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Internet of things-based automation design and organizational innovation of manufacturing enterprises

Materials Today: Proceedings 2022年 56卷 1769-1775页

作者： Babu, T. Roopa, H. Shukla, Arvind Kumar Stalin David, D. Jayadatta, S. Rajesh, A.S. Department of Instrumentation and Control Engineering St. Joseph's College of Engineering Chennai India Department of Information Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Application IFTM University Moradabad India Department of CSE IFET College of Engineering Villupuram India Department of Management Studies KLE's IMSR BVB Campus Hubli India Department of Mechanical Engineering JSS S&T University Mysore India

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing *** support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies. © 2021

关键词： Product design

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Strategic Monitoring of Networked Systems with Heterogeneous Security Levels

arXiv

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arXiv 2023年

作者： Milošević, Jezdimir Dahan, Mathieu Amin, Saurabh Sandberg, Henrik Electrical Engineering and Computer Science The University of Belgrade Belgrade Serbia Electrical Engineering and Computer Science The KTH Royal Institute of Technology Stockholm Sweden Computational Science and Engineering The Massachusetts Institute of Technology CambridgeMA United States Systems Engineering The University of California Berkeley BerkeleyCA United States Engineering Physics Lund University Lund Sweden Automatic Control Lund University Lund Sweden

We consider a strategic network monitoring problem involving the operator of a networked system and an attacker. The operator aims to randomize the placement of multiple protected sensors to monitor and protect components that are vulnerable to attacks. We account for the heterogeneity in the components’ security levels and formulate a large-scale maximin optimization problem. After analyzing its structure, we propose a three-step approach to approximately solve the problem. First, we solve a generalized covering set problem and run a combinatorial algorithm to compute an approximate solution. Then, we compute approximation bounds by solving a nonlinear set packing problem. To evaluate our solution approach, we implement two classical solution methods based on column generation and multiplicative weights updates, and test them on real-world water distribution and power systems. Our numerical analysis shows that our solution method outperforms the classical methods on large-scale networks, as it efficiently generates solutions that achieve a close to optimal performance and that are simple to implement in practice. © 2023, CC BY.

关键词： Game theory

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Safe trajectory design for indoor drones using reinforcement-learning-based methods

Safe trajectory design for indoor drones using reinforcement...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Dénes Tompos Balázs Németh Eötvös Loránd Research Network (ELKH) Kende utca 13-17. Institute for Computer Science and Control (SZTAKI) Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2. Budapest Hungary

This paper proposes a design method for achieving safe trajectory of indoor drones. The trajectory design with a reinforcement-learning-based (RL) agent is facilitated, which can result in efficient and collision-free motion. The method is developed for motion in indoor area with moving mobile robots, and thus, the collision with these obstacles must be avoided. Through RL-based design the fast motion of the drones can be achieved, which must perform a mission between workstations in a manufacturing system. The effectiveness of the design process through a simulation example on a real laboratory environment is illustrated.

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Nonlinear Modes as a Tool for Comparing the Mathematical Structure of Dynamic Models of Soft Robots

Nonlinear Modes as a Tool for Comparing the Mathematical Str...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Pietro Pustina Davide Calzolari Alin Albu-Schäffer Alessandro De Luca Cosimo Della Santina Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Cognitive Robotics Delft University of Technology Delft Netherlands Institute of Robotics and Mechatronics German Aerospace Center Oberpfaffenhofen Germany Department of Informatics Technical University of Munich Garching Germany

Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dynamic conditions necessitates the development of control-oriented reduced-order models (ROMs), which employ as few DoFs as possible while still accurately capturing the core characteristics of the theoretically infinite-dimensional dynamics. However, there is no quantitative way to measure if the ROM of a soft robot has succeeded in this task. In other fields, like structural dynamics or flexible link robotics, linear normal modes are routinely used to this end. Yet, this theory is not applicable to soft robots due to their nonlinearities. In this work, we propose to use the recent nonlinear extension in modal theory -called eigenmanifolds- as a means to evaluate control-oriented models for soft robots and compare them. To achieve this, we propose three similarity metrics relying on the projection of the nonlinear modes of the system into a task space of interest. We use this approach to compare quantitatively, for the first time, ROMs of increasing order generated under the piecewise constant curvature (PCC) hypothesis with a high-dimensional finite element (FE)-like model of a soft arm. Results show that by increasing the order of the discretization, the eigenmanifolds of the PCC model converge to those of the FE model.

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Innate Cyber-Immunity and Acquired Cyber-Immunity across Multi-Area Power Systems

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IEEE Transactions on Power Systems 2025年

作者： Hou, Jiazuo Song, Yue Hou, Yunhe Peng, Jimmy Chih-Hsien National University of Singapore Department of Electrical and Computer Engineering Singapore Tongji University Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology Shanghai201804 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong University of Hong Kong Shenzhen Institute of Research and Innovation Shenzhen518057 China

Organisms have evolved innate and acquired immune systems to defend against pathogens like coronaviruses. Similarly, power networks, threatened by cyber-attacks, desire cyber-immunity. Inspired by the immunology research, this study develops a cyber-immune strategy-characterizing innate cyber-immunity and establishing acquired cyber-immunity-for any selected portion of a power network (termed as power subnetwork) against stealthy false data injection (FDI) cyberattacks. Therein, without extrinsic protection and information asymmetry, this paper for the first time confirms the existence and provides closed-form conditions for power subnetworks that are innately immune to FDI cyber-attacks despite all measurements being compromised. Single-leader-multi-follower bi-level optimization models are then developed to strategically allocate measurement protection, leading to the acquired cyber-immunity, whose conditions for existence and the least cost are explicitly formulated. More importantly, a bi-directional cyber-immunity propagation mechanism is presented to provide the optimal spatial allocation of defense resources across adjacent subnetworks. It also explicitly determines the temporal allocation sequence, revealing its sequential and cumulative impacts on establishing cyber-immunity. This is achieved by designing recursive traversal algorithms, one of which is modelled as a post-order depth-first-search problem of a multi-fork tree. The proposed subnetwork-specific cyber-immune strategy is verified in a modified Australian National Electricity Market. © 1969-2012 IEEE.

关键词： Phishing

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Robust control design using ultra-local model-based approach for vehicle-oriented control problems

Robust control design using ultra-local model-based approach...

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European control Conference (ECC)

作者： Dá niel Fé nyes Tamá s Hegedű s Balá zs Né meth Zoltá n Szabó Pé ter Gá spá r Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Budapest Hungary Department for Control of Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

Model Free control (MFC) with ultra-local model is novel design method to provide high performance control for systems with nonlinear dynamics. The basic concept of this approach is to compute an ultra-local model, which is valid for a short period of time. Although the concept has already been successfully applied for various vehicle control problems, there are some open issues regarding the robustness and the stability of the closed-loop controlled system. This paper presents a method for designing a robust control based on $\mathcal{H}_{\infty}$ synthesis, with which a solution on these issues can be found. In the proposed MFC strategy the ultra-local model-based control is in the inner loop of the control structure. Moreover, a robust $\mathcal{H}_{\infty}$ control for the outer loop is designed, in whose control-oriented model the control of the inner loop is incorporated. The control strategy is implemented in high-fidelity vehicle dynamic environment and its efficiency and operation are demonstrated through a complex simulation example.

关键词： Robust control control design Computational modeling Design methodology Process control Stability analysis Software

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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Robust Data-Driven Predictive control of Unknown Nonlinear Systems using Reachability Analysis

arXiv

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arXiv 2022年

作者： Farjadnia, Mahsa Alanwar, Amr Niazi, Muhammad Umar B. Molinari, Marco Johansson, Karl Henrik Department of Energy Technology School of Industrial Engineering and Management KTH Royal Institute of Technology Stockholm Sweden School of Computer Science and Engineering Constructor University Bremen Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge United States

This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate exact reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins by effectively using the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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Hierarchical control design of automated vehicles for multi-vehicle scenarios in roundabouts

Hierarchical control design of automated vehicles for multi-...

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European control Conference (ECC)

作者： Balá zs Né meth Zsó fia Farkas Zoltá n Antal Pé ter Gá spá r Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

control design for safe and time-efficient motion of automated vehicles in roundabout scenarios poses various challenges, especially adaptation to the actual traffic scenario and coordination of the vehicles. This paper proposes the design of a hierarchical motion control with learning functionality for roundabout scenarios. The control is designed on two levels, such as on cloud level and on vehicle level. The control on the cloud level is designed by using reinforcement learning, with which the maximum speed for the vehicle is achieved. The vehicle level contains a robust controller and a supervisor, with which the collision avoidance of the vehicles is guaranteed. The proposed control on Hardware-in-the-Loop environment with small-scaled indoor vehicles is implemented. The effectiveness of the control and the safe motion of the automated vehicle under various scenarios are demonstrated. The provided scenarios illustrate that safe, i.e., collision-free motion of the automated vehicle can be guaranteed, even if the connection to the cloud has been lost.

关键词： Motion planning Systematics control design Design methodology Europe Reinforcement learning Complexity theory

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