Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis

GANHead: Towards Generative Animatable Neural Head Avatars

To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations. Specifically, GANHead represents coarse geometry, fine-gained details and texture via three networks in canonical space to obtain the ability to generate complete and realistic head avatars. To achieve flexible animation, we define the deformation filed by standard linear blend skinning (LBS), with the learned continuous pose and expression bases and LBS weights. This allows the avatars to be directly animated by FLAME parameters and generalize well to unseen poses and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves superior performance on head avatar generation and raw scan fitting.Comment: Camera-ready for CVPR 2023. Project page: https://wsj-sjtu.github.io/GANHead

Model selection-inspired coefficients optimization for polynomial-kernel graph learning

Graph learning has been extensively investigated for over a decade, in which the graph structure can be learnt from multiple graph signals (e.g., graphical Lasso) or node features (e.g., graph metric learning). Given partial graph signals, existing node feature-based graph learning approaches learn a pair-wise distance metric with gradient descent, where the number of optimization variables dramatically scale with the node feature size. To address this issue, in this paper, we propose a low-complexity model selection-inspired graph learning (MSGL) method with very few optimization variables independent with feature size, via leveraging on recent advances in graph spectral signal processing (GSP). We achieve this by 1) interpreting a finite-degree polynomial function of the graph Laplacian as a positive-definite precision matrix, 2) formulating a convex optimization problem with variables being the polynomial coefficients, 3) replacing the positive-definite cone constraint for the precision matrix with a set of linear constraints, and 4) solving efficiently the objective using the Frank-Wolfe algorithm. Using binary classification as an application example, our simulation results show that our proposed MSGL method achieves competitive performance with significant speed gains against existing node feature-based graph learning methods

A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts

Copyright © 2014 American Geophysical UnionThis paper presents a new Two Stage Bayesian Stochastic Dynamic Programming (TS-BSDP) model for real time operation of cascaded hydropower systems to handle varying uncertainty of inflow forecasts from Quantitative Precipitation Forecasts. In this model, the inflow forecasts are considered as having increasing uncertainty with extending lead time, thus the forecast horizon is divided into two periods: the inflows in the first period are assumed to be accurate, and the inflows in the second period assumed to be of high uncertainty. Two operation strategies are developed to derive hydropower operation policies for the first and the entire forecast horizon using TS-BSDP. In this paper, the newly developed model is tested on China's Hun River cascade hydropower system and is compared with three popular stochastic dynamic programming models. Comparative results show that the TS-BSDP model exhibits significantly improved system performance in terms of power generation and system reliability due to its explicit and effective utilization of varying degrees of inflow forecast uncertainty. The results also show that the decision strategies should be determined considering the magnitude of uncertainty in inflow forecasts. Further, this study confirms the previous finding that the benefit in hydropower generation gained from the use of a longer horizon of inflow forecasts is diminished due to higher uncertainty and further reveals that the benefit reduction can be substantially mitigated through explicit consideration of varying magnitudes of forecast uncertainties in the decision-making process.National Natural Science Foundation of ChinaHun River cascade hydropower reservoirs development company, Ltd.UK Royal Academy of Engineerin

Bifurcation Analysis for a Delayed Predator-Prey System with Stage Structure

<p>Abstract</p> <p>A delayed predator-prey system with stage structure is investigated. The existence and stability of equilibria are obtained. An explicit algorithm for determining the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions is derived by using the normal form and the center manifold theory. Finally, a numerical example supporting the theoretical analysis is given.</p

Preparation and Performance Improvement Mechanism Investigation of High-Performance Cementitious Grout Material for Semi-Flexible Pavement

Semi-flexible pavement material (SFPM) combines the advantages and avoids the disadvantages of asphalt concrete flexible pavement and cement concrete rigid pavement. However, due to the problem of interfacial strength of composite materials, SFPM is prone to cracking diseases, which limits the further application of SFPM. Hence, it is necessary to optimize the composition design of SFPM and improve its road performance. In this study, the effects of cationic emulsified asphalt, silane coupling agent and styrene–butadiene latex on the improvement of SFPM performance were compared and analyzed. The influence of modifier dosage and preparation parameters on the road performance of SFPM was investigated by an orthogonal experimental design combined with principal component analysis (PCA). The best modifier and the corresponding preparation process were selected. On this basis, the mechanism of SFPM road performance improvement was further analyzed by scanning electron microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) spectral analysis. The results show that adding modifiers can significantly enhance the road performance of SFPM. Compared to silane coupling agents and styrene–butadiene latex, cationic emulsified asphalt changes the internal structure of cement-based grouting material and increases the interfacial modulus of SFPM by 242%, allowing cationic emulsified asphalt-SFPM (C-SFPM) to exhibit better road performance. According to the results of the principal component analysis, C-SFPM has the best overall performance compared to other SFPMs. Therefore, cationic emulsified asphalt is the most effective modifier for SFPM. The optimal amount of cationic emulsified asphalt is 5%, and the best preparation process involves vibration at a frequency of 60 Hz for 10 min and 28 days of maintenance. The study provides a method and basis for improving the road performance of SFPM and a reference for designing the material composition of SFPM mixes

Innovative Strategies for Hair Regrowth and Skin Visualization

Today, about 50% of men and 15–30% of women are estimated to face hair-related problems, which create a significant psychological burden. Conventional treatments, including drug therapy and transplantation, remain the main strategies for the clinical management of these problems. However, these treatments are hindered by challenges such as drug-induced adverse effects and poor drug penetration due to the skin’s barrier. Therefore, various efforts have been undertaken to enhance drug permeation based on the mechanisms of hair regrowth. Notably, understanding the delivery and diffusion of topically administered drugs is essential in hair loss research. This review focuses on the advancement of transdermal strategies for hair regrowth, particularly those involving external stimulation and regeneration (topical administration) as well as microneedles (transdermal delivery). Furthermore, it also describes the natural products that have become alternative agents to prevent hair loss. In addition, given that skin visualization is necessary for hair regrowth as it provides information on drug localization within the skin’s structure, this review also discusses skin visualization strategies. Finally, it details the relevant patents and clinical trials in these areas. Together, this review highlights the innovative strategies for skin visualization and hair regrowth, aiming to provide novel ideas to researchers studying hair regrowth in the future