• Smart Structures and Systems
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• 제21권5호
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• pp.677-686
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• 2018

The structural responses are often used to identify the structural local damages. However, it is usually difficult to gain the responses of the track, as the sensors cannot be installed on the track directly. The vehicles running on a track excite track vibration and can also serve as response receivers because the vehicle dynamic response contains the vibration information of the track. A damage identification method using the vehicle responses and sensitivity analysis is proposed for the vehicle-track coupling system in this paper. Different from most damage identification methods of vehicle-track coupling system, which require the structural responses, only the vehicle responses are required in the proposed method. The local damages are identified by a sensitivity-based model updating process. In the vehicle-track coupling system, the track is modeled as a discrete point supported Euler-Bernoulli beam, and two vehicle models are proposed to investigate the accuracy and efficiency of damage identification. The measured track irregularity is considered in the calculation of vehicle dynamic responses. The measurement noises are also considered to study their effects to the damage identification results. The identified results demonstrate that the proposed method is capable to identify the local damages of the track accurately in different noise levels with only the vehicle responses.

• 한국멀티미디어학회논문지
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• 제8권11호
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• pp.1449-1461
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• 2005

본 논문에서는 차량의 식별마크 분류 및 차량번호판 인식을 통한 차량인식 알고리즘을 제안하였다. 제안한 알고리즘에서는 먼저 입력 차량영상으로 부터 잡음제거, 세선화 과정 등 전처리 과정들을 수행하고 명암값 변화 빈도 분포를 사용하여 차량식별마크와 번호판 영역을 추출하였다. 또한 추출된 후보 영역으로부터 차량 식별마크와 번호판 영역의 구조적 특성 정보를 사용하여 차량 식별마크, 번호판의 문자 및 숫자를 분류하였으며, 하이브리드 패턴벡터 및 수직수평 패턴벡터를 사용하여 식별마크, 문자 및 숫자를 인식하여 차량 정보 인식율을 개선하였다. 제안한 알고리즘에서는 차량의 식별마크가 차량의 종류에 따라 독립적인 특성, 식별마크와 번호판 영역에서는 문자와 배경이 뚜렷하게 구별되는 특성 및 수평 및 수직빈도수 분포가 식별마크 및 번호판 이외의 영역과 뚜렷이 구별된다는 특성들을 이용하였다. 제안한 방법의 성능을 확인하기 위하여 다양한 환경에서 촬영된 350여개의 영상에 대하여 차량인식 실험을 수행하였고 제안한 방법이 차량번호판의 크기와 위치에 무관하고 잡음의 영향에 덜 민감하였을 뿐만 아니라 불규칙적인 외부환경에서도 인식율이 개선되었다. 또한 식별마크와 번호판 인식의 실시간 처리가 가능하여 실제 주차장이나 도시화도로등에 적용이 가능하다.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.891-902
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• 2020

The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.

• Interaction and multiscale mechanics
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• 제5권4호
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• pp.347-368
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• 2012

Measuring the bridge frequencies indirectly from an instrumented test vehicle is a potentially powerful technique for its mobility and economy, compared with the conventional direct technique that requires vibration sensors to be installed on the bridge. However, road surface roughness may pollute the vehicle spectrum and render the bridge frequencies unidentifiable. The objective of this paper is to study such an effect. First, a numerical simulation is conducted using the vehicle-bridge interaction element to demonstrate how the surface roughness affects the vehicle response. Then, an approximate theory in closed form is presented, for physically interpreting the role and range of influence of surface roughness on the identification of bridge frequencies. The latter is then expanded to include the action of an accompanying vehicle. Finally, measures are proposed for reducing the roughness effect, while enhancing the identifiability of bridge frequencies from the passing vehicle response.

• Smart Structures and Systems
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• 제13권5호
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• pp.821-848
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• 2014

This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

• Structural Engineering and Mechanics
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• 제46권1호
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• pp.75-90
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• 2013

In order to identify damage of highway bridges rapidly, a method for damage identification using dynamic response of bridge induced by moving vehicle and static test data is proposed. To locate damage of the structure, displacement energy damage index defined from the energy of the displacement response time history is adopted as the indicator. The displacement response time histories of bridge structure are obtained from simulation of vehicle-bridge coupled vibration analysis. The vehicle model is considered as a four-degree-of-freedom system, and the vibration equations of the vehicle model are deduced based on the D'Alembert principle. Finite element method is used to discretize bridge and finite element model is set up. According to the condition of displacement and force compatibility between vehicle and bridge, the vibration equations of the vehicle and bridge models are coupled. A Newmark-${\beta}$ algorithm based professional procedure VBAP is developed in MATLAB, and used to analyze the vehicle-bridge system coupled vibration. After damage is located by employing the displacement energy damage index, the damage extent is estimated through the least-square-method based model updating using static test data. At last, taking one simply supported bridge as an illustrative example, some damage scenarios are identified using the proposed damage identification methodology. The results indicate that the proposed method is efficient for damage localization and damage extent estimation.

• Structural Engineering and Mechanics
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• 제57권1호
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• pp.21-43
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• 2016

A conditional probability based approach known as Particle Filter Method (PFM) is a powerful tool for system parameter identification. In this paper, PFM has been applied to identify the vehicle parameters based on response statistics of the bridge. The flexibility of vehicle model has been considered in the formulation of bridge-vehicle interaction dynamics. The random unevenness of bridge has been idealized as non homogeneous random process in space. The simulated response has been contaminated with artificial noise to reflect the field condition. The performance of the identification system has been examined for various measurement location, vehicle velocity, bridge surface roughness factor, noise level and assumption of prior probability density. Identified vehicle parameters are found reasonably accurate and reconstructed interactive force time history with identified parameters closely matches with the simulated results. The study also reveals that crude assumption of prior probability density function does not end up with an incorrect estimate of parameters except requiring longer time for the iterative process to converge.

• 한국자동차공학회논문집
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• 제20권2호
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• pp.85-89
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• 2012

This paper presents a study on the system identification of the in-situ output shaft torque measurement system using a non-contacting magneto-elastic torque transducer installed in a vehicle drivline. The frequency response (transfer) function (FRF) analysis is conducted to interpret the dynamic interaction between the output shaft torque and road side excitation due to the road roughness. In order to identify the frequency response function of vehicle driveline system, two power spectral density (PSD) functions of two random signals: the road roughness profile synthesized from the road roughness index equation and the stationary noise torque extracted from the original torque signal, are first estimated. System identification results show that the output torque signal can be affected by the dynamic characteristics of vehicle driveline systems, as well as the road roughness.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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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%.

• Structural Engineering and Mechanics
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• 제84권3호
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• pp.413-421
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• 2022

Modal parameters are the main dynamic characteristics of bridge. This study aims to propose an innovative route to estimate the modal parameters for bridges by using a parked vehicle in which mode shapes with high accuracy and spatial resolution are identified by frequency measurement. Based on the theory of dynamic modification and modal identification, the mathematical formulation between the parked mass induced frequency variation and the modal parameters of a bridge is derived. Then this mathematical formulation is extended to a parked vehicle-bridge system. The arithmetic and processes for estimating the modal parameters based on the identified frequency variation of the vehicle-bridge systems when the vehicle locates at sequentially arranged positions are presented. Finally the proposed method is applied to several simulated bridges of different types. The results indicate that it can estimate the modal parameters with high accuracy and efficiency.