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arXiv 2022年

作者： Masum, Mohammad Shahriar, Hossain Haddad, Hisham Faruk, Md Jobair Hossain Valero, Maria Khan, Md Abdullah Rahman, Mohammad A. Adnan, Muhaiminul I. Cuzzocrea, Alfredo Wu, Fan School of Data Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Software Engineering and Game Development Kennesaw State University United States Department Computer Science Kennesaw State University United States Department of Electrical and Computer Engineering Florida International University United States Institute of Natural Sciences United International University United States IDEA Lab University of Calabria Rende Italy LORIA Nancy France Department of Computer Science Tuskegee University United States

Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework's effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score. © 2022, CC BY.

关键词： Deep neural networks

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Secured control systems through integrated IoT devices and control systems

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Measurement: Sensors 2022年 24卷

作者： Rajendran, N. Singh, Rupinder Moudgil, Manu Raj Turukmane, Anil V. Umadevi, M. Glory, K.B. Department of Information Technology B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Applications Chitkara University Rajpura140417 India Department of CSE Bhai Gurdas Institute of Engineering and Technology Punjab Sangrur148001 India Department of Computer Science & Engineering VIT- AP University Vijaywada Amaravati India Department of Computer Science Sri Adi Chunchanagiri Women's College Tamilnadu Cumbum India Department of Engineering English Koneru Lakshmaiah Education Foundation Andhra Pradesh State Vaddeswaram522302 India

The Internet of Things (IoT) provides customers with seamless integration and interoperability between gadgets, technologies, or products, and across multiple networks focus on control schemas. Regardless of the integration node, whether it is a smartphone, tablet, laptop, TV, automobile, or other Internet-compatible gadget, end-users want a fast and clear connection. Intelligent IoT devices can act as flexible connectors with variable structures, allowing rapid responses and possibly universal access. Consequently, gadgets could pose serious security issues. When they are used to manage accounts receivable at IoT terminals, these methods are likely to replay, provoke human attacks in the middle, or by denial of service. Responding to the real-time challenge for safe control operations, this study provides a methodology that blends the characteristics of intelligent IoT systems with power system interfaces. A proposed method combines computation, cryptography, signal/image processing, and communication networks of authentication and authorization tasks, using both terminals and gateways. Unlike conventional methods, they provide a safer, more adaptable, and resilient setting for real-time performance. The researchers also describe Intel's IoT framework for power system inputs that are used in management, industrial, transport, and detailed monitoring contexts. © 2022 The Authors

关键词： Control systems

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AmberTools

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Journal of Chemical Information and Modeling 2023年第20期63卷 6183-6191页

作者： Case, David A. Aktulga, Hasan Metin Belfon, Kellon Cerutti, David S. Cisneros, G. Andrés Cruzeiro, Vinícius Wilian D. Forouzesh, Negin Giese, Timothy J. Götz, Andreas W. Gohlke, Holger Izadi, Saeed Kasavajhala, Koushik Kaymak, Mehmet C. King, Edward Kurtzman, Tom Lee, Tai-Sung Li, Pengfei Liu, Jian Luchko, Tyler Luo, Ray Manathunga, Madushanka Machado, Matias R. Nguyen, Hai Minh O’Hearn, Kurt A. Onufriev, Alexey V. Pan, Feng Pantano, Sergio Qi, Ruxi Rahnamoun, Ali Risheh, Ali Schott-Verdugo, Stephan Shajan, Akhil Swails, Jason Wang, Junmei Wei, Haixin Wu, Xiongwu Wu, Yongxian Zhang, Shi Zhao, Shiji Zhu, Qiang Cheatham, Thomas E. Roe, Daniel R. Roitberg, Adrian Simmerling, Carlos York, Darrin M. Nagan, Maria C. Merz, Kenneth M. Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824-1322 United States FOG Pharmaceuticals Inc. CambridgeMA02140 United States Psivant 451 D Street Suite 205 BostonMA02210 United States Department of Physics Department of Chemistry and Biochemistry University of Texas at Dallas RichardsonTX75801 United States Department of Chemistry and The PULSE Institute Stanford University StanfordCA94305 United States Department of Computer Science California State University Los AngelesCA90032 United States Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States San Diego Supercomputer Center University of California San Diego La Jolla CA92093-0505 United States Institute for Pharmaceutical and Medicinal Chemistry Heinrich Heine University Düsseldorf Düsseldorf40225 Germany Forschungszentrum Jülich GmbH Jülich52425 Germany Pharmaceutical Development Genentech Inc. South San FranciscoCA94080 United States Laufer Center for Physical and Quantitative Biology Department of Chemistry Stony Brook University Stony BrookNY11794 United States Departments of Molecular Biology and Biochemistry Chemical and Biomolecular Engineering Materials Science and Engineering and Biomedical Engineering Graduate Program in Chemical and Materials Physics University of California IrvineCA92697 United States Ph.D. Programs in Chemistry Biochemistry and Biology The Graduate Center of the City University of New York 365 Fifth Avenue New YorkNY10016 United States Department of Chemistry Lehman College 250 Bedford Park Blvd West BronxNY10468 United States Department of Chemistry and Biochemistry Loyola University Chicago ChicagoIL60660 United States Beijing National Laboratory for Molecular Science

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note. ... 详细信息

关键词： Free energy Ligands Molecular mechanics Molecular modeling QM/MM

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Learning-based Event-triggered Adaptive Optimal Output Regulation of Linear Discrete-time Systems 10

Learning-based Event-triggered Adaptive Optimal Output Regul...

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10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Zhao, Fuyu Gao, Weinan Liu, Tengfei Jiang, Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China College of Engineering and Science Florida Institute of Technology Department of Mechanical and Civil Engineering MelbourneFL32901 United States New York University Six MetroTech Center Department of Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9781665424233

In this paper, a data-driven event-triggered output-feedback control approach is proposed to solve the problem of adaptive optimal output regulation for uncertain discrete-time linear systems when only the output information is available. A crucial strategy is to develop a novel co-design scheme for the event-triggering mechanism and the data-driven optimal controller. Theoretical analysis and an application to a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed learning-based, event-triggered, adaptive optimal controller design with output-feedback. © 2021 IEEE.

关键词： Digital control systems

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An ensemble learning-based language identification system

An ensemble learning-based language identification system

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International Conference on Computational Advancement in Communication Circuit and System, ICCACCS 2018

作者： Mukherjee, Himadri Das, Sahana Dhar, Ankita Obaidullah, Sk. Md. Santosh, K.C. Phadikar, Santanu Roy, Kaushik Department of Computer Science West Bengal State University Kolkata India Department of Computer Applications Narula Institute of Technology Kolkata India Department of Computer Science & Engineering Aliah University Kolkata India Department of Computer Science The University of South Dakota Vermillion United States Department of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology Kolkata India

ISBN: (纸本)9789811386862

Speech recognition from multilingual voice signals is a challenging task. It is essential to distinguish the language of the spoken phrases prior to recognizing them. This is known as automatic language identification. Automatic language identification is very much important for multilingual countries like India as people often use more than a single language while talking. In this paper, a language identification system for seven different languages from the IIIT-H Indic Speech Databases is presented. We have used line spectral frequency-based features for modelling the languages. The highest accuracy of 99.71% has been obtained with ensemble learning-based classification technique. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

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Artificial Intelligence Review 2025年第9期58卷 1-83页

作者： Sapkota, Ranjan Flores-Calero, Marco Qureshi, Rizwan Badgujar, Chetan Nepal, Upesh Poulose, Alwin Zeno, Peter Vaddevolu, Uday Bhanu Prakash Khan, Sheheryar Shoman, Maged Yan, Hong Karkee, Manoj Biological & Environmental Engineering Cornell University Ithaca USA Department of Electrical Electronics and Telecommunications Universidad de las Fuerzas Armadas ESPE Sangolquí Ecuador Center for Research in Computer Vision University of Central Florida Orlando USA Biosystems Engineering and Soil Sciences The University of Tennessee Knoxville USA Cooper Machine Company Inc. Wadley USA School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM) Thiruvananthapuram India ZenoRobotics LLC Billings USA Biological and Agricultural Engineering Texas A&M University College Station USA School of Professional Education and Executive Development The Hong Kong Polytechnic University Hong Kong China University of Tennessee Knoxville USA Department of Electrical Engineering City University of Hong Kong Hong Kong China Center for Intelligent Multidimensional Data Analysis CIMDA Hong Kong Science Park Hong Kong China Biological Systems Engineering Washington State University Pullman USA

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and progressing through YOLO11 (or YOLOv11), YOLOv10, YOLOv9, YOLOv8, and subsequent versions to explore each version’s contributions to enhancing speed, detection accuracy, and computational efficiency in real-time object detection. Additionally, this study reviews the alternative versions derived from YOLO architectural advancements of YOLO-NAS, YOLO-X, YOLO-R, DAMO-YOLO, and Gold-YOLO. Moreover, the study highlights the transformative impact of YOLO models across five critical application areas: autonomous vehicles and traffic safety, healthcare and medical imaging, industrial manufacturing, surveillance and security, and agriculture. By detailing the incremental technological advancements in subsequent YOLO versions, this review chronicles the evolution of YOLO, and discusses the challenges and limitations in each of the earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and Artificial General Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

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Robust distributed accelerated stochastic gradient methods for multi-agent networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 9893-9988页

作者： Alireza Fallah Mert Gürbüzbalaban Asuman Ozdaglar Umut Şimşekli Lingjiong Zhu Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Department of Management Science and Information Systems Departments of Electrical Engineering and Statistics Rutgers University Piscataway NJ INRIA - Département d'Informatique de l' École Normale Supérieure PSL Research University Paris France Department of Mathematics Florida State University Tallahassee FL

We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG) for solving decentralized strongly convex stochastic optimization problems where the objective function is distributed over several computational units, lying on a fixed but arbitrary connected communication graph, subject to local communication constraints where noisy estimates of the gradients are available. We develop a framework which allows to choose the stepsize and the momentum parameters of these algorithms in a way to optimize performance by systematically trading off the bias, variance and dependence to network effects. When gradients do not contain noise, we also prove that D-ASG can achieve acceleration, in the sense that it requires O(√κlog(1/ε)) gradient evaluations and O(√κlog(1/ε)) communications to converge to the same fixed point with the non-accelerated variant where κ is the condition number and ε is the target accuracy. For quadratic functions, we also provide finer performance bounds that are tight with respect to bias and variance terms. Finally, we study a multistage version of D-ASG with parameters carefully varied over stages to ensure exact convergence to the optimal solution. It achieves optimal and accelerated O(-k/√κ) linear decay in the bias term as well as optimal O(σ2/k) in the variance term. We illustrate through numerical experiments that our approach results in accelerated practical algorithms that are robust to gradient noise and that can outperform existing methods.

关键词： distributed optimization accelerated methods stochastic optimization robustness multi-agent networks

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Squeezing-enhanced quantum sensing with quadratic optomechanics

Optica Quantum

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Optica Quantum 2024年第4期2卷 222-229页

作者： Sheng-Dian Zhang Jie Wang Qian Zhang Ya-Feng Jiao Yun-Lan Zuo Şahin K. Özdemir Cheng-Wei Qiu Franco Nori Hui Jing Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal Universityhttps://***/053w1zy07 Changsha 410081 China Department of Engineering Science and Mechanics and Materials Research Institute Pennsylvania State University University Park State College Pennsylvania 16802 USA Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research Wako-shi Saitama 351-0198 Japan Physics Department The University of Michigan Ann Arbor Michigan 48109-1040 USA

Cavity optomechanical (COM) sensors, enhanced by quantum squeezing or entanglement, have become powerful tools for measuring ultra-weak forces with high precision and sensitivity. However, these sensors usually rely on linear COM couplings, a fundamental limitation when measurements of the mechanical energy are desired. Very recently, a giant enhancement of the signal-to-noise ratio was predicted in a quadratic COM system. Here we show that the performance of such a system can be further improved surpassing the standard quantum limit by using quantum squeezed light. Our approach is compatible with available engineering techniques of advanced COM sensors and provides new opportunities for using COM sensors in tests of fundamental laws of physics and quantum metrology applications.

关键词： Optical systems Photonic crystals Photonic entanglement Quantum metrology Quantum noise Squeezed states

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Magnetization and Spin Dynamics in Two-Dimensional Magnets

Magnetization and Spin Dynamics in Two-Dimensional Magnets

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Alahmed, Laith Nepal, Bhuwan Macy, Juan Casas, Brian Sapkota, Arjun Mazza, Alessandro R. Brahlek, Matthew Wen, Jiajia Jin, Wencan Zhang, Steven S.-L. Mewes, Claudia Zhang, Li-Chuan Mokrousov, Yuriy Zhang, Wei Lee, Young S. Balicas, Luis Mewes, Tim Zhang, Xiaoqian Li, Peng Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States Department of Physics University of Alabama TuscaloosaAL35487 United States National High Magnetic Field Laboratory Florida State University TallahasseeFL32310 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Physics Auburn University AuburnAL36849 United States Stanford Institute for Materials and Energy Sciences Slac National Accelerator Laboratory Menlo ParkCA94025 United States Department of Physics Case Western Reserve University ClevelandOH44106 United States Peter Grünberg Institut Institute for Advanced Simulation Forschungszentrum Jülich Jara Jülich52425 Germany Department of Physics and Astronomy University of North Carolina at Chapel Hill Chapel HillNC27599 United States Shenzhen Institute for Quantum Science and Engr. Southern University of Science and Technology Shenzhen518055 China School of Microelectronics University of Science and Technology of China Hefei230026 China Institute of Physics Johannes Gutenberg University Mainz Mainz55099 Germany

ISBN: (纸本)9798350338362

The discovery of magnetism in two-dimensional (2D) van der Waals (vdW) materials has flourished a new endeavor of fundamental problems in magnetism. In this talk, we will discuss the magnetization and spin dynamics in two types of 2D magnets: a room-temperature vdW magnetic crystal Fe5GeTe2 with a Curie temperature Tc = 332 K and Cu(1,3-bdc), a quasi-2D magnet with a Kagome lattice. Ferromagnetic resonance was performed with external magnetic fields applied along the c-axis (H||c) and the ab-plane (H||ab). We have found a sizable Landé g-factor difference between the H||c and H||ab cases for both Fe5GeTe2 and Cu(1,3-bdc). The FMR measurements have also revealed that Fe5GeTe2 has a damping constant comparable to Permalloy. Our experiments highlight key information regarding the magnetic state and spin scattering processes in 2D magnets, which can lead to implementations of 2D magnets based spintronic devices. © 2023 IEEE.

关键词： Nickel alloys

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