• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.1-20
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• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.59-67
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• 2009

With the development of high-cost vehicle detectors, low-cost detectors have also been studied due to the advantage that more detectors are provided within limited budgets. This study proposed a method to estimate vehicle speeds using vehicles' track data from auto manufacturers and time stamps obtained when vehicles' tires pass an inclined sensor (here, a tape switch sensor). In speed estimation, small vehicles and large vehicles is distinguished according to the ratio of time stamps for a wheelbase and a rear track obtained from a tape switch sensor. In particular, speed estimation can be adjusted through a parameter to determine vehicles' size so as to take into account location properties such as vehicles' classification ratio. The low-cost vehicle detector with an inclined sensor proposed in this study is expected to be widely utilized to monitor traffic conditions thanks to low cost.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.209-215
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• 2022

As intelligent autonomous driving vehicle development has become a big topic around the world, accurate reference dynamics estimation has been more important than before. Current systems generally use speed and heading information sensed from a distant point as a vehicle reference dynamic, however, the dynamics between different points are not same especially during rotating motions. In order to estimate properly estimate the reference dynamics from the information such as velocity and heading sensed at a point distant from the reference point such as center of gravity, this study proposes estimating reference dynamics from any location in the vehicle by combining the Bicycle and Ackermann models. A test system was constructed by implementing multiple GNSS/INS equipment on an Robot Operating System (ROS) and an actual car. Angle and speed errors of 10° and 0.2 m/s have been reduced to 0.2° and 0.06 m/s after applying the suggested method.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2002.11a
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• pp.196-199
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• 2002

In this paper, variable threshold estimation algorithm for multiple vehicle detection RADAR is proposed and realized by using DSP for real time processing. The algorithm is developed to get the information of velocity and length of vehicles in multiple lanes by using FMCW RADAR. For real time operation, signal processing part is realized with a high speed DSP board to detect and manipulate the vehicle data and some experimental results are given to show the usefulness of the proposed technique.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.577-587
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• 2017

In the design process of dynamic characteristics of a railway vehicle, demand for analysis, testing and estimation methods of running stability are increasing as railway vehicle speed is increasing. Critical speed tests and estimation have been carried out using computer simulation or special test facilities, like roller rigs, because real track testing at critical speed is very dangerous. This paper introduces a test and assessment method for critical speed and estimates the validity using several roller rig tests. The test results show that it is difficult to estimate the critical speed using safety and instability assessment method in UIC 518, but that there is good agreement between the reduction of the equivalent damping ratio and the critical speed.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1352-1355
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• 1997

This paper presents the algorithm for estimating the attitude of an underwater vehicle using EFK. The system model is designed by linerizing the nonlinear Euler angle differential equation and the measurements is a speed logger output. The simulation result shows that the estimation lagorithm is adequate for decreasing attitude errors that grow abruptly during the motion with acceleration and rotation. It also shows that we can adapt the algorithm for compensating initial attitude errors generated after initial leveling.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.467-473
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• 2020

Recently, the number of traffic accidents has also increased with the increase in the penetration rate of cars in Korea. In particular, not only inter-vehicle accidents but also human accidents near crosswalks are increasing, so that more attention to traffic safety around crosswalks are required. In this paper, we propose a system for predicting the safety level around the crosswalk by recognizing an approaching vehicle and estimating the speed of the vehicle using NVIDIA Jetson Nano-class edge devices. To this end, various machine learning models are trained with the information obtained from deep learning-based vehicle detection to predict the degree of risk according to the speed of an approaching vehicle. Finally, based on experiments using actual driving images and web simulation, the performance and the feasibility of the proposed system are validated.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.5
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• pp.478-485
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• 2015

Availability of the climbing angle information is crucial for the intelligent vehicle system. However, the climbing angle information can't be measured with the sensor mounted on the vehicle. In this paper, climbing angle estimation system is proposed. First, longitudinal acceleration obtained from gyro-sensor is compared with the actual longitudinal acceleration of the vehicle. If the vehicle is in yawing motion, actual longitudinal acceleration can't be approximated from time derivative of wheel speed, because lateral velocity and yaw rate affect actual longitudinal acceleration. Wheel speed and yaw rate can be obtained from the sensors mounted on the vehicle, but lateral velocity can't be measured from the sensor. Therefore, lateral velocity is estimated using unknown input observer with nonlinear tire model. Simulation results show that the compensated results using lateral velocity and yaw rate show better performance than uncompensated results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.399-405
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• 2002

Now days, it is based on GIS and GPS, it can search for the shortest path and estimation of arrival time by using the internet and cell phone to driver. But, even though good car navigation system does not create which is the shortest path when there average vehicle speed is 10 -20 Km. Therefore In order to reduce vehicle waiting time and average vehicle speed, we suggest optimal green time algorithm using fuzzy adaptive control , where there are different traffic intersection length and lane. In this paper, it will be able to forecast the optimal traffic Information, estimation of destination arrival time, under construction road, and dangerous road using internet.

• Design & Manufacturing
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• v.14 no.4
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• pp.40-45
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• 2020

In a vehicle-to-vehicle accident, the impact posture, braking status, final stopping position, collision point and collision speed are important factors for accident reconstruction. In particular, the speed of collision is the most important issue. In this study, the collision speed and the final stopping position in the tunnel were estimated using PC-CRASH, a vehicle crash analysis program used for traffic accident analysis, and the final stopping position of the simulation and the final stopping position of the traffic accident report were compared. When the Pride speed was 0km/h or 30km/h and the Sorento speed was 100m/h, the simulation results and reports matched the final stopping positions and posture of the two vehicles. As a result of the simulation, it can be estimated that Pride was collided in an almost stationary state.