• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2011.04a
• /
• pp.224-229
• /
• 2011

In this paper, in order to improve safety of hybrid electric vehicle a fault detection algorithm is introduced. The proposed algorithm uses SVDD techniques. Two methods for learning a lot of data are used in this technique. One method is to learn the data incrementally. Another method is to remove the data that does not affect the next learning. Using lines connecting support vectors selection of removing data is made. Using this method, lot of computation time and storage can be saved while learning many data. A battery data of commercial hybrid electrical vehicle is used in this study. In the study fault boundary via SVDD is described and relevant algorithm for virtual fault data is verified. It takes some time to generate fault boundary, nevertheless once the boundary is given, fault diagnosis can be conducted in real time basis.

• 전자공학회논문지 IE
• /
• v.48 no.2
• /
• pp.19-25
• /
• 2011

This paper presented neural network modeling method using vehicle data to predict fuel consumption. To acquire data for training and testing the proposed neural network, medium-class gasoline vehicle drove at downtown and parameters measured include speed, engine rpm, throttle position sensor (TPS), and mass air flow (MAF) as input data, and fuel consumption as target data from OBD-II port. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the neural network model can predict the vehicle quite well with mean squared error was $1.306{\times}10^{-6}$ for the fuel consumption.

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

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.3
• /
• pp.693-698
• /
• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• Journal of Energy Engineering
• /
• v.19 no.4
• /
• pp.246-250
• /
• 2010

It was measured engine power, specific fuel consumption and exhaust emissions to analyze fuel economy between LNG dual fuel vehicle and base diesel one. The tested LNG dual fuel engine is converted from diesel engine having 12 liter heavy duty class. The power of LNG dual fuel engine is 5% lower than diesel one and the engine efficiency is also lower than diesel case. However the exhaust emission of diesel engine such as PM, NOx, CO and $CO_2$ showed higher than that of LNG duel fuel case except NMHC component. And economical analysis were carried out two cases for an aspect of fuel economy and environmental benefit. As a result, LNG dual fuel vehicle gives some economic benefit to whom both business party and public side respectively though considering the subsidy and price discount for diesel.

• Journal of IKEEE
• /
• v.24 no.1
• /
• pp.97-105
• /
• 2020

A thermal imaging sensor receives the radiating energy from the target and the background, so it has been widely used for detection, tracking, and classification of targets at night for military purpose. In recognizing the target automatically using thermal images, if the correct edges of object are used then it can generate the classification results with high accuracy. However since the thermal images have lower spatial resolution and more blurred edges than color images, the accuracy of the classification using thermal images can be decreased. In this paper, to overcome this problem, a new hierarchical classifier using both shape and local features based on the segmentation reliabilities, and the class/pose updating method for vehicle classification are proposed. The proposed classification method was validated using thermal video sequences of more than 20,000 images which include four types of military vehicles - main battle tank, armored personnel carrier, military truck, and estate car. The experiment results showed that the proposed method outperformed the state-of-the-arts methods in classification accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.3
• /
• pp.86-99
• /
• 2021

This research proposes the Accident Prevention System to prevent collision accident that can occur due to blind spots such as crossway or school zone using V2I communication. Vision sensor and LiDAR sensor located in the infrastructure of crossway somewhere like that recognize objects and warn vehicles at risk of accidents to prevent accidents in advance. Using deep learning-based YOLOv4 to recognize the object entering the intersection and using the Manhattan Distance value with LiDAR sensors to calculate the expected collision time and the weight of braking distance and secure safe distance. V2I communication used ROS (Robot Operating System) communication to prevent accidents in advance by conveying various information to the vehicle, including class, distance, and speed of entry objects, in addition to collision warning.

• The Journal of the Korea Contents Association
• /
• v.11 no.2
• /
• pp.195-203
• /
• 2011

This case study is based on a fundamental class actually done in the film and video department in Sungkyul University. It aimed at suggesting supportive role for typical film production class in universities in Korea. The list of film styles mentioned in this text is selected from the actual ones for the class and it is focused on utilization of sound and camera. It is ultimately designed to guide students to actual making films. First of all, for example, with a humble camcoder, students are encouraged to record both image and his/her narration which is directly recorded into the built-in microphone. Also directional microphone could be used to experience various positions of 'point-of-hearing'. Regarding camera movements, only distinctive ones out of typical utilization are selected to be dealt with. The movements created by moving vehicle such as dolly or crane beyond the limit of human ability could bring up high imagination of students on movement, besides this could be also easily applied to them for using hand-held technique instead of vehicle. This attitude acquired through the course is important for gettig over the resistance they might have before actual experiencing machinary use in production.

• International Journal of Highway Engineering
• /
• v.15 no.4
• /
• pp.127-133
• /
• 2013

PURPOSES : The impact performance of flexible barrier system such as structural response, vehicular motion and occupant safety vary depending on the impact point. Thus, to properly evaluate the performance of a flexible barrier system, impact should be made to a point which will lead to the worst possible results. This point is called the Critical Impact Point (CIP). This paper presents the way to determine the CIP for a SB2 class flexible barrier system which is consisted of Thrie-Beam rail and circular hollow tube post of 2m span. METHODS: Barrier VII simulations were made for impact points; Case 1 at a post, Case 2 at 1/3 span downstream from a post, Case 3 at middle of the span, Case 4 at 2/3 span downstream from a post. For the structural performance (deflections), impact simulation of 8000kg-65km/h-15degree was used, and for vehicle motion and occupant safety, simulation of 1300kg-80km/h-20degree impact was made and analysed. RESULTS: Case 1 gave the largest dynamic deflection of 75.72cm and also gave the largest snag value of 44.3cm. Occupant safety and exit angle of the vehicle after the impact were not sensitive to the impact point and were all below the allowable limit. CONCLUSIONS : For the SB2 class flexible barrier system's CIP can be regarded as a post which is sufficiently away from the end of Length of Need in order to avoid the end-effect of the barrier system. It can be more economic in the long run because the normal concrete pavement material is likely to cost more due to higher probability of maintenance and repair and higher social cost due to traffic accident, etc.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.15 no.1
• /
• pp.69-75
• /
• 2011

Firing tests have been carried out for a technology demonstration model of 75-tonf-class combustor which is to be used on the liquid rocket engine of a Korean space launch vehicle. Firing tests were done at 50% of the nominal flow rate because of incapability of the test facility and limit of the test bed strength. Through the low pressure firing tests of 75-tonf-class channel cooling thrust chamber, operability and stability at the ignition and combustion phases were confirmed. Additionally it was foreseen that the 75-tonf-class thrust chamber would satisfy the performance requirements.