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RETRACTED ARTICLE: An evaluation of AI-based college music teaching using AHP and MOORA

Soft Computing 2023年第2期28卷 689-689页

作者： Han, Xiao Chen, Fuyang Ullah, Ijaz Faisal, Mohammad College of Automation Engineering Control Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China University of Rennes 1 Rennes France Department of Computer Science and Information Technology University of Malakand Chakdara Pakistan

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Optimal CPU scheduling in data centers via a finite-time distributed quantized coordination mechanism

arXiv

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arXiv 2021年

作者： Rikos, Apostolos I. Grammenos, Andreas Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Alan Turing Institute London United Kingdom Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo02150 Finland

In this paper we analyze the problem of optimal task scheduling for data centers. Given the available resources and tasks, we propose a fast distributed iterative algorithm which operates over a large scale network of nodes and allows each of the interconnected nodes to reach agreement to an optimal solution in a finite number of time steps. More specifically, the algorithm (i) is guaranteed to converge to the exact optimal scheduling plan in a finite number of time steps and, (ii) once the goal of task scheduling is achieved, it exhibits distributed stopping capabilities (i.e., it allows the nodes to distributely determine whether they can terminate the operation of the algorithm). Furthermore, the proposed algorithm operates exclusively with quantized values (i.e., the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We also provide examples to illustrate the operation, performance, and potential advantages of the proposed algorithm. Finally, by using extensive empirical evaluations through simulations we show that the proposed algorithm exhibits state-of-the-art performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Multitasking

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Krasovskii and Shifted Passivity Based Output Consensus

arXiv

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arXiv 2022年

作者： Kawano, Yu Cucuzzella, Michele Feng, Shuai Scherpen, Jacquelien M.A. Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia PV Pavia27100 Italy School of Automation Nanjing University of Science and Technology Nanjing210094 China Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands

Motivated by current sharing in power networks, we consider a class of output consensus (also called agreement) problems for nonlinear systems, where the consensus value is determined by external disturbances, e.g., power demand. This output consensus problem is solved by a simple distributed output feedback controller if a system is either Krasovskii or shifted passive, which is the only essential requirement. The effectiveness of the proposed controller is shown in simulation on an islanded DC power network. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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Comparative Analysis of Android Mobile Forensics Tools

Comparative Analysis of Android Mobile Forensics Tools

引用

computer Applications (ICCA), IEEE Conference on

作者： Htar Htar Lwin Wai Phyo Aung Kyaw Kyaw Lin Faculty of Computer Systems and Technologies University of Computer Studies Yangon Department of Automation Control System Moscow Automobile and Road Construction State Technical University Russia Department of Computer Technology Defence Services Academy Pyin Oo Lwin

ISBN: (数字)9781728159256

ISBN: (纸本)9781728159263

This paper performs a comparative analysis of Android mobile forensics tools which are used for acquisition and analyzing of Android mobile devices. The major challenges of Android forensics investigation are manufacturing of Android devices with various operating system versions and there is no single tool which can be used for all sorts of Android devices. Aiming to overcome these challenges and increase more accuracy and integrity in Android forensic investigation, we made comparative analysis on both open source tools and one commercial tool. Logical and physical acquisition methods were utilized to acquire data from Android devices. Android Debug Bridge backup, Linux Data Duplicator utility tool, Magnet Acquire and Belkasoft Acquisition tools were used for acquisition. Two popular analyzing tools such as Autopsy and Belkasoft Evidence Center were utilized to analyze acquired data. The results show that using multiple tools can get more accuracy and integrity of artifacts which is forensically sound.

关键词： Tools Forensics Androids Humanoid robots Magnetic resonance imaging Smart phones Autopsy

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Developing and Analysis of Cyber Security Models for Security Operation Center in Myanmar

Developing and Analysis of Cyber Security Models for Securit...

引用

computer Applications (ICCA), IEEE Conference on

作者： Wai Phyo Aung Htar Htar Lwin Kyaw Kyaw Lin Department of Automation Control System Moscow Automobile and Road Construction State Technical University Russia Faculty of Computer Systems and Technologies University of Computer Studies Yangon Department of Computer Technology Defence Services Academy Pyin Oo Lwin

ISBN: (数字)9781728159256

ISBN: (纸本)9781728159263

In counteraction to the increasing threat of cyber terrorism, the modeling to be predicted in guessing the predictive models for estimating the incidence of cyber-attacks for enterprise network in Myanmar are seriously needed. Although we need these models, there is no record of attacks, defenseless, outcome and threat to utilize the developing predictive models and authentication. The main purpose of this research is to determine whether SOC (Security Operation Center) manager uses cyber security model by using SOC results figures to prepare further cyber defense and incident response plan. The goal of this study was achieved by conducting experiments on various cyber-attacks occurred in security operation center of Industrial control System (ICS).

关键词： computer security Predictive models Mathematical model Analytical models Computational modeling Integrated circuit modeling

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Collaborative Learning of High-Precision Quantum control and Tomography

引用

Physical Review Applied 2021年第1期16卷 014056-014056页

作者： Hai-Jin Ding Bing Chu Bo Qi Re-Bing Wu Department of Automation Tsinghua University Beijing 100084 People’s Republic of China School of Electronic and Computer Science University of Southampton SO17 1BJ United Kingdom Key Laboratory of Systems and Control ISS and National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science CAS Beijing 100190 People’s Republic of China

In the hardware implementation of quantum computation, the control pulses for quantum states and gate operations must be calibrated to correct errors induced by unknown drifts of system parameters. The calibration can be iteratively done by checking the control precision, but the required randomized benchmarking or quantum tomography is experimentally expensive. In this paper, we propose that the control calibration and the quantum tomography can be taken as two separate but collaborative learning processes. Combining the gradient-descent pulse engineering and the adaptive tomography, the resulting c-GRAPE algorithm can substantially reduce the high cost of measurements without sacrificing the control accuracy. The effectiveness is demonstrated by numerical simulation examples.

关键词： Machine learning Quantum control Quantum tomography

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Comparing of transfer process data in PLC and mcu based on IoT 5th

Comparing of transfer process data in PLC and mcu based on I...

引用

5th International Conference on Advanced Engineering Theory and Applications, AETA 2018

作者： Gavlas, Antonin Koziorek, Jiri Rakay, Robert Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 15 Ostrava70833 Czech Republic Department of Automation Control and Human Machine Interactions Faculty of Mechanical Engineering Technical University of Košice Park Komenského 8 Košice042 00 Slovakia

ISBN: (纸本)9783030149062

An article is based on comparing of data processing within PLC and MCU with using Cloud platforms. The last advances in computation and communication technologies are taking shape in the form of IoT (Internet of Things), M2M (Machine to Machine) technology, Industry 4.0 and Cyber Physical Systems. Internet of Things connects an enormous number of devices. Devices within IoT need small number of sensors and control data, but with numerous messages. Today Cloud services work with the volume of these data and support large scale IoT systems. In this paper we would like to represent a comparison of solutions Industrial and Commercial IoT. The proposed systems use one of the newest communication protocols MQTT. © Springer Nature Switzerland AG 2020.

关键词： Internet of things

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

引用

IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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Exact Differentiator with Lipschitz Continuous Output and Optimal Worst-Case Accuracy Under Bounded Noise

Exact Differentiator with Lipschitz Continuous Output and Op...

引用

IEEE Conference on Decision and control

作者： Rodrigo Aldana-López Richard Seeber Hernan Haimovich David Gómez-Gutiérrez Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria Centro Internacional Franco-Argentino de Ciencias de la Información y de Sistemas (CIFASIS) CONICET-UNR Rosario Argentina Intel Tecnología de México Intel Labs Intelligent Systems Research Lab Jalisco Mexico Tecnológico Nacional de México Instituto Tecnológico José Mario Molina Pasquel y Henríquez Unidad Académica Zapopan Jalisco Mexico

The online differentiation of a signal contaminated with bounded noise is addressed. A differentiator is developed that generates a Lipschitz continuous output, is exact in the absence of noise, and provides the optimal worst-case accuracy among all possible exact differentiators when noise is present. This combination of features is not shared by any previously existing differentiator. Tuning of the developed differentiator is very simple, requiring only the knowledge of a bound for the second-order derivative of the signal. The approach consists in regularizing the possibly highly noisy output of a recently introduced linear adaptive robust exact differentiator and feeding it to a first-order sliding-mode filter designed to maintain optimal accuracy. The proposed regularization and filtering of this output allows trading the speed with which exactness is obtained for the feature of a Lipschitz continuous, hence less noisy, output. An illustrative example is provided to highlight the features of the developed differentiator.

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Maturity Model for AI in Smart Production Planning and control System

引用

Procedia CIRP 2022年 107卷 493-498页

作者： Eduardo Colangelo Christian Fries Theresa-Franziska Hinrichsen Ádám Szaller Gábor Nick Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstr. 12 70569 Stuttgart Germany University of Stuttgart Institute of Industrial Manufacturing and Management IFF Allmandring 35 70569 Stuttgart Germany Institute for Computer Science and Control Kende str. 13-17. Budapest 1111 Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest 1111 Hungary EPIC InnoLabs Nonprofit Ltd. Kende str. 13-17. Budapest 1111 Hungary

The utilization of Artificial Intelligence (AI) to improve processes constitutes a main subject for many enterprises. The area of Production Planning and control (PPC) possesses several functions that could profit from such approaches. However, manufacturing companies find themselves often limited in the application of these approaches. This paper concentrates on three elements to assist enterprises: 1) the clarification of what AI is (in the manufacturing context) and its application to the field of PPC; 2) a review performed together with manufacturing enterprises in Germany and Hungary in order to understand the obstacles for the implementation of AI; and 3) the proposal of a maturity model to help enterprises understand where they are in regards to AI, as a way to help them create a roadmap to achieve their objectives.

关键词： Production Planning control Artificial Intelligence Maturity Model Smart Production

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