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检索条件"任意字段=3rd International Conference on Informatics in Control, Automation and Robotics"

共 2685 条记录，以下是461-470 订阅

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Neural Network Approaches for Magnetization Surface Prediction in Switched Reluctance Motors: Classical, Radial Basis Function, and Physics-Informed Models

Neural Network Approaches for Magnetization Surface Predicti...

引用

informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Galina Demidova Andrey Bogdanov Andrei Iaremenko Zhu Xirong Hao Chen Alecksey Anuchin Electric Drive Department Moscow Power Engineering Institute Moscow Russia Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This paper investigates the prediction of magnetization surfaces in Switched Reluctance Motors using three distinct methods: classical Neural Networks, Radial Basis Function networks, and Physics-Informed Neural Networks. The study utilizes experimental data from a 7.5 kW SRM to assess the performance of these approaches, with all methods implemented in Python. Among the methods analyzed, PINN demonstrates the highest accuracy in predicting the magnetization surfaces, effectively capturing the underlying physical behavior of the motor. The computational costs of each method are evaluated, providing insights into training times, inference speeds, and floating-point operations per sample. The findings reveal that while NN offers a reasonable balance between performance and efficiency, the RBF method excels in training speed but shows limitations in capturing the physical dynamics of the system. In contrast, the PINN not only delivers superior predictive performance but also emphasizes the significance of incorporating physical principles into machine learning frameworks. The results highlight the applicability of these methods across various types of SRMs, suggesting their potential for enhancing control strategies in electrical and power engineering applications.

关键词： Training Power engineering Electric potential Accuracy Magnetization Radial basis function networks Machine learning Artificial neural networks Switched reluctance motors Computational efficiency

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Chatbots for Robotic Process automation: Investigating Perceived Trust and User Satisfaction 3

Chatbots for Robotic Process Automation: Investigating Perce...

引用

3rd IEEE international conference on Human-Machine Systems, ICHMS 2022

作者： Casadei, Alessandro Schlogl, Stephan Bergmann, Markus Management Communication & It MCI-The Entrepreneurial School Innsbruck Austria Customer Relationship Management World-Direct EBusiness Solutions GmbH Vienna Austria

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Driven by ongoing improvements in machine learning, chatbots have increasingly grown from experimental interface prototypes to reliable and robust tools for process automation. Building on these advances, companies have identified various application scenarios, where the automated processing of human language can help foster task efficiency. To this end, the use of chatbots may not only decrease costs, but it is also said to boost user satisfaction. People's intention to use and/or reuse said technology, however, is often dependent on less utilitarian factors. Particularly trust and respective task satisfaction count as relevant usage predictors. In this paper, we thus present work that aims to shed some light on these two variable constructs. We report on an experimental study (n = 277), investigating four different human-chatbot interaction tasks. After each task, participants were asked to complete survey items on perceived trust, perceived task complexity and perceived task satisfaction. Results show that task complexity impacts negatively on both trust and satisfaction. To this end, higher complexity was associated particularly with those conversations that relied on broad, descriptive chatbot answers, while conversations that span over several short steps were perceived less complex, even when the overall conversation was eventually longer. © 2022 IEEE.

关键词： robotics

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Uniformly Regular Triangulations for Parameterizing Lyapunov Functions 18

Uniformly Regular Triangulations for Parameterizing Lyapunov...

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18th international conference on informatics in control, automation and robotics (ICINCO)

作者： Giesl, Peter Hafstein, Sigurdur Univ Sussex Dept Math Falmer BN1 9QH England Univ Iceland Sci Inst Dunhagi 3 IS-107 Reykjavik Iceland

ISBN: (纸本)9789897585227

The computation of Lyapunov functions to determine the basins of attraction of equilibria in dynamical systems can be achieved using linear programming. In particular, we consider a CPA (continuous piecewise affine) Lyapunov function, which can be fully described by its values at the vertices of a given triangulation. The method is guaranteed to find a CPA Lyapunov function, if a sequence of finer and finer triangulations with a bound on their degeneracy is considered. Hence, the notion of (h;d)-bounded triangulations was introduced, where h is a bound on the diameter of each simplex and d a bound on the degeneracy, expressed by the so-called shape-matrices of the simplices. However, the shape-matrix, and thus the degeneracy, depends on the ordering of the vertices in each simplex. In this paper, we first remove the rather unnatural dependency of the degeneracy on the ordering of the vertices and show that an (h;d)-bounded triangulation, of which the ordering of the vertices is changed, is still (h;d*)-bounded, where d* is a function of d, h, and the dimension of the system. Furthermore, we express the degeneracy in terms of the condition number, which is a well-studied quantity.

关键词： Triangulation Lyapunov Function CPA Algorithm Linear Programming

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A Comprehensive Review on Integrating IoT and Arm Cortex for Weather Adaptive Street Lighting

A Comprehensive Review on Integrating IoT and Arm Cortex for...

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automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Pallavi H. Ganvir Wani V. Patil Sandeep R. Sonaskar G H Raisoni College of Electronics Engineering Nagpur V S Informatics Pvt Ltd Nagpur

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Smart street lighting systems have emerged as a critical component of sustainable urban infrastructure, offering enhanced energy efficiency, adaptability, and functionality. This review explores advancements in the integration of Internet of Things (IoT) technologies, adaptive control mechanisms, and renewable energy solutions in street lighting. Key studies highlight innovations such as sensor-based dynamic illumination, weather-adaptive lighting control, and the use of ARM Cortex microcontrollers for efficient data processing. However, challenges persist, including high installation costs, sensor reliability, communication limitations in low-connectivity areas, and cybersecurity vulnerabilities. Additionally, the integration of renewable energy sources like solar power faces obstacles in storage and consistency. To address these limitations, future research should focus on leveraging AI and machine learning for predictive analytics, enhancing communication protocols with 5G and LPWAN technologies, and integrating blockchain for secure data management. Advancements in modular retrofitting and hybrid energy systems also hold promise for scalable and cost-effective implementations. This review highlights the need for interdisciplinary approaches to develop robust, secure, and sustainable smart street lighting systems customized to diverse urban and rural environments.

关键词： Renewable energy sources Technological innovation Program processors Reviews Heuristic algorithms Urban areas Green products Lighting Internet of Things Meteorology

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An Iterative Variable Step Error Compensation Approach for Enhancing Robot Trajectory Accuracy

An Iterative Variable Step Error Compensation Approach for E...

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automation, robotics and Computer Engineering (ICARCE), international conference on

作者： Chengzhi Wang Sikai Zhao Tianjiao Zheng Hegao Cai Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

In this paper, an iterative error compensation approach is analyzed and designed to improve trajectory accuracy of industrial robots. Advanced offline programming and robotic machining tasks require high trajectory accuracy of industrial robots, which is significantly affected by parameter inaccuracy, stiffness deformation, backlash, and so on. Such complicated coupling makes it impossible to model each factor separately, thus data-driven direct error compensation is the major solution to improve trajectory accuracy. Previous methods utilize external measurement equipment to obtain tracking error and rely on the error consistency assumption to compensate for the error by modifying the trajectory commands directly. However, such consistency assumption does not hold. Even a minor incremental modification of the initial commands can lead to unexpected influence on the robot's tracking performance, owning to unaccounted factors such as the robot's dynamic stiffness and natural frequency. This paper decomposes trajectory tracking errors based on servo encoder feedback and external laser tracker, and proposes a variable step error compensation scheme to improve tracking performance. Physical experiments are conducted on a standard 6 degree-of-freedom industrial robot to validate the effectiveness of the proposed method. The results of various error compensation approaches are analyzed to draw a possible error correlation model, in order to account for the error inconsistency in the compensation process.

关键词： Accuracy Service robots Trajectory tracking Measurement uncertainty Error compensation Industrial robots Laser modes Trajectory Iterative methods Load modeling

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Specifying Reactive Robotic Applications With Reference Attribute Motion Grammars 3

Specifying Reactive Robotic Applications With Reference Attr...

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3rd IEEE international conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Mey, Johannes Schoene, Rene Podlubne, Ariel Assmann, Uwe Tech Univ Dresden Dresden Germany

ISBN: (数字)9781665451420

ISBN: (纸本)9781665451420

There are several changes when describing the behavior of cyber-physical systems, especially if they act in a complex environment. First, the description should be easily comprehensible to avoid unwanted and unexpected behavior. Second, both the description of the behavior and the description and analysis of the context should be expressive enough to allow complex behavior. Motion grammars have previously been presented as a means to describe high- and low-level behavior of a reactive cyber-physical system. This work proposes Reference Attribute Grammars not only to implement and extend the concept of motion grammars but also to describe and analyze the context. This demonstrator shows how the resulting concept of reference attribute motion grammars is used to control a pickand-place task while reacting to a changing environment.

关键词： Reference Attribute Grammars Motion Grammars robotics Models at runtime

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Motor Imagery Analysis of EEG Signals Using a Low-Cost Ankle Exoskeleton 3

Motor Imagery Analysis of EEG Signals Using a Low-Cost Ankle...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Polo-Hortigüela, Cristina Gracia, Desirée I. Soriano-Segura, Paula Ortiz, Mario Iáñez, Eduardo Cavaliere-Ballesta, Carlo Azorín, José M. Miguel Hernández University of Elche Brain-Machine Interface Systems Lab Elche Spain Engineering Research Institute of Elche - I3E Miguel Hernández University of Elche Elche Spain Valencian Graduated School and Research Network of Artificial Intelligence - ValGRAI Valencia Spain

ISBN: (纸本)9798350378009

The combined use of a brain-machine interface (BMI) and a robotic device, such as an exoskeleton, is one of the state of the art techniques in neurorehabilitation. Among the control methods employed for BMIs, the paradigm of motor imagery is widely used. To achieve this control it is necessary to define an electroencephalographic (EEG) signal decoding algorithm for the extraction of optimal features. The latter is difficult due to the non-stationary character of the EEG signal and the artefacts that can interfere in it. This study focuses on the analysis and processing of EEG signals from two healthy subjects to develop an algorithm for the decoding of motor imagery (MI). As a method for feature extraction, PSD (Power Spectral Density) will be used. For this purpose, the Welch technique, which is an approach measure of the PSD, is applied. This algorithm has been tested for two electrode combinations. In addition, the EEG signals are conditioned by the movement generated in the foot due to an ankle exoskeleton used. This exoskeleton has been designed with a low-cost philosophy and allows dorsiflexion and plantar flexion movements. The result of the MI decoding algorithm for the first combination was an average accuracy value of 68.8% and for the second combination 75%, providing an important baseline for future research in this area. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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Glioma/Glioblastoma Detection in Brain MRI using Pre-trained Deep-Learning Scheme 3

Glioma/Glioblastoma Detection in Brain MRI using Pre-trained...

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3rd international conference on Intelligent Computing, Instrumentation and control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sujitha, R. Angel Balaji, Gangadharam Mohammed, Mazin Abed Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Vytautas Magnus University Department of Applied Informatics Kaunas44404 Lithuania St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Omr Chennai600119 India M/S Tata Consultancy Services Limited Tcsl Siruseri Sez Unit Sipcot I.T. Park TN Chennai603103 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Convolutional Neural Network (CNN) supported medicinal image examination is extensively accepted due to its reputation and improved accuracy. The investigational outcome obtained with DLS along with a chosen classifier helps to achieve better detection results than the traditional and machine-learning methods. The proposed research examines the performance of pre-trained VGG16 and VGG19 schemes in detecting the brain tumour (Glioma/Glioblastoma) grade using different pooling methods. The classification is performed using SoftMax with five-fold cross-validation, and the products are compared and presented. The brain tumour images considered in this study are collected from The Cancer Imaging Archive (TCIA) dataset. This work considered 2000 images (1000 Glioma and 1000 Glioblastoma) of axial-plane with dimension of 224×224×3 pixels for the assessment, and the attained results are compared. The experimental outcome achieved with Python® confirms that the VGG16 with average-pooling provides a better classification accuracy (>99%) with Decision Tree (DT) compared with other methods considered. © 2022 IEEE.

关键词： Python

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Application of Deep Learning Models for Vocal Cords Detection in Laryngoscopic Imagery 28th

Application of Deep Learning Models for Vocal Cords Detectio...

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28th international conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Vrba, Jan Steinbach, Jakub Cejnek, Matouš Jirsa, Tomáš Urbániová, Zuzana Department of mathematics informatics and cybernetics University of Chemistry and Technology in Prague Technická 5 Praha 6-Dejvice Prague166 28 Czech Republic Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University in Prague Technická 1902/4 Praha 6160 00 Czech Republic Department of Otorhinolaryngology 3rd Faculty of Medicine Faculty Hospital Královské Vinohrady Charles University Ruská 87 Praha 10 Prague100 00 Czech Republic

The accurate location of vocal cords is crucial in medical imaging applications for diagnostic and therapeutic purposes. In this study, we evaluated the performance of state-of-the-art object detection models, including the YOLO, EfficientDet D1, Faster RCNN and Single Shot Detector architectures, for the task of vocal cord detection. Using a data set of annotated medical images, we conducted comprehensive experiments to compare the models in terms of mean average precision (mAP) and inference time. Our results indicate that YOLOv8m achieved the highest performance, with a mAP@[0.5:0.95] of 0.778 and an inference time of 4.7 milliseconds. EfficientDet D1, Faster R-CNNs, and Single Shot Detectors trailed behind with slightly lower mAP scores. These findings highlight the efficacy of YOLOv8m for vocal cord localization on the utilized dataset. In addition, our study provides valuable information for the selection of suitable object detection models in medical imaging applications, facilitating better diagnosis and treatment planning in laryngology and otolaryngology. © 2024 The Authors.

关键词： Medical imaging

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SLRNet: Shallow Convolutional Neural Network for Efficient Sign Language Recognition

SLRNet: Shallow Convolutional Neural Network for Efficient S...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Lamia Mostafa Faria Jerin Brishti Eishita Sultana Akther MD. Khaliluzzaman MD Jiabul Hoque Department of Computer Science and Engineering International Islamic University Chittagong (IIUC) Chattogram Bangladesh Department of Computer and Communication Engineering International Islamic University Chittagong (IIUC) Chattogram Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

In recent times, recognizing hand gestures from a sign image has become a vital research field in the areas of computer vision and human-computer interaction. Many researchers have taken advantage of various features and deep learning architectures (DLA) to update the performance of existing sign recognition systems. In small datasets such as ASL, shallow DLAs such as convolutional neural networks (CNN) work well. However, the deeper deep learning architectures are overfitted on the small datasets. To overcome this problem, in this work, two shallow CNN models, SLRNet1 and SLRNet2, are proposed. The proposed models use four convolutional layers to extract the features from the sign image and two fully connected layers to recognize the sign. The first and second models gain 98.94% and 99.14% accuracy, respectively. Both models achieve significant improvements with respect to the present state of the ***— Two regularized hand gesture recognition models, SLRNet1 and SLRNet2, were developed to work efficiently on small datasets without overfitting and perform fine-tuning to achieve better accuracy.

关键词： Hands Human computer interaction Sign language Computer vision Image recognition Accuracy Computational modeling Assistive technologies Feature extraction Convolutional neural networks

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