PURPOSES : This paper is aimed at suggesting a novel approach for determining the pavement condition rating based on the tire-surface friction noise using a machine learning algorithm as a low-end pavement condition monitoring system. METHODS : Vehicle on-board type noise measurement system according to the ISO11819-2, and the K-nearest neighbors with dynamic time warping algorithm were applied. The system and algorithm were empirically tested with a field study. RESULTS : The developed AI- and noise-based pavement condition monitoring system demonstrated significantly positive results with a precision 90.8%, recall 84.8%, and f1-score 86.1%. CONCLUSIONS: We herein confirmed that the acoustic property between the tire and road surface can be used for monitoring pavement conditions. It is believed this finding presented a new paradigm for monitoring pavement conditions based on visual information. However, extensive studies focused on the practical application of this method are required.

A precise control of the launcher in MLRS(Multiple Launcher Rocket System) is important for a mission completion. During the field operation of the MLRS, a friction noise was heard in the elevation clutch that controls the launcher. It was confirmed that unsteady friction between friction plates and clutch plates has caused the noise in the clutch. Conducting the analysis of drive mechnism in the clutch, there was a possibility of the unsteady friction because of moderated spring constant. Therefore, after performing experiments on the elevation clutch varied with spring constant, it was determined the proper spring constant. Finally, an improvement on the spring constant in the elevation clutch is expected in the future by utilizing the result of this study.

PURPOSES: This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS: For measuring the friction noise between the surface and vehicle’s tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.

PURPOSES : The purpose of this study is to eliminate the noise of the vehicle after measuring the friction noise obtained from the NCPX (Noble Close ProXimity) method. The pure friction noise between the tire and road pavement could be determined from filtering the compositeness of sound and the influence of the vehicle noise. METHODS: The noise magnitude could be determined by analyzing the sound pressure level (SPL) and sound power level (PWL) along with the noise frequency of a FFT (Fast Fourier Transform) analysis as well as CPB (Constant Percentage Bandwidth) analysis. RESULTS: When the test for measuring the friction noise originated somewhere between tire and road pavement is performed with NCPX method, it must be fulfilled by attaching the surface microphone near the tire. In this condition, the surface microphone can measure the friction noise occurred at between tire and pavement, the chassis noise from the engine and power transfer units, the fluctuating aerodynamic noise, and the turbulence noise directly affected to the surface microphone. By using the NCPX method, the noise occurred at the vehicle must be eliminated for measuring the friction noise between tire and pavement from the traffic noise. CONCLUSIONS: The vehicle's testing engine noise depends on the vehicle and road types. The effect of vehicle's engine noise is less than the friction noise occurred at between tire and pavement at less than 1% effect.

화장품 사용 전후의 피부 상태는 주로 시각과 촉각 및 피부 구조에 기초한 정성적, 정량적 방법에 의해 평가되어 왔다. 본 연구에서는 화장품을 피부에 도포할 때 문지름으로 인한 마찰 소음이 발생한다는 사실을 기반 으로, 피부 문지름에서 발생하는 소리를 측정하여 청각을 기반으로 하는 새로운 피부 상태 평가법을 개발하고자 하였다. 우선 무향 조건에서 고감도의 계측용 마이크로폰을 사용하여 피부를 문질렀을 때 발생하는 소리를 계측 하였으며, 주파수 영역(1/3 옥타브 스펙트럼) 해석법을 이용하여 세정 전, 세정 후, 화장품 도포 후의 세 가지 피부 조건에 따른 소리의 스펙트럼 변화를 분석하였다. 그 결과, 세정 전보다 세정 후에 피부 문지름에서 발생하 는 소리의 세기가 증가하는 경향을 보였으며, 화장품 도포 후에는 문지름 소음의 총 세기가 가장 작음을 확인하 였다. 또한, 화장품 도포 후는 도포 전과 비교해볼 때, 2 kHz 미만의 저주파 대역의 에너지는 증가하며 반대로 2 kHz 이상의 고주파 영역의 에너지는 상대적으로 감소하는 변곡점이 발생하였다. 화장품 도포 후에 변곡점 근처에서 에너지 차이가 반전되는 이러한 현상은 피부의 물리적 모델인 풍선으로 해석되며, 저주파 영역에서의 소리 세기의 증가를 설명하기 위해 “유연함 지표”를 도입하고, 고주파 영역에서의 소리 세기의 감소를 설명하기 위해서는 “부드러움 지표”를 도입하였다. 그리고, 이러한 소리 측정을 통한 유연함 증가와 부드러움 증가는 각각 cutometer와 primos을 사용한 피부 유연함 측정과 피부 거칠기 측정 결과와 유사함을 확인하였다. 이상의 연구 결과를 통해 유연함과 부드러움 지표를 사용하는 청각에 기초한 평가 방법은 새로운 피부 상태 평가법으로 활용 가능할 것으로 판단된다.