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arXiv 2024年

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

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, CC BY.

关键词： Electric motors

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Adaptive Control Using a Quaternion Wavelet Neural Network

Adaptive Control Using a Quaternion Wavelet Neural Network

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International Workshop on Robot Motion and Control (RoMoCo)

作者： Gerardo Martínez-Terán Eduardo Bayro-Corrochano Dept. Electrical Engineering and Computer Science CINVESTAV Guadalajara Mexico Institute of Automatic Control and Robotics Poznan University of Technology

ISBN: (数字)9798350393965

ISBN: (纸本)9798350393972

robotics and haptic systems have allowed new and diverse applications in the field of medicine, such as assisted surgery and teleoperation which have increasingly stringent requirements for accuracy, convergence, and low computational consumption. In this paper an adaptive PID control law (Proportional Integral Derivative controller, PID), of indirect architecture is presented for movement paths in a haptic system of open chain, where the identification of the plant is through a quaternionic wavelet neural network (Quaternion Wavelet Neural Network, QWNN) for tune the PID values, this allows the optimal movement into the regions of the workspace.

关键词： Uncertainty Quaternions Neural networks Surgery Robustness Haptic interfaces Topology

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Newton-Euler Modeling of Robot Dynamics Using Geometric Algebra

Newton-Euler Modeling of Robot Dynamics Using Geometric Alge...

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International Conference on Methods and Models in Automation and robotics (MMAR)

作者： Eduardo Bayro-Corrochano Daniel Alvarado-Mejia Institute of Automatic Control and Robotics Poznan University of Technology Dept. Electrical Engineering and Computer Science CINVESTAV Guadalajara Mexico

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

In this work, we present the modeling of the dynamics of a robot manipulator using the Newton-Euler algorithm in the conformal algebra framework. The modeling of the dynamics of robot manipulators is currently done using the Euler-Lagrange formulation which is a batch type of computation. In contrast, in this paper, we propose a recursive algorithm for the modeling of the dynamics of robot manipulators using the Newton-Euler algorithm in the conformal geometric algebra framework.

关键词： Algebra Heuristic algorithms Computational modeling Optimal control Motors Real-time systems Mathematical models

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Design and Characterization of a Novel, Low-Cost Resistive Stretch Sensor Band for Interactions in HCI Applications

Design and Characterization of a Novel, Low-Cost Resistive S...

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International Conference on Computing and Networking technology (ICCNT)

作者： B. Sankar Arun Kumar Dhuraisamy S Sindhu S. Rajath Dept. of Mechanical Engineering Indian Institute of Science (IISc) Bangalore India Dept. of Robotics & Automation Engineering PSG College of Technology Coimbatore India Dept. of Electronics Engineering Vellore Institute of Technology (VIT) Chennai India Dept. of Design and Manufacturing Indian Institute of Science (IISc) Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In recent years, Human-Computer Interaction (HCI) technology has progressed significantly toward creating immersive experiences. Yet, the physical interaction within these environments often relies on hand controllers or hand tracking systems using gestures that lack the naturalistic feeling of grasping objects and are computationally expensive, respectively. This study introduces the Resistive Stretch Sensor (RSS) Band, a novel approach to bridging this gap by enabling interactions in HCI through the use of conductive rubber. The RSS is designed as a wearable band that conforms to the user’s forearm, responding to muscle expansion during a full power grasp and release motion. As the RSS band stretches, its resistance value changes, which can be used as input for interactions within a virtual environment. The research characterises the conductive rubber material to determine the optimal RSS band design. The design process of the RSS band is detailed, considering ergonomic factors and the need for reliable and repeatable sensor output. A calibration method is developed to translate resistance changes into accurate input for HCI systems. The study concludes by examining the RSS’s potential applications, such as in human-computer interaction.

关键词： Human computer interaction Technological innovation Sensitivity Ergonomics Virtual environments Muscles Reliability engineering Physiology Rubber Immune system

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IoT-based Autonomous Robot for Smart Farming

IoT-based Autonomous Robot for Smart Farming

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Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), 2020 3rd International Conference on

作者： A S M Ahsanul Sarkar Akib N.M.Raziul Hassan Rishad Salahin Sourov Md Shahin Alam Mozumder Md. Shahriyar Rahman Fatema Jahan Sifa Md Sharif Alom Md Abdullah AL Ahasan Dept. of Robotics Robo Tech Valley Dhaka Bangladesh Dept. of CSE American International University Bangladesh Dhaka Bangladesh Dept. of IT Washington University of Science and Technology USA Dept. of EEE Ahsanullah University of Science and Technology Dhaka Bangladesh Dept. of Pharmacy Independent University Bangladesh Dhaka Bangladesh Dept. of CSE Z. H. Sikder University of Science and Technology Bangladesh dept. of CS University of Regina Regina Canada

ISBN: (数字)9798331532475

ISBN: (纸本)9798331532482

The integration of the Internet of Things (IoT) and artificial intelligence (AI) has revolutionized agriculture and smart farming. These advanced technologies have not only improved cognitive processes but also enabled autonomous systems to navigate and communicate intelligently within agricultural environments. The adoption of smart sensor systems, in conjunction with IoT, has modernized traditional farming practices, particularly through AI-driven crop management solutions. This paper explores how a robotic vehicle (robocar), equipped with sensors to detect nitrogen (N), phosphorus (P), potassium (K), soil moisture, and humidity, collects data and transmits it to a cloud server for analysis. One mobility feature of the robot is its ability to navigate fields autonomously using GPS-guided paths. Starting from a designated home location, the robot follows a pre-set route to the field, where its robotic arm automatically collects soil samples for testing. By utilizing sensors that measure water content, soil moisture, and NPK levels, the system assesses soil conditions and sends the data to the IoT server. Based on this information, AI provides recommendations for the most suitable crops for the area. Furthermore, the robot monitors soil properties and alerts the operator to significant changes in the environment or infrastructure. After completing its tasks, the robot uses its navigation capabilities to autonomously return to its home position by activating the ”return home” function.

关键词： Smart agriculture Navigation Soil moisture Crops Robot sensing systems Internet of Things Servers Artificial intelligence Robots Intelligent sensors

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A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

A Trustable LSTM-Autoencoder Network for Cyberbullying Detec...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Akter, Mst Shapna Shahriar, Hossain Cuzzocrea, Alfredo Wu, Fan Rodriguez-Cardenas, Juanjose University of West Florida Department of Intelligent Systems and Robotics United States University of West Florida Center of Cybersecurity United States University of Calabria IDEA Lab Rende Italy Tuskegee University Dept. of Computer Science Tuskegee United States Kennesaw State University Dept. of Information Technology Georgia United States

ISBN: (纸本)9798350324457

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models' performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper. © 2023 IEEE.

关键词： Cyber-bullying Deep Learning Natural Language Processing Neural Networks

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Probabilistic Modelling and Safety Assurance of an Agriculture Robot Providing Light-Treatment

Probabilistic Modelling and Safety Assurance of an Agricultu...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Mustafa Adam Kangfeng Ye David A. Anisi Ana Cavalcanti Jim Woodcock Robert Morris Robotics Group Faculty of Science & Technology Norwegian University of Life Sciences (NMBU) Norway Dept. of Computer Science University of York UK Dept. of Mechatronics Faculty of Engineering and Science University of Agder (UiA) Norway Saga Robotics AS Norway

Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety-assurance framework can also be used during implementation, and subsequent deployment and operation phases.

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Plant Leaf Diseases Classification Using SVMs, XGBoost, and CNNs Based Texture Features

Plant Leaf Diseases Classification Using SVMs, XGBoost, and ...

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IEEE International Conference on Artificial Intelligence in Engineering and technology (IICAIET)

作者： Abdullah Kamal Sami Khamael Al-Dulaimi Computer Science Dept. Al-Nahrain University Baghdad Iraq School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Plant diseases identification is frequently used by physical examination or laboratory investigation which creates delays that, by the time identification is finished, results in yield loss. Diseases may affect different parts of plant, specifically the leaf. Different Research have made significant contributions to this sector and used computer vision technologies and machine learning effectively. Consequently, having a thorough understanding of the most recent advancements in machine learning technology and its applications for the identification of leaf diseases. In this paper, a technique is proposed by employing texture features with SVM, XGBoost and CNN to identify leaf disease of five types of plants: Alstonia, Gauva, Jamun, Lemon, and Mango. We have used Local Binary Pattern to extract texture features from leaf because the diseases can affect the texture of plant, such as hairs, ridges, and waxy coatings. We have used challenging dataset which taken under different conditions, such as humidity, light, nutrition, water, and temperature which affect the texture, shape, colour and size of leaves. The findings show that the CNN based LBP achieved high accuracy in comparing with others models.

关键词： Plant diseases Accuracy Temperature Shape Computational modeling Machine learning Color Feature extraction Object recognition Diseases

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Free-space photonic quantum memory 3

Free-space photonic quantum memory

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Quantum Computing, Communication, and Simulation III 2023

作者： Arnold, Nathan T. Victora, Michelle Goggin, Michael E. Kwiat, Paul G. Dept. of Physics University of Illinois Urbana-Champaign United States Truman State University United States Illinois Quantum Information Science & Technology Center University of Illinois Urbana-Champaign United States RightHand Robotics Inc United States

ISBN: (纸本)9781510659971

Photonic quantum memories will play an essential role in several quantum information protocols, including distributed quantum computing, quantum sensing, and the synchronization of repeater nodes. Most photonic memories operate by storing the photon in matter-based systems, but those approaches have limitations. Namely, they are inherently narrow bandwidth, often require costly overhead in the form of cryogenics, and typically have low retrieval efficiency into single-mode fiber. In this work, we develop a photonic quantum memory that operates at room temperature in free space, allowing us to avoid the aforementioned drawbacks. © 2023 SPIE.

关键词： Single mode fibers

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Using Dynamic Optical Coherence Elastography to Measure Material Properties 10

Using Dynamic Optical Coherence Elastography to Measure Mate...

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Optical Elastography and Tissue Biomechanics X 2023

作者： Urban, Matthew W. Liu, Hsiao-Chuan Kijanka, Piotr Dept. of Radiology Mayo Clinic 200 First Ave SW RochesterMN55905 United States Dept. of Ophthalmology University of Southern California Los AngelesCA90033 United States Dept. of Mechanical Engineering Massachusetts Institute of Technology 33 Massachusetts Ave CambridgeMA02139 United States Department of Robotics and Mechatronics AGH University of Science and Technology Al. Adama Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9781510658677

Optical coherence elastography (OCE) has been used successfully for characterizing changes tissue mechanical properties particularly in breast tissue and the eye. Many dynamic ultrasound shear wave elastography (SWE) methods have been developed over the past three decades that use propagating waves with different dynamic excitations. We have successfully translated excitation and analysis methods from SWE for applications using OCE. We report here recent developments that utilize focused ultrasound to produce acoustic radiation force or mechanical vibration. We have explored characterizing the rheological properties such as surface tension and viscosity of various fluids. Additionally, we have applied these OCE methods to soft tissues such as blood clots, aorta samples, and porcine kidneys. These techniques have opened new areas for tissue characterization that take advantage of the sensitivity and resolution of optical coherence tomography and the strengths of wave-based approaches for quantifying material properties. © 2023 SPIE.

关键词： Vibrations (mechanical)

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