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15th World Congress of the International Federation of Automatic control, 2002

作者： Verbruggen, Henk B. Park, Jaehyun Halang, Wolfgang Irwin, George Zalewski, Janusz Faculty of Information Technology and Systems Delft University of Technology Delft Netherlands Department of Automation Engineering Inha University Incheon Korea Republic of Faculty of Electrical Engineering Fern Universität Hagen Germany Intelligent Control and Systems Group School of Electrical and Electronic Engineering Queen's University Belfast United Kingdom Department of Electrical and Computing Engineering University of Central Florida Orlando United States

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Method of Robotic Process Automation in Software Testing Using Artificial Intelligence

Method of Robotic Process Automation in Software Testing Usi...

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International Conference on Advanced Computer information Technologies (ACIT)

作者： Nataliya Yatskiv Solomiya Yatskiv Anatoliy Vasylyk Department for Information and Computing Systems and Control Ternopil National Economic University Ternopil Ukraine Department of Computer Science Ternopil National Economic University Ternopil Ukraine

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

Robotic process automation (RPA) offers numerous benefits over typical software for test automation. It allows saving money and freeing up human resources without any modifications in the processes and implemented solutions. Despite many advantages, RPA solution has limited functionality and it is not able to automatically adapt to changes in the system without human interaction. One of the technologies that may be used to leverage these disadvantages is Artificial Intelligence (AI). AI can facilitate process automation by using the intelligent decision making and interface analysis. In this paper the main approaches of RPA usage, their advantages and disadvantages are investigated. This paper describes the proposed method for software test automation with RPA which allows to make RPA software more flexible and decrease human involvement in the support processes.

关键词： Automation Artificial intelligence Software Tools Business Computer vision

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$H_{\bm \infty}$ Fuzzy control for systems With Repeated Scalar Nonlinearities and Random Packet Losses

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IEEE Transactions on Fuzzy systems 2009年第2期17卷 440-450页

作者： Hongli Dong Zidong Wang Huijun Gao College of Electrical and Information Engineering Daqing Petroleum Institute Daqing China Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin China Department of Information Systems and Computing Brunei University Uxbridge Middlesex UK

This paper is concerned with the H infin fuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. The measurement transmission between the plant and controller is assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random packet losses. Attention is focused on the analysis and design of H infin fuzzy controllers with the same repeated scalar nonlinearities such that the closed-loop T-S fuzzy control system is stochastically stable and preserves a guaranteed H infin performance. Sufficient conditions are obtained for the existence of admissible controllers, and the cone complementarity linearization procedure is employed to cast the controller design problem into a sequential minimization one subject to linear matrix inequalities, which can be readily solved by using standard numerical software. Two examples are given to illustrate the effectiveness of the proposed design method.

关键词： Fuzzy control Takagi-Sugeno model Fuzzy sets Nonlinear equations Nonlinear systems Recurrent neural networks Loss measurement Stochastic processes Propagation losses Performance analysis

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Feedback control of quantum entanglement in a two-spin system

Feedback control of quantum entanglement in a two-spin syste...

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IEEE Conference on Decision and control

作者： N. Yamamoto K. Tsumura S. Hara Control and Dynamical Systems California Institute of Technology Pasadena CA USA Department of Information Physics and Computing Graduate School of Information Science and Technology University of Tokyo Tokyo Japan

A pair of spins is the most simple quantum system that can possess entanglement, a non-classical property playing an essential role in quantum information technologies. In this paper, feedback control problems of the two-spin system conditioned on a continuous measurement are considered. In order to make some useful formulas in stochastic control theory directly applicable, we first derive a two-dimensional description of the system. We then prove that a feedback controller stabilizes an entangled state of the two spins almost globally with probability one. Furthermore, it is shown that another entangled state, which corresponds to a non-equilibrium point of the dynamics, is stabilized via feedback in the sense that the expectation of the distance from the target can be made arbitrarily small.

关键词： Feedback control Quantum entanglement Quantum computing Quantum mechanics Adaptive control Stochastic processes control theory Differential equations control systems information technology

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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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Resource-Efficient and Self-Adaptive Quantum Search in a Quantum-Classical Hybrid System

arXiv

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arXiv 2024年

作者： Jiang, Zihao Du, Zefan Ruan, Shaolun Chen, Juntao Wang, Yong Cheng, Long Buyya, Rajkumar Mao, Ying The Department of Computer and Information Science Fordham University New York City United States The School of Computing and Information Systems Singapore Management University Singapore The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computing and Information Systems The University of Melbourne Australia

Over the past decade, the rapid advancement of deep learning and big data applications has been driven by vast datasets and high-performance computing systems. However, as we approach the physical limits of semiconductor fabrication in the post-Moore's Law era, questions arise about the future of these applications. In parallel, quantum computing has made significant progress with the potential to break limits. Major companies like IBM, Google, and Microsoft provide access to noisy intermediate-scale quantum (NISQ) computers. Despite the theoretical promise of Shor's and Grover's algorithms, practical implementation on current quantum devices faces challenges, such as demanding additional resources and a high number of controlled operations. To tackle these challenges and optimize the utilization of limited onboard qubits, we introduce ReSaQuS, a resource-efficient index-value searching system within a quantum-classical hybrid framework. Building on Grover's algorithm, ReSaQuS employs an automatically managed iterative search approach. This method analyzes problem size, filters less probable data points, and progressively reduces the dataset with decreasing qubit requirements. Implemented using Qiskit and evaluated through extensive experiments, ReSaQuS has demonstrated a substantial reduction, up to 86.36% in cumulative qubit consumption and 72.72% in active periods, reinforcing its potential in optimizing quantum computing application deployment. © 2024, CC BY.

关键词： Hybrid systems

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Challenges in differentiating between attention disorders based on EEG recordings in neurofeedback therapy

Challenges in differentiating between attention disorders ba...

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Applications of Electromagnetics in Modern Techniques and Medicine (PTZE)

作者： Żołubak Magda Pelc Mariusz Kawala-Janik Aleksandra Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Department of Computing and Information Systems University of Greenwitch Greenwitch England

The aim of this article is to show problems with the analysis of various cognitive disorders in the registered EEG signal and the effects of the neurofeedback therapy. Most of the disorders tested in ICD-10 have electroencephalographic readings associated with Beta bands and slow waves. All problems with attention deficit have similar records in the EEG signal and similar training protocols are used in their therapy and the results of the therapy are visible in more than 10 training sessions.

关键词： Electroencephalography Training Neurofeedback Medical treatment Protocols Cancer Pediatrics

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Iterative Learning control for Optimal Multiple-Point Tracking

Iterative Learning Control for Optimal Multiple-Point Tracki...

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IEEE Conference on Decision and control

作者： Tong Duy Son Dinh Hoa Nguyen Hyo-Sung Ahn Distributed Control and Autonomous Systems Laboratory Department of Mechatronics Gwangju Institute of Science and Technology (GIST) Korea Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9781612848006

This paper presents a new optimization-based iterative learning control (ILC) framework for multiple-point tracking control. Conventionally, one demand prior to designing ILC algorithms for such problems is to build a reference trajectory that passes through all given points at given times. In this paper, we produce output curves that pass close to the desired points without considering the reference trajectory. Here, the control signals are generated by solving an optimal ILC problem with respect to the points. As such, the whole process becomes simpler;key advantages include significantly decreasing the computational cost and improving performance. Our work is then examined in both continuous and discrete systems.

关键词： Iterative learning control Discrete systems control SIGNAL trajectory

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Local reachability of stochastic quantum dynamics with application to feedback control of single-spin systems

Local reachability of stochastic quantum dynamics with appli...

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IEEE Conference on Decision and control

作者： N. Yamamoto H. Machida K. Tsumura S. Hara Control and Dynamical Systems California Institute of Technology Pasadena CA USA Department of Information Physics and Computing Graduate School of Information Science and Technology University of Tokyo Bunkyo Tokyo Japan

A quantum state subject to a continuous measurement obeys a nonlinear stochastic differential equation called the stochastic master equation. In this paper, we utilize the technique of local reachability developed in control theory to characterize a class of the measurement and the controller in such a way that the local transition of the controlled quantum state is restricted to a low-dimensional manifold. We examine the proposed controller for single-spin systems with two typical measurements.

关键词： Stochastic systems Feedback control control systems Differential equations control theory Nonlinear equations Quantum mechanics Stochastic processes Filtering theory Measurement standards

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Hybrid quantum-classical convolutional neural networks

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Science China(Physics,Mechanics & Astronomy) 2021年第9期64卷 1-8页

作者： Junhua Liu Kwan Hui Lim Kristin LWood Wei Huang Chu Guo He-Liang Huang Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore Forth AI Singapore 487372Singapore College of Engineering Design and ComputingUniversity of Colorado DenverColorado 80208USA Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic TechnologyGuilin 541004China 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 ApplicationsHunan Normal UniversityChangsha 410081China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Shanghai Branch CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefei 201315China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou 450000China

Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or *** parallel,quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations,which could generate distributions that are hard for a classical computer to *** we propose a hybrid quantum-classical convolutional neural network(QCCNN),inspired by convolutional neural networks(CNNs)but adapted to quantum computing to enhance the feature mapping *** is friendly to currently noisy intermediate-scale quantum computers,in terms of both number of qubits as well as circuit’s depths,while retaining important features of classical CNN,such as nonlinearity and *** also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical *** demonstrate the potential of this architecture by applying it to a Tetris dataset,and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.

关键词： quantum computing quantum machine learning hybrid quantum-classical algorithm convolutional neural network

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