is proposed. The proposed algorithm consists of three steps. The first one is a step for background estimates, low computational complexity and memory consumption Running Gaussian Average (RGA) is used. The second step is vehicle detection step, Adaboost algorithm is applied to this step. In order to reduce false detection from a relatively remote location of the vehicles, local features according to height of vehicles are used to detect vehicles. If the local features of an object are more than the threshold value, the object is classified as a vehicle. The last step is a vehicle tracking step, the Kalman filter is applied to track moving objects. Through computer simulations, the proposed algorithm was found that useful to detect and track vehicles in the tunnel.

• 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 Advanced Navigation Technology
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• v.25 no.6
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• pp.441-449
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• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.2
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• pp.26-33
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• 2009

New equipment that is useful for driving is developing every day. Navigation is one of the most popular equipment among them. Telematics market including navigation is getting bigger and bigger. However, traffic accident from using telematics equipment is also increasing. Drivers may lose glance using navigation, and driver's workload will also grow by driver's distraction. This thesis is base on the study about the influence on the drivers' workload by using the telematics equipment. Previous study of drivers' workload used psychological method and behavior test method, but it was less connection with telematics equipment. The main object of this thesis is measuring the workload according to the telematics usage by HMI (Human Machine Interface) in the virtual reality. Therefore, we developed GPS simulator, and made an experiment of whether using the navigation or not on the highway and an experiment of the location of navigation in downtown. The result of these experiments is that workload when driver used navigation was higher than when driver didn't use navigation. In addition, workload was different according to the location, and HUD (Head-Up Display) was especially higher than other locations but also its information delivery ability was the best.

• International Journal of Highway Engineering
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• v.18 no.6
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• pp.173-180
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• 2016

OBJECTIVES : Fixed roadside objects are a threat to drivers when their vehicles deviate from the road. Therefore, such roadside objects need to be suitably dealt with to decrease accidents. This study determines the factors affecting the severity of accidents because of fixed roadside objects. METHODS : This study analyzed the crash severity impact of fixed roadside objects by using ordered probit regression as the analysis methodology. In this research, data from 896 traffic accidents reported in the last three years were used. These accidents consisted of sole-car accidents, fixed roadside object accidents, and lane-departure accidents on the national highway of Korea. The accident severity was classified as light injury, severe injury, and death. The factors relating to the road and the driver were collected as independent variables. RESULTS : The result of the analysis showed that the variables of the crash severity impact are the collision location (left side), gender of the driver (female), alcohol use, collision facility (roadside trees, traffic signals, telephone poles), and type of road (rural segments). Additionally, the collision location (left side), gender of the driver (female), alcohol use, collision facility (street trees, traffic signals, telephone poles), and type of road (rural segments), in order of influence, were found to be the factors affecting the crash severity in accidents due to fixed roadside objects. CONCLUSIONS : An alternative solution is urgently required to reduce the crash severity in accidents due to fixed roadside objects. Such a solution can consider the appropriate places to install breakaway devices and energy-absorbing systems.

• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.494-502
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• 2007

Ubiquitous location based services (u-LBS) will be interested to an important services. They can easily recognize object position at anytime, anywhere. At present, many researchers are making a study of the position recognition and tracking. This paper consists of postion recognition and user identification system. The position recognition is based on location under services (LBS) using a signal strength map, a database is previously made use of empirical measured received signal strength indicator (RSSI). The user identification system automatically controls instruments which is located in home. Moreover users are able to measures body signal freely. We implemented the multi-hop routing method using the Star-Mesh networks. Also, we use the sensor devices which are satisfied with the IEEE 802.15.4 specification. The used devices are the Nano-24 modules in Octacomm Co. Ltd. A RSSI is very important factor in position recognition analysis. It makes use of the way that decides position recognition and user identification in narrow indoor space. In experiments, we can analyze properties of the RSSI, draw the parameter about position recognition. The experimental result is that RSSI value is attenuated according to increasing distances. It also derives property of the radio frequency (RF) signal. Moreover, we express the monitoring program using the Microsoft C#. Finally, the proposed methods are expected to protect a sudden death and an accident in home.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.297-303
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• 2009

Integral imaging is a well-known 3D image recording and display technique. The huge size of integral imaging data requires a compression scheme to store and transmit 3D scenes. In the conventional compression scheme, the data amount of elemental images depends on the various recording condition such as the positional location of a 3D object, the illumination and specification of the lenslet array even if an identical pickup system is used. In this paper, to reduce the dependence of the image characteristics of elemental images on the pickup conditions, a compression scheme using block division on the elemental image of integral imaging is proposed. The proposed scheme provides an improved compression ratio by considering the local similarity of elemental images picked up from three-dimensional objects according to a positional location. To test the proposed scheme, various elemental images are picked up and a compression process is then carried out u sing a standard MPEG-4. Based on compression ratio results, the proposed compression scheme is improved by approximately 9% compared with the conventional compression method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2162-2166
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• 2016

This paper describes the implementation of AUSV system for sonar image acquisition to survey the seabed. The system is controlled by Feed Forward PID algorithm on the vessel for bearing of the thrusters composed of motion sensor and DGPS which calculates the differences between the current location and the destination location for longitude and latitude based on GPS coordinates. As experimental results, the bearing control performance is good that the error distance from the destination positions are under 6m in total survey track of 1km. And the sonar image deviation of a object is under 12 pixels from the manned survey method, which the comparison with the total image quality is almost the same as the manned survey one. Thus the proposed AUSV system is a new method of system can be utilized at the limited survey areas as the surveyor should not be able to approach on sea surface by onboard vessel.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.71-80
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• 2012

This paper proposes vehicle detection and tracking algorithm using a CCD camera. The proposed algorithm uses Haar-like wavelet edge detector to detect features of vehicle and estimates vehicle's location using calibration information of an image. After that, extract accumulated vehicle information in continuous k images to improve reliability. Finally, obtained vehicle region becomes a template image to find same object in the next continuous image using SURF(Speeded Up Robust Features). The template image is updated in the every frame. In order to reduce SURF processing time, ROI(Region of Interesting) region is limited on expended area of detected vehicle location in the previous frame image. This algorithm repeats detection and tracking progress until no corresponding points are found. The experimental result shows efficiency of proposed algorithm using images obtained on the road.