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arXiv 2024年

作者： Suzuki, Yudai Aoki, Shiori Key, Fabian Endo, Katsuhiro Matsuda, Yoshiki Tanaka, Shu Behr, Marek Muramatsu, Mayu Quantum Computing Center Keio University Yokohama Kanagawa Japan Graduate School of Science and Technology Keio University Yokohama Kanagawa Japan Institute of Lightweight Design and Structural Biomechanics TU Wien Austria Tsukuba Ibaraki Japan Fixstars Corporation Tokyo Japan Green Computing System Research Organization Waseda University Tokyo Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Kanagawa Japan Keio University Tokyo Japan Keio University Tokyo Japan Computational Analysis of Technical Systems RWTH Aachen University Germany Department of Mechanical Engineering Keio University Yokohama Kanagawa Japan

Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, including digital or quantum annealers, have been used as efficient solvers for combinatorial optimization problems. Beyond combinatorial optimization, recent works have demonstrated applicability to other engineering tasks by tailoring corresponding problem formulations. In this study, we present a novel Ising machine formulation for computing design updates during topology optimization with the goal of minimizing dissipation energy in flow channels. We explore the potential of this approach to improve the efficiency and performance of the optimization process. To this end, we conduct experiments to study the impact of various factors within the novel formulation. Additionally, we compare it to a classical method using the number of optimization steps and the final values of the objective function as indicators of the time intensity of the optimization and the performance of the resulting designs, respectively. Our findings show that the proposed update strategy can accelerate the topology optimization process while producing comparable designs. However, it tends to be less exploratory, which may lead to lower performance of the designs. These results highlight the potential of incorporating Ising formulations for optimization tasks but also show their limitations when used to compute design updates in an iterative optimization process. In conclusion, this work provides an efficient alternative for design updates in topology optimization and enhances the understanding of integrating Ising machine formulations in engineering optimization. © 2024, CC BY-NC-ND.

关键词： Shape optimization

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An Improved Deep Learning-Based Intrusion Detection for Reliable Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年

作者： Suman, Preetam Padhy, Sasmita Kumar, Naween Suman, Amrit Singh, Akansha Singh, Krishna Kant Castilla, Angel Kuc Al-Zahrani, Turki Saad S. VIT Bhopal University School of Computing Science and Engineering Bhopal India Bennett University SCSET Greater Noida India Sharda University Department of CSE Greater Noida India Delhi Technical Campus Greater Noida India C/Isabel Torres 21 Engineering Research and Innovation Group Universidad Europea del Atlántico Santander39011 Spain Universidad Internacional Iberoamericana Department of Project Management C.P. Campeche24560 Mexico Universidad Internacional Iberoamericana Department of Project Management Arecibo00613 Puerto Rico University of Jeddah Learning Design and Technology Department Jeddah Saudi Arabia

The research area of Vehicular Ad-Hoc Network (VANET) is rapidly expanding for different reasons. The main factors responsible for this are the unstable topology, fast vehicle movement, and security vulnerabilities. Identifying malicious attacks in vehicular ad-hoc networks is necessary to enhance the security and reliability of communication between all vehicles in the system. The intruders carry out numerous malicious attacks. The focus of this paper is to identify and mitigate attacks like Botnet, Sybil, DoS, Wormhole, PortScan, Blackhole, and BruteForce. The present work proposes an Improved LeeNET (I-LeeNet) architecture to identify and mitigate unidentified attack types. The suggested architecture intelligently blends Convolutional Neural Networks (CNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to provide real-time solutions to unidentified attacks. The proposed approach includes a module KIDS for known attack detection and another module UIDS for learning and identifying previously unidentified attacks. The KIDS module uses ANFIS classification to identify destructive attacks. ANFIS and CNN use the UIDS module to identify unknown attacks on VANET further. The efficacy of the proposed I-LeeNet is tested on three different datasets, such as i-VANET, ToN-IoT, and CIC-IDS 2017. The results section discusses the testing in a regressive manner. In addition, the experimental results are compared to other advanced methods. The average accuracy achieved by the proposed method is 97.21% on i-VANET, 97.75% on ToN-IoT, and 96.66% on the CIC-IDS 2017 dataset. The analysis suggests the proposed method is promising and can be applied in real-time. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Channel Drop Out:A Simple Way to Prevent CNN from Overfitting in Motor Imagery Based BCI

Channel Drop Out:A Simple Way to Prevent CNN from Overfittin...

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2021国际计算机前沿大会

作者： Jing Luo Yaojie Wang Rong Xu Guangming Liu Xiaofan Wang Yijing Gong Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and EngineeringXi'an University of Technology Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design

With the development of deep learning,many motor imagery brain-computer interfaces based on convolutional neural networks(CNNs) show outstanding ***,the trial number of EEG in the training set is usually limited,and redundancy extensively exists in multiple channel ***,overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of *** this paper,channel drop out is proposed to address this problem by data augmentation and ensemble ***,one of all EEG channels will be dropped and replaced by the mean signal of all EEG *** this way,the trial number in the training set was enlarged by channel drop *** at the testing stage,all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the *** experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2 a to verify the performances of the proposed channel drop out *** results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83%and 2.65% compared with the original CNN *** the channel drop out approach significantly improves the performances of motor imagery based BCI.

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Time-Domain Transfer Path Analysis Based on Gaussian Process Latent Force Model and Least Squares

SSRN

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SSRN 2024年

作者： Zhu, Yu He, Zhicheng Zhou, Enlin L. Li, Eric Chen, Y.J. Gao, H. State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha410082 China School of Computing Engineering & Digital Technologies Teesside University Middlesbrough United Kingdom Chinese Scholartree Ridge Laboratory China North Vehicle Research Institute Beijing100072 China Liuzhou Wuling New Energy Vehicle Co. Ltd. Guangxi Automobile Group Co. Ltd. Liuzhou545005 China

The appearance of Time-domain transfer path analysis (TPA) has provided an effective means for addressing the issues of noise, vibration, and harshness (NVH) in mechanical systems under transient conditions. However, two primary challenges exist with this approach. Firstly, computing the generalized inverse of the frequency response functions (FRFs) matrix from excitations to indicators is prone to numerical instabilities, leading to ill-conditioned problems. Secondly, the accuracy of time-domain information extracted from existing frequency-domain data is relatively low. These issues diminish the precision of classical time-domain TPA methods in time-domain loads identification and impulse response functions (IRFs) recognition, thereby affecting their accuracy. In this work, we propose a novel time-domain TPA method that integrates the gaussian process latent force model (GPLFM) with the least squares (LS). Initially, this method explores the GPLFM in fluid-structure interaction (FSI) system. Secondly, a genetic algorithm (GA) is utilized to determine optimal parameters of the indicators. Subsequently, the vibration IRFs are identified using the LS. Finally, the contribution of each path to the targets' vibration is calculated using linear convolution, while a Kalman filter without measurement updates predicts the noise contribution. Case studies have demonstrated that the proposed method achieved higher accuracy in time-domain loads identification under multiple loading conditions compared to traditional methods using deconvolution filters. Additionally, the accuracy of calculating the contributions surpasses that of traditional methods employing linear convolution. © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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Freezing dynamics of wetting droplet under a uniform electric field

arXiv

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arXiv 2024年

作者： Huang, Jiangxu Li, Hanqing Che, Jiaqi Chai, Zhenhua Wang, Lei Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China Marine Design & Research Institute of China China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Mathematics and Physics China University of Geosciences Wuhan430074 China

Electrofreezing is a powerful technique that employs the electric field to control and enhance the freezing process. In this work, a phase-field-based lattice Boltzmann (LB) method is developed to study the electrofreezing process of sessile droplet on a cooled substrate. The accuracy of the present LB method is first validated through performing some simulations of the three-phase Stefan problem, the droplet freezing on a cold wall, and the droplet deformation under a uniform electric field. Then it is used to investigate the effect of an electric field on the freezing of a wetting droplet on a cold substrate, and the numerical results show that the electric field has a significant influence on the freezing time of the droplet mainly through changing the morphology of the droplet. In particular, under the effect of the electric field, the freezing time is increased for the droplet with a prolate pattern, while the freezing time of the droplet with an oblate pattern is decreased. These numerical results bring some new insights on the electrofreezing and provide a valuable guidance for the precise regulation of droplet freezing. Copyright © 2024, The Authors. All rights reserved.

关键词： Wetting

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Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies

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Philosophy & technology 2021年第4期34卷 1945-1960页

作者： Maurice D Mulvenna Raymond Bond Jack Delaney Fatema Mustansir Dawoodbhoy Jennifer Boger Courtney Potts Robin Turkington School of Computing Ulster University Shore Road Newtownabbey Northern Ireland UK. Imperial College School of Medicine Imperial College London South Kensington London UK. Department of Systems Design Engineering University of Waterloo University Avenue West Waterloo Canada.

Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail.

关键词： Digital health Digital phenotyping Ecological momentary assessment Ethics Event log analysis Experience sampling method Unsupervised machine learning

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Correction: Variational Rectification Inference for Learning with Noisy Labels

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International Journal of Computer Vision 2024年第3期133卷 1434-1434页

作者： Sun, Haoliang Wei, Qi Feng, Lei Hu, Yupeng Liu, Fan Fan, Hehe Yin, Yilong School of Software Shandong University Jinan China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Computing National University of Singapore Singapore Singapore School of Computer Science and Technology Zhejiang University Hangzhou China

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The contribution of dilatational motion to energy flux in homogeneous compressible turbulence

arXiv

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arXiv 2025年

作者： Luo, Chensheng Fang, Le Fang, Jian Xu, Haitao Pumir, Alain Yang, Ping-Fan School of Aeronautics Institute of Extreme Mechanics Northwestern Polytechnical University Xi’an710072 China Research Institute of Aero-Engine Beihang University Beijing100191 China Laboratory of Complex System Ecole Centrale de Pékin School of General Engineering Beihang University Beijing100191 China Scientific Computing Department STFC Daresbury Laboratory WarringtonWA4 4AD United Kingdom Center for Combustion Energy School of Aerospace Engineering Tsinghua University Beijing100084 China Laboratoire de Physique Ecole Normale Supérieure de Lyon CNRS Université de Lyon LyonF-69007 France National Key Laboratory of Aircraft Configuration Design Xi’an710072 China

We analyze the energy flux in compressible turbulence by generalizing the exact decomposition recently proposed by Johnson (Phys. Rev. Lett., vol. 124, 2020. 104501) to study incompressible turbulent flows. This allows us to characterize the effect of dilatational motion on the inter-scale energy transfer in three-dimensional compressible turbulence. Our analysis reveals that the contribution of dilatational motion to energy transfer is due to three different physical mechanisms: the interaction between dilatation and strain, between dilatation and vorticity, and the self-interaction of dilatational motion across scales. By analyzing numerical simulations of flows at moderate turbulent Mach numbers (Mat 0.3), we validate our theoretical derivations and provide a quantitative description of the role of dilatational motion in energy transfer. In particular, we determine the scaling dependence of the dilatational contributions on the turbulent Mach number. Moreover, our findings reveal that the eddy-viscosity assumption often used in large-eddy simulations, in the spirit of the approach used for incompressible flows, effectively neglects the interaction between solenoidal-dilatational energy transfer and overestimate dilatational effects. Copyright © 2025, The Authors. All rights reserved.

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Efficient Statistics for Sparse Graphical Models from Truncated Samples

arXiv

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arXiv 2020年

作者： Bhattacharyya, Arnab Desai, Rathin Nagarajan, Sai Ganesh Panageas, Ioannis School of Computing National University of Singapore Engineering Systems and Design Singapore University of Technology and Design Information Systems Singapore University of Technology and Design

In this paper, we study high-dimensional estimation from truncated samples. We focus on two fundamental and classical problems: (i) inference of sparse Gaussian graphical models and (ii) support recovery of sparse linear models. (i) For Gaussian graphical models, suppose d-dimensional samples x are generated from a Gaussian N(µ, Σ) and observed only if they belong to a subset S ⊆ Rd. We show that µ and Σ can be estimated with error Ε in the Frobenius norm, using(Formula Presented)samples from a truncated N(µ, Σ) and having access to a membership oracle for S. The set S is assumed to have non-trivial measure under the unknown distribution but is otherwise arbitrary. (ii) For sparse linear regression, suppose samples (x, y) are generated where y = x>Ω∗ + N(0, 1) and (x, y) is seen only if y belongs to a truncation set S ⊆ R. We consider the case that Ω∗ is sparse with a support set of size k. Our main result is to establish precise conditions on the problem dimension d, the support size k, the number of observations n, and properties of the samples and the truncation that are sufficient to recover the support of Ω∗. Specifically, we show that under some mild assumptions, only O(k2 log d) samples are needed to estimate Ω∗ in the `∞-norm up to a bounded error. For both problems, our estimator minimizes the sum of the finite population negative log-likelihood function and an `1-regularization term. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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AUTOMATA-BASED CONTROLLER SYNTHESIS FOR STOCHASTIC SYSTEMS: A GAME FRAMEWORK VIA APPROXIMATE PROBABILISTIC RELATIONS

arXiv

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arXiv 2021年

作者： Zhong, Bingzhuo Lavaei, Abolfazl Zamani, Majid Caccamo, Marco TUM School of Engineering and Design Technical University of Munich Germany School of Computing Newcastle University United Kingdom Department of Computer Science University of Colorado Boulder United States Department of Computer Science LMU Munich Germany

In this work, we propose an abstraction and refinement methodology for the controller synthesis of discrete-time stochastic systems to enforce complex logical properties expressed by deterministic finite automata (a.k.a. DFA). Our proposed scheme is based on a notion of so-called Ε, δ)-approximate probabilistic relations, allowing one to quantify the similarity between stochastic systems modeled by discrete-time stochastic games and their corresponding finite abstractions. Leveraging this type of relations, the lower bound for the probability of satisfying the desired specifications can be well ensured by refining controllers synthesized over abstract systems to the original games. Moreover, we propose an algorithmic procedure to construct such a relation for a particular class of nonlinear stochastic systems with slope restrictions on the nonlinearity. The proposed methods are demonstrated on a quadrotor example, and the results indicate that the desired lower bound for the probability of satisfaction is guaranteed. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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