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Rayleigh-Taylor instability under multimode perturbation:Discrete Boltzmann modeling with tracers

Communications in Theoretical Physics 2022年第11期74卷 180-198页

作者： Hanwei Li Aiguo Xu Ge Zhang Yiming Shan Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China HEDPS Center for Applied Physics and TechnologyCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China Energy Resources Engineering Department Stanford University367 Panama StStanfordCA 94305-2220United States of America

The two-dimensional Rayleigh-Taylor Instability(RTI)under multi-mode perturbation in compressible flow is probed via the Discrete Boltzmann Modeling(DBM)with *** distribution of tracers provides clear boundaries between light and heavy fluids in the position ***,the position-velocity phase space offers a new perspective for understanding the flow behavior of RTI with intuitive geometrical *** effects of viscosity,acceleration,compressibility,and Atwood number on the mixing of material and momentum and the mean nonequilibrium strength at the interfaces are investigated separately based on both the mixedness defined by the tracers and the non-equilibrium strength defined by the *** mixedness increases with viscosity during early stage but decreases with viscosity at the later ***,compressibility,and Atwood number show enhancement effects on mixing based on different *** the system relaxes from the initial state,the mean non-equilibrium strength at the interfaces presents an initially increasing and then declining trend,which is jointly determined by the interface length and the macroscopic physical quantity *** conclude that the four factors investigated all significantly affect early evolution behavior of an RTI system,such as the competition between interface length and macroscopic physical quantity *** results contribute to the understanding of the multi-mode RTI evolutionary mechanism and the accompanied kinetic effects.

关键词： Rayleigh-Taylor instability multi-mode perturbation Discrete Boltzmann modeling tracers non-equilibrium effects kinetic modeling

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IoT-Enhanced Path Planning and Obstacle Avoidance for Autonomous Mobile Robots 3

IoT-Enhanced Path Planning and Obstacle Avoidance for Autono...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Selvanathan, N. Ramya, S.R. Sureshkumar, C. Kiruthiga, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Sona College of Technology Department of Information Technology Salem India Dr NGP Institute of Technology Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Department of AI&DS Erode India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350354171

Path planning and obstacle avoidance play a key role in how well autonomous mobile robots work and stay safe when they move through tricky areas. Traditional path-planning methods often can't keep up when new obstacles pop up or existing ones change, which makes them less useful in the real world. To fix these problems, the proposed research work brings in a new IoT-powered path planning and obstacle avoidance system that uses the D∗ Lite and Deep Reinforcement Learning (DRL) algorithms. Our approach brings together D∗ Lite's quick replanning skills with DRL's ability to learn and adapt made even better by live data from IoT sensors. We've put our model through its paces in different simulated environments set up to be as tricky as real-world navigation. Quantitative analysis shows it's a big step up from older models. Adding IoT data leads to a 30% boost in how paths are planned and cut the time robots need to get through complex routes by 20%. Plus, using DRL helps robots deal with moving obstacles 25% better, which means fewer potential crashes. This is a big step forward in the field of autonomous robotics. It gives a robust answer that would not just be reactive to the given environmental inputs but also, through proactivity, prompt possible obstacles, further contributing to smoother and safer output. As we move along with the development of our methodology, we foresee broad applications in many sectors enhancing the autonomy and efficiency of robotic systems working in dynamic and unpredictable environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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AI-Driven CASGN Framework for Evaluating Descriptive Answers in Online Education

AI-Driven CASGN Framework for Evaluating Descriptive Answers...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sridevi Ponmalar, P. Vinodini, S. Srithar, V. Morankar, Gouri Jebaseelan, S. D. Sundarsingh Harikrishnan, R. School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai India Chennai600119 India PeriyarManiammai Institute of Science & Technology Department of Computer Applications Vallam Thanjavur India Shri Ramdeobaba College of Engineering and Management Nagpur India Sathyabama Institute of Science and Technology Department of Eee Chennai119 India Symbiosis Institute of Technology Symbiosis International Deemed University Pune Campus Pune412115 India

ISBN: (纸本)9798331543617

There is an enormous demand for automated assessment systems, which can handle the bulk assessments efficiently and effectively. While existing systems perform well in grading objective questions, they break down with subjective or descriptive answers that require a deeper analysis of context and understanding. To address this challenge, we have proposed the AI-Enhanced Context-Aware Semantic Grading Network-state-of-the-art algorithm for instant auto-descriptive response scoring in e-assessments. The AI-CASGN utilizes state-of-the-art AI technologies, particularly NLP and deep learning models-very specifically transformer architectures-during context-based semantic analysis. Beyond mere keyword matching, the system scores what students say and how relevant it is. This takes the concept of grading to an even more fine-tuned art. Scores change with depth and accuracy in the answer given to show complete evaluation. With the dynamic grading framework, adaptation on scores both regarding the accuracy and depth of the answer for a precise evaluation is possible. Inbuilt adaptive learning within the system performance evolves day by day by learning from the new data. AI-CASGN is designed to handle biases in mitigation by considering that there may be different patterns for a response to ensure that responses with varied wording but conceptually correct are fairly graded. It will have to deal with the volumes at scale, hence it is envisaged to process large volumes of responses in minimum time and with high accuracy to be deployed in real time in any educational institution. In future, the system will also provide personalized feedback to students for the purpose of continuous learning and improvement. This represents a scalable, real-time, unbiased tool that assesses subjective answers in modern online learning environments. © 2024 IEEE.

关键词： Semantics

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Modified Glowworm Swarm Optimization Algorithm based on Task Scheduling for Cyber-Physical System 2

Modified Glowworm Swarm Optimization Algorithm based on Task...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Anitha, D. Al-Farouni, Mohammed Shilpa, N. Revanasiddappa Lekshmy, P.L. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India The Islamic University Najaf Iraq Sr University School of Engineering Department of Ece Telangana India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Lbs Institute of Technology for Women Department of CSE Trivandrum India

ISBN: (纸本)9798350317558

The extensive application of Cyber-Physical Systems (CPS) requires efficient optimization of computational units as well as physical plant. Task scheduling (TS) is serious to enlightening resource utilization and system efficacy within CPS. Standard task schedulers in embedded real-time systems fall short of achieving CPS performance criteria due to task diversity and system heterogeneity. This research proposes a novel task-scheduling technique called Multi objective based Modified Glowworm Swarm Optimization algorithm (MMGSO) for TS that is specifically designed for the CPS context. MMGSO involves assigning number of autonomous tasks to various resources during the TS process. The objective function focuses on reducing task completion time and optimizing resource use. A series of simulations were run to demonstrate MMGSO improved performance. The comparison analysis demonstrated that the MMGSO system performed better across a variety of parameters. The proposed approach attains the better performance in Task Execution (ms) of 347ms and Average Load Balancing Ratio (%) of 69.91% from 100 iterations. © 2024 IEEE.

关键词： Cyber-Physical Systems Glowworm Swarm Optimization Multi-Objective Task Diversity Task Scheduling

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Improved time integration for phase-field crystal models of solidification

Improved time integration for phase-field crystal models of ...

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92nd Annual Meeting of the International Association of Applied mathematics and Mechanics (GAMM)

作者： Punke, Maik Wise, Steven M. Voigt, Axel Salvalaglio, Marco Institute of Scientific Computing TU Dresden Dresden01062 Germany Department of Mathematics The University of Tennessee KnoxvilleTN37996 United States Dresden Center for Computational Materials Science TU Dresden Dresden01062 Germany

We optimize a numerical time-stabilization routine for a class of phase-field crystal (PFC) models of solidification. By numerical experiments, we demonstrate that our simple approach can improve the accuracy of underlying time integration schemes by a few orders of magnitude. We investigate different time integration schemes. Moreover, as a prototypical example for applications, we extend our numerical approach to a PFC model of solidification with an explicit temperature coupling. © 2023 The Authors. Proceedings in Applied mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Solidification

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A Spectral Method of Moments for Hierarchical Imitation Learning 22

A Spectral Method of Moments for Hierarchical Imitation Lear...

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22nd IFAC World Congress

作者： Nguyen, Nguyen Molloy, Timothy L. Nair, Girish N. Paschalidis, Ioannis Ch. College of Engineering and Hariri Institute for Computing and Computational Science and Engineering Boston University 8 St. Mary's St. BostonMA02215 United States Australian National University Canberra Australia University of Melbourne Melbourne Australia

ISBN: (纸本)9781713872344

Recent empirical success has led to a rise in popularity of the options framework for Hierarchical Reinforcement Learning (HRL). This framework tackles the scalability problem in Reinforcement Learning (RL) by introducing a layer of abstraction (i.e. high-level options) over the (low-level) decision process. Hierarchical Imitation Learning (HIL) is the problem of learning low-level and high-level policies within HRL from expert demonstrations consisting only of the low-level actions and states, with the high-level options being hidden (or latent). Due to the latent options, recent work on HIL has focused on the development of Expectation-Maximization (EM) algorithms inspired by approaches such as the celebrated Baum-Welch algorithm for hidden Markov models (HMMs). In this work, we take a different approach and derive a new HIL framework inspired by the spectral method of moments for HMMs. The method of moments offers global and consistent convergence under mild regulatory conditions, whilst only requiring one sweep through the data set of state and action pairs, giving it a competitive run time. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Imitation Learning Learning for control Machine learning Markov Decision Processes Method of Moments Options

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A Classification of Face Recognition System Using Faster Region-Based Convolutional Neural Network 2

A Classification of Face Recognition System Using Faster Reg...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Maheswari, M. Alabdeli, Haider Aruna, T.M. Reddy, R. Archana Revathi, R. School of Computing Srm Institue of Science and Technology Department of Computational Intelligence Kattankulathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India School of Computer Science and Engineering Sr University Warangal India Karpagam Academy of Higher Education Department of Computer Science Echanari Coimbatore India

ISBN: (纸本)9798350373110

In recent times, face recognition is a kind of biometric authentication which is widely used for security purpose in many areas like image processing, pattern recognition. However, recognizing the human face was very difficult as the poses in the input images vary, various conditions of light, brightness, expressions, ages. To overcome from it, a proposed method was introduced in this paper known as Faster Region-based Convolutional Neural Network (RCNN). The main advantage of using the proposed method is it will minimize the training process time as it consisting of two fully connected layers namely classifier and regression layer. Initially, the proposed method was implemented on ORL database consisting of 40 classes, then the images in the database undergoes image resizing and enhancing in the preprocessing stage. Where, Image resizing id done to reduce images size so that every image is same in size that will minimize the processing time and image enhancing was done to adjust the brightness of the image. In further, for extracting the features from preprocessed images, CNN is used, so that extracted features are given to the classification stage to detect the face of human or not. In detecting the faces, proposed Faster RCNN achieved high accuracy of 98.64% when compared with other existing methods like IFC, EFRA and VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT based deep learning approach for online fault diagnosis against cyber attacks

IoT based deep learning approach for online fault diagnosis ...

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Felix, A. Yovan Sharmila, V. Devi, S. Nandhini Deena, S. Yadav, Ajay Singh Jeyalakshmi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering VSB Engineering College Tamil Nadu Karur India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Physics PSNA College of Engineering and Technology Tamil Nadu Dindigul India

ISBN: (纸本)9781032669663

The Internet of Things (IoT) connects systems, software, cloud computing, activities and could provide new access points for cyber-attacks. Virus attacks and unauthorized downloading are the two main risks to IoT security at the moment. These dangers run the risk of obtaining private information, damaging their reputation and their finances. A frame is employed in this paper’s deep learning to identify threat avoidance in IoT. The cyber security detection systems following database has to be built, after which the benchmark variables are chosen and assessed in order to establish a cyber-security alert system in a large data set. The deep learning strategy to identifying threat avoidance was described for the simulation of this scenario. From the Mailing database, malware samples have been gathered for analysis. The research results demonstrate that the proposed method for assessing risks to cyber security in IoT provides highest accuracy to current approaches. Because of this, Deep Convolution Neural Network (DCNN) is utilized in IoT to detect attack avoidance. © 2024 The Author(s).

关键词： Internet of things

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Password Manager with Multi-Factor Authentication 1

Password Manager with Multi-Factor Authentication

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Basha, Saleem Vellore Institute of Technology School of Computer Science and Engineering Chennai India KCG College of Technology Department of Mathematics Chennai India Mazoon University College Computing and Informatics Department muscat Oman

ISBN: (纸本)9798350336009

Data breach is a serious issue as it leaks the personal information of more than billions of users and their privacy is compromised. More than 77% of organizations do not have a Cyber Security Incident Response plan. So, it is necessary to be informed of network security and ways to store and generate secured passwords. Having different and random passwords for ones digital accounts can exponentially increase the security of user's data. The goal of this project is to build a password manager which can securely store and encrypt passwords and other data. The multifactor authentication will provide increased security to validate the user into password management system. Multifactor authentication system includes physical security key and graphical password authentication. © 2023 IEEE.

关键词： Cryptography

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Open-Source Security Testing Tools for IoT Protocols - MQTT and Zigbee

Open-Source Security Testing Tools for IoT Protocols - MQTT ...

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2023 Advances in science and engineering Technology International Conferences, ASET 2023

作者： Praveen, Meghna Raza, Ali Hasib, Maheen Rochester Institute of Technology Dept. of Electrical Engineering and Computing Sciences Dubai United Arab Emirates School of Mathematics and Computer Science Herriot Watt University Dubai United Arab Emirates

ISBN: (纸本)9781665454742

The Internet of Things (IoT) has become an integral part of our lives, but it also poses a major security threat. As the number of IoT devices grows and more data is collected about us, malicious actors are taking advantage and launching attacks on vulnerable systems. Offensive security testing is a proactive approach of securing computing infrastructure through adversary simulation. This involves, understanding different attack tools that are available and how they can be used to attack devices. This paper studies the state of open-source tools which are available to test IoT networks that use the Zigbee and MQTT protocols. Usage of these tools are explained with attack examples, challenges are listed and enhanced tooling capabilities are proposed. © 2023 IEEE.

关键词： Zigbee

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