Research on sound insulation characteristics of the friction stir welding magnesium alloy sheet

The friction stir welding (FSW) on magnesium alloy has already been widely used. Therefore, the research on its sound insulation characteristics appears particularly significant, based on ALE (Arbitrary Lagrangian Eulerian) adaptive meshing technique of ABAQUS/Explicit, the FSW procedure was numerically simulated and the modal solution, just a little different from the experimental result, was finally obtained, which has verified the validity of the established model, and obtain the response result to be imported into professional acoustic software to calculate the sound insulation characteristics. Subsequently, the structure-acoustic coupling method was employed to calculate the noise reduction in FSW on magnesium alloy, and through comparison with the experimental result, this coupling method proved feasible to predict the sound insulation characteristics in FSW on magnesium alloy. Furthermore, the result has also revealed that FSW could increase the noise reduction at intermediate or low frequency, in addition, which was 2 dB higher on the frontal welding surface than the reverse one. Consequently, at the installation of magnesium alloy welding parts, the frontal or reverse surface shall be reasonably selected to face the noise source in accordance with the practical situation, so as to improve the sound insulation performance to a greater extent. To some extent, the research achieves the combination of welding and acoustic

SVDinsTN: An Integrated Method for Tensor Network Representation with Efficient Structure Search

Tensor network (TN) representation is a powerful technique for data analysis and machine learning. It practically involves a challenging TN structure search (TN-SS) problem, which aims to search for the optimal structure to achieve a compact representation. Existing TN-SS methods mainly adopt a bi-level optimization method that leads to excessive computational costs due to repeated structure evaluations. To address this issue, we propose an efficient integrated (single-level) method named SVD-inspired TN decomposition (SVDinsTN), eliminating the need for repeated tedious structure evaluation. By inserting a diagonal factor for each edge of the fully-connected TN, we calculate TN cores and diagonal factors simultaneously, with factor sparsity revealing the most compact TN structure. Experimental results on real-world data demonstrate that SVDinsTN achieves approximately $10^2\sim{}10^3$ times acceleration in runtime compared to the existing TN-SS methods while maintaining a comparable level of representation ability

A METHOD AND SYSTEM FOR SELECTION OF PAYMENT CARDS FOR TRANSACTIONS AFTER PRE-STIPULATED TIME PERIODS

The present disclosure relates to a method and system for providing selection of payment cards for transactions after pre-stipulated time periods. The method includes generating a universal token for a consumer, such that the universal token is mapped to the consumer’s payment cards. Thereafter, the universal token is utilised for a transaction. In order for the transaction to be authorised, the universal token is authorised by issuing agencies. At an end of the pre-stipulated time period, the consumer may pay for purchases by choosing a payment card from a plurality of payment cards for each transaction made using the universal token. The transaction is ultimately cleared after the pre-stipulated time period using the payment card chosen by the consumer

METHOD AND SYSTEM FOR MANAGING MERCHANT TICKETS

The present disclosure provides a method and a system for managing the merchant tickets. The present disclosure provides a centralized platform that stores and manages the merchant tickets offered and sold by the merchant. The present disclosure discloses associating the merchant tickets of a user with a digital wallet of the user. The merchant tickets are managed and stored by a merchant system associated with the merchant and a service provider system. The user can view, manage, and redeem the merchant tickets from the digital wallet

Detecting and removing visual distractors for video aesthetic enhancement

Personal videos often contain visual distractors, which are objects that are accidentally captured that can distract viewers from focusing on the main subjects. We propose a method to automatically detect and localize these distractors through learning from a manually labeled dataset. To achieve spatially and temporally coherent detection, we propose extracting features at the Temporal-Superpixel (TSP) level using a traditional SVM-based learning framework. We also experiment with end-to-end learning using Convolutional Neural Networks (CNNs), which achieves slightly higher performance than other methods. The classification result is further refined in a post-processing step based on graph-cut optimization. Experimental results show that our method achieves an accuracy of 81% and a recall of 86%. We demonstrate several ways of removing the detected distractors to improve the video quality, including video hole filling; video frame replacement; and camera path re-planning. The user study results show that our method can significantly improve the aesthetic quality of videos

Poly[[aqua­(μ5-1H-benzimidazole-5,6-dicarboxyl­ato)(μ4-1H-benzimidazole-5,6-dicarboxyl­ato)dibarium] 4.5-hydrate]

The polymeric title compound, {[Ba2(C9H4N2O4)2(H2O)]·4.5H2O}n, adopts a layer structure parallel to (001) in which adjacent BaII atoms are connected by two benzimidazole-5,6-dicarboxyl­ate dianions, one functioning in a μ4-bridging mode and the other in a μ5-bridging mode. The Ba atom having water in its coordination environment as well as the Ba atom without water exist in a nine-coordinate polyhedron of O atoms; the geometry is difficult to derive. Lattice water mol­ecules occupy the space between layers and inter­act with the layers through O—H⋯O, O—H⋯N and N—H⋯O hydrogen bonds. ne of the five lattice water molecules is equally disordered around an inversion centre and shows half-occupancy

The Effect of Surfactants on the Diameter and Morphology of Electrospun Ultrafine Nanofiber

Different surfactants are introduced to study the diameter and morphology transformation characteristics of electrospun nanofiber. Surfactants increase the net charge density and instability motion of charged jet. The instability motion provides a good way to stretch the charged jets into finer ones, by which the beaded structures are also prevented. Ultrafine nanofiber with average diameter less than 65 nm can be fabricated. The nanofiber diameter decreases with the increase of surfactant concentration in polymer solution. The nanofibers with anionic surfactant sodium dodecyl sulfate (SDS) have the smallest diameter. The cationic surfactant hexadecyl trimethyl ammonium bromide (HTAB) plays the best role to prevent the formation of beaded structures in nanofibers, and helps to increase the uniformity of electrospun nanofiber. The effects of surfactants on the nanofiber diameter and morphology have been studied, which would promote the industrial application of ultrafine polymeric nanofibers