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18th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2024

作者： Ji, Haoshuai Liu, Hongye Sun, Maoxun Chen, Xiaorong Shen, Liang Qiu, Chongpeng Tian, Zhenhua School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Mechanical Engineering Virginia Polytechnic Institute State University 1145 Perry Street BlacksburgVA24061 United States

ISBN: (纸本)9798331523732

Estimating elastic constants in materials is markedly important in engineering and technology. Ultrasonic guided wave non-destructive testing provides an effective means for accurately assessing material properties. This paper presents a method for inverting the elastic constants of hollow pipes using the longitudinal mode of ultrasonic guided waves combined with a genetic algorithm (GA). The Legendre orthogonal polynomial expansion (LOPE) method serves as the forward model for analyzing the dispersion curves of axisymmetric guided wave modes, while the GA performs the inversion. A carefully designed finite element (FE) model generates pseudo-experimental data, which is transformed into the frequency-wave number domain for input into the GA. The comparison between simulation results and theoretical analysis confirms the validity of the FE model and the accuracy of the detected modes. The results demonstrate that the proposed method effectively inverts the elastic constants of isotropic hollow cylindrical structures, with an inversion error of less than 2% based on pseudo-experimental data. © 2024 IEEE.

关键词： Nondestructive examination

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WAVEFIELD CHARACTERIZATION OF LAMB WAVE PROPAGATION IN COMPOSITE PLATES BASED ON GUIDED WAVE OMNIDIRECTIONAL WAVENUMBER SIMULATION 18

WAVEFIELD CHARACTERIZATION OF LAMB WAVE PROPAGATION IN COMPO...

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18th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2024

作者： Wang, Lei Liu, Hongye Sun, Maoxun Chen, Xiaorong Shen, Liang Qiu, Chongpeng Tian, Zhenhua School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Mechanical Engineering Virginia Polytechnic Institute State University 1145 Perry Street BlacksburgVA24061 United States

ISBN: (纸本)9798331523732

With the growing application of composite materials in sectors such as aerospace, automotive, and wind energy, accurately assessing their mechanical properties is critical for effective non-destructive testing (NDT) and structural health monitoring (SHM). In this paper, a three-dimensional finite element model was developed to generate the fundamental A0 Lamb wave mode within an 8-layer orthotropic laminate, addressing the complexities associated with the anisotropic behavior of composites. Utilizing the Legendre Orthogonal Polynomial Expansion (LOPE) method, we characterized the wave number profile, revealing significant directional dependence in wave propagation across multilayer laminates. The results demonstrated strong agreement between the analytical predictions from the LOPE method and the finite element simulations, confirming the robustness of our modeling approach. Additionally, we introduced an innovative omnidirectional wavefield representation method, effectively mitigating the influence of phase and group velocity steering angles. These findings enhance our understanding of guided wave dynamics in composite materials, offering valuable insights for the development of more effective detection strategies and assessments of structural integrity in complex composite systems. © 2024 IEEE.

关键词： Gravity waves

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Study on the Deformation Mechanism of a Soft Rock Tunnel

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Fluid Dynamics & Materials Processing 2022年第2期18卷 243-255页

作者： Jianhui Yang Kai Shen Shoudong Pan Shuren Wang Zhengsheng Zou School of Civil Engineering and Architecture Zhejiang University of Science and TechnologyHangzhou310023China China Railway 16 Bureau Group Co. Ltd.Beijing100018China School of Civil Engineering Henan Polytechnic UniversityJiaozuo454003China

The large deformation of soft rock tunnel is one of the key problems to be overcome in the tunnel construction *** the present study,the deformation mechanism of a representative tunnel and some related countermeasures are investigated using field tests and engineering geological *** to the scarce performances of methods based on other criteria such as small pipe spacing,anchor bolt length and steel frame spacing,a new support scheme is implemented and *** show that shear failure and bedding sliding are produced under high horizontal stress *** low strength of the surrounding rock results in the uneven convergence of both sides of the *** the aforementioned new support scheme,however,most of such problems can be mitigated leading to good stability properties and ensuing economic advantages.

关键词： Soft rock tunnel high stress deformation mechanism supporting countermeasure

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A Mobile Driving Simulator to Improve Driving Experience for Novice and Learner Drivers 9

A Mobile Driving Simulator to Improve Driving Experience for...

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9th International Symposium on Educational technology, ISET 2023

作者： Fung, Yin-Chun Lee, Lap-Kei Chui, Kwok Tai Hao, Tianyong Wu, Nga-In Tai, Kam-Cheung Tang, Wai-Leung Wong, Cheuk-Hei Hong Kong Metropolitan University School of Science and Technology Hong Kong South China Normal University School of Computer Science Guangzhou China The Hong Kong Polytechnic University Division of Languages and Communication College of Professional and Continuing Education Hong Kong

ISBN: (纸本)9798350304916

Statistics show that novice and learner drivers have a higher crash risk and lower passing rate for driving tests compared to experienced drivers, posing a risk to road safety. Therefore, we propose a mobile driving simulator game that is designed to provide an alternative driver training solution for novice and learner drivers to improve their driving experiences and prepare for the driving test. This game focuses on promoting safe driving by providing a driver training platform through smartphones, with high portability and functionality. The game consists of three modes-Tutorial, Free Driving, and Driving Test Mode. The Tutorial Mode offers driver training tutorials to teach and remind drivers of their driving knowledge. The Free Driving Mode provides an authentic Hong Kong street view to enhance drivers' recognition of roads in Hong Kong. The Driving Test Mode offers a simulated driving test for learner drivers to practice for their driving test. The game features a high-quality and authentic map of Hong Kong, with street views, buildings, and road objects built according to real-life situations in Ho Man Tin. Players can explore the detailed map while practicing driving with the game. © 2023 IEEE.

关键词： Serious games

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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4861-4875页

作者： Yang, Shangshang Tian, Ye He, Cheng Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Department of Computer Science University of Surrey Guildford U.K

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

关键词： Artificial neural networks Training Optimization Search problems Scalability Complexity theory Genetic algorithms Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

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Robustness Challenges in Deep Learning: Strategies for Enhancing Model Resilience 2

Robustness Challenges in Deep Learning: Strategies for Enhan...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Joshi, Prakash Shaikh, M.Z. Varshney, Neeraj Dwivedy, Bhoopendra Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Gla University Department of Computer Engineering and Applications UP Mathura India Galgotias University Department of School of Computer Science & Engineering UP Greater Noida India

ISBN: (纸本)9798350371055

Deep knowledge is the main way the improvement of gadgets, gaining knowledge of generation for more than a few applications from facial popularity to self-reliant vehicles. Deep learning fashions have carried out outstanding successes, even outperforming people in complex obligations. However, robustness demanding situations of deep getting-to-know systems remain an essential difficulty. Deep studying fashions are at risk of malicious inputs that could result in misclassifications or denial-of-provider attacks. Robustness towards entering noise, in addition to robust extraction of beneficial capabilities from noisy datasets, are just a number of the main challenges for deep mastering systems. Current research has discovered a plethora of attack vectors that can be deployed against deep getting-to-know fashions along with 'adversarial' assaults, which contain manipulating the enter records in a particular manner to purpose models to misclassify items. Moreover, dataset bias and absence of right validation can cause structures that are not sturdy towards facts from a domain extraordinary from the facts used for education. A good way to enhance the robustness of deep learning models, researchers are exploring solutions that include countermeasures, specialized architectures, and higher education and checking out approaches. While progress has been made, the robustness challenges of deep learning remain an important study vicinity that should be addressed so one can fulfill the promise of this generation. © 2024 IEEE.

关键词： Architectures Discovered Extraordinary Manipulating Researchers

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DOA Estimation Based on Sparse Reconstruction via Acoustic Vector Sensor Array under Non-uniform Noise

DOA Estimation Based on Sparse Reconstruction via Acoustic V...

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2022 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2022

作者： Li, Xiangshui Wang, Weidong Li, Hui Shi, Wentao Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Northwestern Polytechnical University School of Marine Science and Technology Xi'an710072 China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

This paper aims to solve the reduction in direction of arrival estimated accuracy for the acoustic vector sensor array in the existence of non-uniform noise, and proposes an iterative sparse covariance matrix reconstruction (ISCMR) method. We first define a virtual manifold matrix and establish the cost function based on the covariance matrix fitting criterion. Then, using the properties of Frobenius norm to derive the analytical expression of the cost function. Furthermore, the sparse covariance matrix is reconstructed according to the estimated power of sparse signal and noise until the iteration is terminated. Finally, a more exactly DOA estimation is completed by searching spectral peaks on the sparse signal power. The superior performance of the proposed method, comparison with the existing methods, is proved by simulation results in the existence of non-uniform noise. © 2022 IEEE.

关键词： Cost functions

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The Number of Solutions of Certain Equations over Finite Fields

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Wuhan university Journal of Natural sciences 2022年第1期27卷 49-52页

作者： HU Shuangnian LIU Jianghan QIN Zhentao School of Mathematics and Statistics Nanyang Institute of TechnologyNanyang 473004HenanChina College of Computer and Information Science Southwest UniversityChongqing 400715China School of Mathmatics and Computer Panzhihua UniversityPanzhihua 617000SichuanChina

Let s be a positive integer,p be an odd prime,q=p^(s),and let F_(q)be a finite field of q *** N_(q)be the number of solutions of the following equations:(x_(1)^(m_(1))+x_(2)^(m_(2))+…+x_(n)^(m_(n)))^(k)=x_(1)x_(2)…x_(n)x^(k_(n+1))_(n+1)…x^(k_(t))_(t)over the finite field F_(q),with n≥2,t>n,k,and k_(j)(n+1≤j≤t),m_(i)(1≤i≤n)are positive *** this paper,we find formulas for N_(q)when there is a positive integer l such that dD|(p^(l)+1),where D=1 cm[d_(1),…,d_(n)],d=gcd(n∑i=1M/m_(i)-kM,(q-1)/D),M=1 cm[m_(1),…,m_(n)],d_(j)=gcd(m_(j),q-1),1≤j≤*** we determine N_(q)explicitly under certain *** extends Markoff-Hurwitz-type equations over finite field.

关键词： finite field rational point diagonal equation Markoff-Hurwitz-type equations

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Secure parallel Outsourcing Scheme for Large-scale Matrix Multiplication on Distributed Cloud Servers 29

Secure parallel Outsourcing Scheme for Large-scale Matrix Mu...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Yinlong Tao, Yunting Kong, Fanyu Gu, Zhaoquan Yu, Jia Zhang, Hanlin Binzhou Polytechnic College of Information Engineering Shandong Binzhou256603 China Shandong University School of Software Shandong Jinan250101 China Shandong Sansec Information Technology Co. Ltd Jinan250101 China School of Computer Science and Technology Shenzhen China Qingdao University College of Computer Science and Technology Shandong Qingdao266071 China

ISBN: (纸本)9798350330717

Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n3) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce the client-side computational time. This paper presents a parallel matrix multiplication outsourcing scheme based on Cannon's algorithm. By distributing the matrix across multiple cloud servers for parallel computation, we can get a significant efficiency speedup. Our scheme employs multiple cloud servers to perform parallel matrix computation, reducing the computational load by 89-97% when utilizing 4-16 servers as opposed to using a single server. We provide a comprehensive analysis of the scheme's correctness, security, and verifiability, substantiating the benefits of our approach through the experimental data. © 2023 IEEE.

关键词： Cloud computing Data privacy Matrix multiplication Parallel outsourcing

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An Enhanced Broad Learning System with Mean Time Series Difference for Aided Diagnosis of Mild Cognitive Impairment

An Enhanced Broad Learning System with Mean Time Series Diff...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Jing Wang Yong Jun He C. L. Philip Chen Xiping Jia Zhiyong Lin Huiming Zhao School of Computer Science Guangdong Polytechnic Normal University Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

Alzheimer's disease is a progressive neurological disorder. The disease is not reversible, but mild cognitive impairment is a transitional state between Alzheimer's disease and cognitively normal, it is a stage in which the brain is so minimally diseased that it can be treated or slowed or prevented from developing further lesions. At present, there are many methods for the aided diagnosis of mild cognitive impairment, among which the method based on fMRI medical images has emerged in recent years. However, the current aided diagnosis methods in this direction have some problems such as low accuracy and cumbersome feature extraction. A novel feature extraction method is put forward, namely the mean time series difference method in this paper. The feature extraction method effectively improves the accuracy in the auxiliary diagnosis task of mild cognitive impairment. An enhanced broad learning system with mean time series difference also was proposed. The results show that the enhanced broad learning system with mean time series can not only effectively optimize the feature extraction process, but also the accuracy of mild cognitive impairment classification tasks., which has significance for the clinical auxiliary diagnosis of mild cognitive impairment.

关键词： Learning systems Neurological diseases Accuracy Time series analysis Functional magnetic resonance imaging Feature extraction Lesions

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