Browsing by Subject "data"
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Item Analysis of WACSIS data using a directional hybrid wave model(Texas A&M University, 2007-04-25) Zhang, ShaosongThis study focuses on the analysis of measured directional seas using a nonlinear model, named Directional Hybrid Wave Model (DHWM). The model has the capability of decomposing the directional wave field into its free wave components with different frequency, amplitude, direction and initial phase based on three or more time series of measured wave properties. With the information of free waves, the DHWM can predict wave properties accurately up to the second order in wave steepness. In this study, the DHWM is applied to the analyses of the data of Wave Crest Sensor Inter-comparison Study (WACSIS). The consistency between the measurements collected by different sensors in the WACSIS project was examined to ensure the data quality. The wave characteristics at the locations of selected sensors were predicted in time domain and were compared with those recorded at the same location. The degree of agreement between the predictions and the related measurements is an indicator of the consistency among different sensors. To analyze the directional seas in the presence of strong current, the original DHWM was extended to consider the Doppler effects of steady and uniform currents on the directional wave field. The advantage of extended DHWM originates from the use of the intrinsic frequency instead of the apparent frequency to determine the corresponding wavenumber and transfer functions relating wave pressure and velocities to elevation. Furthermore, a new approach is proposed to render the accurate and consistent estimates of the energy spreading parameter and mean wave direction of directional seas based on a cosine-2s model. In this approach, a Maximum Likelihood Method (MLM) is employed. Because it is more tolerant of errors in the estimated cross spectrum than a Directional Fourier Transfer (DFT) used in the conventional approach, the proposed approach is able to estimate the directional spreading parameters more accurately and consistently, which is confirmed by applying the proposed and conventional approach, respectively, to the time series generated by numerical simulation and recorded during the WACSIS project.Item Hi! I’m a TDR User(Texas Digital Library, 2023-05-16) Chan-Park, Christina; Sare, Laura; Waugh, Laura; Weber, Millicent; Zhou, Xuan"Come meet Jaime, Chris, Lee, Ali, and Sami! They are typical users of the Texas Data Repository (TDR) based on responses from a 2022 user survey. The TDR uses the Dataverse platform for publishing and archiving datasets (and other data products) created by faculty, staff, and students at Texas higher education institutions and is hosted by the Texas Digital Library. There are currently nine participating member institutions. Jaime is a typical downloader. Chris is a typical TDR user who has created a Dataverse collection and uploaded a dataset. Lee is a faculty TDR user, Ali is a non-faculty researcher, and Sami is a graduate researcher. Find out how each of these users learned about the TDR, why they use it, and how they use it. They think it’s important to share data and want you to know that it is easy to use the TDR."Item The Robust Classification of Hyperspectral Images Using Adaptive Wavelet Kernel Support Vector Data Description(2012-07-16) Kollegala, RevathiDetection of targets in hyperspectral images is a specific case of one-class classification. It is particularly relevant in the area of remote sensing and has received considerable interest in the past few years. The thesis proposes the use of wavelet functions as kernels with Support Vector Data Description for target detection in hyperspectral images. Specifically, it proposes the Adaptive Wavelet Kernel Support Vector Data Description (AWK-SVDD) that learns the optimal wavelet function to be used given the target signature. The performance and computational requirements of AWK-SVDD is compared with that of existing methods and other wavelet functions. An introduction to target detection and target detection in the context of hyperspectral images is given. This thesis also includes an overview of the thesis and lists the contributions of the thesis. A brief mathematical background into one-class classification in reference to target detection is included. Also described are the existing methods and introduces essential concepts relevant to the proposed approach. The use of wavelet functions as kernels with Support Vector Data Description, the conditions for use of wavelet functions and the use of two functions in order to form the kernel are checked and analyzed. The proposed approach, AWKSVDD, is mathematically described. The details of the implementation and the results when applied to the Urban dataset of hyperspectral images with a random target signature are given. The results confirm the better performance of AWK-SVDD compared to conventional kernels, wavelet kernels and the two-function Morlet-Radial Basis Function kernel. The problems faced with convergence during the Support Vector Data Description optimization are discussed. The thesis concludes with the suggestions for future work.