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检索条件"机构=Dept. of Robotics Science and Technology"

共 318 条记录，以下是1-10 订阅

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Autonomous Water Quality Monitoring Surface Vehicle 3

Autonomous Water Quality Monitoring Surface Vehicle

引用

3rd IEEE International Conference on Recent Advances in Systems science and Engineering, RASSE 2023

作者： Nambiar, Manushri Saljith Ramakrishnan, Arjun Lakshman, Anirudh Abhiram, P.V. Girish, P. Toc H Institute of Science and Technology Dept. of Robotics and Automation India

ISBN: (纸本)9798350341676

Water pollution is the contamination of water sources by contaminants that render the water unfit for drinking, cooking, cleaning, swimming, and other uses. Pollutants include chemicals, waste, bacteria, and parasites. Traditional monitoring methods entail manually collecting water samples from vari-ous water resources, followed by testing and analysis in the laboratory. This procedure is frequently ineffective because itis time-consuming and does not produce results in real-Time. Water quality should be continuously monitored to ensure a safe supply of water to end users from any water resources or water bodies. Designing and creating a cost-effective system for real-Time monitoring of water quality utilising the Internet of Thingsis now required. A water vehicle is employed here to house the water quality monitoring system. The vehicle is self-contained, and set locations for measuring water quality can be provided. Using IoT devices and a Wi-Fi module, the proposed system aids in the continuous monitoring of water quality. The Wi-Fi moduleenables internet connectivity and sends sensor data to the Cloud. Various sensors are used to measure various factors in order to assess the water quality of water resources © 2023 IEEE.

关键词： Potable water

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Probabilistic Modelling and Safety Assurance of an Agriculture Robot Providing Light-Treatment 19

Probabilistic Modelling and Safety Assurance of an Agricultu...

引用

19th IEEE International Conference on Automation science and Engineering, CASE 2023

作者： Adam, Mustafa Ye, Kangfeng Anisi, David A. Cavalcanti, Ana Woodcock, Jim Morris, Robert Robotics Group Faculty of Science & Technology Norway University of York Dept. of Computer Science United Kingdom Faculty of Engineering and Science Dept. of Mechatronics Norway Saga Robotics As Norway

ISBN: (纸本)9798350320695

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. © 2023 IEEE.

关键词： Agricultural robots

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks 50

Detection of Electric Motor Damage Through Analysis of Sound...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

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

ISBN: (纸本)9781665464543

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 IEEE.

关键词： Damage detection

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IoT-based Autonomous Robot for Smart Farming 5

IoT-based Autonomous Robot for Smart Farming

引用

5th IEEE International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering, ELECOM 2024

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

ISBN: (纸本)9798331532475

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. © 2024 IEEE.

关键词： Smart agriculture

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Plant Leaf Diseases Classification Using SVMs, XGBoost, and CNNs Based Texture Features 6

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

引用

6th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2024

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

ISBN: (纸本)9798350389692

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. © 2024 IEEE.

关键词： Machine learning

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Aerogel defect detection using terahertz waves 11

Aerogel defect detection using terahertz waves

引用

Infrared, Millimeter-Wave, and Terahertz Technologies XI 2024

作者： Maa, Yehao Shen, Liran Cao, Yuqi Hou, Dibo Zhang, Guangxin State Key Laboratory of Industrial Control Technology Dept. of Control Science and Engineering Zhejiang University Hangzhou310027 China Robotics Institute Ningbo University of Technology Ningbo315211 China

ISBN: (数字)9781510682238

ISBN: (纸本)9781510682221

Aerogel is known as super insulation materials and has already been applied to manufacture thermal protection parts of aerospace equipment. Terahertz (THz) wave has good penetration to non-metallic and non-polar materials. To explore its potential in aerogel defect detection, the absorption coefficient and refractive index of aerogel were studied. As a result, the absorption spectrum of aerogel has not obvious absorption peak, and THz wave has great penetration for aerogel. Additionally, two aerogel samples with different sizes of defects were imaged with two time domain parameters (pulse peak and pulse valley) and two frequency domain parameters (absorption coefficient and refractive index). In contrast to other three parameters, the refractive index presents the greatest performance in defect detection, defects in the aerogel can be observed more clearly through the refractive index. The study is of key importance for THz wave in nondestructive testing for aerogel, offering a rapid and non-destructive detection method for aerogel defects. © 2024 SPIE.

关键词： Refractive index

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine 14

A Sentiment Analysis Study of Twitter Users' Reactions to th...

引用

14th International Conference on Information and Communication Systems, ICICS 2023

作者： Alkhushayni, Suboh Alomari, Zakaria Al-Zaleq, Du'a Yarmouk University Faculty of Information Technology and Computer Science Dept. Information Systems Irbid Jordan New York Institute of Technology Dept. Computer Science VancouverBC Canada Al-Ahliyya Amman University Dept. Robotics and Artificial Intelligence Engineering Amman Jordan

ISBN: (纸本)9798350307870

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution. © 2023 IEEE.

关键词： COVID-19

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Unsupervised Anomaly Detection of a Home Appliance by Monitoring EMI Data

Unsupervised Anomaly Detection of a Home Appliance by Monito...

引用

2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Yu, Hyeonwoo Jeong, Sangyeong Kim, Jingook Sungkunkwan University Dept. of Intelligent Robotics Suwon Korea Republic of Ulsan National Institute of Science and Technology Korea Republic of EMcoretech Co. Ulsan Korea Republic of

ISBN: (纸本)9798350360394

We propose an anomaly detection method by approximating the system state using electromagnetic interference (EMI) data. Since the harmonics of switching frequency cause characteristic patterns in conducted emission (CE) currents, understanding CE patterns can be exploited to detect an anomaly state caused by physical or functional defects in the system. To capture the CE patterns that follow an intractable distribution, we introduce a method based on a variational generative model. The anomaly data in a real-world scenario is challenging to obtain, and as such, we determined an approximated distribution for the normal states of a system to detect an outlier. Further, we designed a manifold space with a multi-modal prior distribution, thus our method can be extended to consider the entire system. To evaluate our approach, we manually collected normal and anomaly EMI data from the outdoor unit of an air conditioner. Using the EMI data from a normal state, we approximated the manifold distribution that follows an tractable distribution and demonstrate the possibilities for outlier detection. While common-mode (CM) CE EMI noise are mainly used for configuring the state of a system, we also apply our approach to the differential-mode (DM) as well as for direct power line noise. © 2024 IEEE.

关键词： Risk assessment

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Design and Analysis of a Real-Time Control Architecture Towards Software-Defined EtherCAT Devices 49

Design and Analysis of a Real-Time Control Architecture Towa...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Delgado, Raimarius Oh, Sang-Rok You, Bum-Jae Choi, Byoung Wook Ihn, Yong Seok Korea Institute of Science and Technology Center for Intelligent & Interactive Robotics Seoul Korea Republic of Seoul National University of Science and Technology Dept. of Electrical and Information Engineering Seoul Korea Republic of

ISBN: (纸本)9798350331820

EtherCAT has been widely adopted in industrial automation and motion control owing to its real-time performance and flexible topology. However, an EtherCAT network with devices from different vendors requires extensive software modification which eventually increases development costs and effort. To address this issue, this paper presents a real-time control architecture focusing on a software-defined approach to easily reconfigure EtherCAT devices with minimal user intervention. This is achieved by simplifying the integration of EtherCAT slaves while exposing required parameters in a configuration interface through an API based on open-source EtherCAT master. We also provide a real-time variation of the POSIX library for a hard real-time multi-tasking environment considering task synchronization. Moreover, the real-time publish-subscribe protocol is also utilized to ensure seamless communication with devices outside of the EtherCAT network. To validate feasibility, practical example involving control of multi-axis robots and real-time performance analysis has been conducted. © 2023 IEEE.

关键词： Real time control

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Localization of Dengue Virus Likelihood Using Image Processing Technique 6

Localization of Dengue Virus Likelihood Using Image Processi...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Hosen, Md. Mahbub Islam, Md. Rahatul Tabassum, Faria Tonni, Syeda Thamina Noor, Malik Mohammad Nafiur Rahman, Shahid Northern University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Kyushu Institute of Technology Dept. of Human Intelligence Systems Wakamatsu Kitakyushu Japan Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Advance Robotics Research Center Human Intelligence Lab Dhaka Bangladesh Canadian University of Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306613

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo V4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes;it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image. © 2024 IEEE.

关键词： aedes mosquito dengue-prone image processing yolo v4 tiny

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