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Fixed-point quantum continuous search algorithm with optimal query complexity

science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 80-91页

作者： Shan Jin Yuhan Huang Shaojun Wu Guanyu Zhou Chang-Ling Zou Luyan Sun Xiaoting Wang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610051China Key Laboratory of Quantum Information CASUniversity of Science and Technology of ChinaHefei 230022China Center for Quantum Information Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing 100084China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China Hefei National Laboratory Hefei 230088China

Continuous search problems(CSPs),which involve finding solutions within a continuous domain,frequently arise in fields such as optimization,physics,and *** discrete search problems,CSPs require navigating an uncountably infinite space,presenting unique computational *** this work,we propose a fixed-point quantum search algorithm that leverages continuous variables to address these challenges,achieving a quadratic *** by the discrete search results,we manage to establish a lower bound on the query complexity of arbitrary quantum search for CSPs,demonstrating the optimality of our *** addition,we demonstrate how to design the internal structure of the quantum search oracle for specific ***,we develop a general framework to apply this algorithm to a range of problem types,including optimization and eigenvalue problems involving continuous variables.

关键词： quantum search quantum algorithm quantum computation optimization spectral problems

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

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CDGNet: A Cross-Time Dynamic Graph-Based Deep Learning Model for Vehicle-Based Traffic Speed Forecasting

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Yuchen Luo, Haiyong Zhao, Fang Sun, Poly Z.H. Qin, Yanjun Zeng, Liang Hui, Bo Wang, Chenxing School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Industrial Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Tsinghua University Beijing China University of Tulsa Tulsa USA

Vehicle-based traffic speed forecasting aims to predict the average speed of vehicles on the road in the future, which is an essential side information in intelligent vehicles and beneficial to safe autonomous driving, yet is very challenging due to the complex and dynamic spatio-temporal dependencies in real-world vehicle-based traffic. To extract intricate correlations among multiple vehicle-based speed time series, previous methods have used graph convolution networks. However, the conventional static and dynamic graphs fail to reflect the traffic evolution and the hysteresis spatial influence caused by vehicle movement. To address this issue, we propose a novel cross-time dynamic graph-based deep learning model, named CDGNet, for vehicle-based traffic speed forecasting. The model is able to effectively capture hysteresis spatial dependencies between each time slice and its historical time slices through the cross-time dynamic graph-based GCN. Meanwhile, a gating mechanism is integrated into our cross-time dynamic graph, which conforms to the sparse correlation in the real world. Besides, GCNs are incorporated into a novel encoder-decoder architecture to forecast multi-step speed. Experimental results on three real-world vehicle-based traffic speed datasets demonstrate the superiority of our CDGNet over various state-of-the-art spatio-temporal forecasting methods and the effectiveness of each component. We additionally provide a visualization of our cross-time dynamic graph to show the capability of assisting intelligent vehicles to avoid congestion. IEEE

关键词： Forecasting

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Self-adaptive Bat Algorithm With Genetic Operations

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1284-1294页

作者： Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou H.Vincent Poor School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electrical Engineering Princeton UniversityPrinceton NJ 08544 USA IEEE

Swarm intelligence in a bat algorithm(BA)provides social *** operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization *** integration provides an opportunity for improved search ***,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions *** from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated ***,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and *** by these exemplars,SBAGO improves both BA’s efficiency and global search *** evaluate this approach by using 29 widely-adopted problems from four test *** is also evaluated by a real-life optimization problem in mobile edge computing *** results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

关键词： Bat algorithm(BA) genetic algorithm(GA) hybrid algorithm learning mechanism meta-heuristic optimization algorithms

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具有高度(220)取向垂直纳米孪晶异质结构铜薄膜的强化机制研究

引用

Acta Mechanica Sinica 2024年第1期40卷 44-53页

作者：韦小丁张鹏马瑜薇刘俊杰於中良丛超男 State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceCollege of EngineeringPeking UniversityBeijing100871China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing100083China Department of Engineering Mechanics Beijing University of TechnologyBeijing100124China College of Mechanical Engineering Yangzhou UniversityYangzhou225127China College of Science China Agricultural UniversityBeijing100083China

在本工作中,我们通过直流电沉积方法制备了含有不同体积比的(220)取向垂直纳米孪晶异质结构的铜薄膜,并表征了其力学性能.单轴拉伸测试表明,当垂直纳米孪晶的体积比增加时,机械性能显著提高:具有88%垂直孪晶的薄膜的极限拉伸强度为455 M... 详细信息

在本工作中,我们通过直流电沉积方法制备了含有不同体积比的(220)取向垂直纳米孪晶异质结构的铜薄膜,并表征了其力学性能.单轴拉伸测试表明,当垂直纳米孪晶的体积比增加时,机械性能显著提高:具有88%垂直孪晶的薄膜的极限拉伸强度为455 MPa,约比等轴晶粒铜薄膜的强度高83%.通过对拉伸后样品进行高分辨电子显微镜表征,并辅以分子动力学模拟,我们揭示了含有等轴晶粒和垂直纳米孪晶的特殊异质结构有助于激活铜薄膜材料内部多种模式的滑移系统,并导致更高的位错密度,从而在赋予材料更强的机械强度和加工硬化率的同时,不会显著损失材料的韧性.

关键词：纳米孪晶高分辨电子显微镜极限拉伸强度异质结构铜薄膜等轴晶粒滑移系统强化机制

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Interior Scene Intelligent Generated for Smart Homes Based on GAN-ID Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays 1

引用

1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Karkera, Thejas Singh, Chandra Bekal, Anush Shetty, Shailesh Prajwal, P. Department of Electronics Communication Engineering Sahyadri College of Engineering and Management Mangalore India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

Covid-19 pandemic is spreading across the world at a breakneck pace. As of September 2022, JHU CSSE COVID-19 Data and Our World in Data have recorded up to 5.2 M deaths due to Covid-19. One of the essential steps of fighting this pandemic is to detect the disease early. Studies show some abnormalities in the chest X-rays of the Covid-19 patients, and these features help classify the covid-19 positive patients from the negative patients. In recent years in the field of medical x-rays, Deep Learning models have proven to have the ability to learn and identify features that a trained person can only identify. Due to the exponential increase in the covid-19 cases, the waiting time for covid results has increased, resulting in the late diagnosis of patients for Covid-19. Classifying a patient as Covid-19 positive for a genuinely covid positive patient is as essential as classifying a patient Covid-19 negative who is genuinely covid-negative. Here we present a detailed quantitative analysis on the performance of state-of-the-art models like ResNet-50, Dense-Net, Mobile-Net-V2, and MNAS-Net on classifying patients for Covid positive, Pneumonia positive, and Normal by evaluating Accuracy, Precision, Recall, and Jaccard Index. We propose a new deep neural network classification model for low-end devices that uses two Attention mechanisms. Figure 1. shows the attention map with confidence percentage for each classes. Our model uses fewer parameters and FLOPS than other state-of-the-art models and recorded a 2% increase in Accuracy and other evaluation parameters. The dataset used for implementation is a public dataset COVID-DATASET and NIH Chest X-ray from kaggle. It has over 1000 images of Covid Chest X-ray, Pneumothrax, Mass, Pneumonia, Cardiomegaly, Nodule, Effusion, Atelectasis and inflitration. All the model implementations are implemented on PyTorch. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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