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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Deep Learning control for Autonomous Robot

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Computers, Materials & Continua 2022年第8期72卷 2811-2824页

作者： Rihem Farkh Saad Alhuwaimel Sultan Alzahrani Khaled Al Jaloud Mohammad Tabrez Quasim College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical Engineering National Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these *** cars are one such *** is expected to have a significant and revolutionary influence on *** with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving *** autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human *** are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic ***,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate *** majority of self-driving car technologies are based on computer systems that automate vehicle control *** forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and *** researches on computer vision and deep learning are used to control autonomous driving *** self-driving automobiles,lane-keeping is *** study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the *** propose an advanced control for a selfdriving robot by using two controllers *** neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering *** stu

关键词： Autonomous car cascade PID control deep learning convolutional neural network differential drive system raspberry PI road lane detector

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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Computer systems Science & Engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt 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 Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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An Aircraft Identification System Using Convolution Neural Networks

An Aircraft Identification System Using Convolution Neural N...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Viktor Turchenko Iryna Turchenko Nadiia Vasylkiv Department for Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine Department of Computing and Software McMaster University Hamilton Canada Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

The task of aircraft identification using their registration number is important as visual identification remains the only method of their identification in case of air traffic control systems or radar failure. In this paper, an analysis of known solutions was carried out and an aircraft identification system was developed using a pre-trained YOLO (You Only Look Once) deep convolutional neural network and Optical Character Recognition (OCR) technology. The system consists of three modules: the M1 module is designed for the detection of region of interest-tail, the M2 module is designed for the detection of region of interest-registration number, the M3 module is designed for the classification of the registration number. 2309 aircraft images were used for training, 426 for validation and 408 for testing from the Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft) dataset. Our results show that the mean average precision for the M1 and M2 YOLOv7 models on the test set were 0.92 and 0.85 respectively, and for the module M3 OCR technology the classification error was 0.2.

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Sustainability and Time Complexity Estimation of Cryptographic Algorithms Main Operations on Elliptic Curves 11

Sustainability and Time Complexity Estimation of Cryptograph...

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11th International Conference on Advanced Computer information Technologies, ACIT 2021

作者： Yakymenko, Igor Kasianchuk, Mykhailo Yatskiv, Vasyl Shevchuk, Ruslan Koval, Vasyl Yatskiv, Solomiya West Ukrainian National University Department of Cyber Security Ternopil Ukraine West Ukrainian National University Department of Computer Science Ternopil Ukraine West Ukrainian National University Department of Information and Computing Systems and Control Ternopil Ukraine

This paper presents the time complexity estimates for the methods of points exponentiation, which are basic for encrypting information flows in computer systems. As a result of numerical experiments, it is determined that the method of doubling-addition-subtraction has the lowest complexity. Mathematical models for determining the execution time of each considered algorithm for points exponentiation on elliptic curves were developed, which allowed to conduct in-depth analysis of their performance and resistance to special attacks, in particular timing analysis attack. The dependences of the cryptographic operations execution time on the key length and the sustainability of each method on the Hamming weight are investigated. It is proved that under certain conditions the highest sustainability of the system is achieved by the doubling-addition-subtraction algorithm. This allows to justify the choice of algorithm and its parameters for the implementation of cryptographic information security, which is resistant to special attacks. © 2021 IEEE.

关键词： Security of data

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A Sub-mother UAV System Capable of Autonomous Aerial Docking

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年

作者： Li, Gang Lu, Yi He, Bin Pang, Tao Cheng, Bin Zhang, Pengpeng College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200120 China Shanghai Sunshine Rehabilitation Center Shanghai201613 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai200120 China East China Institute of Computing Technology Shanghai201822 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China College of Electronic and Information Engineering Tongji University Shanghai China

Completing the docking between two multi-rotor unmanned aerial vehicles (UAVs) in the air has great significance for expanding the application scenarios of multiple UAVs and improving their endurance capability. In this regard, this paper first designs a safe and autonomous docking scheme and constructs a sub-mother UAV *** address the issue of unstable flight in downwash airflow, this paper designs a high-performance control method suitable for the sub-mother UAV system based on Incremental Nonlinear Dynamic Inversion (INDI) and achieves robust attitude control with strong disturbance rejection capability. A UWB-based Ortho-UWB 3D positioning method is proposed in this paper with orthogonal installation, which, after being integrated with visual methods, enables high-precision 3D relative positioning during the docking process of the sub-mother UAV ***, the paper designs a complete docking process for the sub-mother UAVs and incorporates safety constraints into the docking process to ensure the safety of the docking process for both UAVs. Experiments of successfully docking the sub UAV to the mother UAV from bottom to top prove the feasibility and effectiveness of this scheme. © 2024 IEEE.

关键词： Disturbance rejection

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Bayesian Monitoring of COVID-19 in Sweden

arXiv

引用

arXiv 2022年

作者： Marin, Robin Runvik, Håkan Medvedev, Alexander Engblom, Stefan Division of Scientific Computing Department of Information Technology Uppsala University UppsalaSE-751 05 Sweden Division of Systems and Control Department of Information Technology Uppsala University UppsalaSE-751 05 Sweden

In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. © 2022, CC BY.

关键词： COVID-19

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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