Acoustics Performance Research and Analysis of Light Timber Construction Wall Elements Based on Helmholtz Metasurface

Based on the efficient sound absorption characteristics of Helmholtz resonance structures in the range of medium and low frequency acoustic waves,this paper investigates an effective solution for light timber construction walls with acoustic problems.This study takes the light timber construction wall structure as the research object.Based on the Helmholtz resonance principle,the structure design of the wall unit,impedance tube experiment and COMSOL MULTIPHYSICS simulation calculation were carried out to obtain the change rule of acoustic performance of the Helmholtz resonance wall unit structure.The research results show that the overall stability of sound insulation of the structure is improved,and the frequency range with sound transmission loss more than 50 dB in the experimental group is 640–1600 Hz,while in the control group is 500–906 Hz and 1238–1600 Hz;the sound absorption performance of the structure is obviously better than that of the ordinary structure,especially in the low frequency acoustic wave range of 100–320 Hz,the sound absorption coefficient of the experimental group is more than 0.49,while the sound absorption coefficient of the control group is less than 0.1.It is expected that these results will contribute to the optimization of the acoustic performance of light timber construction walls and have high application and popularization value.

NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.

New progress in geometric computing for image and video processing

In recent years, geometry-based image and video processing methods have aroused significant interest. This paper considers progress from four aspects： geometric characteristics and shape, geometric transformations, embedded geometric structure, and differential geometry methods. Current research trends are also pointed out.

A Simplified-Syntax-Based Static Structure Model for Embedded Software Analysis

In order to solve the problem that the embedded software has the shortcoming of the platform dependence, this paper presents an embedded software analysis method based on the static structure model. Before control flow and data flow analysis, a lexical analysis/syntax analysis method with simplified grammar and sentence depth is designed to analyze the embedded software. The experiments use the open source code of smart meters as a case, and the artificial faults as the test objects, repeating 30 times. Compared with the popular static analyzing tools PC-Lint and Splint, the method can accurately orient 91% faults, which is between PC-Lint＇s 95% and Splint＇s 85%. The result indicates that the correct rate of our method is acceptable. Meanwhile, by removing the platform-dependent operation with simplified syntax analysis, our method is independent of development environment. It also shows that the method is applicable to the compiled C（including embedded software） program.