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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bauer, Waldemar Zagórowska, Marta Baranowski, Jerzy Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9781665464543

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided. © 2024 IEEE.

关键词： Damage detection

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Challenges in AI-supported Process Analysis in the Italian Judicial System: what After Digitalization?

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Digital Government: Research and Practice 2024年第1期5卷 1-10页

作者： Bianchini, Devis Bono, Carlo Campi, Alessandro Cappiello, Cinzia Ceri, Stefano De Luzi, Francesca Mecella, Massimo Pernici, Barbara Plebani, Pierluigi Dept. of Computer Science University of Brescia Via Branze 38 Brescia25123 Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Piazza Leonardo da Vinci 32 Milan20133 Italy Dept. of Computer Control and Management Engineering Sapienza Universitá di Roma Via Ariosto 25 Roma00185 Italy

In this commentary article, we outline research challenges and possible directions for the potential applications of AI in the judicial domain by specifically considering process analysis in the Italian context. Applying AI to process analysis poses several challenges, including information extraction from legacy information systems and analysis of legal documents, process modeling with a particular emphasis on temporal analysis, real-time process monitoring, conformance and compliance checking, predictive techniques for accurate predictions, and analysis of judges' workload. Solutions to these challenges include methods and tools for data identification and collection, innovative approaches to process modeling, reactive techniques for real-time monitoring, conformance checking with explainability, language models adapted to specific domains, and the identification of suitable indicators for the analysis of case handling efficiency and case classification. Copyright © 2024 held by the owner/author(s).

关键词： Process monitoring

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Game-based Resource Allocation for Secure UAV Communication in Wireless Networks 15

Game-based Resource Allocation for Secure UAV Communication ...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Kakkar, Riya Gupta, Rajesh Agrawal, Smita Tanwar, Sudeep Pricop, Emil Inst. of Tech. Nirma University Dept. of Computer Science and Engg. Ahmedabad India Petroleum-Gas University of Ploiesti Automatic Control Comp. & Elect. Dept. Ploieşti Romania

ISBN: (纸本)9798350321388

Unmanned aerial vehicles (UAVs) are one of the applications of device-to-device (D2D) technology, where two UAVs can communicate with or without the existence of a base station. It facilitates high content delivery, low latency, low cost, and high data rate communication in wireless networks. Despite the aforementioned benefits, an eavesdropping attack is one of the critical challenges that cause interference in UAV communication in the presence of an eavesdropper. To mitigate the aforementioned challenges, we proposed a secure resource allocation for UAV communication in wireless networks. The applied single carrier orthogonal frequency division multiple access (SC-OFDMA) technique utilize the orthogonality feature to provide secure UAV communication in wireless networks. Moreover, a zero-sum game approach is considered to enable the secure and optimal resource allocation for UAV communication. The main aim of the zero-sum game theory is to maximize the communication channel's data rate and secrecy capacity to achieve a secure and optimal UAV communication scenario in wireless networks. Finally, the performance and simulation of the proposed approach are evaluated against the existing random approach in terms of various metrics such as data rate and secrecy capacity of the communication channel. © 2023 IEEE.

关键词： Frequency division multiple access

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Home Care Assistance Solution based on a Software Multi-Agents System 24

Home Care Assistance Solution based on a Software Multi-Agen...

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Rasras, Mohammad Marin, Iuliana Radu, Serban University Politehnica of Bucharest Doctoral School of Automatic Control and Computers Bucharest Romania University Politehnica of Bucharest Dept. of Engineering in Foreign Languages Bucharest Romania University Politehnica of Bucharest Dept. of Computer Science Bucharest Romania

ISBN: (纸本)9798350313390

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ' age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home's residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies. © 2023 IEEE.

关键词： Multi agent systems

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Zero-Load: A Zero Touch Network based router management scheme underlying 6G-IoT ecosystems 15

Zero-Load: A Zero Touch Network based router management sche...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Bhattacharya, Pronaya Mukherjee, Anwesha Tanwar, Sudeep Pricop, Emil School of Eng. and Tech. Amity University Dept. of Computer Science and Engg. Kolkata India Mahishadal Raj College Dept. of Computer Science West Bengal Mahishadal India Inst. of Tech. Nirma University Dept. of Computer Science and Engg. Ahmedabad India Petroleum-Gas University of Ploiesti Automatic Control Comp. & Elect. Dept. Ploiesti Romania

ISBN: (纸本)9798350321388

The rising data volumes force significant bottlenecks on the 6G for IoT (6G-IoT) network management functions, which limits the control, flexibility, and interoperability among devices, protocols, and end applications. Solutions like software-defined networking (SDN), and network function virtualization (NFV) are proposed with 6G, but the core management operations are still manual. Thus, to automatically upscale these 6G-IoT networks at reduced cost orchestration complexity, zero-touch networks (ZTN) are proposed. ZTN in 6G-IoT allows a high degree of automation and seamless integration of services. The article proposes a scheme, Zero-Load, that integrates ZTN at the core routing functionality of the 6G-IoT applications. We present a load balancing and traffic classification scheme through the ZTN networking stack for core routers. The ZTN router configuration fabric connects applications with the core services. Further, we present a Gaussian kernel-based support vector machine (SVM) classifier at the ZTN automation layer, which classifies the normal traffic and attack traffic. The proposed work is compared for parameters like mean time to response (MTTR), and resolution latency against baseline SDN and NFV schemes. Using ZTN, an average improvement of 32.45% is obtained in MTTR, and 87.89% in resolution latency (against a query). Using the Gaussian RBF kernel, an accuracy of 0.9914 is reported. These results indicate that ZTN-based management paves the way toward a more dense and intelligent 6G-IoT network. © 2023 IEEE.

关键词： Network function virtualization

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Dengue Vector control Mobile Application: Architecture and Features 8

Dengue Vector Control Mobile Application: Architecture and F...

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8th International Conference on Business and Information Management, ICBIM 2024

作者： Jitnupong, Boonchoo Suraseing, Jaruwan Gumseda, Teerayuth Dept. of Computer Science and Information Faculty of Science at Sriracha Kasetsart University Sriracha Campus Chonburi Thailand The Office of Disease Prevention and Control 6 Chonburi Department of Disease Control Chonburi Thailand

ISBN: (纸本)9798350379099

Mobile applications have been developed for many domains, including the public health sector. They can be used to track disease outbreaks and provide real-time information. Herein, we present a mobile application integrated with a dengue fever surveillance initiative in Thailand called Dengue Manager Application. Smartphone-specific characteristics such as communication, location-based services, and notification systems are used with appropriate vector control such as insecticide spraying. The architecture and features of the app, for instance its scalability and maintainability, and its implementation are explained in detail. Model-View-controller was used in its development. Another important feature is the data layer comprising an on-premises database and cloud storage to collect the necessary data. The controller API that plays an essential role along with the user interface are also covered. We also make some suggestions concerning how to bundle the proposed application into a Thai Ministry of Public Health super app by its development into an API that another application can connect to. © 2024 IEEE.

关键词： Vector control (Electric machinery)

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Tuning the Feedback controller Gains is a Simple Way to Improve Autonomous Driving Performance

Tuning the Feedback Controller Gains is a Simple Way to Impr...

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UKACC International Conference on control (control)

作者： Wenyu Liang Pablo R. Baldivieso Ross Drummond Donghwan Shin Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Dept. of Computer Science University of Sheffield Sheffield UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics. The aim of this paper is to highlight the limitations of this approach; it is shown that re-tuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness.

关键词： Machine learning algorithms Neural networks control systems Robustness Feedback control control theory Adaptive control

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Physics-Informed Neural Networks: A Step Towards Data-Driven Optimization of Additive Manufacturing 29

Physics-Informed Neural Networks: A Step Towards Data-Driven...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Depaoli, Fabio Felicioni, Stefano Ponzio, Francesco Aliberti, Alessandro MacIi, Enrico Bondioli, Federica Padovano, Elisa Di Cataldo, Santa Dept. of Control and Computer Engineering Politecnico di Torino Italy Dept. of Applied Science and Technology Politecnico di Torino Italy Interuniv. Dept. of Reg. and Urban Studies and Planning Politecnico di Torino Italy

ISBN: (纸本)9798350361230

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach. © 2024 IEEE.

关键词： Smart manufacturing

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Tuning the feedback controller gains is a simple way to improve autonomous driving performance

arXiv

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arXiv 2024年

作者： Liang, Wenyu Baldivieso, Pablo R. Drummond, Ross Shin, Donghwan Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Dept. of Computer Science University of Sheffield Sheffield United Kingdom

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle’s closed loop dynamics. The aim of this paper is to highlight the limitations of this approach;it is shown that retuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness. © 2024, CC BY.

关键词： Feedback control

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BIOPHYSICAL SIGNAL PROCESSING FOR automatic ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

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