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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Guonian Jin Shejie Lu Siping Hu Haihong Xu Jinfeng Gao Fang Liu Haitao Wu School of Computer Science and Technology Institute of Engineering and Technology Hubei University of Science and Technology Xianning PR China International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Huanghuai University Zhumadian PR China

In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system performance degradation. Through in-depth analysis of the root cause of this problem, this paper designs a simple and effective algorithm, which uses ARMA filtering algorithm to measure the conditional collision probability and then calculate the number of nodes in the network. The AP tells all nodes in the network in the form of broadcast when the number of nodes in the network changes, after all nodes receive the broadcast sent by the AP, they dynamically adjust the network parameters accordingly. Finally, NS2 simulation software is used to conduct simulation experiments in a variety of network scenarios, and the experimental results verify that the algorithm in this paper is simple and effective, with less changes to IEEE 802.11, and is suitable for use and promotion in wireless networks. It can optimize the network system performance based on the number of nodes, improve the system throughput, and significantly improve the network performance. According to the number and density of nodes, the algorithm reduces the collision probability by dynamically adjusting the timeslot, accordingly increases the total network throughput, and achieves the purpose of effectively improving the network performance.

关键词： Degradation Wireless LAN Heuristic algorithms Wireless networks System performance Software algorithms Throughput

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SECURE-BY-CONSTRUCTION SYNTHESIS FOR control SYSTEMS

arXiv

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arXiv 2023年

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Department of Computer Science University of Colorado Boulder United States Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden TUM School of Engineering and Design Technical University of Munich Germany

In this paper, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system’s ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions, which we introduce and discuss in detail. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Moreover, we demonstrate the flexibility of our approach by incorporating user-defined cost functions into the construction of secure-by-construction controllers. Finally, we validate the effectiveness of our results through two case studies, illustrating the practical applicability and benefits of our proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： controllers

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Applying Predictive Machine Learning Algorithms to Petroleum Refining Processes as Part of Intelligent Automation 23

Applying Predictive Machine Learning Algorithms to Petroleum...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Nekrasov, Ivan S. Tynchenko, Vadim S. Bukhtoyarov, Vladimir B. Kachaeva, Vera A. Bashmur, Kirill A. Sinitskaya, Aleksandra E. Siberian Federal Univercity Laboratory of Biofuel Composition Krasnoyarsk Russia Siberian Federal Univercity Dept. of Technological Machines and Equipment of Oil and Gas Complex Krasnoyarsk Russia Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Information-Control Systems Department Krasnoyarsk Russia

ISBN: (纸本)9781665498043

Intelligent automation is a term that can be applied to the more complex field of workflow automation, consisting of robotic workplace automation, robotic process automation, machine learning, and artificial intelligence. Depending on the type of business, companies often use one or more types of automation to improve efficiency or effectiveness. As you move from process-driven automation to more flexible data-driven automation, additional costs arise in the form of training datasets, technical development, infrastructure, and expertise. But the potential benefits in terms of new ideas and financial development can increase significantly. The development of mechanized oil production in recent years has been accompanied by significant achievements in the field of digitalization. Machine learning, as an important element of digitalization, can successfully solve many production problems. The paper describes the application of some machine learning algorithms for solving the problem of classifying and predicting failures of hydrocracking process equipment that occur during oil refining and diesel fuel production. The application of random forest, principal component analysis and hyperparameter tuning methods is considered. The effectiveness of their application is compared. © 2022 IEEE.

关键词： Decision trees

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Mathematical Models and Data Processing Methods for Improving the Efficiency of High-Tech Production of Polymeric Films

Mathematical Models and Data Processing Methods for Improvin...

引用

Information Systems Theory and Practice (ISTP), Seminar on

作者： Tamara B. Chistyakova Andrey N. Polosin Viacheslav G. Ksenofontov Aleksandr S. Razygraev Computer-Aided Design and Control System Department Saint-Petersburg State Institute of Technology (Technical University) Saint Petersburg Russia Managing Director Klöckner Pentaplast Rus LLC Saint Petersburg Russia

Mathematical models and data processing methods used for the digitalization of high-tech production of polymeric films, characterized by flexibility, large-capacity, multi-assortment, returnable waste, have been developed. The models describe the physical processes at the main stages of production (preparation of the extrudate, forming the extrudate, fixing the film structure), take into account the variability of equipment configurations, and make it possible to calculate the quality indices of the extrudate and film that are not monitored in production. Machine learning methods are used to process production data with large volume, high accumulation rate, variety of sources, and predict the quality of films. Mathematical models and methods have been implemented in the form of a software package that is adjusted for various types of films and configurations of production systems. It helps to solve the tasks of controlling the consumer characteristics of films (thickness difference, number of surface defects of various types, shrinkage, color, telescoping). Testing according to the extrusion-calender production of pharmaceutical and food packaging films at factories in Russia and Germany has confirmed the adequacy of the models and the operability of the software package. Their use contributes to improving the production efficiency by increasing the yield of high-quality film with the same amount of raw materials and the possibility of increasing film price for customers due to a significant improvement in the consumer characteristics.

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Model Based Parameterizations of Different Topologies 1

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20th Polish control Conference, PCC 2020

作者： Bányász, Csilla Keviczky, Laszlo Bars, Ruth Institute for Computer Science and Control Budapest Hungary Budapest University of Technology and Economics Budapest Hungary

The optimization of simple two-degree-of-freedom control systems is very easy with the new parameterizations, as Youla, Keviczky-Banyasz, etc. The comparison of their model-based versions is important at the practical... 详细信息

ISBN: (数字)9783030509361

ISBN: (纸本)9783030509354

关键词： Parameterization

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Research on Black-Box Adversarial Attack Based on Transformer

Research on Black-Box Adversarial Attack Based on Transforme...

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Frontiers technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Wenjuan Zheng Hanxu Luo YuXin Ji Haipeng Li Longjian Cong Master of Computer Vision National Key Laboratory of Aerospace Intelligent Control Technology Beijing China Beijing Aerospace Automatic Control Institute Beijing China Department of Information Engineering Wuhan University of Technology Wuhan Hubei Province China

The classic adversarial samples of black-box attacks are all aimed at the models of Convolutional neural networks (CNNs), but they do not perform well on the new recognition networks based on Transformer. In this paper, we propose an adversarial sample generation algorithm based on Vision transformers (VITs)‘ self-attention mechanism and patch partition. We noticed that different blocks in the VIT have uneven attention distribution, so we first generated a patch-based attention map and performed threshold segmentation, which is used as a mask to perform data enhancement operations based on patches with high weight and patches with low weight, and then exchanged information between patches to generate adversarial samples. The experiment of simulating black-box attack shows that the adversarial samples generated by the algorithm in this paper have a high success rate in all kinds of models based on Transformer of attacks, and also perform well on CNNs.

关键词： Closed box Benchmark testing Transformers Partitioning algorithms Convolutional neural networks Convergence

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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IEEE/SICE International Symposium on System Integration

作者： Mert Bozkurtlar Benjamin Yen Katsutoshi Itoyama Kazuhiro Nakadai Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Department of Industrial Engineering Faculty of Management Istanbul Technical University Harbiye Maçka/Beşiktaş/İstanbul Türkiye Department of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Ayazaga Sariyer/İstanbul Türkiye Japan Society for the Promotion of Science (JSPS) at the Nakadai Lab Tokyo Institute of Technology Honda Research Institute Japan Co. Ltd Saitama Japan

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments.

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On the Optimal Topology of Time-Delay control Systems 1

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20th Polish control Conference, PCC 2020

作者： Bars, Ruth Bányász, Csilla Keviczky, Laszlo Budapest University of Technology and Economics Budapest Hungary Institute for Computer Science and Control Budapest Hungary

It is shown that the Smith predictor is a subclass of the Youla parameterization based generic two-degree of freedom controllers. Comparing the algorithms the application of the new approach is suggested. © 2020,... 详细信息

ISBN: (数字)9783030509361

ISBN: (纸本)9783030509354

关键词： Time delay

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A Geometric Flow Perspective on Continuous-Depth Networks

SSRN

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SSRN 2024年

作者： Chen, Jun Zhu, Tianyi Liu, Lina Jiang, Yunliang Liu, Yong Zhejiang Normal University Jinhua321004 China China Mobile Research Institute Beijing100032 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

The continuous-depth model, introduced by neural ordinary differential equations, have revived interest in exploring dynamic systems based on deep learning prototypes. The studies employed to investigate their theoretical properties mainly rely on Euclidean space, however, the geometric principle of general neural networks has been developed on the Riemannian manifold. Motivated by this open problem, we construct a formalized geometric theory of continuous-depth networks through the lens of diffeomorphic flows. From this perspective, the Riemannian metric with coordinate representations learned by the continuous-depth network itself is the closed-form solution of diffeomorphic flows. With the presence of ordinary differential equations, the continuous-depth network governs the velocity field to drive the different kinds of data apart from each other, which is a novel observation between the velocity field and data separation. Toy experiments confirm the proposed theory, as well as provide intuitions and visualizations as to how the velocity field governed by continuous-depth networks evolves on the manifold to operate on data. © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

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