The noise control of minicar body in white based on acoustic panel participation method

It is very important to predict the acoustic radiation of vehicle body for the control of interior noise. Firstly, the kinetic equations of coupled acoustic-structural finite element method are explained and the numerical analytical methods of noise transfer function and acoustic panel participation are further obtained. Then the coupled acoustic-structural finite element model of body in white and passenger compartment cavity of a minicar is established and verified by modal test. The passive side of engine mounting points are chosen as the excitation points, and driver’s right ear is the output point of sound pressure response. The noise transfer function is calculated and the critical frequency of vehicle interior noise is obtained. The acoustic panel participation analysis of vehicle roof and floor are conducted, and the key acoustic panels are identified. In order to reduce the noise of critical frequency, the measures, pasting damping material and welding beam, are adopted. The results indicate that, compared with the results of structure improvement of modal method, the vehicle interior noise is controlled more effectively by using the acoustic panel participation analytical method

ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization

Due to its simplicity and outstanding ability to generalize, stochastic gradient descent (SGD) is still the most widely used optimization method despite its slow convergence. Meanwhile, adaptive methods have attracted rising attention of optimization and machine learning communities, both for the leverage of life-long information and for the profound and fundamental mathematical theory. Taking the best of both worlds is the most exciting and challenging question in the field of optimization for machine learning. Along this line, we revisited existing adaptive gradient methods from a novel perspective, refreshing understanding of second moments. Our new perspective empowers us to attach the properties of second moments to the first moment iteration, and to propose a novel first moment optimizer, \emph{Angle-Calibrated Moment method} (\method). Our theoretical results show that \method is able to achieve the same convergence rate as mainstream adaptive methods. Furthermore, extensive experiments on CV and NLP tasks demonstrate that \method has a comparable convergence to SOTA Adam-type optimizers, and gains a better generalization performance in most cases.Comment: 25 pages, 4 figure

SPAN: A Stochastic Projected Approximate Newton Method

Second-order optimization methods have desirable convergence properties. However, the exact Newton method requires expensive computation for the Hessian and its inverse. In this paper, we propose SPAN, a novel approximate and fast Newton method. SPAN computes the inverse of the Hessian matrix via low-rank approximation and stochastic Hessian-vector products. Our experiments on multiple benchmark datasets demonstrate that SPAN outperforms existing first-order and second-order optimization methods in terms of the convergence wall-clock time. Furthermore, we provide a theoretical analysis of the per-iteration complexity, the approximation error, and the convergence rate. Both the theoretical analysis and experimental results show that our proposed method achieves a better trade-off between the convergence rate and the per-iteration efficiency.Comment: Appeared in the AAAI 2020, 25 pages, 6 figure

The noise control of minicar body in white based on acoustic panel participation method

It is very important to predict the acoustic radiation of vehicle body for the control of interior noise. Firstly, the kinetic equations of coupled acoustic-structural finite element method are explained and the numerical analytical methods of noise transfer function and acoustic panel participation are further obtained. Then the coupled acoustic-structural finite element model of body in white and passenger compartment cavity of a minicar is established and verified by modal test. The passive side of engine mounting points are chosen as the excitation points, and driver’s right ear is the output point of sound pressure response. The noise transfer function is calculated and the critical frequency of vehicle interior noise is obtained. The acoustic panel participation analysis of vehicle roof and floor are conducted, and the key acoustic panels are identified. In order to reduce the noise of critical frequency, the measures, pasting damping material and welding beam, are adopted. The results indicate that, compared with the results of structure improvement of modal method, the vehicle interior noise is controlled more effectively by using the acoustic panel participation analytical method

The noise control of minicar body in white based on acoustic panel participation method

It is very important to predict the acoustic radiation of vehicle body for the control of interior noise. Firstly, the kinetic equations of coupled acoustic-structural finite element method are explained and the numerical analytical methods of noise transfer function and acoustic panel participation are further obtained. Then the coupled acoustic-structural finite element model of body in white and passenger compartment cavity of a minicar is established and verified by modal test. The passive side of engine mounting points are chosen as the excitation points, and driver’s right ear is the output point of sound pressure response. The noise transfer function is calculated and the critical frequency of vehicle interior noise is obtained. The acoustic panel participation analysis of vehicle roof and floor are conducted, and the key acoustic panels are identified. In order to reduce the noise of critical frequency, the measures, pasting damping material and welding beam, are adopted. The results indicate that, compared with the results of structure improvement of modal method, the vehicle interior noise is controlled more effectively by using the acoustic panel participation analytical method

Effects of helix deviation on load distributions and bending stresses of continuous engaged helical gear drives

The research of the load distributions and bending stresses with helix deviations in power transmission systems is important for effectively improving gear load capacity. The equations of tooth surface with helix slope and form deviation were established by the given forming rack-cutter tool and the path for processing rack-cutter tool. And various kinds of engaged helical gear models with helix slope and form deviation were developed using finite element method software. Finally, tooth surface load distribution and tooth root bending stress were numerically calculated. The effects of the helix slope deviation, different shape, period, and amplitude of helix form deviation on tooth surface load distribution and tooth root bending stress were investigated and the results were compared to each other as specified by grades 5 and 7. It is found that the single helix slope deviation on tooth surface load distribution and tooth root bending stress shows “superposition” effect. Especially, the different shapes, periods, and amplitudes of helix form deviation exhibit significant effect on tooth surface load distribution and tooth root bending stress. Helix form deviation mainly affects the tooth surface load distribution and tooth root bending stress along the tooth longitudinal direction, while has little impact between tooth pairs. The study benefits gear load capacity analysis and provides valuable guidelines for improving the performance of power transmission systems

Confidence-Aware Matrix Factorization for Recommender Systems

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, existing matrix factorization methods rarely consider confidence of the rating prediction and thus cannot support advanced recommendation tasks. In this paper, we propose a Confidence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction and measure the prediction confidence in the model. Specifically, we introduce variance parameters for both users and items in the matrix factorization process. Then, prediction interval can be computed to measure confidence for each predicted rating. These confidence quantities can be used to enhance the quality of recommendation results based on Confidence-aware Ranking (CR). We also develop two effective implementations of our framework to compute the confidence-aware matrix factorization for large-scale data. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness of our framework from multiple perspectives

Investigation on Strain Hardening and Failure in Notched Tension Specimens of Cold Rolled Ti6Al4V Titanium Alloy

Uniaxial and notched tension samples are utilized to investigate the damage and failure of titanium alloy Ti6Al4V. The strain fields on the samples are obtained by the digital image correlation (DIC) method. Strain localization occurs before fracturing in all samples, and the width and size of the localized zone are characterized. Slant fractures are observed in uniaxial and notched tension specimen, which indicate that the initiation and propagation of cracks in thin sheet specimens are highly affected by the shear stress. Numerical simulations were performed for identification of hybrid hardening laws, and the results were compared with the experiments. The influence of the stress triaxiality on damage mechanism of Ti6Al4V was analyzed by observation of the specimen fracture surfaces using SEM. The results show that a higher stress triaxiality facilitates the formation and growth of micro-voids, which leads to a decrement of strain at failure