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A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning

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arXiv 2023年

作者： Abouheaf, Mohammed Gueaieb, Wail Spinello, Davide Al-Sharhan, Salah College of Technology Architecture & Applied Engineering Bowling Green State University Bowling GreenOH43402 United States School of Electrical Engineering and Computer Science Department of Mechanical Engineering University of Ottawa OttawaONK1N 6N5 Canada Machine Intelligence Research Labs AuburnWA98071-2259 United States

Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding surfaces, and backstepping are typically employed to advise adaptive control strategies. The resulting solutions are often challenged by the complexity of the reference model and those of the derived control strategies. Additionally, the explicit dependence of the control strategies on the process dynamics and reference dynamical models may contribute in degrading their efficiency in the face of uncertain or unknown dynamics. A model-reference adaptive solution is developed here for autonomous systems where it solves the Hamilton-Jacobi-Bellman equation of an error-based structure. The proposed approach describes the process with an integral temporal difference equation and solves it using an integral reinforcement learning mechanism. This is done in real-time without knowing or employing the dynamics of either the process or reference model in the control strategies. A class of aircraft is adopted to validate the proposed technique. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Data Fusion in Neuromarketing: Multimodal Analysis of Biosignals, Lifecycle Stages, Current Advances, Datasets, Trends, and Challenges

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arXiv 2022年

作者： Pérez, Mario Quiles Beltrán, Enrique Tomás Martínez Bernal, Sergio López Prat, Eduardo Horna Del Campo, Luis Montesano Maimó, Lorenzo Fernández Celdrán, Alberto Huertas Department of Information and Communications Engineering University of Murcia Murcia30100 Spain Bit&Brain Technologies S.L Zaragoza50006 Spain Department of Computer Architecture and Technology University of Murcia Murcia30100 Spain Communication Systems Group CSG Department of Informatics IfI University of Zurich UZH ZürichCH-8050 Switzerland

The primary goal of any company is to increase its profits by improving both the quality of its products and how they are advertised. In this context, neuromarketing seeks to enhance the promotion of products and generate a greater acceptance on potential buyers. Traditionally, neuromarketing studies have relied on a single biosignal to obtain feedback from presented stimuli. However, thanks to new devices and technological advances studying this area of knowledge, recent trends indicate a shift towards the fusion of diverse biosignals. An example is the usage of electroencephalography for understanding the impact of an advertisement at the neural level and visual tracking to identify the stimuli that induce such impacts. This emerging pattern determines which biosignals to employ for achieving specific neuromarketing objectives. Furthermore, the fusion of data from multiple sources demands advanced processing methodologies. Despite these complexities, there is a lack of literature that adequately collates and organizes the various data sources and the applied processing techniques for the research objectives pursued. To address these challenges, the current paper conducts a comprehensive analysis of the objectives, biosignals, and data processing techniques employed in neuromarketing research. This study provides both the technical definition and a graphical distribution of the elements under revision. Additionally, it presents a categorization based on research objectives and provides an overview of the combinatory methodologies employed. After this, the paper examines primary public datasets designed for neuromarketing research together with others whose main purpose is not neuromarketing, but can be used for this matter. Ultimately, this work provides a historical perspective on the evolution of techniques across various phases over recent years and enumerates key lessons learned. © 2022, CC BY-NC-SA.

关键词： Life cycle

A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset

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A Horizontal Federated Learning Approach to IoT Malware Traf...

International Conference on Advanced Communication technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.

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IALF-YOLO: Insulator defect detection method combining improved attention mechanism and lightweight feature fusion network

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Mei, Zhiyu Xu, Hongzhen Yan, Liyue Wang, Kafeng School of Software East China University of Technology Nanchang330013 China School of Architecture and Urban Planning Shenzhen University Shenzhen518061 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Effect and efficient insulator defect detection is critical for advancing smart grid technologies. Current deep learning-based methods face limitations in small-object recognition accuracy, insufficient key feature extraction, and high computational complexity, which restrict their application in grid inspections. We propose IALF-YOLO, an improved YOLOv5s-based model, to address these problems. Firstly, by fusing the shallow feature map of Backbone and the deep feature map of Neck, a detection layer dedicated to small objects is created in Head, significantly improving small objects’ detection accuracy. Secondly, a S-CBAM attention mechanism is proposed, which addresses the issue of feature information loss in conventional CBAM by synchronizing the extraction channel with spatial attention. Finally, the lightweight GSConv module replaces the convolutional layer in the Neck network to construct a lightweight feature fusion network, which improves detection accuracy while reducing model complexity and the number of parameters. Our method improves mAP by 2.8% and 2.5% on both datasets, respectively. The detection speed is 2× faster than other methods. © 2025 Elsevier Ltd

关键词： Leak detection

Implementation of an Intelligent Animal Monitoring System Using Wireless Sensor Network and IoT Platform

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Implementation of an Intelligent Animal Monitoring System Us...

Cyber Resilience (ICCR), International Conference on

作者： Ateeq Ur Rehman Jehangir Arshad Muhammad Tariq Sadiq Abdul Rehman Munir Ahmad Mohammad Kamrul Hasan Hussam Al Hamadi Tauqeer Faiz Department of Electrical Engineering GC University Lahore Pakistan Department of Electrical & Computer Engineering Lahore Campus COMSATS University Islamabad Lahore Pakistan School of Architecture Technology and Engineering University of Brighton Brighton United Kingdome NCBAE School of Computer Science Lahore Pakistan Faculty of Information Science and Technology Center for Cyber Security Universiti Kebansaan Malaysia (UKM) Bangi Selangor Malaysia College of Engineering and IT University of Dubai School of IT Skyline University College Sharjah UAE Faculty of Engineering Computing and Science (FECS) - Swinburne University of Technology Australia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

Meat and dairy products are negatively impacted by a lack of technology in the livestock industry in developing countries. To cater for this challenge, the Internet of Things (IoT), Node-MCU, and intelligent wireless sensor nodes are deployed to create a new smart dairy monitoring system. A cow collar with a temperature sensor, a GPS module, and an environmental parameter regularization system are included. Data from modules is stored in a separate database using an innovative IoT -based front end. All the deployed WS nodes can determine whether the environment is stable or not at any given time. Sensors built inside cow collars can assess vital indications like temperature and pulse rate, as well as the animal's exact location. The design of the Cow collar also has a feature that automatically alerts the owner. The plug-and-play technology offered is designed to be easy to adapt. When a farm has many animals, automation decreases the need for human involvement and so lowers labor expenses. The exploitation of remote monitoring systems improves the health of the animals and hence yields a better amount of dairy. This study also includes a detailed comparison of the proposed implementation with current systems to demonstrate its originality. Other applications, such as smart monitoring of zoo animals and poultry, may be derived from the proposed technology.

关键词： Temperature sensors Wireless communication Temperature measurement Wireless sensor networks Animals Databases Cows

Localization Research Based on Low Cost Sensor 15th

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Localization Research Based on Low Cost Sensor

15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Zuo, Jian Zhang, Chenghao Shu, Kuang-I Zhang, Heng College of Computer and Information Science Southwest University Chongqing China E/E Architecture Department Beijing New Energy Vehicle Technology Innovation Center Co. Ltd. Beijing China

ISBN: (纸本)9783030642426

With the increasing demand for indoor navigation applications, indoor navigation has become a research hotspot in many technical fields. High-precision sensors are expensive, and they are often used in industrial and aerospace applications. Low-cost sensors can cause severe drift and noise. This paper uses a simple, low-cost Inertial measurement unit (IMU) and effectively evaluates the precise position of pedestrians. This paper designs a pedestrian position estimation based on low-cost inertial sensors. To solve the problem of inertial sensing noise and accumulated errors during the movement, based on the EKF algorithm, a multi-condition threshold detection method is proposed, and then a zero-speed update, an angular speed update, and a yaw angle update are designed. In the measurement state, the proposed measurement error vector effectively eliminates the cumulative error during walking. Finally, using this algorithm to perform straight and rectangular walking routes, corresponding experiments were performed on the effectiveness of the method used. The proposed method for estimating the inertial navigation of pedestrians has an error within 3.8%. This basic research is of great significance in rehabilitation training and somatosensory play. © 2020, Springer Nature Switzerland AG.

关键词： Aerospace applications

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GöwFed

Journal of Network and computer Applications 2023年 217卷

作者： Aitor Belenguer Jose A. Pascual Javier Navaridas Department of Computer Architecture and Technology University of the Basque Country UPV/EHU Manuel Lardizabal 1 20018 Donostia-San Sebastian Spain

Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or IoT devices. Current approaches rely on powerful centralized servers that receive data from all their parties — violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach, where different agents collaboratively train a shared model, without exposing training data to others or requiring a compute-intensive centralized infrastructure. This work presents GöwFed , a novel network threat detection system that combines the usage of Gower Dissimilarity matrices and Federated averaging . Different approaches of GöwFed have been developed based on state-of the-art knowledge: (1) a vanilla version — achieving a median point of [0.888, 0.960] in the PR space and a median accuracy of 0.930; and (2) a version instrumented with an attention mechanism — achieving comparable results when 0.8 of the best performing nodes contribute to the model. Furthermore, each variant has been tested using simulation oriented tools provided by TensorFlow Federated framework. In the same way, a centralized analogous development of the Federated systems is carried out to explore their differences in terms of scalability and performance — the median point of the experiments is [0.987, 0.987]) and the median accuracy is 0.989. Overall, GöwFed intends to be the first stepping stone towards the combined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats in industrial-level networks.

关键词： Federated Learning Intrusion Detection Systems Internet of Things Gower distance

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

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arXiv 2023年

作者： Nikpour, Maryam Yousefi, Parisa Behvand Jafarzadeh, Hadi Danesh, Kasra Shomali, Roya Asadi, Saeed Lonbar, Ahmad Gholizadeh Ahmadi, Mohsen Architecture Department Ahvaz Branch Islamic Azad University Ahvaz Iran university of technology Tehran Iran School of E-Learning Shiraz University Shiraz Iran Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Department of Information Systems Statistics and Management Science The University of Alabama Tuscaloosa United States Department of Civil Engineering The University of Texas at Arlington ArlingtonTX United States Department of Civil Construction and Environmental Engineering University of Alabama TuscaloosaAL United States

This study addresses the growing challenges of energy consumption and the depletion of energy resources, particularly in the context of smart buildings. As the demand for energy increases alongside the need for efficient building maintenance, it becomes imperative to explore innovative energy management solutions. We present a review of Internet of Things (IoT)-based frameworks aimed at managing smart city energy consumption, the pivotal role of IoT devices in addressing these issues due to their compactness, sensing, measurement, and computing capabilities. Our review methodology involves a thorough analysis of existing literature on IoT architectures and frameworks for intelligent energy management applications. We focus on systems that not only collect and store data but also support intelligent analysis for monitoring, controlling, and enhancing system efficiency. Additionally, we examine the potential for these frameworks to serve as platforms for the development of third-party applications, thereby extending their utility and adaptability. The findings from our review indicate that IoT-based frameworks offer potential to reduce energy consumption and environmental impact in smart buildings. By adopting intelligent mechanisms and solutions, these frameworks facilitate effective energy management, leading to improved system efficiency and sustainability. Considering these findings, we recommend further exploration and adoption of IoT-based wireless sensing systems in smart buildings as a strategic approach to energy management. Our review highlights the importance of incorporating intelligent analysis and enabling the development of third-party applications within the IoT framework to efficiently meet evolving energy demands and maintenance challenges. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart homes

Scalable and probabilistically complete planning for robotic spatial extrusion

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arXiv 2020年

作者： Garrett, Caelan Reed Huang, Yijiang Lozano-Pérez, Tomás Mueller, Caitlin Tobin Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Department of Architecture Massachusetts Institute of Technology

There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures. However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees. In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms. The key planning challenge is, throughout the printing process, satisfying both stiffness constraints that limit the deformation of the structure and geometric constraints that ensure the robot does not collide with the structure. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware heuristic is able to successfully balance both constraints. We empirically compare our methods on a benchmark of over 40 simulated extrusion problems. Finally, we apply our approach to 3 real-world extrusion problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrusion