• Earthquakes and Structures
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• 제11권5호
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• pp.841-859
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• 2016

Bridge bearings are important connection elements between bridge superstructures and substructures, whose health states directly affect the performance of the bridges. This paper systematacially presents a new method to identify the bridge bearing damage based on the neural network theory. Firstly, based on the analysis of different damage types, a description of the bearing damage is introduced, and a uniform description for all the damage types is given. Then, the feasibility and sensitivity of identifying the bearing damage with bridge vibration modes are investigated. After that, a Radial Basis Function Neural Network (RBFNN) is built, whose input and output are the beam modal information and the damage information, respectively. Finally, trained by plenty of data samples formed by the numerical method, the network is employed to identify the bearing damage. Results show that the bridge bearing damage can be clearly reflected by the modal information of the bridge beam, which validates the effectiveness of the proposed method.

• 한국정밀공학회지
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• 제25권12호
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• pp.107-116
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• 2008

The excitation force of a powertrain is one of major sources for the interior noise of a vehicle. This paper presents a novel approach to predict the interior noise caused by the vibration of the power rain by using the hybrid TPA (transfer path analysis) method. Although the traditional transfer path analysis (TPA) is useful for the identification of powertrain noise sources, it is difficult to modify the structure of a powertrain by using the experimental method for the reduction of vibration and noise. In order to solve this problem, the vibration of the power rain in a vehicle is numerically analyzed by using the finite element method (FEM). The vibration of the other parts in a vehicle is investigated by using the experimental method based on vibrato-acoustic transfer function (VATF) analysis. These two methods are combined for the prediction of interior noise caused by a power rain. Throughout this research, two papers are presented. This paper presents a simulation of the excitation force of the power rain exciting the vehicle body based on numerical simulation. The other paper presents a prediction of interior noise based on the hybrid TPA, which uses the VATF of the car body and the excitation force predicted in this paper.

• 토지주택연구
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• 제11권2호
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• pp.1-14
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• 2020

The aim of this study is to empirically identify the differentiating characteristics of determinant factors on sing-person households' commuting mode choice compared to multi-person households' one in order to establish the customized police directions to decrease private car use in commuting. While the study use the 2% sample survey data on the population and housing in 2015, it employ multinomial logit models on relative choice probability of such alternative commuting modes as bus, subway or rail, and walking, rather than driving. As potential determinant factors, the study employs demographic, socio-economic, and housing and residential one for both models of single-person and multi-person households. The study finds that the behavior of commuting mode choice has distinctive difference by gender, marriage status, physical activity constraint, job type, residential period in current housing of the single-person household's workers compared to the multi-person households' ones. Based on the findings, the study deduce ten commuting policy directions customized for the single-person household.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.911-915
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• 2004

Recently, several experimental techniques for identifying the noise sources distributed over a moving vehicle are being developed and used in order to design a low noise vehicle. The beamforming method, which uses phase information between several microphones to localize the source position, is proved to be one of the promising techniques applicable even under complicated test environments. In this study a beamforming algorithm is developed and applied to measure the dominant noise sources on a passenger car moving at constant speed. Unlike the acoustic signals from a stationary noise source, the sound generated from a moving source is distorted due to the Doppler effects. The sound pressure are measured with an spiral array system composed of 26 microphones and a pair of photo sensors are used to measure the. vehicle speed. The information about the speed and relative position of the vehicle are used to eliminate the Doppler effects from the measured pressure signal by using a de-Dopplerization algorithm. The noise generated from a moving vehicle can be grouped in many ways, however, tire noise and the noise generated from the engine are distinguishable at the speeds being tested.

• Smart Structures and Systems
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• 제25권3호
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• pp.265-277
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• 2020

Recently, the concept of "drive-by" bridge monitoring system using indirect measurements from a passing vehicle to extract key parameters of a bridge has been rapidly developed. As one of the most key parameters of a bridge, the natural frequency has been successfully extracted theoretically and in practice using indirect measurements. The frequency of bridge is generally calculated applying Fast Fourier Transform (FFT) directly. However, it has been demonstrated that with the increase in vehicle velocity, the estimated frequency resolution of FFT will be very low causing a great extracted error. Moreover, because of the low frequency resolution, it is hard to detect the frequency drop caused by any damages or degradation of the bridge structural integrity. This paper will introduce a new technique of bridge frequency extraction based on Hilbert Transform (HT) that is not restricted to frequency resolution and can, therefore, improve identification accuracy. In this paper, deriving from the vehicle response, the closed-form solution associated with bridge frequency removing the effect of vehicle velocity is discussed in the analytical study. Then a numerical Vehicle-Bridge Interaction (VBI) model with a quarter car model is adopted to demonstrate the proposed approach. Finally, factors that affect the proposed approach are studied, including vehicle velocity, signal noise, and road roughness profile.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• Journal of Korean Neurosurgical Society
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• 제66권4호
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• pp.356-381
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• 2023

Although immunotherapy has been broadly successful in the treatment of hematologic malignancies and a subset of solid tumors, its clinical outcomes for glioblastoma are still inadequate. The results could be due to neuroanatomical structures such as the blood-brain-barrier, antigenic heterogeneity, and the highly immunosuppressive microenvironment of glioblastomas. The antitumor efficacy of endogenously activated effector cells induced by peptide or dendritic cell vaccines in particular has been insufficient to control tumors. Effector cells, such as T cells and natural killer (NK) cells can be expanded rapidly ex vivo and transferred to patients. The identification of neoantigens derived from tumor-specific mutations is expanding the list of tumor-specific antigens for glioblastoma. Moreover, recent advances in gene-editing technologies enable the effector cells to not only have multiple biological functionalities, such as cytokine production, multiple antigen recognition, and increased cell trafficking, but also relieve the immunosuppressive nature of the glioblastoma microenvironment by blocking immune inhibitory molecules, which together improve their cytotoxicity, persistence, and safety. Allogeneic chimeric antigen receptor (CAR) T cells edited to reduce graft-versus-host disease and allorejection, or induced pluripotent stem cell-derived NK cells expressing CARs that use NK-specific signaling domain can be a good candidate for off-the-shelf products of glioblastoma immunotherapy. We here discuss current progress and future directions for T cell and NK cell therapy in glioblastoma.

• 한국ITS학회 논문지
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• 제1권1호
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• pp.22-32
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• 2002

본 연구는 기존 지점검지기와 비콘검지기 및 매설식 자동차량인식장치(Automatic Vehicle Identification : AVI)의 한계점을 극복하고자 비매설식 AVI를 개발하고 이를 통하여 도로상을 주행하는 일반적인 차량들을 프루브 차량으로 활용하여 신뢰성 있는 구간교통정보를 산출하는 방법을 개발하는 것을 목적으로 한다. 이를 위해 비매설식 자동차량인식장치를 개발하고, 국도1호선 수원$\~$평택구간(9.5km)에 설치되어 운용중인 장비에 대하여, 현장에서 수집된 자료를 분석하여 신뢰성 있는 구간교통정보 수집 가능성을 살펴보았다. 현장 실험 결과 레이저센서의 차량 검지율은 95$\%$ 이상, 차량 인식률은 87.8$\%$이며, 차량 매칭률은 약 14.3$\%$로 분석되어 도로의 괴통상황 추이를 잘 반영하는 것으로 판단되었다. 또 시스템의 신뢰도 시험 및 지속성 시험 방법에 의한 시험결과의 성능을 비친 평가하기 위하여 기존의 이와 유사한 장비를 설치하여 검수하는 각 기관의 검수기준을 적용하여 평가한 결과 본 시스템은 각 검수 기준을 모두 만족하는 것으로 나타나 현장 적용성에서 매우 뛰어난 성능을 보이고 있다 향후 연구과제로서, 설치 대상 차로 및 적정 설치구간 거리, 정보 제공 주기 등에 대한 상세한 연구 및 기존 지점 검지기 자료와의 퓨전(Fusion) 방안에 대한 연구가 필요할 컷으로 사료된다.

• 한국정보통신학회논문지
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• 제15권5호
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• pp.1087-1096
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• 2011

지능형 차량 네트워크(VANET)는 무선통신을 이용하여 차량 간 (V2V, Vehicle to Vehicle), 차량과 노변장치 간(V2I, Vehicle to Infrastructure)의 통신을 제공하는 네트워킹 기술이다. 현재 VANET통신은 자동차산업의 급속한 발전과 차량자동화로 인하여 산업계와 학계를 중심으로 연구가 활발히 진행되고 있다. VANET을 통해 유통되는 차량의 속도, 가속도, 도로 및 환경 모니터링정보는 운전자에게 안전운전과 관련된 서비스를 제공하는 분야로써 통신에서의 보안은 필수적인 요건이다. 지금까지 안전한 메시지 인증을 위한 많은 인증프로토콜들이 제시되어 왔다. 그 중에서도 Jung에 의해 제안된 VANET 알고리즘은 데이터베이스 검색 알고리즘인 블룸 필터를 RAISE 알고리즘에 적용하여 차량 밀집환경에서의 인증에 보다 효율적인 알고리즘을 제안하였다. 하지만 RAISE에서 사용한 k-anonymity는 정확한 차량의 ID정보를 얻기 위해 모든 메시지에 대해 전수조사 연산을 수행해야 하므로 차량의 수가 증가함에 따라 해시연산량이 지수적으로 증가한다. 또한 핸드오버가 발생하는 경우 완벽한 키전달 알고리즘을 제공하지 못한다. 본 논문에서는 RSSI기반 속도 및 거리 추정 알고리즘을 사용하여 사용자의 ID를 위치화하며 프로토콜의 핸드오버부분의 오류를 수정하여 안전하고 효율적인 알고리즘을 제공한다.

• 분석과학
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• 제24권1호
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• pp.51-59
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• 2011

유전자 감식은 다양한 범죄현장에서 발견되는 생체시료의 분석을 통해 신원을 식별하는 중요한 법과학적 수사과정으로 자리 잡았다. 최근에는 범인이 사용했던 펜, 뺑소니 차량에서의 핸들, 기어, 각종 버튼스위치 등에 남겨져 있는 touch evidence-type sample로 알려져 있는 low copy number (LCN) DNA에서의 A-STR분석을 위해 의뢰되는 감정물들이 증가하는 추세에 있다. 본 연구에서는 총기의 뭉개진 지문 등에 남겨져 있는 touch evidence-type의 LCN DNA를 추출하고 유전자형의 분석 성공률을 확인하고 자 하였다. 4종류의 총기(M16, K1A, COLT 45 권총, M29 리볼버)를 각각 격발한 후 총기별로 4곳의 부위에서 시료를 채취한 다음 LCN DNA의 추출을 위해 Microkit과 $Prepfiler^{TM}$ 등 2종류의 시약을 이용하여 DNA 검출량과 유전자형 분석 성공률을 비교 분석하였다. 분석결과 $Prepfiler^{TM}$가 Microkit에 비해 평균 1.7배 DNA검출량이 많았으며, 유전자형 분석 성공률에 있어서도 Microkit은 0%인데 비해 $Prepfiler^{TM}$에서는 평균 24.9%의 성공률을 보였으며, K1A의 손잡이 부위에서 50.6%의 성공률을 나타냈다.