• 제목/요약/키워드: spectral study

검색결과 2,799건 처리시간 0.031초

Understanding of unsteady pressure fields on prisms based on covariance and spectral proper orthogonal decompositions

  • Hoa, Le Thai;Tamura, Yukio;Matsumoto, Masaru;Shirato, Hiromichi
    • Wind and Structures
    • /
    • 제16권5호
    • /
    • pp.517-540
    • /
    • 2013
  • This paper presents applications of proper orthogonal decomposition in both the time and frequency domains based on both cross spectral matrix and covariance matrix branches to analyze multi-variate unsteady pressure fields on prisms and to study spanwise and chordwise pressure distribution. Furthermore, modification of proper orthogonal decomposition is applied to a rectangular spanwise coherence matrix in order to investigate the spanwise correlation and coherence of the unsteady pressure fields. The unsteady pressure fields have been directly measured in wind tunnel tests on some typical prisms with slenderness ratios B/D=1, B/D=1 with a splitter plate in the wake, and B/D=5. Significance and contribution of the first covariance mode associated with the first principal coordinates as well as those of the first spectral eigenvalue and associated spectral mode are clarified by synthesis of the unsteady pressure fields and identification of intrinsic events inside the unsteady pressure fields. Spanwise coherence of the unsteady pressure fields has been mapped the first time ever for better understanding of their intrinsic characteristics.

변동풍속의 파워 스펙트럴 밀도에 관한 평가 (Estimation on the Power Spectral Densities of Daily Instantaneous Maximum Fluctuation Wind Velocity)

  • 오종섭
    • 한국방재안전학회논문집
    • /
    • 제10권2호
    • /
    • pp.21-28
    • /
    • 2017
  • 시공간적으로 불규칙하게 작용하는 변동 풍속 난류의 자료는 풍공학적으로 돌풍계수 평균풍속 변동 풍하중등의 계산에서 요구되지만, 내풍 및 사용성에 따른 동적응답의 평가에서는 변동 풍속의 파워 스펙트럴 밀도함수가 요구된다. 본 논문에서는 1987-2016.12.1일까지의 일순간최대풍속 자료를 확률과정으로 가정했고, 이 실측된 자료와 확률이론을 근거로 평균류방향 파워 스펙트럴 밀도 함수에 대한 기초적 자료를 얻고자 대표지점(6개 지점)을 선정했다. 선정된 각 지점에 대한 일순간최대풍속자료는 기상청으로부터 획득했다. 해석결과 본 논문에서 평가된 스펙트럼 모델은 저진동수 영역에서는 Solari, 고진동수 영역에서는 von Karman의 모델과 근접한 현상을 나타냈다.

Estimation of Forest LAI in Close Canopy Situation Using Optical Remote Sensing Data

  • Lee, Kyu-Sung;Kim, Sun-Hwa;Park, Ji-Hoon;Kim, Tae-Geun;Park, Yun-Il;Woo, Chung-Sik
    • 대한원격탐사학회지
    • /
    • 제22권5호
    • /
    • pp.305-311
    • /
    • 2006
  • Although there have been several attempts to estimate forest LAI using optical remote sensor data, there are still not enough evidences whether the NDVI is effective to estimate forest LAI, particularly in fully closed canopy situation. In this study, we have conducted a simple correlation analysis between LAI and spectral reflectance at two different settings: 1) laboratory spectral measurements on the multiple-layers of leaf samples and 2) Landsat ETM+ reflectance in the close canopy forest stands with fieldmeasured LAI. In both cases, the correlation coefficients between LAI and spectral reflectance were higher in short-wave infrared (SWIR) and visible wavelength regions. Although the near-IR reflectance showed positive correlations with LAI, the correlations strength is weaker than in SWIR and visible region. The higher correlations were found with the spectral reflectance data measured on the simulated vegetation samples than with the ETM+ reflectance on the actual forests. In addition, there was no significant correlation between the forest.LAI and NDVI, in particular when the LAI values were larger than three. The SWIR reflectance may be important factor to improve the potential of optical remote sensor data to estimate forest LAI in close canopy situation.

Acoustic analysis of fricatives in dysarthric speakers with cerebral palsy

  • Hernandez, Abner;Lee, Ho-young;Chung, Minhwa
    • 말소리와 음성과학
    • /
    • 제11권3호
    • /
    • pp.23-29
    • /
    • 2019
  • This study acoustically examines the quality of fricatives produced by ten dysarthric speakers with cerebral palsy. Previous similar studies tend to focus only on sibilants, but to obtain a better understanding of how dysarthria affects fricatives we selected a range of samples with different places of articulation and voicing. The Universal Access (UA) Speech database was used to select thirteen words beginning with one of the English fricatives (/f/, /v/, /s/, /z/, /ʃ/, /ð/). The following four measurements were taken for both dysarthric and healthy speakers: phoneme duration, mean spectral peak, variance and skewness. Results show that even speakers with mild dysarthria have significantly longer fricatives and a lower mean spectral peak than healthy speakers. Furthermore, mean spectral peak and variance showed significant group effects for both healthy and dysarthric speakers. Mean spectral peak and variance was also useful for discriminating several places of articulation for both groups. Lastly, spectral measurements displayed important group differences when taking severity into account. These findings show that in general there is a degradation in the production of fricatives for dysarthric speakers, but difference will depend on the severity of dysarthria along with the type of measurement taken.

드론 초분광 스펙트럼과 분광각매퍼를 적용한 생태계교란식물 탐지 (Detection of Ecosystem Distribution Plants using Drone Hyperspectral Spectrum and Spectral Angle Mapper)

  • 김용석
    • 한국환경과학회지
    • /
    • 제30권2호
    • /
    • pp.173-184
    • /
    • 2021
  • Ecological disturbance plants distributed throughout the country are causing a lot of damage to us directly or indirectly in terms of ecology, economy and health. These plants are not easy to manage and remove because they have a strong fertility, and it is very difficult to express them quantitatively. In this study, drone hyperspectral sensor data and Field spectroradiometer were acquired around the experimental area. In order to secure the quality accuracy of the drone hyperspectral image, GPS survey was performed, and a location accuracy of about 17cm was secured. Spectroscopic libraries were constructed for 7 kinds of plants in the experimental area using a Field spectroradiometer, and drone hyperspectral sensors were acquired in August and October, respectively. Spectral data for each plant were calculated from the acquired hyperspectral data, and spectral angles of 0.08 to 0.36 were derived. In most cases, good values of less than 0.5 were obtained, and Ambrosia trifida and Lactuca scariola, which are common in the experimental area, were extracted. As a result, it was found that about 29.6% of Ambrosia trifida and 31.5% of Lactuca scariola spread in October than in August. In the future, it is expected that better results can be obtained for the detection of ecosystem distribution plants if standardized indicators are calculated by constructing a precise spectral angle standard library based on more data.

Toward Practical Augmentation of Raman Spectra for Deep Learning Classification of Contamination in HDD

  • Seksan Laitrakun;Somrudee Deepaisarn;Sarun Gulyanon;Chayud Srisumarnk;Nattapol Chiewnawintawat;Angkoon Angkoonsawaengsuk;Pakorn Opaprakasit;Jirawan Jindakaew;Narisara Jaikaew
    • Journal of information and communication convergence engineering
    • /
    • 제21권3호
    • /
    • pp.208-215
    • /
    • 2023
  • Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

Spectral Pooling: DFT 기반 풀링 계층이 보여주는 여러 가능성에 대한 연구 (Spectral Pooling: A study on the various possibilities of the DFT-based Pooling layer)

  • 이성주;조남익
    • 한국방송∙미디어공학회:학술대회논문집
    • /
    • 한국방송∙미디어공학회 2020년도 추계학술대회
    • /
    • pp.87-90
    • /
    • 2020
  • GPU의 발전과 함께 성장한 딥러닝(Deep Learning)은 영상 분류 문제에서 최고의 성능을 보이고 있다. 그러나 합성곱 신경망 기반의 모델을 깊게 쌓음에 따라 신경망의 표현력이 좋아짐과 동시에 때로는 학습이 잘되지 않고 성능이 저하되는 등의 부작용도 등장했다. 성능 향상을 방해하는 주요 요인 중 하나는, 차원감소 목적에 따라 필연적으로 정보 손실을 겪어야 하는 풀링 계층에 있다. 따라서 특성맵(Feature map)의 차원감소를 통해 얻게 되는 비용적 이득과 모델의 분류 성능 사이의 취사선택(Trade-off)이 존재한다. 그리고 이로부터 자유로워지기 위한 다양한 연구와 기법이 존재하는데 Spectral Pooling도 이 중 하나이다. 본 논문에서는 이산 푸리에 변환(Discrete Fourier Transform, DFT)을 이용한 Spectral Pooling에 대한 소개와, 해당 풀링의 성질을 통상적으로 사용되고 있는 Max Pooling과의 성능 비교를 통해 분석한다. 또한 영상 내 고주파수 부분에서 특히 더 강건하지 못하다는 맥스 풀링의 고질적인 문제점을, Spectral Pooling과의 하이브리드(Hybrid) 구조를 통해 어떻게 극복해나갈 것인지 그 가능성을 중심으로 실험을 수행했다.

  • PDF