• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.203-207
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• 2006

Nowadays, Cars are continuing to grow at an alarming rate but they also cause many problems such as traffic accident, pollutions and so on. One of the most effective methods that prevent traffic accidents is the use of traffic monitoring systems, which are already widely used in many countries. The monitoring system is beginning to be used in domestic recently. An intelligent monitoring system generates photo images of cars as well as identifies cars by recognizing their plates. That is, the system automatically recognizes characters of vehicle plates. An automatic vehicle plate recognition consists of two main module: a vehicle plate locating module and a vehicle plate number identification module. We study for a vehicle plate number identification module in this paper. We use image preprocessing, feature extraction, multi-layer neural networks for recognizing characters of vehicle plates and we present a feature-comparison method for improving the performance of vehicle plate number identification module. In the experiment on identifying vehicle plate number, 300 images taken from various scenes were used. Of which, 8 images have been failed to identify vehicle plate number and the overall rate of success for our vehicle plate recognition algorithm is 98%.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1449-1461
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• 2005

In this paper, we propose a vehicle recognition system based on the classification of vehicle identification mark and recognition of vehicle license plate. In the proposed algorithm, From the input vehicle image, we first simulate preprocessing procedures such as noise reduction, thinning etc., and detect vehicle identification mark and license plate region using the frequency distribution of intensity variation. And then, we classify extracted vehicle candidate region into identification mark, character and number of vehicle by using structural feature informations of vehicle. Lastly, we recognize vehicle informations with recognition of identification mark, character and number of vehicle using hybrid and vertical/horizontal pattern vector method. In the proposed algorithm, we used three properties of vehicle informations such as Independency property, discriminance property and frequency distribution of intensity variation property. In the vehicle images, identification mark is generally independent of the types of vehicle and vehicle identification mark. And also, the license plate region between character and background as well as horizontal/vertical intensity variations are more noticeable than other regions. To show the efficiency of the propofed algorithm, we tested it on 350 vehicle images and found that the propofed method shows good Performance regardless of irregular environment conditions as well as noise, size, and location of vehicles.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.6-10
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• 2019

This paper describes effectiveness of accident reduction on vehicles equipped with AEB using accident data occurring in Korea. During the statistical period, we used the number of vehicles which are covered by auto insurance and the number of accidents. To maximize the reduction effect of accidents caused by the driver's carelessness, the analysis was limited to Physical Damage Coverage that covers the cost of repairing or replacing the damaged vehicle caused by the driver's fault. Due to Personal Information Protection Law, it was not capable of comparing the same vehicle using Vehicle Identification Number in this study. Instead of that, we used it as a similar vehicle, so there are limits to the comparison and analysis results. As a result of this study, we have found that the effect of reducing accidents was different depending on the vehicle class, but it was generally concluded that the number of accidents decreased when the vehicle was equipped with an AEB system. Domestic research on the AEB effect of reducing accidents is not active yet. Therefore, it is absolutely essential to analyze the effects according to various conditions such as driver's age, occupation and gender as well as expanding the study models in the future.

• Journal of Auto-vehicle Safety Association
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• v.9 no.4
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• pp.48-54
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• 2017

This paper presents the motorcycle inspection standards development on the vehicle identification, engine and transmission, tyres and wheels, steering and suspension, and brake system. 187 real-world motorcycles are visually and mechanically inspected according to the developed inspection standards. The non-compliance rate of the vehicle identification is 20.3% and main causes are insecure, damaged, and not clearly visible number plate. The non-compliance rate of the brake system is 15.5% and main cause is failing to meet the brake performance requirements. The motorcycle inspections standards are improved reflecting 187 cases of real-world motorcycle inspection results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.100-108
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• 1993

In this paper, noticing the point of human's ability which appropriately cope with vague conditions, we design fuzzy traffic signal light controller similar to human's distinction ability and decide the optimum cycle most suited to any traffic junction using fuzzy identification algorithm. In this study, for the control output decision process we design fuzzy controller better than electronic vehicle actuated controller in performance. We propose the cycle decision method which is not limited by the variance of traffic junction vehicle number through overcoming the limit of Webster's method which is adopted by the fixed cycle controller. Simulated experimental results show that fuzzy controller and fuzzy identification algorithm are better than the existing electronic vehicle actuated controller and fixed cycle controller in delay time per vehicle.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.7-11
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• 2023

In this study, we presented a method to recognize vehicle license plates using CNN techniques. A vehicle plate is normally used for the official identification purposes by the authorities. Most regular Optical Character Recognition (OCR) techniques perform well in recognizing printed characters on documents but cannot make out the registration number on the number plates. Besides, the existing approaches to plate number detection require that the vehicle is stationary and not in motion. To address these challenges to number plate detection we make the following contributions. We create a database of captured vehicle number plate's images and recognize the number plate character using Convolutional Neural Networks. The results of this study can be usefully used in parking management systems and enforcement cameras.

• Smart Structures and Systems
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• v.30 no.1
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• pp.107-119
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• 2022

Modal parameters are widely used in bridge damage detection, finite element model (FEM) updating and design optimization. However, the conventional modal identification approaches require large number of sensors, enormous data processing workload, but normally result in mode shapes with low accuracy. This paper proposes a modal identification method of time-varying vehicle-bridge system using a single sensor. Firstly, the essential physical relationship between the instantaneous frequency of the vehicle-bridge system and the bridge mode shapes are derived. Subsequently, based on the synchroextracting transform, the instantaneous frequency of the system is tracked through the dynamic response collected by a single sensor, and further the modal parameters are estimated by using the derived physical relationship. Then numerical and experimental examples are conducted to examine the feasibility and effectiveness of the proposed method. Finally, the modal parameters identified by the proposed method are applied in bridge FEM updating. The results manifest that the proposed method identifies the modal parameters with high accuracy via a single sensor, and can provide reliable data for the FEM updating.

• Smart Structures and Systems
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• v.13 no.5
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• pp.797-819
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• 2014

This paper presents a theoretical algorithm for constructing the mode shapes of a bridge from the dynamic responses of a test vehicle moving over the bridge. In comparison with those approaches that utilize a limited number of sensors deployed on the bridge, the present approach can offer much more spatial information, as well as higher resolution in mode shapes, since the test vehicle can receive the vibration characteristics of each point during its passage on the bridge. Basically only one or few sensors are required to be installed on the test vehicle. Factors that affect the accuracy of the present approach for constructing the bridge mode shapes are studied, including the vehicle speed, random traffic, and road surface roughness. Through numerical simulations, the present approach is verified to be feasible under the condition of constant and low vehicle speeds.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.135-154
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• 2008

The moving vehicle loads on a bridge deck is one of the most important live loads of bridges. They should be understood, monitored and controlled before the bridge design as well as when the bridge is open for traffic. A MOM-based algorithm (MOMA) is proposed for identifying the timevarying moving vehicle loads from the responses of bridge deck in this paper. It aims at an acceptable solution to the ill-conditioning problem that often exists in the inverse problem of moving force identification. The moving vehicle loads are described as a combination of whole basis functions, such as orthogonal Legendre polynomials or Fourier series, and further estimated by solving the new system equations developed with the basis functions. A number of responses have been combined, some numerical simulations on single axle, two axle and multiple-axle loads, being either constant or timevarying, have been carried out and compared with the existing time domain method (TDM) in this paper. The illustrated results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent when it is used to identify the moving force from bridge responses.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.22-32
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• 2002

This paper describes about non-burial AVI (Automatic Vehicle Identification) system using general vehicle as probe car for obtaining more accurate traffic information while conserving road pavement surface. Existing spot traffic detectors have their own limits of not obtaining right information owing to its mathematical method. Burial AVI systems have some defects, causing traffic jam, needing much maintenance cost because of frequent cutting of loop and piezo-electric sensors. Especially, they have hard time to make right detection, when it comes to jamming time. Therefore, in this paper, we propose non-burial AVI system with laser trigger unit. Proposed non-burial AVI system is developed to obtain regional traffic information from normal Passing vehicle by automatic license number recognition technology. We have adapted it to national highway section between Suwon city and Pyong$\~$Taek city(9.5km) and get affirmative results. Vehicle detection rate of laser trigger unit is more than 95$\%$, vehicle recognition rate is 87.8$\%$ and vehicle matching rate is about 14.3$\%$. So we regard these as satisfying results to use the system for traffic information service. We evaluate proposed AVI system by regulation of some institutions which are using similar AVI system and the proposed system satisfies all conditions. For future study, we have plan of detailed research about proper lane number from all of the target lanes, optimal section length, information service period, and data fusion method for existing spot detector.