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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Warchol, Dawid Oszust, Mariusz Department of Computer and Control Engineering Faculty of Electrical and Computer Engineering Rzeszów University of Technology W. Pola 2 Rzeszów35-959 Poland

In this paper, an approach to augment action recognition time series datasets, devoted to improving the accuracy of deep learning classifiers, is proposed. In the introduced method, two operators are sequentially introduced that perform linear and nonlinear modifications in the time scale of the input time series. The resulting data samples contribute to the variability within classes and allow a deep learning-based classifier to better capture their boundaries, leading to a significant improvement in the classification accuracy. The extensive experiments performed on eight publicly available action recognition datasets using the popular Bidirectional Long Short-Term Memory (BiLSTM) classifier reveal the superiority of the proposed algorithm over related approaches. © 2022 The Authors. Published by Elsevier B.V.

关键词： Time series

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A LESO Based Backstepping controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Vision-based gesture modeling for signed expressions recognition 26

Vision-based gesture modeling for signed expressions recogni...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Kapuscinski, Tomasz Inglot, Krzysztof Rzeszow University of Technology Department of Computer and Control Engineering W. Pola 2 Rzeszow35-959 Poland

A vision-based method for signed expressions recognition consisting of searching for previously prepared word models in the analyzed utterances was developed. The models were created as temporal sequences of grouped descriptors of the place of articulation and the shape of the hand. The descriptors were created using the skeletons determined by the deep neural network and histogram of oriented gradients features. The classification was carried out using the Wagner-Fischer algorithm for determining Levenshtein distance, which was modified to search for fragments in sequences of discrete symbols. Considering the practical application of the developed method, even in the case of partially inaccurate recognition results, it was proposed to visualize the answers in the form of a word cloud. The method was tested for a challenging set of sign language expressions used in the office when applying for an ID card. © 2022 The Authors. Published by Elsevier B.V.

关键词： Human computer interaction

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Deep Learning-based Source Code Classification and Visualization of Decision Rationales 28th

Deep Learning-based Source Code Classification and Visualiza...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Oeda, Shinichi Mitsumori, Seiya Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan

In programming education, it is crucial to provide instruction that is tailored to students' proficiency levels. For this purpose, an objective evaluation of each student's coding ability is essential. Additionally, it is necessary to understand the characteristics of source code written by both beginners and advanced students. Previous research has successfully converted the structural information of source code into graphs and assessed coding skills using deep learning, achieving high accuracy. However, it remains unclear which specific structural elements significantly influence these assessments. This study addresses this gap by transforming source code into abstract syntax trees and developing a model that uses Graph Convolutional Networks to categorize code as either beginner or advanced users based on learned structural information. Furthermore, we apply Integrated Gradients to visualize the decision-making basis of our model and elucidate the structural characteristics distinguishing source code written by beginner and advanced users. © 2024 The Authors.

关键词： Adversarial machine learning

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Data-driven energy demand forecasting for electric vehicle charging infrastructure

Data-driven energy demand forecasting for electric vehicle c...

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2023 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2023

作者： Meddi, Silvia Cavaglion, Sara Cerquitelli, Tania Manfredi, Emanuele Regalia, Andrea Menolascino, Raffaele Zardo, Guido Accenture S.p.A. Italy Department of Control and Computer Engineering Politecnico di Torino Italy F2M eSolutions Italy Politecnico di Torino Italy

In the era of Big Data and electric vehicles growth by market, data-driven methodologies assume a crucial role to create valuable information. The focus is on supporting the decision-making process for the development of an accurate charging infrastructure. Forecast analysis allows prediction of energy demand over the network. This supports growing trends with a consequent increase in customer satisfaction. By anticipating potential breakdowns due to infrastructure overloads, maintenance costs are reduced. In this paper, we focus on analyzing charging sessions data together with external data (weather and population information and energy/fuel prices) collected from different sources. The proposed methodology, named GEORGE (enerGy dEmand fOrecasting foR charGing infrastructurE), offers a building-blocks based approach for the monthly energy demand forecasting. The approach is both generalisable and data-specific. We discuss the results of a classification learning approach to predict a belonging range of kwh for a charge point. In particular the most promising model has good performances in predicting high utilization and is more advantageous to support the company's decision-making process. Many possible developments are discussed to improve the prediction. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Data mining

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Output Tracking control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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Learning-Based State Estimation for Automated Lane-Changing 27

Learning-Based State Estimation for Automated Lane-Changing

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Ha, Won Yong Chakraborty, Sayan Lin, Xiaoyi Ozbay, Kaan Jiang, Zhong-Ping New York University Control and Networks Lab Department of Electrical and Computer Engineering 370 Jay Street BrooklynNY11201 United States C2SMARTER Center New York University Department of Civil and Urban Engineering 6 MetroTech Center BrooklynNY11201 United States

ISBN: (纸本)9798331505929

This paper introduces a learning-based optimal control strategy enhanced with nonmodel-based state estimation to manage the complexities of lane-changing maneuvers in autonomous vehicles. Traditional approaches often depend on comprehensive system state information, which may not always be accessible or accurate due to dynamic traffic environments and sensor limitations. Our methodology dynamically adapts to these uncertainties and sensor noise by iteratively refining its control policy based on real-time sensor data and reconstructed states. We implemented an experimental setup featuring a scaled vehicle equipped with GPS, IMUs, and cameras, all processed through an Nvidia Jetson AGX Xavier board. This approach is pivotal as it addresses the limitations of simulations, which often fail to capture the complexity of dynamic real-world conditions. The results from real-world experiments demon-strate that our learning-based control system achieves smoother and more consistent lane-changing behavior compared to traditional direct measurement approaches. This paper underscores the effectiveness of integrating Adaptive Dynamic Program-ming (ADP) with state estimation techniques, as demonstrated through small-scale experiments. These experiments are crucial as they provide a practical validation platform that simulates real-world complexities, representing a significant advancement in the control systems used for autonomous driving. © 2024 IEEE.

关键词： State estimation

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AI and data-driven infrastructures for workflow automation and integration in advanced research and industrial applications

AI and data-driven infrastructures for workflow automation a...

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Forni, Tommaso Vozza, Mario Le Piane, Fabio Lorenzoni, Andrea Baldoni, Matteo Mercuri, Francesco DAIMON Lab CNR-ISMN via P. Gobetti 101 Bologna40129 Italy Department of Control and Computer Engineering Polytechnic University of Turin Corso Castelfidardo 34/d Turin10138 Italy Department of Computer Science and Engineering University of Bologna Viale del Risorgimento 2 Bologna40136 Italy

The use of AI and data-driven technologies and infrastructures for innovation and development of advanced research and industrial applications requires a strong degree of integration across a broad range of tools, disciplines and competences. In spite of a huge disruptive potential, the role of AI for research and development in the context of industrial applications is often hampered by the lack of consolidated and shared practices for transforming domain-specific processes for generating knowledge into added value. These issues are particularly striking for small-medium enterprises (SMEs), which must adopt clear and effective policies for implementing successful technology transfer paths for innovation. The activities of the DAIMON Lab of the CNR-ISMN focus on the design, development, implementation and application of integrated modelling, data-driven and AI methods and infrastructures for innovation in hi-tech applications. Our approach is based on the development of horizontal platforms, which can be applied to a broad range of vertical use-cases. Namely, we target the realisation of high-throughput workflows, related to specific domains and use cases, which are able to collect and process simulations and/or physical data and information. The implementation of an interoperable integration framework is a prerequisite for further application of AI tools for predictivity and automation. With a strong focus on the development of key enabling technologies (KETs), such as advanced materials, the approach pursued is extended to a broad range of application fields and scenarios of interest in industry, including electronic and ICT, advanced and sustainable manufacturing, energy, mobility. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Automation

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Exploring waste-collection fleet data: challenges in a real-world use case from multiple data providers

Exploring waste-collection fleet data: challenges in a real-...

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2022 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2022

作者： Monaco, Simone Bethaz, Paolo Apiletti, Daniele Baldini, Fabrizio Pio Caso, Carlo Cerquitelli, Tania Department of control and computer engineering Politecnico di Torino Torino Italy Italy T2D Transfer to Digital Salerno Italy

In the age of connected vehicles, large amounts of data can be collected while driving through a variety of on-board sensors. The information collected can be used for various types of data-driven analytics that can be of great benefit to both vehicle owners, e.g., to reduce costs by means of predictive maintenance, and to society as a whole, e.g., to optimize mobility behavior. Prior to any real-world data analysis, an investigation and characterization of the available data is of utmost importance in order to evaluate the quality and quantity of the data and to set the right expectations. In this paper, we focus on the data exploration and characterization step, which is necessary to avoid inconsistencies in the collected parameters and to enable valid, data-driven modeling. The proposed data exploration considers both the frequency of samples and their values for all monitored parameters. A specific cross-provider data comparison is performed to compare values collected for the same vehicle at the same time from different fleet monitoring data providers. The study is applied to a real-world use case with months of data from dozens of vehicles deployed in the waste collection service managed by SEA, Soluzioni Eco Ambientali, in Italy. The analyzes uncover unexpected behaviors in the measurements and lead to their early identification, bringing great benefits to the company operating the fleet by improving data collection and enabling a safe modeling phase. © 2022 Copyright for this paper by its authors.

关键词： Vehicles

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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