550 results on '"industrial control"'
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2. Secure Connections in Local Networks Despite Multiple Stakeholders
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Karnapke, Reinhardt, Heuer, Dilmari Seidel, Hoeckner, Soeren, Walther, Karsten, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor more...
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- 2024
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3. A Taxonomy of Industrial Control Protocols and Networks in the Power Grid
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Ortiz, Neil, Cardenas, Alvaro A, and Wool, Avishai
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Information and Computing Sciences ,Engineering ,Engineering Practice and Education ,Protocols ,Industrial control ,Taxonomy ,Power grids ,Critical infrastructure ,Security ,Monitoring ,Distributed Computing ,Electrical and Electronic Engineering ,Communications Technologies ,Networking & Telecommunications ,Communications engineering ,Distributed computing and systems software - Published
- 2023
4. Design of Intrusion Detection and Response Mechanism for Power Grid SCADA Based on Improved LSTM and FNN
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Yu Huang and Liangyuan Su
- Subjects
Industrial control ,machine learning ,intrusion detection ,neural networks ,standardization ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The current behavior pattern extraction methods in intrusion detection systems cannot fully extract information. To improve the accuracy of such systems, the study first uses sequence feature construction algorithms to explicitly represent sequence feature information. Afterwards, an intrusion detection system is designed that combines long short-term memory networks and feed-forward neural networks to remember sequence information and adjust output dimensions, thereby mapping the results to classification labels. According to the simulation comparison results, the designed system had a significantly higher packet capture rate per second compared with the other three intrusion detection systems. When the intrusion rates were 10% and 22%, respectively, the designed system had a packet capture rate of 7000ps per second and a system occupancy rate of 23%. Compared with other intrusion detection systems, the intrusion detection and response mechanism of the proposed power grid monitoring and data acquisition control system was more outstanding in terms of functionality and practicality. The loss value of the proposed method was less than 0.22, and the F1 value was 99.97%. The F1 value of the model combining convolutional neural network and bidirectional long short-term memory network improved by 0.90%. This indicates that the proposed method has higher accuracy and reliability in intrusion detection tasks. This study contributes to the development of power system security protection technology, providing important references for intrusion detection research in other fields. more...
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- 2024
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5. Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control.
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Zhang, Mingguang, Li, Feng, Yu, Yang, and Cao, Qingfeng
- Abstract
Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...
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- 2024
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6. Multi‐rate event‐triggered control with imperfect data for dense medium separation.
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Dai, Wei, Yang, Yi‐Zhuo, Zhang, Qi‐Rui, Yang, Chun‐Yu, and Ma, Xiao‐Ping
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HARDWARE-in-the-loop simulation , *COAL preparation , *KALMAN filtering , *MANUFACTURING processes , *MAXIMUM power point trackers , *COAL - Abstract
The stability control of suspension density is an important means to ensure the quality of fine coal in dense medium separation (DMS) process. This paper focuses on the tracking control issue of the dense medium suspension density in a non‐uniform sampling and networked control environment, and proposes a multi‐rate event trigger control design method. Firstly, lifting technique and multi‐rate identification method are adopted to establish the multi‐rate model. Secondly, a multi‐rate event‐triggered predictive control (MEPC) algorithm is proposed to reduce the waste of communication resources and ensure tracking performance. Furthermore, by using a Kalman filter to eliminate the effects of noise and developing a compensation mechanism to predict the lost data, a robust MEPC (RMEPC) algorithm is further proposed for imperfect data in the actual production process. Finally, the hardware‐in‐the‐loop simulation experiments with actual parameters of coal preparation plant have been carried out, showing the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR] more...
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- 2024
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7. Development of an Industrial Control Virtual Reality Module for the Application of Electrical Switchgear in Practical Applications
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Atiaja, Kevin R., Toapanta, Jhon P., Corrales, Byron P., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Chatterjee, Prasenjit, editor, Pamucar, Dragan, editor, Yazdani, Morteza, editor, and Panchal, Dilbagh, editor more...
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- 2023
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8. Fast-ICA Algorithm in Industrial Control Network Anomaly Detection System
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Ma, Yuanyuan, Xhafa, Fatos, Series Editor, Atiquzzaman, Mohammed, editor, Yen, Neil Yuwen, editor, and Xu, Zheng, editor
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- 2023
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9. Optimal Designs in Multi-Agent Systems and Industrial Refrigeration
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Konda, Rohit
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Engineering ,Control Theory ,Game Theory ,Incentive Design ,Industrial Control ,Multi-Agent Systems ,Optimization - Abstract
The focus of this thesis is on developing control strategies for large-scale systems. We look at two distinct problem areas: the design of coordination algorithms for multi-agent systems and the optimization of industrial refrigeration systems.In the first part of this thesis, we focus on multi-agent systems, where various decision- makers interact with each other, each with their own local objectives. To understand and design control strategies within these systems, we employ a game-theoretic viewpoint. Under this framework, the utility functions and strategic interactions are explicitly modeled to understand emergent behavior. Optimal incentive mechanisms can then be derived to align joint outcomes with the collective objective. The subsequent chapters explore different scenarios within this framework, such as collective transient behavior, coordination in limited information settings, and run-time analysis. Overall, we aim to classify multi-agent behavior and optimize outcomes under varying conditions.In the second part of this thesis, we focus on designing control strategies for industrial refrigeration settings. Industrial refrigeration plays a significant role in various sectors and represents a major portion of total global energy usage. Despite this, there are significant control opportunities in increasing the efficiency of such systems by carefully modulating system variables, such as pressure and temperature, to operate close to optimal thermodynamic conditions. We explore the control opportunities of compressor sequencing and scheduling as a viable option to significantly reduce energy usage, byutilizing techniques from inventory control and scheduling theory. By leveraging optimization ixmethodologies, the manuscript seeks to enhance the efficiency of refrigeration systems, thereby reducing energy usage and environmental impact. more...
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- 2024
10. GOPS: A general optimal control problem solver for autonomous driving and industrial control applications
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Wenxuan Wang, Yuhang Zhang, Jiaxin Gao, Yuxuan Jiang, Yujie Yang, Zhilong Zheng, Wenjun Zou, Jie Li, Congsheng Zhang, Wenhan Cao, Genjin Xie, Jingliang Duan, and Shengbo Eben Li
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Industrial control ,Reinforcement learning ,Approximate dynamic programming ,Optimal control ,Neural network ,Benchmark ,Transportation engineering ,TA1001-1280 - Abstract
Solving optimal control problems serves as the basic demand of industrial control tasks. Existing methods like model predictive control often suffer from heavy online computational burdens. Reinforcement learning has shown promise in computer and board games but has yet to be widely adopted in industrial applications due to a lack of accessible, high-accuracy solvers. Current Reinforcement learning (RL) solvers are often developed for academic research and require a significant amount of theoretical knowledge and programming skills. Besides, many of them only support Python-based environments and limit to model-free algorithms. To address this gap, this paper develops General Optimal control Problems Solver (GOPS), an easy-to-use RL solver package that aims to build real-time and high-performance controllers in industrial fields. GOPS is built with a highly modular structure that retains a flexible framework for secondary development. Considering the diversity of industrial control tasks, GOPS also includes a conversion tool that allows for the use of Matlab/Simulink to support environment construction, controller design, and performance validation. To handle large-scale problems, GOPS can automatically create various serial and parallel trainers by flexibly combining embedded buffers and samplers. It offers a variety of common approximate functions for policy and value functions, including polynomial, multilayer perceptron, convolutional neural network, etc. Additionally, constrained and robust algorithms for special industrial control systems with state constraints and model uncertainties are also integrated into GOPS. Several examples, including linear quadratic control, inverted double pendulum, vehicle tracking, humanoid robot, obstacle avoidance, and active suspension control, are tested to verify the performances of GOPS. more...
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- 2023
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11. Design of FPGA remote debugging system based on Internet
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Guan Jian, Qian Xuelei, Han Liujun, Xue Pei, and Shao Chunwei
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fpga ,remote debugging ,internet ,industrial control ,dynamic reconfigurable ,Electronics ,TK7800-8360 - Abstract
Due to highly customized feature, once the program of FPGA system is fixed, it is extremely difficult to keep maintenance. The system uses MicroBlaze processor to implement TCP/IP protocol stack, realizes the real-time online update of modules in FPGA system by upgrading files through Internet, and completes remote command injection and data upload for remote debugging. Based on ethernet mode, the system can be separated from the traditional debugging method, realize remote equipment debugging without manual participation on the equipment site, and reduce maintenance costs. The system has the characteristics of reliability, fast configuration, and no need to restart using this method, and can also be used in other directions such as cloud computing and real-time simulation. more...
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- 2023
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12. Actuator and Sensor Fault Detection and Isolation System Applied to a Distillation Column
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Gerardo Ortiz-Torres, Jesus E. Valdez-Resendiz, Carlos Alberto Torres-Cantero, Jesse Yoe Rumbo-Morales, Moises Bulmaro Ramos-Martinez, and Jorge Salvador Valdez-Martinez
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Chemical processes ,distillation equipment ,fault diagnosis ,fault tolerant control ,industrial control ,nonlinear control systems ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
This article presents the results of the simulation of a binary distillation column for which a composition controller and fault detection and isolation strategy was applied. In the simulation study, a distillation column comprising nine trays was adopted. A nonlinear feedback composition controller was designed to track the composition in tray 2 and tray 9 of this distillation column. The actuator and sensor fault detection and isolation system were developed by using a linear sliding mode observer. Fault detection was achieved by comparing the fault estimation signal to a predefined threshold. Fault isolation was achieved by analyzing the maximum value of fault estimation signals and a proposed isolation logic. The control and the fault detection and isolation system were tested via simulation to illustrate the effectiveness of the scheme. The results show that the algorithms provide good performance for the composition control of the process and for actuator and sensor fault detection and isolation. more...
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- 2023
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13. Cyber Security Controls in Nuclear Power Plant by Technical Assessment Methodology
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Daun Jung, Jiho Shin, Chaechang Lee, Kookheui Kwon, and Jung Taek Seo
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Control system security ,industrial control ,nuclear facility regulation ,security ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
With the rapid increase in cyber attacks on industrial control systems, the significance of the application of cyber security controls and the evaluation of security against such attacks has also increased. Among them, cyber attacks on nuclear power plants (NPPs) can cause not only economic loss, but also human casualties. Thus, the application of cyber security controls is necessary for mitigating security threats, especially to NPPs. However, currently, there are limited resources pertaining to information protection, which is essential to uniformly deploy all the controls required to meet cyber security regulations. To overcome this challenge, effective cyber security controls need to be identified and adequate information protection resources must be allocated to each NPP. Although NPPs apply a differential security control according to its characteristics based on NEI 13–10 (Cyber Security Control Assessments), this alone is not only insufficient in reflecting the latest security threats, but also fails to confirm whether the security controls have actually mitigated such threats. To address this challenge, the Electric Power Research Institute (ETRI) developed the technical assessment methodology (TAM), which can be used to generate a quantitative score by assessing the effects of potential cyber attacks on an asset and the relevant security controls. This methodology allows for the application of differential security control based on the score to identify whether the security controls have actually mitigated the risks. Considering this context, the purpose of this paper is to conduct a comparative analysis of the results derived from applying security controls and assessing risks using only NEI 13–10 as well as both NEI 13–10 and TAM on the plant protection system of the nuclear power reactor APR1400. Furthermore, this paper discusses the scopes for subsequent research by addressing the limitations of the TAM and considerations for its use. more...
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- 2023
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14. Application and Requirements of AIoT-Enabled Industrial Control Units
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Nishimura, Everton Hideo, Iano, Yuzo, de Oliveira, Gabriel Gomes, Vaz, Gabriel Caumo, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Iano, Yuzo, editor, Saotome, Osamu, editor, Kemper Vásquez, Guillermo Leopoldo, editor, Cotrim Pezzuto, Claudia, editor, Arthur, Rangel, editor, and Gomes de Oliveira, Gabriel, editor more...
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- 2022
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15. Evaluating Performance of Scalable Fair Clustering Machine Learning Techniques in Detecting Cyber Attacks in Industrial Control Systems
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Handa, Akansha, Semwal, Prabhat, Choo, Kim-Kwang Raymond, editor, and Dehghantanha, Ali, editor
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- 2022
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16. Reliability of Replicated Distributed Control Systems Applications Based on IEC 61499
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Santos, Adriano A., da Silva, António Ferreira, Magalhães, António, de Sousa, Mário, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, di Mare, Francesca, Series Editor, Machado, José, editor, Soares, Filomena, editor, and Yildirim, Sahin, editor more...
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- 2022
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17. Hydraulic Data Preprocessing for Machine Learning-Based Intrusion Detection in Cyber-Physical Systems.
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Mboweni, Ignitious V., Ramotsoela, Daniel T., and Abu-Mahfouz, Adnan M.
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INTRUSION detection systems (Computer security) , *CYBER physical systems , *PROCESS control systems , *INFRASTRUCTURE (Economics) , *WATER distribution , *ENVIRONMENTAL infrastructure , *WATER purification equipment - Abstract
The protection of critical infrastructure such as water treatment and water distribution systems is crucial for a functioning economy. The use of cyber-physical systems in these systems presents numerous vulnerabilities to attackers. To enhance security, intrusion detection systems play a crucial role in limiting damage from successful attacks. Machine learning can enhance security by analysing data patterns, but several attributes of the data can negatively impact the performance of the machine learning model. Data in critical water system infrastructure can be difficult to work with due to their complexity, variability, irregularities, and sensitivity. The data involve various measurements and can vary over time due to changes in environmental conditions and operational changes. Irregular patterns and small changes can have significant impacts on analysis and decision making, requiring effective data preprocessing techniques to handle the complexities and ensure accurate analysis. This paper explores data preprocessing techniques using a water treatment system dataset as a case study and provides preprocessing techniques specific to processing data in industrial control to yield a more informative dataset. The results showed significant improvement in accuracy, F1 score, and time to detection when using the preprocessed dataset. [ABSTRACT FROM AUTHOR] more...
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- 2023
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18. 面向模糊测试的工业控制协议逆向方法研究.
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刘俐媛, 霍朝宾, and 贺敏超
- Abstract
Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...
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- 2023
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19. PCA mix‐based Hotelling's T2 multivariate control charts for intrusion detection system
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Mo Shaohui, Gulanbaier Tuerhong, Mairidan Wushouer, and Tuergen Yibulayin
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computer network security ,data compression ,industrial control ,security of data ,Computer engineering. Computer hardware ,TK7885-7895 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Abstract Most of the data, which is in the field of network intrusion detection, have the characteristics of a mixture of high‐dimensional datasets of continuous and categorical variables. It easily leads the traditional multivariate control chart to get the error detection results. Hotelling's T2 multivariate control charts based on Principal Component Analysis mix (PCA mix) with bootstrap control limit were proposed, and applied to the network intrusion detection system. It was compared with the conventional Hotelling's T2 control chart based on PCA and the performance of the control limits obtained with the bootstrap method was compared to the ones calculated using the most commonly used kernel density estimation. The experimental results revealed that the proposed method had better performance in intrusion detection than its counterparts. more...
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- 2022
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20. Enhanced Interference Management for 6G in-X Subnetworks
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Ramoni Adeogun, Gilberto Berardinelli, and Preben E. Mogensen
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6G ,in-X subnetworks ,industrial control ,ultra-reliable low latency communication (URLLC) ,interference mitigation ,resource allocation ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Short-range low-power 6th generation (6G) in-X subnetworks are proposed as a viable radio concept for supporting extreme communication requirements in emerging applications such as wireless control of robotic arms and control of critical on-body devices, e.g. wireless heart pacemaker. For these applications, ultra-high reliability (e.g., above 6 nines) with sub-ms latency must be guaranteed at all spatio-temporal instants. To meet these requirements, radio systems that are robust against fading and interference are crucial. In this paper, we present a comprehensive investigation on technology enablers and techniques for interference mitigation in 6G in-X subnetworks. We present several techniques including blind-repetition with pseudo-random frequency hopping and environment-aware mechanisms for interference management via dynamic channel allocation. We further propose two novel enhancements viz: (1) repetition order adaptation involving real-time selection of the number of repetitions based on current channel conditions; and (2) anticipatory packet duplication in which each subnetwork duplicates its transmission on a secondary channel group whenever it detects the presence of a potentially harmful neighbouring subnetwork. We perform extensive simulations in an industrial factory environment with mobile in-X subnetworks using models and parameters defined by the 3rd Generation Partnership Project (3GPP). Results show that in-X subnetworks requires large bandwidth (≥ 1 GHz), up to 2 packet repetitions and environment-aware interference coordination in order to support packet loss rate below 10−6 with a latency $ < 100~\mu \text{s}$ . The number of repetitions can however be reduced for systems with survival time greater than the cycle time. The proposed enhancements also result in up to $\times 10^{4}$ packet loss rate reduction for systems with survival time above 2 cycle times. more...
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- 2022
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21. Conceptual Method and Empirical Practice of Building Digital Capability of Industrial Enterprises in the Digital Age.
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Li, Jun, Zhou, Jian, and Cheng, Yu
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DIGITAL communications , *SUPPLY chain management , *INFORMATION & communication technologies , *EMPIRICAL research , *BUSINESS enterprises , *PRODUCTION control - Abstract
Systematically identification and building of digital capability in the Digital Age are of great significance to the obtainment of enterprises’ sustainable competitive advantages. This article first proposes typical application paradigms for the general framework of building digital capability of industrial enterprises in the Digital Age (FBDC) announced as international standard. Then data of over 4100 practical cases of building digital capabilities using FBDC by over 4000 Chinese industrial enterprises are collected. Based on the data, major directions, core implementations of digital capabilities building are systematically demonstrated through statistical analysis. Furthermore, we divide the investigated enterprises into different groups by sector, then organize in-depth research works to illustrate the major directions and typical practices of building digital capabilities in China's raw materials sector, equipment sector, and consumer goods sector. The results show that the major concerns of Chinese industrial enterprises building digital capabilities are centered on six aspects: R&D and innovation, production management and control, supply chain management, financial management and control, business management, and customer service. The results of sector-wise analysis also provide practical reference to industrial enterprises and other stakeholders to identify suitable digital capability based on information and communication technologies application in the Digital Age and efficiently implement the digital capability building process. [ABSTRACT FROM AUTHOR] more...
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- 2022
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22. Design and Implementation of Fuzzy-based Fine-tuning PID Controller for Programmable Logic Controller
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Nguyen, Van-Khanh, Tran, Vy-Khang, Pham, Hai, Nguyen, Hoang-Dung, Nguyen, Chi-Ngon, Nguyen, Van-Khanh, Tran, Vy-Khang, Pham, Hai, Nguyen, Hoang-Dung, and Nguyen, Chi-Ngon
- Abstract
The Proportional-Integral-Derivative (PID) controller, already known for its stability, is widely used in industrial applications and integrated into many Programmable Logic Controllers (PLCs). However, most PLCs do not support the self-tuning mechanism for PID controller parameters. Therefore, users must manually adjust several times to achieve the desired outcomes. This manual adjustment is time-consuming and must be repeated as control object parameters change over time. This study proposed a fine-tuning mechanism for the PID controller’s parameters based on a fuzzy-PD controller. The mechanism was designed and simulated using MATLAB/Simulink on an identified plant, then converted into a Structured Control Language (SCL) code for implementation on the PLC programs. Experimental results on the Siemens S7-1200 PLC demonstrated the proposed mechanism’s effectiveness in stabilizing the thermal plant by adjusting the initial parameters of the integrated PID controller. The system response was more stable, and the overshoot was minimized in comparison with the built-in auto-tuning feature on the S7-1200. Specifically, overshoot decreased to 0.79% from 0.94%, and the setting error declined to 0.1 °C from 0.45 °C. The above results indicate the effectiveness of the proposed self-tuning mechanism when used to improve the quality of PID controllers in PLCs. In addition, due to its ability to self-tuning parameters, it helps users reduce the time required to design PID controllers. more...
- Published
- 2024
23. Modeling Input Data of Control System of a Mining Production Unit Based on ISA-95 Approach
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Semmar, Atae, Machkour, Nadia, Boutaleb, Reda, Bnouachir, Hajar, Medromi, Hicham, Chergui, Meriyem, Deshayes, Laurent, Elouazguiti, Mohamed, Moutaouakkil, Fouad, Zegrari, Mourad, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Hamlich, Mohamed, editor, Bellatreche, Ladjel, editor, Mondal, Anirban, editor, and Ordonez, Carlos, editor more...
- Published
- 2020
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24. Generating Synthetic Data To Solve Industrial Control Problems By Modeling A Belt Conveyor.
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Reutov, Ilya, Moskvin, Denis, Voronova, Alyona, and Venediktov, Maxim
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CONVEYOR belts ,BELT conveyors ,COMPUTER vision ,GRAPHICAL user interfaces ,INDUSTRIAL controls manufacturing ,ELECTRONIC data processing - Abstract
The paper illustrates the process of generating synthetic data used to develop a computer vision industrial control system, based on the simulation of an industrial belt conveyor. The result of this study represents the implementation of a new approach when the process of generating synthetic data is wrapped in completed software with user graphical interface. This software includes modules for loading objects and the conveyor itself. The experiments conducted to test the performance of the software showed its high speed in generating synthetic data relative to the process of real data obtaining. The software developed within the frames of this study has the potential to be improved to the universal tool level due to which it is possible to generate the synthetic data sets from tens and hundred thousands of images by means of adding and modeling digital duplicates of complex industrial objects to solve the tasks not only in the sphere of industrial control but in human-centric computer vision as well. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
- Full Text
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25. Structural Synthesis of PLC Program for Real-Time Specification Patterns.
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Xie, Kai, Wei, Zijian, Yin, Kang, Li, Songsong, Yao, Xinyan, and Zhou, Xiaoyu
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PROGRAMMABLE controllers , *LINGUISTICS , *PROBLEM solving - Abstract
Among all the reactive synthesis methods, the results of the structural synthesis have the best interpretability and traceability. However, existing structural synthesis method cannot deal with real-time specifications and specifications with circular dependencies. A structural synthesis method for Programmable Logic Controller programs is proposed. First, a real-time specification pattern language, RTSPS4Syn, is proposed. Under the condition that there is no circular dependency between the specification items, the implementations of a specification item are assembled structurally from the implementation of its scope and property expression; and the programs for the specification items are concatenated according to the order of priority and dependency to obtain the synthesized program. This paper presents a method to eliminate the circular dependence of specifications, such that the synthesis approach can be applied to specifications with circular dependency. Furthermore, this paper presents an approach for setting the preset values of timers when considering tolerance on the duration of the delay. The synthesis method does not need to check the conflict and determine the nondeterministic part of the specifications. These problems are solved in existing research by constraint solving in PSPACE. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
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26. A Trust Management Method Against Abnormal Behavior of Industrial Control Networks Under Active Defense Architecture.
- Author
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Wang, Jingpei, Zhang, Zhenyong, and Wang, Mufeng
- Abstract
Trusted computing is a typical active defense technology. Trust management is a core support technology of trusted computing. However, when trust management is applied in the industrial control systems, how to identify malicious behavior effectively, model trust relationships, and make a decision based on behavior trustworthiness, meanwhile how to ensure deployed trust mechanism does not affect the control network’s availability, is a significant issue that has not been solved in the previous literature. This paper proposes a trust management method against abnormal behavior of industrial control networks under active defense architecture. Firstly, we review the difficulties of trust management when applied to industrial control networks and analyze abnormal behaviors of the control operations under unknown threats. Then we extract trust information, model the trust relationship of abnormal behaviors, and establish a trust update and decision-making mechanism under the availability constraints of industrial control networks. Furthermore, we provide a deployment method of the proposed trust management in a distributed control network. Finally, we take five typical abnormal operations on control instruction in an industrial control network as an example and perform a detailed analysis and experimental verification of the proposed method. The results prove that the proposed trust management method has good immunity to abnormal behaviors of the control flow and can be deployed in an industrial control system with availability constraints. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
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27. Multi-Agent Dynamic Resource Allocation in 6G in-X Subnetworks with Limited Sensing Information.
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Adeogun, Ramoni and Berardinelli, Gilberto
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RESOURCE allocation , *REINFORCEMENT learning , *SENSES - Abstract
In this paper, we investigate dynamic resource selection in dense deployments of the recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of the minimum capacity per inXS while minimizing overhead from intra-subnetwork signaling. Since inXSs are expected to be autonomous, selection decisions are made by each inXS based on its local information without signaling from other inXSs. A multi-agent Q-learning (MAQL) method based on limited sensing information (SI) is then developed, resulting in low intra-subnetwork SI signaling. We further propose a rule-based algorithm termed Q-Heuristics for performing resource selection based on similar limited information as the MAQL method. We perform simulations with a focus on joint channel and transmit power selection. The results indicate that: (1) appropriate settings of Q-learning parameters lead to fast convergence of the MAQL method even with two-level quantization of the SI, and (2) the proposed MAQL approach has significantly better performance and is more robust to sensing and switching delays than the best baseline heuristic. The proposed Q-Heuristic shows similar performance to the baseline greedy method at the 50th percentile of the per-user capacity and slightly better at lower percentiles. The Q-Heuristic method shows high robustness to sensing interval, quantization threshold and switching delay. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
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28. Automatic system for deformation measurement of anodes in an electrolytic process.
- Author
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delaCalle, F.J., Fernández, A., Lema, D.G., Usamentiaga, R., and García, D.F.
- Subjects
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INDUSTRIAL robots , *MANUFACTURING processes , *COMPUTER vision , *IMAGE processing , *ANODES - Abstract
This paper proposes a novel system for measuring the deformation of anodes automatically in an electrolytic process, eliminating the need for manual intervention. The system employs cameras to acquire lateral perspective images of the anodes. These images are processed using a computer vision algorithm to give measurements of anode deformation, while considering potential errors arising from scene and object geometry. The system's results align with measurements conducted by operators across 71 anodes and were validated over 3900 more anodes in four different locations under different lightning and environmental conditions. This system improves efficiency, by automating a task that was previously carried out manually, and also safety by eliminating the operators need of handling heavy loads and operating in hazardous environments. • The system measures each anode pre- and post-maintenance, avoiding manual checks. • The system prevents handling heavy loads and operating in risky environments. • Tested and validated in real conditions, the system proves its performance. [ABSTRACT FROM AUTHOR] more...
- Published
- 2025
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29. Development of a cognitive mnemonic scheme for an optical Smart-technology of remote learning of the Experions PKS distributed control system on the basis of Artificial Immune Systems
- Author
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G.A. Samigulina and T.I. Samigulin
- Subjects
optical smart-technology ,industrial control ,color ,psychology of perception ,distance learning for people with vision and psycho type ,cognitive mimic ,artificial immune system ,Information theory ,Q350-390 ,Optics. Light ,QC350-467 - Abstract
The article discusses current issues related to the development of an information optical Smart technology for distance learning of Honeywell's distributed Experion PKS control system for the oil and gas industry. About 70 % of industrial accidents are caused by the human factor through the fault of operators. The work of operators consists in monitoring and managing high-tech proc-esses through mnemonic scheme circuits and is characterized by increased tension in the visual apparatus, as well as general fatigue and loss of concentration. The innovative personalized tech-nology of distance learning takes into account the peculiarities of students' vision by adjusting the color supply of educational material and the dynamic presentation of information depending on the person's psychotype and is based on the use of cognitive, optical, multi-agent technologies, as well as ontological and immuno-network approaches. The development of cognitive mnemonic schemes is carried out taking into account these features, which allows one to reduce the load on the visual apparatus and increase the effectiveness of teaching practical skills when working with mnemonic schemes. An artificial immune systems approach is used to predict and evaluate the learning process and promptly adjust the knowledge obtaining process. A modified algorithm for the functioning of a distance learning system based on the use of optimization algorithms for arti-ficial intelligence and an algorithm for immuno-network modeling has been developed. General principles of creating mimic diagrams and existing Honeywell mnemonic schemes are considered. An example of the implementation of the proposed remote technology is presented and results of the simulation of cognitive mnemonic scheme for various categories of students with special needs are discussed. more...
- Published
- 2021
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30. Research on a soft PLC system architecture based on industrial cloud
- Author
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Chen Hai
- Subjects
industrial control ,cloud ,soft plc ,Electronics ,TK7800-8360 - Abstract
In the field of industrial automation, factory control is often performed by Pro-grammable Logic Controller(PLC).With the application of automation becoming more and more miniaturized and flexible, it is uneconomical to use, design and maintain a full hardware-based PLC in these fields. The software PLC based on industrial cloud can become a Control-as-a-Service solution. This paper introduces a multi-instance and scalable soft-PLC system architecture based on industrial cloud.Furthermore,the real-time and scalability of the architecture based on industrial cloud are evaluated. Finally, an outlook to the prospect of cloud-based control scene in future industrial applications is given. more...
- Published
- 2021
- Full Text
- View/download PDF
31. Robust optimal tuning of a reduced active disturbance rejection controller based on first order plus dead time model approximation.
- Author
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Paek, Su-Yong, Kong, Yong-Su, Pak, Song-Ho, Kang, Jong-Su, Yun, Jong-Nam, Kil, Ho-Il, and Hwang, Chol-Jun
- Subjects
- *
PROCESS control systems , *PARTICLE swarm optimization , *CLOSED loop systems , *DIFFERENTIAL evolution , *MANUFACTURING processes - Abstract
The Active Disturbance Rejection Control (ADRC) has been employed in many industrial applications in recent year. One of the key issues when applying ADRC to industrial process control is determining controller parameters. This paper presents a new robust optimal tuning rule for first order reduced ADRC for the industrial processes which can be approximated to First Order Plus Dead Time (FOPDT) model. The tuning rule is derived to achieve a good control performance and robustness of the closed loop system by minimizing the Integrated Absolute Error (IAE) or Integrated Time-weighted Absolute Error (ITAE) under robustness constraints. It is formulated as an optimization problem with the strong nonlinear inequality constraints such as stability margin and maximum sensitivity function constraint. It is difficult to solve these optimization problems analytically. From this, the optimization problem is effectively solved by using the Particle Swarm Optimization - Differential Evolution (PSO-DE) hybrid intelligence algorithm, which is one of the swarm intelligent optimization techniques. The validity of the proposed tuning rule is verified via simulations for the benchmark processes and the temperature control for yeast culture process. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
32. Hydraulic Data Preprocessing for Machine Learning-Based Intrusion Detection in Cyber-Physical Systems
- Author
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Ignitious V. Mboweni, Daniel T. Ramotsoela, and Adnan M. Abu-Mahfouz
- Subjects
critical infrastructure ,critical water system infrastructure ,cyber-physical systems ,data preprocessing ,industrial control ,intrusion detection systems ,Mathematics ,QA1-939 - Abstract
The protection of critical infrastructure such as water treatment and water distribution systems is crucial for a functioning economy. The use of cyber-physical systems in these systems presents numerous vulnerabilities to attackers. To enhance security, intrusion detection systems play a crucial role in limiting damage from successful attacks. Machine learning can enhance security by analysing data patterns, but several attributes of the data can negatively impact the performance of the machine learning model. Data in critical water system infrastructure can be difficult to work with due to their complexity, variability, irregularities, and sensitivity. The data involve various measurements and can vary over time due to changes in environmental conditions and operational changes. Irregular patterns and small changes can have significant impacts on analysis and decision making, requiring effective data preprocessing techniques to handle the complexities and ensure accurate analysis. This paper explores data preprocessing techniques using a water treatment system dataset as a case study and provides preprocessing techniques specific to processing data in industrial control to yield a more informative dataset. The results showed significant improvement in accuracy, F1 score, and time to detection when using the preprocessed dataset. more...
- Published
- 2023
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33. PCA mix‐based Hotelling's T2 multivariate control charts for intrusion detection system.
- Author
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Shaohui, Mo, Tuerhong, Gulanbaier, Wushouer, Mairidan, and Yibulayin, Tuergen
- Subjects
QUALITY control charts ,PROBABILITY density function ,PRINCIPAL components analysis ,COMPUTER network security ,CUSUM technique - Abstract
Most of the data, which is in the field of network intrusion detection, have the characteristics of a mixture of high‐dimensional datasets of continuous and categorical variables. It easily leads the traditional multivariate control chart to get the error detection results. Hotelling's T2 multivariate control charts based on Principal Component Analysis mix (PCA mix) with bootstrap control limit were proposed, and applied to the network intrusion detection system. It was compared with the conventional Hotelling's T2 control chart based on PCA and the performance of the control limits obtained with the bootstrap method was compared to the ones calculated using the most commonly used kernel density estimation. The experimental results revealed that the proposed method had better performance in intrusion detection than its counterparts. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
- Full Text
- View/download PDF
34. TC-Flow: Chain Flow Scheduling for Advanced Industrial Applications in Time-Sensitive Networks.
- Author
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Yang, Dong, Gong, Kai, Ren, Jie, Zhang, Weiting, Wu, Wen, and Zhang, Hongke
- Subjects
- *
PRODUCTION scheduling , *INDUSTRIAL applications , *LINEAR programming , *SCHEDULING , *HEURISTIC programming , *COMPUTER scheduling - Abstract
Time-sensitive networking (TSN) can help standardize deterministic Ethernet across industrial automation. The deterministic guarantee of TSN is based on network resource scheduling in the unit of flow. However, the state-of-the-art TSN single flow scheduling scheme cannot meet the coordinated scheduling requirements of multiple data flows in advanced industrial applications (e.g., control and safety applications). In this article, we propose a TSN chain flow abstraction, TC-Flow, for a coordinated multiple-flow scheduling model in industrial control and safety applications. Based on the proposed TC-Flow model, we design an offline TC-Flow scheduling algorithm using integer linear programming and an online heuristic TC-Flow scheduling algorithm to handle network dynamics. To deploy the proposed TC-Flow model and scheduling algorithms in the TSN, we design a CF-TSN network architecture that is compatible with the existing TSN single-flow scheduling scheme. Finally, we implement the proposed CF-TSN architecture and TC-Flow scheduling algorithms in real-world network environments. Experimental results show that the proposed scheduling algorithms can increase the number of schedulable flows by 26 percent compared to the state-of-the-art TSN scheduling benchmark. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
- Full Text
- View/download PDF
35. Deep Learning Based Intelligent Industrial Fault Diagnosis Model.
- Author
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Surendran, R., Khalaf, Osamah Ibrahim, and Tavera Romero, Carlos Andres
- Subjects
DEEP learning ,FAULT diagnosis ,ROTATING machinery ,FEATURE extraction ,SIGNAL processing ,WAVELET transforms ,DIAGNOSIS ,INDUSTRIAL revolution - Abstract
In the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven and monitoring approaches for achieving quick, trustable, and high-quality analysis in an automated way. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The advent of deep learning (DL) methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals. This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. The proposed model operates on three major processes namely signal representation, feature extraction, and classification. The proposed model uses a Continuous Wavelet Transform (CWT) is for preprocessed representation of the original vibration signal. In addition, Inception with ResNet v2 based feature extraction model is applied to generate high-level features. Besides, the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer. Finally, a multilayer perceptron (MLP) is applied as a classification technique to diagnose the faults proficiently. Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset. The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6% and 99.64% on the applied gearbox dataset and bearing dataset. The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
- Full Text
- View/download PDF
36. Assessment of reinforcement learning applications for industrial control based on complexity measures.
- Author
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Grothoff, Julian, Camargo Torres, Nicolas, and Kleinert, Tobias
- Subjects
REINFORCEMENT learning ,COLD rolling ,MACHINE learning ,INDUSTRIAL applications ,MAINTAINABILITY (Engineering) ,ROLLING-mills - Abstract
Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...
- Published
- 2022
- Full Text
- View/download PDF
37. Deep Learning-Guided Production Quality Estimation for Virtual Environment-Based Applications
- Author
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Akm Ashiquzzaman, Hyunmin Lee, Tai-Won Um, Kwangki Kim, Hye-Young Kim, and Jinsul Kim
- Subjects
data rebalancing ,deep learning ,ensemble Learning ,industrial control ,information management ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
In modern smart factories, quality estimation is vital for maximum productivity. However, quality estimation by definition relies on an imbalanced dataset, as most smart factories are highly efficient. In this research, we propose a guided quality estimation system that can recognize faulty data among a highly imbalanced production dataset. We also propose a customized LSTM model that is trained to ensure high accuracy in the quality estimation system. This is achieved by our proposed batch-wise balanced training method. Moreover, traditional means of evaluation for this type of method are not suitable, again due to the highly imbalanced nature of the dataset. Thus, a proper evaluation metric is also discussed. The proposed customized LSTM model with custom batch-wise SMOTE + ENN achieved 99.9% accuracy with an f1 score of 95%. This new proposed method for the imbalanced smart factory quality estimation will improve drastically and give pathway to more improved quality. Finally, we discuss practical implementation for the edge server consisting of the proposed guided production estimation system and real-time visualization. Feasibility analysis of this virtual environment-based application of the proposed framework ensured low computational overhead and faster processing. more...
- Published
- 2020
- Full Text
- View/download PDF
38. A Survey on Industrial Internet With ISA100 Wireless
- Author
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Theofanis P. Raptis, Andrea Passarella, and Marco Conti
- Subjects
Industry 4.0 ,industrial internet ,industrial control ,Internet of Things ,manufacturing automation ,process control ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
We present a detailed survey of the literature on the ISA100 Wireless industrial Internet standard (also known as ISA100.11a or IEC 62734). ISA100 Wireless is the IEEE 802.15.4-compatible wireless networking standard “Wireless Systems for Industrial Automation: Process Control and Related Applications”. It features technologies such as 6LoWPAN, which renders it ideal for industrial Internet edge applications. The survey focuses on the state of the art research results in the frame of ISA100 Wireless from a holistic point of view, including aspects like communication optimization, routing mechanisms, real-time control, energy management and security. Additionally, we present a set of reference works on the related deployments around the globe (experimental testbeds and real-terrain installations), as well as of the comparison to and co-existence with another highly relevant industrial standard, WirelessHART. We conclude by discussing a set of open research challenges. more...
- Published
- 2020
- Full Text
- View/download PDF
39. Cloud Model for Security State Recognition Based on Factor Space.
- Author
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Yang, Li, Qin, Hongmei, Zhang, Jian, Su, Huawen, Li, Guoshu, and Bai, Sichang
- Abstract
With the integration of informatization and industrialization, the oil-gas industry control system is currently confronting huge security risks. To more effectively assess and predict the security state of industry control systems, this study proposed a cloud model based on factor space for security state recognition. First, the malicious behavior factors of oil-gas industry control system were obtained and were described in the factor state space. Next, the expectation, entropy and hyper entropy of each factor were constructed to convert the fuzzy concepts in the cloud model into quantitative value. Finally, based on the forward and reverse generators, single-condition, multi-condition and multi-rule cloud generators were applied to rule-based reasoning model to realize the transformation from expected value to qualitative value. Experimental simulation demonstrates that cloud-reasoning model based on the factor space can predict the impact of unknown malicious program behavior on the security state and achieve a better evaluation effect. Moreover, this study can provide a new approach to the security state recognition of industrial control systems. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
- Full Text
- View/download PDF
40. Reliable Minimum Cycle Time of 5G NR Based on Data-Driven Channel Characterization.
- Author
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Jiang, Xiaolin, Luvisotto, Michele, Pang, Zhibo, and Fischione, Carlo
- Abstract
Wireless communication is evolving to support critical control in automation systems. The fifth-generation (5G) mobile network air interface New Radio adopts a scalable numerology and mini-slot transmission for short packets that make it potentially suitable for critical control systems. The reliable minimum cycle time is an important indicator for industrial communication techniques but has not yet been investigated within 5G. To address such a question, this article considers 5G-based industrial networks and uses the delay optimization based on data-driven channel characterization (CCDO) approach to propose a method to evaluate the reliable minimum cycle time of 5G. Numerical results in three representative industrial environments indicate that following the CCDO approach, 5G-based industrial networks can achieve, in real-world scenario, millisecond-level minimum cycle time to support several hundred nodes with reliability higher than 99.9999%. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
- Full Text
- View/download PDF
41. Beam-Steered Optical Wireless Communication for Industry 4.0.
- Author
-
Koonen, Ton, Mekonnen, Ketemaw Addis, Cao, Zizheng, Huijskens, Frans, Pham, Ngoc Quan, and Tangdiongga, Eduward
- Abstract
By means of narrow 2D-steerable infrared beams, interference-free individual wireless connections to densely-spaced devices in industry 4.0 settings can be made. Beam steering by fiber-fed passive diffractive beam steering modules and wavelength-tunable transceivers located in a separate controller location enable readily scalable connectivity to many devices and their nomadic mobility. To accommodate the dynamics in industry 4.0 settings, self-calibrating localization of the devices by means of retro-reflecting corner cube foils and beam scanning is demonstrated, and a low-complexity broadband wide field-of-view optical wireless receiver is presented, based on a matrix of photodiodes. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
- Full Text
- View/download PDF
42. Reinforcement learning methods based on GPU accelerated industrial control hardware.
- Author
-
Schmidt, Alexander, Schellroth, Florian, Fischer, Marc, Allimant, Lukas, and Riedel, Oliver
- Subjects
- *
REINFORCEMENT learning , *CONVOLUTIONAL neural networks , *MANUFACTURING processes - Abstract
Reinforcement learning is a promising approach for manufacturing processes. Process knowledge can be gained automatically, and autonomous tuning of control is possible. However, the use of reinforcement learning in a production environment imposes specific requirements that must be met for a successful application. This article defines those requirements and evaluates three reinforcement learning methods to explore their applicability. The results show that convolutional neural networks are computationally heavy and violate the real-time execution requirements. A new architecture is presented and validated that allows using GPU-based hardware acceleration while meeting the real-time execution requirements. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
- Full Text
- View/download PDF
43. Data Acquisition System of the CLOUD Experiment at CERN.
- Author
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Weber, Stefan K., Miotto, Giovanna Lehmann, Almeida, Joao, Blanc, Pascal Herve, Dias, Antonio, Malaguti, Giulio, Manninen, Hanna E., Pfeifer, Joschka, Ravat, Sylvain, Onnela, Antti, Mathot, Serge, Kirkby, Jasper, Tome, Antonio, and Amorim, Antonio more...
- Subjects
- *
DATA acquisition systems , *ATMOSPHERIC nucleation , *CLOUD droplets , *DATABASES , *WEATHER , *NUCLEAR research , *ACQUISITION of data - Abstract
The Cosmics Leaving OUtdoor Droplets (CLOUD) experiment at the European Organization for Nuclear Research (CERN) is investigating the nucleation and growth of aerosol particles under atmospheric conditions and their activation into cloud droplets. The experiment comprises an ultraclean 26 m3 chamber and its associated systems (the CLOUD facility) together with a suite of around 50 advanced instruments attached to the chamber via sampling probes to analyze its contents. The set of instruments changes for each experimental campaign according to the scientific goals. The central function of the CLOUD DAQ (data acquisition) system is to combine the data from these autonomous and inhomogeneous instruments into a single, integrated CLOUD experiment database. The DAQ system needs to be highly adaptable to allow a fast setup over a single installation week at the start of each campaign when the instruments are brought to CERN and installed at the CLOUD chamber. Each campaign requires high flexibility and fast response to changes in instrument configuration or experimental parameters. The experiments require online monitoring of the physical and chemical measurements with delays of only a few seconds. In addition, the raw data, the monitoring databases, and the processed data must be archived and provided to the international collaboration for both real-time and later analyses. We will describe the various components of the CLOUD DAQ and computing infrastructure, together with the reasons for the chosen solutions. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
- Full Text
- View/download PDF
44. FEATURES FORMALIZATION TASKS OF FORMATION OF SOLUTIONS FOR FUNCTIONAL AND PRODUCTION MANAGEMENT INDUSTRIAL ENTERPRISES IN THE MARKET
- Author
-
S. Logvinov
- Subjects
industrial plants ,industrial control ,simulation ,manufacturing processes ,optimization ,network models ,Sociology (General) ,HM401-1281 ,Economics as a science ,HB71-74 - Abstract
The article deals with issues related to the characteristics of the formalization of tasks of forming solutions for functional and production management of industrial enterprises in market conditions. Particular attention is paid to the economic and mathematical modeling of structural and manufacturing processes in industrial plants. more...
- Published
- 2019
45. On the Generation of Anomaly Detection Datasets in Industrial Control Systems
- Author
-
Angel Luis Perales Gomez, Lorenzo Fernandez Maimo, Alberto Huertas Celdran, Felix J. Garcia Clemente, Cristian Cadenas Sarmiento, Carlos Javier Del Canto Masa, and Ruben Mendez Nistal
- Subjects
Anomaly detection ,critical infrastructures ,industrial control ,industrial control systems ,industry applications ,machine learning ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
In recent decades, Industrial Control Systems (ICS) have been affected by heterogeneous cyberattacks that have a huge impact on the physical world and the people's safety. Nowadays, the techniques achieving the best performance in the detection of cyber anomalies are based on Machine Learning and, more recently, Deep Learning. Due to the incipient stage of cybersecurity research in ICS, the availability of datasets enabling the evaluation of anomaly detection techniques is insufficient. In this paper, we propose a methodology to generate reliable anomaly detection datasets in ICS that consists of four steps: attacks selection, attacks deployment, traffic capture and features computation. The proposed methodology has been used to generate the Electra Dataset, whose main goal is the evaluation of cybersecurity techniques in an electric traction substation used in the railway industry. Using the Electra dataset, we train several Machine Learning and Deep Learning models to detect anomalies in ICS and the performed experiments show that the models have high precision and, therefore, demonstrate the suitability of our dataset for use in production systems. more...
- Published
- 2019
- Full Text
- View/download PDF
46. LSTM-Based Wastewater Treatment Plants Operation Strategies for Effluent Quality Improvement
- Author
-
Ivan Pisa, Ignacio Santin, Antoni Morell, Jose Lopez Vicario, and Ramon Vilanova
- Subjects
Artificial neural networks ,BSM2 framework ,industrial control ,violation reduction ,water pollution ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Wastewater Treatment Plants (WWTPs) are facilities devoted to managing and reducing the pollutant concentrations present in the urban residual waters. Some of them consist in nitrogen and phosphorus derived products which are harmful for the environment. Consequently, certain constraints are applied to pollutant concentrations in order to make sure that treated waters comply with the established regulations. In that sense, efforts have been applied to the development of control strategies that help in the pollutant reduction tasks. Furthermore, the appearance of Artificial Neural Networks (ANNs) has encouraged the adoption of predictive control strategies. In such a fashion, this work is mainly focused on the adoption and development of them to actuate over the pollutant concentrations only when predictions of effluents determine that violations will be produced. In that manner, the overall WWTP's operational costs can be reduced. Predictions are generated by means of an ANN-based Soft-Sensor which adopts Long-Short Term Memory cells to predict effluent pollutant levels. These are the ammonium (SNH,e) and the total nitrogen (SNtot,e) which are predicted considering influent parameters such as the ammonium concentration at the entrance of the WWTP reactor tanks (SNH,po), the reactors' input flow rate (Qpo), the WWTP recirculation rate (Qa) and the environmental temperature (Tas). Moreover, this work presents a new multi-objective control scenario which consists in a unique control structure performing the reduction of SNH,e and SNtot,e concentrations simultaneously. Performance of this new control approach is contrasted with other strategies to determine the improvement provided by the ANN-based Soft-Sensor as well as by the fact of being controlling two pollutants at the same time. Results show that some brief and small violations are still produced. Nevertheless, an improvement in the WWTPs performance w.r.t. the most common control strategies around 96.58% and 98.31% is achieved for SNH,e and SNtot,e, respectively. more...
- Published
- 2019
- Full Text
- View/download PDF
47. Multi-Agent Dynamic Resource Allocation in 6G in-X Subnetworks with Limited Sensing Information
- Author
-
Ramoni Adeogun and Gilberto Berardinelli
- Subjects
6G ,reinforcement learning ,in-X subnetworks ,resource allocation ,Q-learning ,industrial control ,Chemical technology ,TP1-1185 - Abstract
In this paper, we investigate dynamic resource selection in dense deployments of the recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of the minimum capacity per inXS while minimizing overhead from intra-subnetwork signaling. Since inXSs are expected to be autonomous, selection decisions are made by each inXS based on its local information without signaling from other inXSs. A multi-agent Q-learning (MAQL) method based on limited sensing information (SI) is then developed, resulting in low intra-subnetwork SI signaling. We further propose a rule-based algorithm termed Q-Heuristics for performing resource selection based on similar limited information as the MAQL method. We perform simulations with a focus on joint channel and transmit power selection. The results indicate that: (1) appropriate settings of Q-learning parameters lead to fast convergence of the MAQL method even with two-level quantization of the SI, and (2) the proposed MAQL approach has significantly better performance and is more robust to sensing and switching delays than the best baseline heuristic. The proposed Q-Heuristic shows similar performance to the baseline greedy method at the 50th percentile of the per-user capacity and slightly better at lower percentiles. The Q-Heuristic method shows high robustness to sensing interval, quantization threshold and switching delay. more...
- Published
- 2022
- Full Text
- View/download PDF
48. Securing the Internet of Things
- Author
-
Dorey, Paul, Mayes, Keith, editor, and Markantonakis, Konstantinos, editor
- Published
- 2017
- Full Text
- View/download PDF
49. Efficient and Lightweight Data Streaming Authentication in Industrial Control and Automation Systems.
- Author
-
Xu, Jian, Meng, Qingyu, Wu, Jun, Zheng, James Xi, Zhang, Xuyun, and Sharma, Suraj
- Abstract
The industrial control and automation systems have played an increasingly important role in critical manufacturing processes. In such systems, many Internet of Things devices continuously collect large number of streaming data for real-time processing. Verifiable data streaming (VDS) addresses such authenticity issue for streaming data, but most VDS schemes are not efficient and lightweight, do not support range querying, and cannot be used in practice. To improve the efficiency and achieve a verifiable range query in data streaming, we present here a new primitive, namely, a chameleon authentication tree with prefixes (PCAT), which is extended from the PBTree and chameleon authentication tree. Our scheme is not only lightweight but also supports dynamic expansion and verifiable range query in data streaming, making it more suitable for resource-constrained devices. We separate the PCAT's algorithms into the following phases: initialization, data appending, query, and verification. Our analyses prove that the PCAT satisfies all the security requirements of VDS. Moreover, an efficiency analysis and performance evaluation demonstrate that our scheme not only supports lightweight data streaming authentication but also has high efficiency, which means that the PCAT is easier to apply in the industrial control and automation systems. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
- Full Text
- View/download PDF
50. VISE: Combining Intel SGX and Homomorphic Encryption for Cloud Industrial Control Systems.
- Author
-
Coppolino, Luigi, D'Antonio, Salvatore, Formicola, Valerio, Mazzeo, Giovanni, and Romano, Luigi
- Subjects
- *
INDUSTRIAL controls manufacturing , *BLOCK ciphers , *ELECTRONIC data processing - Abstract
Protecting data-in-use from privileged attackers is challenging. New CPU extensions (notably: Intel SGX) and cryptographic techniques (specifically: Homomorphic Encryption) can guarantee privacy even in untrusted third-party systems. HE allows sensitive processing on ciphered data. However, it is affected by i) a dramatic ciphertext expansion making HE unusable when bandwidth is narrow, ii) unverifiable conditional variables requiring off-premises support. Intel SGX allows sensitive processing in a secure enclave. Unfortunately, it is i) strictly bonded to the hosting server making SGX unusable when the live migration of cloud VMs/Containers is desirable, ii) limited in terms of usable memory, which is in contrast with resource-consuming data processing. In this article, we propose the VIrtual Secure Enclave (VISE), an approach that effectively combines the two aforementioned techniques, to overcome their limitations and ultimately make them usable in a typical cloud setup. VISE moves the execution of sensitive HE primitives (e.g., encryption) to the cloud in a remotely attested SGX enclave, and then performs sensitive processing on HE data–outside the enclave–leveraging all the memory resources available. We demonstrate that VISE meets the challenging security and performance requirements of a substantial application in the Industrial Control Systems domain. Our experiments prove the practicability of the proposed solution. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
- Full Text
- View/download PDF
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