Analysis of Magnetohydrodynamic Perturbations in the Radial-field Solar Wind from Parker Solar Probe Observations

We report analysis of sub-Alfvénic magnetohydrodynamic (MHD) perturbations in the low-β radial-field solar wind employing the Parker Solar Probe spacecraft data from 2018 October 31 to November 12. We calculate wavevectors using the singular value decomposition method and separate MHD perturbations into three eigenmodes (Alfvén, fast, and slow modes) to explore the properties of sub-Alfvénic perturbations and the role of compressible perturbations in solar wind heating. The MHD perturbations show a high degree of Alfvénicity in the radial-field solar wind, with the energy fraction of Alfvén modes dominating (∼45%–83%) over those of fast modes (∼16%–43%) and slow modes (∼1%–19%). We present a detailed analysis of a representative event on 2018 November 10. Observations show that fast modes dominate magnetic compressibility, whereas slow modes dominate density compressibility. The energy damping rate of compressible modes is comparable to the heating rate, suggesting the collisionless damping of compressible modes could be significant for solar wind heating. These results are valuable for further studies of the imbalanced turbulence near the Sun and possible heating effects of compressible modes at MHD scales in low-β plasma.

Real-time and automatic map stitching through aerial images from UAV

Real-time aerial map stitching through aerial images had been done through many different methods. One of the popular methods was a features-based algorithm to detect features and to match the features of two and more images to produce a map. There are several feature-based methods such as ORB, SIFT, SURF, KAZE, AKAZE and BRISK. These methods detect features and compute homography matrix from matched features to stitch images. The aim for this project is to further optimize the existing image stitching algorithm such that it will be possible to run in real-time as the UAV capture images while airborne. First, we propose to use a matrix multiplication method to replace a singular value decomposition method in the RANSAC algorithm. Next, we propose to change the workflow to detect the image features to increase the map stitching rate. The proposed algorithm was implemented and tested with an online aerial image dataset which contain 100 images with the resolution of 640 × 480. We have successfully achieved the result of 1.45 Hz update rate compared to original image stitching algorithm that runs at 0.69 Hz. The improvement shown in our proposed improved algorithm are more than two folds in terms of computational resources. The method introduced in this paper was successful speed up the process time for the program to process map stitching.

Applying the R-solution method for designing a tsunami observational system

One of the promising methods of the early warning of a tsunami is obtaining data of the wave heights based on the numerical solution of the inverse tsunami problem by using the truncated singular value decomposition method as a variant of the least-squares method. The problem is considered within the framework of the linear theory of wave propagation. The technique proposed allows one to avoid the inevitable instability of the numerical solution. It is possible to choose the most informative directions for the placement of the observation stations, which is based on the analysis of the energy transfer by the spatial modes generated by each right singular vector. As it has turned out, the best location of the stations is closely related to the directions of the most intense distribution of the tsunami energy. One of the significant advantages of the approach presented is the possibility, without additional calculations of the tsunami wave propagation from a reconstructed source, to obtain the tsunami wave heights at the points at which there are no observations but which are associated when calculating the matrix of the direct problem operator. The implementation of the approach proposed of the actual event of the Chilean Illapel Tsunami of September 16, 2015, is presented.

Classification of Slamming Events in Irregular Waves Measured through Tow Tank Experiments

Slamming events are the source of critical design loads for small, high-speed craft. Categorization of slamming events can prove useful by identifying cases of interest for more in-depth analysis, such as high-fidelity modeling and experiments. Inspired by developments in facial recognition techniques, a quantitative method is proposed to sort slamming events using various experimental measurements. A singular value decomposition method on a matrix assembled of vectors of time-histories of rigid body motions recorded in free-to-heave-and-pitch tow tank experiments on a planing hull. While some of the categories identified in this work show distinct features in slamming accelerations consistent with previously identified categories, other categories have also been identified. These results can be used when evaluating ride quality, and design loads, and performing more in-depth studies on specific slamming categories.

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

Early Bearing Fault Diagnosis based on the Improved Singular Value Decomposition Method

The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information, and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.

Singular vector filtering method for mitigation of disturbance enhancement in multichannel active noise control systems

In the design of multichannel active noise control filters, the disturbance enhancement phenomenon will sometimes occur, i.e., the resulting sound is enhanced instead of being reduced in some frequency bands, if the control filter is designed to minimize the power of error signals in other frequency bands or across all frequencies. In previous work, a truncated singular value decomposition method was applied to the system autocorrelation matrix to mitigate the disturbance enhancement. Some small singular values and the associated singular vectors are removed, if they are responsible for unwanted disturbance enhancement in some frequency bands. However, some of these removed singular vectors may still contribute to the noise control performance in other frequency bands; thus, a direct truncation will degrade the noise control performance. In the present work, through an additional filtering process, the set of singular vectors that causes the disturbance enhancement is replaced by a set of new singular vectors whose frequency responses are attenuated in the frequency band where disturbance enhancement occurs, while the frequency responses in other frequency bands are unchanged. Compared with truncation approach, the proposed method can maintain the performance in the noise reduction bands, while mitigating the influence in disturbance enhancement bands.

Development of QSAR Models Using Singular Value Decomposition Method: A Case Study for Predicting Anti-HIV-1 and Anti-HCV Biological Activities

This study performed a detailed approach derived by coupling singular value decomposition (SVD) with multiple linear regression (MLR) methods on the performance and predictive capability of the quantitative structure-activity relationship (QSAR). The study was carried out on two different datasets of 128 HIV-1 attachment inhibitors and 115 HCV analogs. For both datasets, the structure of each compound was represented by suitable molecular descriptors. Then, the two datasets were divided into training and test sets employing the Kennard-Stone procedure (K-S). Both MLR and SVD-MLR models were developed to link the structure of the studied compounds to their reported biological activities. The selected models were subjected to the internal leave-one-out cross-validation method, and their predictive abilities were evaluated using the external test set. The developed SVD-MLR models were robust and reliable with an external determination coefficient (R_test^2) of 0.9755 and a mean-square error (MSE) of 0.0205, as well as an R_test^2 of 0.9179 and MSE of 0.0298 for the HCV and the HIV set, respectively. In return, this model could be developed to predict the activities of a non-seen extra set of organic molecules for the purpose of either virtual screening or lead/hit optimization.