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Automatic Temperature control of Blast Furnace Based on Data Analysis Model 2

Automatic Temperature Control of Blast Furnace Based on Data...

引用

2nd international conference on Artificial Intelligence, automation, and High-Performance computing, AIAHPC 2022

作者： Wang, Xijuan Feng, Jingxiao School of Physics and Electronic Information Luoyang Normal University Luoyang471000 China

ISBN: (纸本)9781510657717

The heating process of the blast furnace is a complex and huge controlled object, which has the characteristics of nonlinear, multivariable, distributed parameters, fast and slow processes intertwined. It is impossible to achieve satisfactory control results only by traditional control. With the development of artificial intelligence in recent years, neural networks, expert technology, fuzzy and predictive control provide new ideas for the heating control of the blast furnace. In this paper, based on the deep learning framework of artificial intelligence, the LSTM algorithm is proposed. Based on the massive production data, through the model simulation comparison, the results show that the LSTM network modeling has stronger generalization ability, smaller error, and higher accuracy. The LSTM neural network algorithm can effectively deal with the characteristics of time series data, provide guidance for the production practice, and lay a good foundation for the predictive control of blast furnaces. © 2022 SPIE. All rights reserved.

关键词： Blast furnaces

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Integrated Data-Driven Framework for Automatic controller Tuning with Setpoint Stabilization Through Reinforcement Learning 19

Integrated Data-Driven Framework for Automatic Controller Tu...

引用

19th international conference on Software Technologies, ICSOFT 2024

作者： Mohajer, Babak Singh, Neelaksh Liebeskind, Joram BELIMO Automation AG Brunnenbachstrasse 1 Hinwil8340 Switzerland

ISBN: (纸本)9789897587061

We introduce a three-stage framework for designing an optimal controller. First, we apply offline black-box optimization algorithms to find optimal controller parameters based on a heuristically chosen setpoint profile and a novel cost function for penalizing control signal oscillations and direction changes. Then, we leverage cloud data to generate device-specific setpoint profiles and tune the controller parameters to perform well on the device with respect to the same cost function. Finally, we train a control policy on top of the offline tuned controller after deployment on device through an online learning algorithm to handle unseen setpoint variations. A novel reward function encouraging setpoint stabilization is added for preventing destabilization from coupling effects. Bayesian Optimization and Nelder-Mead methods are used for offline optimization, and a state-of-the-art model free Reinforcement Learning algorithm namely Soft Actor-Critic is used for online optimization. We validate our framework using a realistic HVAC hydraulic circuit simulation. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Reinforcement learning

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Insider Threat Detection Using Bidirectional Encoder Representations from Transformers and Optimized AdaBoost Classifier

Insider Threat Detection Using Bidirectional Encoder Represe...

引用

2024 international conference on Circuit, Systems and communication, ICCSC 2024

作者： Kumpf, Katarina Protic, Mihajlo Jovanovic, Luka Cajic, Miroslav Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Faculty of Informatics and Computing Belgrade Serbia University 'Union Nikola Tesla' Faculty of Information Technology and Engineering Belgrade Serbia

ISBN: (纸本)9798350365306

Cybersecurity plays an increasingly vital role in contemporary times, particularly as large companies face escalating threats and substantial losses, with ransomware attacks standing out as one of the more sophisticated forms of intrusion. In such attacks, hackers infiltrate systems, encrypt crucial company data, and then demand a ransom for data decryption or the restoration of control over the system. The application of natural language processing (NLP) emerges as a promising avenue, offering the potential to analyze attack patterns and recognize malicious intentions before actual attempts occur. Given the inherent specificity of each system, fine-tuning AI algorithms with special settings becomes imperative for optimal efficiency. This research delves into the synergy of NLP and robust classification algorithms to detect cyber attacks through website content analysis. Recognizing that classifier performance hinges on the judicious selection of hyperparameters, we explore a modified version of the recently introduced red fox algorithm (RFO). This adaptation is combined with bidirectional encoder representations from transformers to identify malicious intent in email content. The proposed approach undergoes testing on a real-world dataset, with the best models demonstrating an accuracy of 0.975822 suggesting viability. © 2024 IEEE.

关键词： Natural language processing systems

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Mapping of Newcomer Clients in Federated Learning Based on Activation Strength 32nd

Mapping of Newcomer Clients in Federated Learning Based on A...

引用

32nd international conference on Flexible automation and Intelligent Manufacturing

作者： Legler, Tatjana Hegiste, Vinit Ruskowski, Martin RPTU Kaiserslautern Landau Inst Machine Tools & Control Syst WSKL Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany Technol Initiat SmartFactory KL eV Kaiserslautern Germany

ISBN: (纸本)9783031381645;9783031381652

Federated learning is a collaborative machine learning approach that allows multiple parties to train a model without exchanging sensitive data. In manufacturing, where different parties may have proprietary or sensitive data that cannot be shared, this is especially useful. However, traditional federated learning approaches (as proposed by McMahan et al.) do not consider the differences in data and computing resources across different parties, leading to sub-optimal model performance. Personalized federated learning addresses this issue by allowing each party to contribute to the model training according to its specific data and resources. Furthermore, most common approaches only consider a limited set of data and a short period of time, without considering the system's long-term usefulness. It is important to consider the integration of new clients and the continuous change of data, which could result in the addition of new classes. This paper will explore the potential of federated learning in manufacturing and present a flexible and expandable approach, focusing on mapping newcomer clients based on activation strength of weights.

关键词： Federated Learning Continual Learning Multi-Headed Neural Networks

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NILM-Based Solutions for the automation of Energy Services in Residential Buildings 13

NILM-Based Solutions for the Automation of Energy Services i...

引用

13th international conference on Smart Cities and Green ICT Systems, SMARTGREENS 2024

作者： Ferrez, Pierre Gabioud, Dominique Roduit, Pierre Institute of Sustainable Energy University of Applied Sciences and Arts Western Switzerland Rue de l'industrie 23 Sion1950 Switzerland

ISBN: (纸本)9789897587023

In Switzerland, about 40 % (90 TWh) of the energy needs are due to buildings and about 70 % of these needs come from heating. Therefore, improving the efficiency of buildings has a high potential for energy savings. This paper presents innovative solutions for the automation of energy services in residential buildings, based solely on the disaggregation of the centralized electricity consumption measurement of the household. The developed method was successfully used on electricity consumption data recorded in 14 households over a year to (1) extract the heat pump consumption from the aggregated consumption, (2) split the heat pump consumption into space heating and domestic hot water consumption and (3) split the remaining consumption into four categories: base load, low power, medium power, and high power. With a daily average error on the prediction of the heat pump consumption below 3.5 % and daily average errors on the prediction of the number of cycles and the operating time of the heat pump both around 1 %, the described method can be used for the development of energy service prototypes allowing to better understand and optimize the energy functioning of residential buildings with potential savings for the residents. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Energy efficiency

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IoT Based Intruder Detection System Integrated with Chatbot for Authentication 5

IoT Based Intruder Detection System Integrated with Chatbot ...

引用

5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Edward Kennady, L. Bethanny Janney, J. Bernatin, T. Sathyabama Institute of Science and Technology Department of Electronics and Communication Chennai India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350321425

Internet of Things (IoT) enables connectivity and interoperability between users' devices, systems, services, networks, and especially control systems. Smart video surveillance is an IOT application as it uses the internet for a variety of functions. IOT-based Intruder Detection System (IDS) is the latest technology used in a wide range of places. It serves as a remote eye for the security force. In the existing technique, the AdaBoost algorithm needs a quality data set. And also, noisy data and outliershave to be avoided before adopting this algorithm. So, in this study, machine learning techniques such as the Haar-Cascade and local binary pattern histogram algorithms are used to overcome the above disadvantage to clearly detect the face. The camera module captures an image of a person as soon as the smart home switch is pressed, sends the still image to the Android device through the Telegram app, and alerts the owner about the person who is trying to enter through the door. The entire systems programming has been established in Python and Embedded C for a controller. © 2023 IEEE.

关键词： Machine learning

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Safeguarding Industrial automation: A Fuzzing Framework for PLC control Logic

Safeguarding Industrial Automation: A Fuzzing Framework for ...

引用

international conference on computing, Networking and communications (ICNC)

作者： Marie Louise Uwibambe Qinghua Li Department of Electrical Engineering and Computer Science University of Arkansas

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

As Programmable Logic controllers (PLCs) become more integrated into complex industrial systems and networks, their software components face increased security vulnerabilities. Fuzz testing, which uses unexpected inputs to find vulnerabilities, has been used to enhance PLC security but is primarily focused on communication protocols, often overlooking control logic. This paper presents a fuzzing framework for PLC control logic programs. The framework is designed to handle control logic’s unique execution process, which may involve translating PLC languages into general-purpose languages. It generates structurally valid test cases and is platform-independent. We implement our prototype around the OpenPLC environment and expand our experiments to a Wago PLC image to evaluate the feasibility of our method on real-world devices. Our approach exhibits strong performance in terms of code coverage, test case pass rate, and crash detection, while ensuring high time efficiency.

关键词： Performance evaluation Protocols Programmable logic devices Prototypes Process control Fuzzing Software Logic Security Faces

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Experimental research on Assisted control of Lower limb exoskeleton Robot 2

Experimental research on Assisted Control of Lower limb exos...

引用

2nd international conference on Artificial Intelligence, automation, and High-Performance computing, AIAHPC 2022

作者： Zhou, Xiong Yu, Zhipeng Wang, Meiling Ye, Xiaodong Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510657717

With the rapid increase of aging population in China, more and more power-assisted devices need to be used. In this paper, aiming at the walking problem of the elderly or the disabled, a lower limb exoskeleton assisted robot based on motor drive is designed. This article first briefly introduces the mechanical structure, hardware system and software control system of the lower limb exoskeleton, and then proposes the assist control algorithm based on the dynamic model of the lower limb exoskeleton robot. Plantar pressure is used as the metrics to evaluate the effectiveness of the power control algorithm. Experimental results verify the effectiveness of our proposed assisted control algorithm. Experimental results show that the proposed algorithm can significantly reduce the plantar pressure of the wearer, which verified the effectiveness of the assisted control algorithm. © 2022 SPIE. All rights reserved.

关键词： Robots

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Design and Implementation of Power Dispatching automation System Based on Deep Learning 5

Design and Implementation of Power Dispatching Automation Sy...

引用

5th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Shao, Mingchun Jia, Peng Wang, Chen Jin, Yiqi State Grid Henan Energy Interconnection Electric Power Design Institute Co. Ltd Zhengzhou450000 China State Grid Henan Economic Research Institute. Ltd Zhengzhou450000 China State Grid Zhengzhou Electric Power Supply Company Zhengzhou450000 China

ISBN: (纸本)9798350310603

The power prediction, state estimation and fault diagnosis methods based on deep learning are introduced. Take the cloud platform of electric power automation as the analysis object to study its architecture. The modified particle swarm optimization algorithm is combined with the structural topology of the cloud resource scheduling model to establish three-level data nodes. A new scheduling method for deep learning cloud computing system based on particle swarm optimization is proposed. By analyzing the information of primary equipment, secondary equipment and surrounding environment, the "dual core"model of power network is constructed. The real-time monitoring and forecasting of power system are carried out in the forecasting subsystem. Finally, through the analysis of the application case of artificial intelligent control system, the system has a wide range of applications. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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Autonomous Industrial Rovers

Autonomous Industrial Rovers

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Advancements in Electrical, Electronics, communication, computing and automation (ICAECA), international conference on

作者： Shivappriya SN Vijay M Vasanthan L Yogesh Adhithya A Hari Priya A Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

To automate the material handling duties in industries the Autonomous Industrial Rovers (AIRs) with the emerging techniques like Collaborative robots (COBOTS), indoor localization techniques, and Light Detection and Ranging (LIDAR) plays a vital role. COBOTS can communicate and collaborate with human workers, adjusting to changes in the environment in real time. The indoor localization technology establishes precise location within a space. LIDAR allows the rovers to map their environments in real time, detecting obstacles and navigating with extreme precision. The robots can detect and avoid impairments because LIDAR can create 3D maps of the surroundings. This ensures smooth operations even in congested regions or when they experience unanticipated changes in their surroundings, clients, and environment. The Master-Slave configurations of these robots enable them to work synchronized and uniform. This AIR provides enhanced efficiency, accuracy and safety by lowering reliance on human operators for repetitive, physically taxing activities.

关键词： Location awareness Laser radar automation Simultaneous localization and mapping Accuracy Three-dimensional displays Materials handling Collaborative robots Real-time systems Safety

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