• 대한기계학회논문집A
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• 제29권12권
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• pp.1597-1604
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• 2005

The identification of a moving noise source is important in reducing the source power of the transport systems such as airplanes or high speed trains. However, the direct measurement using a microphone running with noise source is usually difficult due to wind noise, white the source motion distorts the frequency characteristics of the pass-by sound measured at a fixed point. In this study the relationship between the spectra of the source and the pass-by sound signal is analyzed for an acoustic source moving at a constant velocity. Spectrum of the sound signal measured at a fixed point has an integral relationship with the source spectrum. Nevertheless direct conversion of the measured spectrum to the source spectrum is ill-posed due to the singularity of the integral kernel. Alternatively a differential equation approach is proposed, where the source characteristics can be recovered by solving a differential equation relating the source signal to the distorted measurement in time domain. The parameters such as the source speed and the time origin, required beforehand, are also determined only from the frequency-phase relationship using an auxiliary measurement. With the help of the regularization method, the source signal is successfully recovered. The effects of the parameter errors to the estimated frequency characteristics of the source are investigated through numerical simulations.

• 한국소음진동공학회논문집
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• 제23권2호
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• pp.176-184
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• 2013

Quantification of road noise is a challenging issue in the development of tire noise since its transfer paths are complicated. In this paper, a simplified model to estimate the road noise is developed. Transfer path of the model is from wheel to interior. The method uses the wheel excitation force estimated throughout inverse method. In inversion procedure, the Tikhonov regularization method is used to reduce the inversion error. To estimate the wheel excitation force, the vibration of knuckle is measured and transfer function between knuckle and wheel center is also measured. The wheel excitation force is estimated by using the measured knuckle vibration and the inversed transfer function. Finally interior noise due to wheel force is estimated by multiplying wheel excitation force in the vibro-acoustic transfer function. This vibro-acoustic transfer function is obtained throughout measurement. The proposed method is validated by using cleat excitation method. Finally, it is applied to the estimation of interior noise of the vehicle with different types of tires during driving test.

• Smart Structures and Systems
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• 제31권4호
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Steel and Composite Structures
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• 제44권1호
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• pp.119-139
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• 2022

This paper aims to use the artificial intelligence approach to develop a new model for predicting the ultimate axial strength of the circular concrete-filled steel tubular (CFST) stub columns. For this, the results of 314 experimentally tested circular CFST stub columns were employed in the generation of the design model. Since the influence of the column diameter, steel tube thickness, concrete compressive strength, steel tube yield strength, and column length on the ultimate axial strengths of columns were investigated in these experimental studies, here, in the development of the design model, these variables were taken into account as input parameters. The model was developed using the backpropagation algorithm named Bayesian Regularization. The accuracy, reliability, and consistency of the developed model were evaluated statistically, and also the design formulae given in the codes (EC4, ACI, AS, AIJ, and AISC) and the previous empirical formulations proposed by other researchers were used for the validation and comparison purposes. Based on this evaluation, it can be expressed that the developed design model has a strong and reliable prediction performance with a considerably high coefficient of determination (R-squared) value of 0.9994 and a low average percent error of 4.61. Besides, the sensitivity of the developed model was also monitored in terms of dimensional properties of columns and mechanical characteristics of materials. As a consequence, it can be stated that for the design of the ultimate axial capacity of the circular CFST stub columns, a novel artificial intelligence-based design model with a good and robust prediction performance was proposed herein.

• 전자공학회논문지CI
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• 제39권5호
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• pp.11-20
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• 2002

이 논문에서 우리는 블록화 현상과 DCT 계수의 양자화 에러를 최소화 하면서 영상을 복호하는 새로운 기술을 제안한다. 영상의 복호화 과정에서 영상의 DCT 계수는 양자화 된 DCT 계수와 양자화 행렬의 곱으로 구해지게 되고, 양자화 간격의 절반 크기의 에러가 유도 될 수 있다. 이때 역 양자화 과정에서 원 영상의 DCT 계수는 알 수 없으며, 만약 DCT 계수를 양자화 간격 절반 크기 내로 대응 시킨다면 무한개의 해답이 존재하게 된다. 이 논문에서 우리는 하나의 해답을 구하기 위한 단서로, 양자화 에러는 양자화 간격의 절반 크기 내로 제한되어 있으며, 적어도 블록 경계면에서의 불연속성으로 나타나는 고주파 성분은 원 영상에 존재하지 않는다는 사실을 이용하게 된다. 이 두 가지 조건으로 우리는 역 양자화기의 정규화 과정을 거치게 된다. 정규화 된 역 양자화기는 역 양자화 과정에서 얻어지는 DCT 계수를 항상 양자화 에러, 즉 양자화 간격의 절반 크기 범위 내로 대응 시키게 된다. 논문에서 제안된 기술은 JPEG, MPEG-1, H.263+의 영상 압축 표준과 비교하였으며, 비교과정은 시각적인 효과로 기존의 일반적인 방법으로 영상을 복호할 때보다 블록화 현상이 감소한다는 것을 보여주게 되고 또한 원 영상과의 Peak Signal to Noise Ratio (PSNR), Blockiness Measure (BM)에 대한 수치적인 비교 결과를 보여주게 된다.

• Computers and Concrete
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• 제20권5호
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• pp.563-572
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• 2017

Ordinary reinforced concrete (RC) and prestressed concrete bridges are two popular and typical types of short- and medium-span bridges that accounts for the vast majority of all existing bridges. The cost of maintaining, repairing or replacing degraded existing RC bridges is immense. Detecting the abnormality of RC bridges at an early stage and taking the protective measures in advance are effective ways to improve maintenance practices and reduce the maintenance cost. This study proposes a systematic method from influence line (IL) identification to damage detection with applications to RC bridges. An IL identification method which integrates the cubic B-spline function with Tikhonov regularization is first proposed based on the vehicle information and the corresponding moving vehicle induced bridge response time history. Subsequently, IL change is defined as a damage index for bridge damage detection, and information fusion technique that synthesizes ILs of multiple locations/sensors is used to improve the efficiency and accuracy of damage localization. Finally, the feasibility of the proposed systematic method is verified through experimental tests on a three-span continuous RC beam. The comparison suggests that the identified ILs can well match with the baseline ILs, and it demonstrates that the proposed IL identification method has a high accuracy and a great potential in engineering applications. Results in this case indicate that deflection ILs are superior than strain ILs for damage detection of RC beams, and the performance of damage localization can be significantly improved with the information fusion of multiple ILs.

• Computers and Concrete
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• 제24권6호
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• pp.541-553
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• 2019

Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.

• 지구물리와물리탐사
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• 제23권3호
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• pp.192-207
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• 2020

탄성파 탐사를 수행할 때 경제적, 환경적 제약 또는 탐사 장비의 문제 등에 의해 탄성파 자료의 일부가 규칙적 또는 불규칙적으로 손실되는 경우가 발생하게 된다. 이러한 자료 손실은 탄성파 자료 처리와 해석 결과에 부정적인 영향을 주기 때문에 사라진 탄성파 자료를 복원할 필요가 있다. 탄성파 자료 복원을 위해 재탐사 또는 추가적인 탐사를 진행하는 경우 시간적, 경제적 비용이 발생하기 때문에, 많은 연구자들이 사라진 탄성파 자료를 정확히 복원하기 위한 보간 기법 연구를 진행해왔다. 최근에는 머신러닝 기술 발달에 따라 머신러닝 기법을 활용한 연구들이 진행되고 있고, 다양한 머신러닝 기술들 중에서도 서포트 벡터 회귀, 오토인코더, 유넷, 잔차넷, 생성적 적대 신경망 등의 알고리즘을 활용한 탄성파 자료의 보간 연구가 활발하게 진행되고 있다. 이 논문에서는 이러한 연구들을 조사하고 분석하여 복잡한 신경망 모델뿐 아니라 상대적으로 구조가 간단한 서포트 벡터 회귀 모델을 통해서도 뛰어난 보간 결과를 얻을 수 있다는 것을 확인했다. 추후 머신러닝 기법들을 사용하는 탄성파 자료 보간 연구들에서 오픈소스로 공개된 실제 자료를 이용하며 데이터 증식, 전이학습, 기존 기법을 이용한 규제 등의 기술을 활용하면 탄성파 자료 보간 성능을 향상시킬 수 있을 것으로 기대된다.