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cyber-Physical systems Based Smart Manufacturing of Disinfectants: A Need, and Solution Driven by COVID-19 Pandemic 32nd

Cyber-Physical Systems Based Smart Manufacturing of Disinfec...

引用

32nd international conference on Flexible automation and intelligent Manufacturing

作者： Iqbal, Faiz Semwal, Tushar Stokes, Adam A. Univ Lincoln Sch Engn Lincoln LN6 7TS England Univ Edinburgh Inst Integrated Micro & Nano Syst Sch Engn Kings Bldg Edinburgh EH9 3FF Midlothian Scotland

ISBN: (纸本)9783031382406;9783031382413

cyber-physical systems and Industry-4.0 have made smart manufacturing possible. The multi-national companies were able to quickly adapt and cope with changing times, but the micro small-scale enterprises (m-SMEs) were at crossroads to adopt new technology due to extensive costs involved. This gap of no existing solutions for micro-SMEs is the result of literature survey for this work. Then the COVID-19 pandemic made it extremely difficult for micro-SMEs to survive. This paper implements a smart manufacturing solution for production facility at one such micro-SME. Pre-pandemic, their facility had enough production to satisfy the demand, but pandemic overwhelmed their production with rapid rise in demand. The main goal for this study is to make the micro-SME cope this unprecedented rise in demand. The production facility was upgraded, and smart features were incorporated based on cyber-physical production systems framework to predict demand and transform a manual manufacturing into smart manufacturing. With more than one product being produced, optimum production was achieved avoiding over/underproduction for different products given the limited storage capacity available with the micro-SMEs.

关键词： Smart Manufacturing cyber-Physical systems Small scale enterprises AI COVID-19

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An intelligent Digital Twin Model for Attack Detection in Zero-Touch 6G Networks 59

An Intelligent Digital Twin Model for Attack Detection in Ze...

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Bolat-Akca, Burcu Bozkaya-Aras, Elif Canberk, Berk Buchanan, Bill Schmid, Stefan Natl Def Univ Dept Comp Engn Turkish Naval Acad Istanbul Turkiye Edinburgh Napier Univ Sch Comp Engn & Built Environm Edinburgh Midlothian Scotland Berlin Tech Univ Dept Elect Engn & Comp Sci Berlin Germany

ISBN: (纸本)9798350304060;9798350304053

The rapid adoption of Internet of Things (IoT) services and the increasingly stringent dependability and performance requirements are transforming next-generation wireless network management towards zero-touch 6G networks. Zero-touch management is also particularly attractive in the context of smart industrial production (Industry 5.0) as it allows more autonomous control over all operations without human intervention. However, such a paradigm requires real-time remote monitoring and control throughout the life cycle of all physical entities. Against this backdrop, digital twin is a critical technology for Industry 5.0 and beyond for novel network automation solutions. A digital twin enables fine-grained monitoring, testing and experimentation by providing a model of its physical counterparts. In this regard, we propose a three-layered digital twin framework's architecture to effectively bridge the gap between the physical world and the digital world in zero-touch 6G networks. As a case study, we consider an intelligent digital twin model to detect cyber attacks, such as brute force, web attacks, and DDoS attacks. However, an excessive increase in data volume requires exploratory efforts for efficient and scalable attack detection. In addition, due to the insufficient annotation and imbalanced distribution of classes, challenges are introduced regarding the achievable accuracy. To address these issues, we propose the use of Long Short-Term Memory Network (LSTM) with the combination of the Synthetic Minority Over-sampling Technique (SMOTE) for attack detection for the new and unseen samples with the assistance of digital twin. We also analyze samples within the cluster and eliminate samples that are somewhere between the clusters and negatively affect the final accuracy by silhouette score. Extensive experiments show that our proposed intelligent digital twin model for attack detection improves the accuracy performance.

关键词： Digital twin zero-touch network management attack detection Long Short Term Memory (LSTM)

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Voltage control of DC microgrid based on fuzzy neural network and improved particle swarm optimization algorithm 1

Voltage control of DC microgrid based on fuzzy neural networ...

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1st international conference on cyber-Energy systems and intelligent Energy, ICCSIE 2022

作者： Sun, Jianfeng State Grid Corporation of China Department of Technology and Innovation Beijing China

ISBN: (纸本)9781665452151

DC microgrid, characterized by high conversion efficiency, occupies a large part of the distribution network. However, due to the complexity and randomness of renewable energy and load, the voltage control of DC microgrid is inaccurate, which is an important indicator to measure the stable operation of the system. Considering the randomness, this paper studies the voltage control of DC microgrid including photovoltaic, wind power, energy storage and DC load systems. In this paper, the mathematical model of DC micro grid under multi energy coupling is established. Furthermore, fuzzy neural PID control with improved particle swarm optimization is proposed. Finally, the controller in this paper is proved to be effective through simulation experiments. © 2023 ieee.

关键词： Voltage control

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Vulnerability Assessment of Industrial control systems for Colonial Pipeline and WannaCry Ransomware 65

Vulnerability Assessment of Industrial Control Systems for C...

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65th ieee Annual international Scientific conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2024

作者： Musluoglu, Mert Kunicina, Nadezhda Caiko, Jelena Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia

ISBN: (纸本)9798350365771

Industrial control systems (ICS) allow a high level of automation of industrial processes, which, in its turn, results in their higher efficiency and lower cost [1]. ICS are core components in many industrial setups, being used to automatically and manually control processes in the fields of manufacturing, energy, and utilities, among others. ICS environments are increasingly becoming susceptible to cyber-attacks as a result of the merging of operational technology (OT) with information technology (IT) [2]. This paper has addressed common vulnerabilities in ICS environments, which range from unsecure communication protocols, weak authentication mechanisms, outdated software, and improper network configurations. Through the exploitation of such vulnerabilities, malicious attackers are able to obtain unauthorized access, disrupt operations, or cause much damage on physical infrastructure. In an attempt to minimize such risks, this paper has set out some of the mitigation efforts that span from the adoption of network segmentation, robust mechanisms of authentication and encryption, regular updating and patching of systems, and total maintenance and around-the-clock monitoring and vulnerability assessments. Such measures are of greatest necessity in improving the security posture of ICS environments as well as securing core industrial processes. © 2024 ieee.

关键词： Authentication

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Building Trust in AI-Driven Decision Making for cyber-Physical systems (CPS): A Comprehensive Review 29

Building Trust in AI-Driven Decision Making for Cyber-Physic...

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29th ieee international conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Mhapsekar, Rahul Umesh Umrani, Muhammad Iftikhar Faizan, Malik Ali, Omer Abraham, Lizy South East Technological University Waterford Ireland Waterford Ireland

ISBN: (纸本)9798350361230

Recent advancements in technology have led to the emergence of cyber-Physical systems (CPS), which seamlessly integrate the cyber and physical domains in various sectors such as agriculture, autonomous systems, and healthcare. This integration presents opportunities for enhanced efficiency and automation through the utilization of Artificial Intelligence (AI) and Machine Learning (ML). However, the complexity of CPS brings forth challenges related to transparency, bias, and trust in AI-enabled decision-making processes. This research explores the significance of AI and ML in enabling CPS in these domains and addresses the challenges associated with interpreting and trusting AI systems within CPS. Specifically, the role of Explainable AI (XAI) in enhancing trustworthiness and reliability in AI-enabled decision-making processes is discussed. Key challenges such as transparency, security, and privacy are identified, along with the necessity of building trust through transparency, accountability, and ethical considerations. © 2024 ieee.

关键词： Adversarial machine learning

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Reinforcement Learning with Reward Shaping and Hybrid Exploration in Sparse Reward Scenes 6

Reinforcement Learning with Reward Shaping and Hybrid Explor...

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ieee 6th international conference on Industrial cyber-Physical systems (ICPS)

作者： Yang, Yulong Cao, Weihua Guo, Linwei Gan, Chao Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geo Explorat Wuhan 430074 Peoples R China

ISBN: (纸本)9798350311259

High precision modeling in industrial systems is difficult and costly. Model-free intelligent control methods, represented by reinforcement learning, have been applied in industrial systems broadly. The hard evaluated of production states and the low value density of processing data causes sparse rewards, which lead to an insufficient performance of reinforcement learning. To overcome the difficulty of reinforcement learning in sparse reward scenes, a reinforcement learning method with reward shaping and hybrid exploration is proposed. By perfecting the rewards distribution in the state space of environment, the reward shaping can make the state-value estimation of reinforcement learning more accurate. By improving the rewards distribution in time dimension, the hybrid exploration can make the iteration of reinforcement learning more efficient and more stable. Finally, the effectiveness of the proposed method is verified by simulations.

关键词： reinforcement learning sparse reward reward shaping hybrid exploration

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Study of Machine Learning Algorithms for Detecting Web Bot 21

Study of Machine Learning Algorithms for Detecting Web Bot

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21st international conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information technology (ECTI-CON)

作者： Poptiphueng, Thanu Siribunyaphat, Nannaphat Sukpongthai, Warattha Moolwat, Onuma Dhurakij Pundit Univ Coll Innovat Technol & Engn Bangkok Thailand

ISBN: (纸本)9798350381566;9798350381559

Nowadays, automation software systems play a crucial role in assisting humans with specific tasks, particularly in online communication for timely interactions. However, the misuse of such systems poses risks, impacting website performance, leading to data access violations, or even posing threats to the system's security. To address these challenges, an automatic software inspection system has been developed as a prototype, aimed at preventing potential issues in the online domain. Machine learning algorithms are employed to enhance detection accuracy, with the decision tree algorithm demonstrating a maximum accuracy of 97.4%. This promising result suggests its suitability for further development into a JavaScript library, offering a valuable resource for future website applications.

关键词： Detection System automation Software cyber Security Machine Learning

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Switched Sliding Mode control for Networked T-S Fuzzy Delayed systems: A Membership Function-Dependent Event-Triggered Mechanism

Switched Sliding Mode Control for Networked T-S Fuzzy Delaye...

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2024 ieee international conference on control Science and systems Engineering, ICCSSE 2024

作者： Liu, Hao Hu, Jun Zhang, Hongxu Zuo, Zhiyuan Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China School of Automation Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9798331517199

This paper investigates the switched sliding mode control (SMC) problem for networked T-S fuzzy delayed systems with limited communication capability, where the event-triggered mechanism is adopted to schedule the information transmission. Using the fuzzy model and controller membership function information, a membership function-dependent dynamic event-triggered mechanism (DETM) is constructed with hope to enhance effective data utilization. Furthermore, a novel switched fuzzy sliding mode controller is designed. Then, by combining with the membership function analysis method, sufficient conditions are provided to guarantee that the closed-loop fuzzy system is asymptotically stable with a prescribed H∞ performance and the sliding surface is reachable. Finally, a simulation example is employed to demonstrate the rationality of proposed switched SMC strategy. © 2024 ieee.

关键词： Sliding mode control

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Global Multi-Object Transformer Tracking with Detecting and Attention Mechanism 13

Global Multi-Object Transformer Tracking with Detecting and ...

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13th ieee international conference on cyber technology in automation, control, and intelligent systems, cyber 2023

作者： Liu, Hengming Liu, Chang Hao, Zhicheng Zhou, Zhehai Qiu, Jun Institute of Applied Mathematics Beijing Information Science and Technology University Beijing China Beijing Information Science and Technology University Beijing Engineering Research Center of Optoelectronic Information and Instruments Beijing100192 China

ISBN: (纸本)9798350315196

Visual multiple-object tracking (MOT) has always been a research hotspot in the fields of computer vision and video image intelligence analysis. Tracking algorithms must not only accurately locate targets but also adapt to various types of interference while meeting robustness requirements. However, the uncertainty of the number of objects in the scene, mutual occlusion between objects, and the low discriminability of object features have led to slow progress in the practical application of visual multiple-object tracking. To address this issue, we designed a modified attention mechanism module and embedded it into a transformer-based global multiple-object tracking architecture, generating global trajectories for all objects. This forms the proposed efficient algorithm for object tracking. For larger convolution kernels, our method effectively extracts each feature in the receptive field while reducing computation costs, thus improving algorithm efficiency. We conducted the experiments on MOT17 and TAO, and achieved the state-of-the-art results. Experimental results show that our proposed method outperforms existing models overall, ensuring stable object trajectories while maintaining high-quality detection. © 2023 ieee.

关键词： Computational efficiency

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Data-Driven System Identification of Quadrotors Subject to Motor Delays

Data-Driven System Identification of Quadrotors Subject to M...

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2024 international conference on intelligent Robots and systems

作者： Eschmann, Jonas Albani, Dario Loianno, Giuseppe NYU Tandon Sch Engn Brooklyn NY 11201 USA Technol Innovat Inst Autonomous Robot Res Ctr Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350377712;9798350377705

Recently non-linear control methods like Model Predictive control (MPC) and Reinforcement Learning (RL) have attracted increased interest in the quadrotor control community. In contrast to classic control methods like cascaded PID controllers, MPC and RL heavily rely on an accurate model of the system dynamics. The process of quadrotor system identification is notoriously tedious and is often pursued with additional equipment like a thrust stand. Furthermore, low-level details like motor delays which are crucial for accurate end-to-end control are often neglected. In this work, we introduce a data-driven method to identify a quadrotor's inertia parameters, thrust curves, torque coefficients, and first-order motor delay purely based on proprioceptive data. The estimation of the motor delay is particularly challenging as usually, the RPMs can not be measured. We derive a Maximum A Posteriori (MAP)-based method to estimate the latent time constant. Our approach only requires about a minute of flying data that can be collected without any additional equipment and usually consists of three simple maneuvers. Experimental results demonstrate the ability of our method to accurately recover the parameters of multiple quadrotors. It also facilitates the deployment of RL-based, end-to-end quadrotor control of a large quadrotor under harsh, outdoor conditions.

关键词： Predictive control systems

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