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Nonlinear Iterative Learning control: A Frequency-Domain Approach for Fast Convergence and High Accuracy

IFAC-PapersOnLine 2023年第2期56卷 1889-1894页

作者： Leontine Aarnoudse Alexey Pavlov Tom Oomen Dept. of Mechanical Engineering Control Systems Technology Eindhoven University of Technology Eindhoven The Netherlands Dept. of Geoscience and Petroleum Norwegian University of Science and Technology Trondheim Norway Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

Iterative learning control (ILC) involves a trade-off between perfect, fast attenuation of iteration-invariant disturbances and amplification of iteration-varying ones. The aim of this paper is to develop a nonlinear ILC framework that achieves fast convergence, robustness, and low converged error values in ILC. To this end, the method includes a deadzone nonlinearity in the learning update, which uses the difference in amplitude characteristics of repeating and varying disturbances to modify the learning gain for each error sample. A criterion for monotonic convergence of the nonlinear ILC algorithm is provided, which is used in combination with system measurements to select suitable design parameters. The proposed algorithm is validated using simulations, in which fast convergence to low error values is demonstrated.

关键词： Iterative learning control variable-gain control nonlinear control feedforward control mechatronic systems

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FPGA-based Low Power Acceleration of HARK Sound Source Localization

FPGA-based Low Power Acceleration of HARK Sound Source Local...

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IEEE Cool Chips

作者： Zirui Lin Katsutoshi Itoyama Kazuhiro Nakadai Hideharu Amano Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

ISBN: (数字)9798350384147

ISBN: (纸本)9798350384154

This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes substantial processing time in edge computing devices. To balance processing time and low power consumption, two functions in sound source localization that include many matrix operations are targeted and migrated on an FPGA SoC board called M-KUBOS. Compared to CPU-based computing on ARM Cortex A53, our implementation achieved a 2.0× speedup and 1.7× lower energy consumption.

关键词： Location awareness Program processors Power demand Parallel processing Energy efficiency Matrix decomposition Robots

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An Architectural Approach for Enhanced Data Interoperability Across Building systems 7

An Architectural Approach for Enhanced Data Interoperability...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Yefi, Peter Ejaz, Sikandar Menon, Ramanunni Parakkal Eicker, Ursula Guéhéneuc, Yann-Gaël Concordia University Computer Science and Software Engineering Dept. Montreal Canada Concordia University Institute for Information Systems Engineering Montreal Canada Institute for Solar Research German Aerospace Center Jülich Germany Concorida University NextGen Cities Institute Montreal Canada

ISBN: (纸本)9798350356717

Access to building data is crucial for creating portable applications and improving building operation and energy efficiency. Ensuring data transmutability is essential for facilitating research and overcoming the diverse data representation, management, and collection methods across various building management systems (BMSs). Different BMSs use multiple interfaces for data exchange. Some BMSs provide Application Programming Interfaces (APIs) for data exchanges, while others have gateways connecting to cloud services for application subscription and data access. This study introduces a five-layered architecture that describes how client applications, researchers, and other interested stakeholders can exchange data with different BMSs. Our objective is to help researchers and practitioners understand the various ways of accessing transmutable (capable of being transformed into a compatible format) data from BMSs, regardless of the building management system a building uses. Through transmutable access to building systems' data, we aim to enable transferable energy-efficient-related applications for buildings. We evaluate our work with a building model of the Varennes Library in Varennes, QC, Canada. The building model includes information about the building, floors, rooms, electric meter and historical time series data of the meter, weather station and its historical time series data. The model also has carbon dioxide concentration sensors, temperature sensors, humidity sensors, and historical time series data of the sensors. We include two test client programs to show how they access the transmutable data of the building through a model. We conclude that the five-layered architecture facilitates the exchange of transmutable data across diverse BMSs, making it a valuable tool for researchers and practitioners. © 2024 IEEE.

关键词： Middleware

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Deep Reinforcement Learning Platooning control of Non-Cooperative Autonomous Vehicles in a Mixed Traffic Environment

Deep Reinforcement Learning Platooning Control of Non-Cooper...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Danilo Menegatti Andrea Wrona Antonio Di Paola Simone Gentile Alessandro Giuseppi Dept. of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome Italy Dept. of Electrical and Information Engineering Polytechnic of Bari Bari Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Ensuring secure spacing between vehicles is vital for road safety, efficient traffic flow, and system stability in autonomous driving. While traditional cooperative platooning approach, relying on centralized coordination exploiting wireless network, faces practical implementation challenges due to communication constraints and diverse driving behaviors, this work introduces a scalable non-cooperative multi-agent platooning strategy based on Deep Reinforcement Learning, leveraging on decentralized decision-making principles. The agents’ aim is to adjust their velocities dynamically to ensure safe following distances and adapt to surrounding vehicle behavior, without the possibility of exchanging information over a wireless network. Extensive simulations validate the effectiveness and robustness of the proposed approach, making it suitable for real-world autonomous driving scenarios.

关键词： Wireless networks Deep reinforcement learning control systems Aerodynamics Robustness Stability analysis Sensors Vehicle dynamics Autonomous vehicles Optimization

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Geographical Self-Organizing Map Clustering in Large-Scale Urban Networks for Perimeter control 10

Geographical Self-Organizing Map Clustering in Large-Scale U...

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10th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2024

作者： Elouni, Maha Rakha, Hesham A. Menendez, Monica Abdelghaffar, Hossam M. Department of Computer Science Randolph-Macon College AshlandVA United States Charles E. Via Jr. Dept. of Civil and Environmental Engineering Virginia Tech BlacksburgVA United States Division of Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9789897587030

Traffic congestion in urban areas presents a major challenge to efficient transportation systems. Recent advancements in traffic management provide promising solutions, with perimeter control emerging as a technique to tackle network-wide congestion. However, it is crucial to identify geographically connected homogeneously congested areas for effective implementation. This research explores the application of clustering techniques, particularly geographical self-organizing maps (GeoSOM), to identify spatially connected and homogeneously congested areas within transportation networks. While GeoSOM has found applications across various domains, its adaptation to transportation networks for congestion clustering is novel. This study introduces and implements an adaptation of the GeoSOM algorithm tailored for the large-scale urban environment of downtown Los Angeles. Its performance is assessed through a comparative evaluation with two other clustering algorithms, namely DBSCAN and K-means. The results demonstrate that GeoSOM surpasses other clustering algorithms, exhibiting improvements of up to 43% in traffic density variance, up to 61% in the spatial quantization error, and 15% in the quantization error. This finding demonstrates that the proposed clustering algorithm is effective in identifying a spatially homogeneous congested area within a large-scale transportation network. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Traffic congestion

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Game-Theoretic-Based Resource Allocation Algorithm for SCMA Max-Min Problem to Maximize Fairness 7

Game-Theoretic-Based Resource Allocation Algorithm for SCMA ...

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7th IEEE International Conference on Electronics and Communication engineering, ICECE 2024

作者： Cheraghy, Maryam Soltanpour, Meysam Abuhaija, Belal Abdalla, Hemn Barzan Ehimwenma, Kennedy Wenzhou-Kean University dept. Computer Science and Technology Wenzhou China Shandong University dept. Control Science and Engineering Jinan China

ISBN: (纸本)9798331517755

This paper tackles a joint optimization problem in uplink sparse code multiple access (SCMA) networks to enhance network fairness, i.e., Jain's fairness index (JFI). We propose a novel game-theoretic approach, the worst-case player first-strategy selection (WCPFSS) algorithm, to maximize the minimum achievable rate for all subcarriers while ensuring fair power allocation and optimized subcarrier assignment. This approach effectively manages user interference because each subcarrier acts as a player to exchange their strategies, which are clusters of users associated with each subcarrier. The simulation results confirm the superiority of the proposed WCPFSS method w.r.t. JFI for differential setup parameters. © 2024 IEEE.

关键词： Code division multiple access

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Software Defined Network Based Next Generation Mobile Communication Network Architecture

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 138-148页

作者： Kumar, Sumit Ben Sujin, B. Bhavani, K. Srilatha, Doddi Anand, Ketan Dept. of CSE Haridwar University Uttarakhand Roorkee India University of Technology and Applied Sciences Nizwa Oman Department of Computer Science and Business Systems Panimalar Engineering College Tamil Nadu Chennai India Department of CSE Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Sreenidhi Institute of Science and Technology Telangana Hyderabad India

As mobile networks and network speeds become more prevalent, the demand for marketing strategies increases. The operators are thinking about this, and the development of 5G communication networks is one of their main concerns. As the need for higher transmission speeds increases, 5G networks face challenges of scalability and adaptability. The next-generation mobile network (NMCN-SDN) architecture proposed in this study is based on Software-Defined Networks (SDN). A new network model called computer-defined networking allows for dynamic network definition and programming. A network simulator is created to examine the efficiency of the built infrastructure under different network conditions, including throughput, latency, and resource consumption. In this paper, the comparison of end-toend latency between the standard communication architecture and the proposed NMCN-SDN architecture is done. The results show that the proposed architecture has less space in various conditions compared to existing communication architectures in various conditions. © 2024 by the authors of this article.

关键词： 5G end-to-end latency mobile communication resource utilization Software-Defined Networks (SDN) throughput

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Understanding Statistical Correlation of Application Security Vulnerability Data from Detection and Monitoring Tools 33

Understanding Statistical Correlation of Application Securit...

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33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Kasturi, Santanam Li, Xiaolong Pickard, John Li, Peng Indiana State University Technology Management Terre Haute United States Indiana State University Dept. of Electronics and Computer Engineering Terre Haute United States East Carolina University Dept. of Technology Sys Greenville United States East Carolina University Dept. of Technology Systems Greenville United States

ISBN: (纸本)9798350317138

Vulnerability data gathered from multiple detection and monitoring capabilities at different layers of an application using a time-series analysis will provide value and insights by doing a statistical correlation with attack requests observed using a Web Application Firewall (WAF) monitoring solution. Static Analysis Security Testing, Software Composition Analysis, Dynamic Analysis Security Testing (DAST), Application Ethical Hack, Application Programming Interface (API) testing are the tests / scans that have been used to gather vulnerability data for this study. Correlations can further help track abnormal transaction paths if we follow specific ones pointed out by the statistical analysis for those requests that are not blocked by the rules and are allowed as valid transactions to pass through. This provides a narrowed down focus on the convergence of observability and security, critical to realizing a near-real time rapid action. Observations must continue for many days as a time series ensuring consistency and reliability in data collections and analysis. Multiple applications must be observed in a similar manner for ensuring validity of the process for analysis. Also, gathering sufficient data that is large enough to represent a reasonable population of web applications within an organization is a significant factor in achieving reliable correlation. Applying Pearson Correlation (or Spearman Correlation for distributions that are not normal) technique provides insight into Significance (two-tailed) as to whether a correlation is present over large number of data points. Results of analysis show evidence of correlations among specific attack requests monitored by the WAF and corresponding vulnerabilities in applications, detected using one or more methods. This is significant to looking for more insights into how these correlations can further explored into predicting attack patterns based on existing vulnerabilities. © 2023 IEEE.

关键词： Time series analysis

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Indoor Occupancy Estimation Based on Synergy of Physical Modeling, Environmental Data Fusion, and Machine Learning Frameworks

Indoor Occupancy Estimation Based on Synergy of Physical Mod...

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Electrical, Electronic and Computing engineering (IcETRAN), International Conference on

作者： Ivan Popović Igor Krčmar Aleksandar Rakić Dept. of Electronics University of Belgrade - School of Electrical Engineering Belgrade Serbia Dept. of Control Systems University of Banja Luka - Faculty of Electrical Engineering Banja Luka Bosnia and Herzegovina Dept. of Signals and Systems University of Belgrade - School of Electrical Engineering Belgrade Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

In a modern society, driven by both economic interests and a concern for personal privacy, indoor occupancy, either at workspace or in other type of accommodation, became a matter of stressed research interest. Though various methodologies have been developed so far, the proposed solutions of a technical problem of occupancy estimation still suffer from narrow perspectives and partial solutions, usually formulated as an optimization of a chosen mathematized problem. This paper considers the broader context of the problem, particularly its physical background, basic assumptions for the effective use of the environmental variables in occupancy estimations and the viable frameworks, either conventional or modern machine learning ones, that are to meet those assumptions. The approach is experimentally verified in a case study.

关键词： Training Accuracy Estimation Machine learning Data models Transient analysis Task analysis

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Enhancing Diabetes Prediction using Hybrid Feature Selection and Ensemble Learning with AdaBoost 8

Enhancing Diabetes Prediction using Hybrid Feature Selection...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Thuraka, Bharadwaj Pasupuleti, Vikram Kodete, Chandra Shikhi Chigurupati, Ravi Swaroop Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States Eastern Illinois University School of Technology CharlestonIL61920 United States Dept. of Cse Bhimavaram India Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798350376425

This research relies on a feature selection method termed LasNO to analyse the progress of diabetes prediction. The ANOVA integrated with Lasso regularization identifies significant predictors like Age, BMI, DiabetesPedigreeFunction, and (Glucose). An AdaBoost model with the following characteristics was developed, and the results were exceptional performance metrics: 97.02% accuracy, 87.65% precision, and 82.5% recall. The results of the primary focus on glucose levels to differentiate between events with and without diabetes have been affirmed from the visual analyses. It also calls into attention that due to the appropriate cross-validation, LasNO is very successful on improving the interpretability and the possibility of generalization of the model. Such findings make it clear that efforts towards early detection of the condition and subsequent timely implementation of measures to contain the illness can go quite a long way in enhancing the quality of the lives of people that contract the disease. Further research is likely to be devoted to the assessing of the improved and broadened validation of the dataset as a critical approach for the enhancement and expansion of the model for use in clinics. As a result, this study aims to advance diabetes theragnostic possibilities and healthcare recommendations for better action based on ML approaches enhancing the accuracy of predictions. In this study, approaches to feature selection used for improving the performance while also making the models more understandable are the focus. The idea is to ensure that medical practitioners have reliable tools to undertake early detection and management of diabetes. Thus, the main objective of dealing with patients' Health-Related Quality of Life is to simultaneously increase the quality of patients' living while decreasing the costs of their treatment. © 2024 IEEE.

关键词： Prediction models

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