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arXiv 2024年

作者： Liu, Zhenning Li, Xiantao Wang, Chunhao Liu, Jin-Peng Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Computer Science University of Maryland United States Department of Mathematics Pennsylvania State University United States Department of Computer Science and Engineering Pennsylvania State University United States Yau Mathematical Sciences Center Tsinghua University China Beijing Institute of Mathematical Sciences and Applications China

Modeling and simulating the protein folding process overall remains a grand challenge in computational biology. We systematically investigate end-to-end quantum algorithms for simulating various protein dynamics with effects, such as mechanical forces or stochastic noises. We offer efficient quantum simulation algorithms to produce quantum encoding of the final states, history states, or density matrices of inhomogeneous or stochastic harmonic oscillator models. For the read-in setting, we design (i) efficient quantum algorithms for initial state preparation, utilizing counter-based random number generator and rejection sampling, and (ii) depth-efficient approaches for molecular structure loading. Both are particularly important in handling large protein molecules. For the read-out setting, our algorithms estimate various classical observables, such as energy, low vibration modes, density of states, correlation of displacement, and optimal control of molecular dynamics. We also show classical numerical experiments focused on estimating the density of states and applying the optimal control to facilitate conformation changes to verify our arguments on potential quantum speedups. Overall, our study demonstrates that the quantum simulation of protein dynamics can be a solid end-to-end application in the era of early or fully fault-tolerant quantum computing. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Dielectric Rod Antenna Array with Planar Folded Slot Antenna Excitation

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 664-673页

作者： Saffold, Gabriel L. Weller, Thomas M. Antenna Systems Division Sensors and Electromagnetic Applications Laboratory Georgia Tech Research Institute AtlantaGA30080 United States School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States

A dielectric rod antenna array fed by slot antenna radiators without the use of metallic waveguide is presented here. Communication systems, especially those related to 5G, are moving up into mm-wave bands where metal losses can become significant and many traditional fabrication and manufacturing techniques become more difficult. Dielectric rod antennas (DRA) are entirely made of dielectric and may be injection-molded or 3D-printed as solid rods, layered rods, or tubes. While the normal approach to feeding DRAs involves some version of metallic waveguide, we show here that the DRA may be integrated with a planar radiator feed and used effectively in an array configuration. The array demonstrated is a 2 × 2 array designed for operation at 15 GHz. Each DRA is 6 λ long. The array is fabricated on a 100 mm × 100 mm substrate with 52 mm separation between elements. The array radiation efficiency is 80% for an array gain of 19 dBi. © 2020 IEEE.

关键词： Radiators

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Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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An Optimized DDoS Attack Detection Using Deep Convolutional Generative Adversarial Networks

An Optimized DDoS Attack Detection Using Deep Convolutional ...

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International Conference on Inventive Research in Computing applications (ICIRCA)

作者： Chandra Mouli Venkata Srinivas Akana Arvind Kumar Mohit Tiwari Aslanov Zabit Yunus Vijayakumar E Mukesh Singh Bonam Venkata Chalamayya Engineering College Odalarevu School of Computer Applications Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India Azerbaijan State University of Economics - UNEC Baku Azerbaijan Department of Computer Applications KIT-Kalaignar Karunanidhi Institute of Technology Coimbatore Tamilnadu India Department of Computer Science and Engineering Graphic Era University Dehradun India

Attacks known as Distributed Denial-of-Service (DDoS) are rising as a result of recent, dramatic increase in demand for Internet access. When the amount and characteristics of network traffic, which may include harmful DDoS contents, expand dramatically, traditional basic algorithms using machine learning for classifying DDoS attacks frequently fail because it cannot automatically extract high-value characteristics. In order to effectively identify and classify DDoS attacks, a hybrid technique called DFNN-SAE-DCGAN that combines three deep learning-based models is suggested. The Deep Feed-Forward Neural Network (DFNN) and Stacked Auto encoder offers an efficient method for extracting features that identifies the most pertinent feature sets without the assistance of a person. To avoid the operational overhead and presumption associated with processing massive set of features with distortion and redundant characteristic values, the Deep Convolutional Generative Adversarial Networks (DCGAN) component of the proposed model classifies the attacks into various DDoS attack types using the restricted and minimized characteristic sets generated by the DFNN-SAE as inputs. The experimental results show a very high and resilient accuracy rate and an F1-score of 98.5%, which is higher than the performance of many similar approaches. These results were acquired by thorough and extensive trials on various performance aspects on the CICDDoS2019 dataset. This demonstrates that the suggested methodology may be utilized to defend against the increasing amount of DDoS attacks.

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： M. Ashok Kumar Laith H. Jasim Sudhakar K R. Archana Reddy N. Naga Saranya Department of Computer Science Software Engineering and Cyber Security Skyline University Nigeria Kano State Nigeria Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal India Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%).

关键词： Geometry Deformable models Visualization Three-dimensional displays Sensitivity Event detection Feature extraction

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Real-time Abnormal Address Detection for Mobile Devices in Location-based Services

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IEEE Transactions on Mobile Computing 2025年

作者： Hong, Zhiqing Yang, Heng Wang, Haotian Lyu, Wenjun Yang, Yu Wang, Guang Liu, Yunhuai Wang, Yang Zhang, Desheng Rutgers University Department of Computer Science PiscatawayNJ08854 United States Beijing Institute of Computer Technology and Application Beijing100854 China JD Logistics Beijing100176 China Lehigh University BethlehemPA18015 United States Florida State University TallahasseeFL32306 United States Peking University Department of Computer Science and Engineering Beijing100871 China University of Science and Technology of China Suzhou215000 China

An address, a textual description of a geographical location, plays an important role in location-based services such as instant delivery. However, abnormal addresses (i.e., an address without detailed or accurate information) have led to significant costs. In real-world settings, abnormal address detection is not trivial because it needs to be completed in real-time to support massive online queries from mobile devices. In this study, we design FastAddr, a fast abnormal address detection framework, which detects abnormal addresses in real time. FastAddr consists of a novel contrastive address augmentation module and a lightweight multi-head attention model. We further design FastAddr+ to enhance FastAddr by utilizing large-scale spatial entities. A comprehensive three-phase evaluation is conducted. (i) We evaluate FastAddr on a real-world dataset and it yields the average F1 of 85.7% in 0.058 milliseconds, which outperforms the state-of-the-art models by 47.4% with a similar detection time. (ii) An offline A/B test shows that FastAddr outperforms the previous model significantly. (iii) We also conduct an online A/B test to compare FastAddr with the deployed model, which shows an improvement of F1 by more than 20%. Moreover, we conduct two case studies on real industry data, demonstrating both the efficiency and effectiveness of FastAddr. © 2002-2012 IEEE.

关键词： Location

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High Security for De-Duplicated Big Data Using Optimal SIMON Cipher

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computers, Materials & Continua 2021年第5期67卷 1863-1879页

作者： A.Muthumari J.Banumathi S.Rajasekaran P.Vijayakarthik K.Shankar Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Information Technology University College of EngineeringNagercoil629004India Department of EEE PSN College of Engineering(Anna University)Tirunelveli627152India Department of Information Science and Engineering Sir M.Visvesvaraya Institute of TechnologyBangalore56215India Department of Computer Applications Alagappa UniversityKaraikudi63003India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cloud computing offers internet location-based affordable,scalable,and independent *** computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events,for instance,pandemic *** handle massive information,clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data,with elevated velocity and modified *** deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the *** deduplication also saves network *** this paper,a new cloud-based big data security technique utilizing dual encryption is *** clustering model is utilized to analyze the Deduplication process hash *** kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption *** confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is *** security process involving dual encryption with the optimization model develops the productivity *** this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering *** results proved that the proposed technique achieved maximum accuracy and minimum encryption time.

关键词： Cloud security deduplication clustering optimization big data dual encryption

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Nitrous oxide fluxes during a winter cover crop season in a Mississippi corn cropping system

Soil and Environmental Health

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Soil and Environmental Health 2024年第4期2卷

作者： Li, Zihan Miles, Dana M. Hammons, Leah Podrebarac, Frances A. Brooks, John P. Adeli, Ardeshir Smith, Renotta Prince Czarnecki, Joby M. Moorhead, Robert J. Hu, Jing Department of Civil Environmental and Construction Engineering College of Engineering and Computer Science University of Central Florida USDA-ARS Genetics and Sustainable Agriculture Research Geosystems Research Institute Mississippi State University

Agroecosystems are the largest source of anthropogenic N2O fluxes. While cover crops (CC) offer benefits for soil health, their impacts on greenhouse gas fluxes are inconsistent. In the southeastern US, where intensiv... 详细信息

Agroecosystems are the largest source of anthropogenic N₂O fluxes. While cover crops (CC) offer benefits for soil health, their impacts on greenhouse gas fluxes are inconsistent. In the southeastern US, where intensive agriculture and low CC adoption are prevalent, few studies have investigated CC impact on soil N₂O fluxes. Our study explored the effects of CC species and management practices on soil N₂O fluxes during the winter CC growing season in Mississippi, which was conducted in a non-tilled corn cropping system with seven CC treatments. We measured in situ soil N₂O fluxes, along with soil moisture and temperature, throughout the CC growing season from 2022 to 2023. Surface soil samples were also collected to analyze soil mineral nitrogen (N) content and enzyme activity. Over the study period (a total of 188 days), cumulative N₂O fluxes were 0.50–1.03 kg N₂O–N ha⁻¹, with the lowest values from the annually-rotated Elbon rye treatment and the highest from the annually-rotated Austrian winter pea. Our results show that both CC treatments and sampling time significantly affected soil N₂O fluxes. There was a strong positive correlation (r = 0.34, p < 0.05) between N₂O fluxes and NO₃–N content, which was lowest under continuous rye and rotated-rye treatments (0.31 and 0.34 mg kg⁻¹ ). The results suggest that Elbon rye effectively reduced soil N₂O fluxes during this period by lowering the soil NO₃–N content, the primary substrate for denitrification. This study is one of the few studies to examine the impacts of cover crops on soil N₂O fluxes in cropping systems in the southeastern US, offering insights into the cover crop effects on soil N₂O fluxes during their growing season. © 2024

关键词： Ammonia oxidizers Cover crop mixture Cover crop rotation Denitrification Enzyme activity Nitrate contents Nitrite oxidizers Radish, rye and pea Soil moisture Soil temperature

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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Fraud Detection in E-commerce Transactions: A Machine Learning Perspective

Fraud Detection in E-commerce Transactions: A Machine Learni...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Geetha Manoharan S Dada Noor Hayath Ali Dr. Manoj Sathe A Karthik Amandeep Nagpal Ajay Sidana SR University School of Business Warangal Telangana India Master of Computer Applications Ballari Institute of Technology and Management Ballari Karnataka State Computer Science PES Modern Institute of Business Studies Pune India Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Amity International business school Amity University Noida

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

As the number of online purchases increases at an alarming rate, fraudulent activities have surfaced as a major concern for retailers and consumers alike. Fraudsters frequently face setbacks when attempting to bypass automated rule-based systems that are specifically engineered to identify fraudulent activities. The objective of this research is to investigate methods by which fraudulent online transactions can be detected using machine learning algorithms. To detect fraudulent activities, an analysis is performed on various machine learning methodologies, including supervised, unsupervised, and semi-supervised learning. This research primarily examines the evaluation criteria, feature engineering, and model selection that are distinct to the domain of electronic commerce. Furthermore, obstacles that manifest during the detection of fraudulent activities in electronic commerce must be confronted, including the dissemination of deceptive ideas and manipulated data. By subjecting machine learning models to rigorous testing on real-world datasets, we demonstrate their efficacy in detecting fraudulent activities and underscore their capacity to dynamically adjust to evolving fraud patterns. In conclusion, we discuss the practical obstacles and possible avenues for future research concerning machine learning-based e-commerce fraud detection systems.

关键词： Machine learning algorithms Machine learning Semisupervised learning Fraud Electronic commerce Informatics Monitoring

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