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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Towards Efficient Aerial Object Detection: Techniques, Datasets and Challenges 3

Towards Efficient Aerial Object Detection: Techniques, Datas...

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3rd International Conference on Communication Technologies, ComTech 2021

作者： Anwar, Hirra Javed, Kashif Rubab, Saddaf Khan, Muhammad Jawad Department of Robotics and Intelligent Machine Engineering SMME Islamabad Pakistan Military College of Signals Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665419017

In the past decade, object detection has gained significant attention in computer vision due to variety of its applications including picture retrieval, object tracking, self-driving cars, security, activity recognition, traffic surveillance and many more. Latest research in computer vision is making remarkable progress in multiple directions;aerial object detection is one of the foremost. Object detection in natural scenes has been extensively researched however, challenges remain in aerial object detection due to factors such as variation in scale and shape of objects, occlusion, monotonic appearance, and arbitrary orientation of objects in a scene. Literature highlights deep learning models surpass all others in terms of accuracy and efficiency for aerial object detection and recognition. It is well-known that datasets directly impact deep learning models as deep learning itself is a data driven approach. It is therefore crucial to analyze not only the object detection algorithms in aerial imagery, but also the remote sensing datasets that are available. In this paper, we analyze multiple state-of-the-art models that are used in the field of object detection specific to aerial images. We also present a detailed timeline of the prominent aerial imagery datasets;analyze them in detail and present their strengths and weaknesses. At the end, we discuss various challenges present and future research directions in this field. This research work will further pave way for future advancement in the field of aerial object detection and recognition. © 2021 IEEE.

关键词： Antennas

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Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks

arXiv

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arXiv 2023年

作者： Conde, Pedro Barros, Tiago Lopes, Rui L. Premebida, Cristiano Nunes, Urbano J. The Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal Critical Software South Africa

With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. This requires a thorough look not only at the accuracy of such systems, but also at their predictive uncertainty. Hence, we propose a novel technique (with two different variations, named M-ATTA and V-ATTA) based on test time augmentation, to improve the uncertainty calibration of deep models for image classification. By leveraging an adaptive weighting system, M/V-ATTA improves uncertainty calibration without affecting the model’s accuracy. The performance of these techniques is evaluated by considering diverse metrics related to uncertainty calibration, demonstrating their robustness. Empirical results, obtained on CIFAR-10, CIFAR-100, Aerial Image Dataset, as well as in two different scenarios under distribution-shift, indicate that the proposed methods outperform several state-of-the-art post-hoc calibration techniques. Furthermore, the methods proposed also show improvements in terms of predictive entropy on out-of-distribution samples. Code for M/V-ATTA available at: https://***/pedrormconde/MV-ATTA Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles

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Computers, Materials & Continua 2022年第10期73卷 1-14页

作者： Jaeyong Kang Chul-Su Kim Jeong Won Kang Jeonghwan Gwak Department of Software Korea National University of TransportationChungju27469Korea School of Railroad Engineering Korea National University of TransportationUiwang16106Korea Graduate School of Transportation Korea National University of TransportationUiwang16106Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of IT Convergence(Brain Korea PLUS 21) Korea National University of TransportationChungju27469Korea

The anomaly detection of the brake operating unit (BOU) in thebrake systems on metro vehicle is critical for the safety and reliability ofthe trains. On the other hand, current periodic inspection and maintenanceare unable to detect anomalies in an early stage. Also, building an accurateand stable system for detecting anomalies is extremely difficult. Therefore,we present an efficient model that use an ensemble of recurrent autoencodersto accurately detect the BOU abnormalities of metro trains. This is the firstproposal to employ an ensemble deep learning technique to detect BOUabnormalities in metro train braking systems. One of the anomalous caseson metro vehicles is the case when the air cylinder (AC) pressures are less thanthe brake cylinder (BC) pressures in certain parts where the brake pressuresincrease before coming to a halt. Hence, in this work, we first extract the dataof BC and AC pressures. Then, the extracted data of BC and AC pressuresare divided into multiple subsequences that are used as an input for bothbi-directional long short-term memory (biLSTM) and bi-directional gatedrecurrent unit (biGRU) autoencoders. The biLSTM and biGRU autoencodersare trained using training dataset that only contains normal subsequences. Fordetecting abnormalities from test dataset which consists of abnormal subsequences, the mean absolute errors (MAEs) between original subsequences andreconstructed subsequences from both biLSTM and biGRU autoencoders arecalculated. As an ensemble step, the total error is calculated by averaging twoMAEs from biLSTM and biGRU autoencoders. The subsequence with totalerror greater than a pre-defined threshold value is considered an *** carried out the experiments using the BOU dataset on metro vehiclesin South Korea. Experimental results demonstrate that the ensemble modelshows better performance than other autoencoder-based models, which showsthe effectiveness of our ensemble model for detecting BOU anomalies onmetro trains.

关键词： Anomaly detection brake operating unit deep learning machine learning signal processing

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Collaborating for Success: Optimizing System Efficiency and Resilience Under Agile Industrial Settings

Collaborating for Success: Optimizing System Efficiency and ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sunny Katyara Suchita Sharma Praveen Damacharla Carlos Garcia Francis O’Farrell Philip Long Department of Robotics & Automation Irish Manufacturing Research Company Ltd Ireland Software Engineering Division Shutterfly USA KineticAI INC Woodlands Texas USA

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Designing an efficient and resilient human-robot collaboration strategy that not only upholds the safety and ergonomics of shared workspace but also enhances the performance and agility of collaborative setup presents significant challenges concerning environment perception and robot control. In this research, we introduce a novel approach for collaborative environment monitoring and robot motion regulation to address this multifaceted problem. Our study proposes novel computation and division of safety monitoring zones, adhering to ISO 13855 and TS 15066 standards, utilizing 2D lasers information. These zones are not only configured in the standard three-layer arrangement but are also expanded into two adjacent planes, thereby enhancing system uptime and preventing unnecessary deadlocks. Moreover, we also leverage 3D visual information to track dynamic human articulations and extended intrusions. Drawing upon the fused sensory data from 2D and 3D perceptual spaces, our proposed hierarchical controller stably regulates robot velocity, validated using Lasalle in-variance principle. Empirical evaluations demonstrate that our approach significantly reduces task execution time and system response delay, resulting in improved efficiency and resilience within collaborative settings.

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Real-Time Plant Disease Detection Using YOLOv5 and Autonomous Robotic Platforms for Scalable Crop Monitoring

Real-Time Plant Disease Detection Using YOLOv5 and Autonomou...

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International Conference on Cybernetics, robotics and Control (CRC)

作者： M. Hassan Tanveer Zainab Fatima Muzammil Ali Khan Razvan Cristian Voicu M. Farrukh Shahid Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta USA Department of Software Engineering NED University of Engineering and Technology Karachi Pakistan Department of AI and Data Science FAST University of computing and Emerging Science Karachi Pakistan

ISBN: (数字)9798331517489

ISBN: (纸本)9798331517496

Plant diseases pose a significant threat to global food security, with annual losses amounting to billions of dollars. Early detection and intervention are critical for mitigating these losses. Traditional methods of disease detection, such as visual inspection, are time-consuming, labor-intensive, and often inefficient. This paper presents an advanced approach using the YOLOv5 (You Only Look Once) model, a state-of-the-art object detection algorithm, to identify and classify plant diseases in real-time. The YOLOv5 model offers high accuracy and efficiency, particularly for tasks requiring simultaneous detection of multiple objects, making it ideal for agricultural applications. This paper explores using novel data collection methods, such as mounting cameras on Husky UGV (Unmanned Ground Vehicle), to capture diverse images of crops from various angles in the field. These autonomous systems enhance dataset variety, contributing to more accurate detection results. Our methodology focuses on training the YOLOv5 model using annotated datasets, which enables the model to identify diseases such as apple rust leaf, bell pepper leaf spot, and others. The experimental results demonstrate a detection accuracy of 92 %, showcasing the potential of YOLOv5 for realtime crop disease monitoring. Integrating robotics for field analysis is further discussed as a future direction, providing a scalable solution for continuous crop monitoring and disease management.

关键词： YOLO Plant diseases Visualization Accuracy Autonomous systems Crops Food security Real-time systems Robots Monitoring

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Potholes Detection Using Yolo Algorithms and Their Integration Into Gazebo Simulation

Potholes Detection Using Yolo Algorithms and Their Integrati...

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International Conference on Cybernetics, robotics and Control (CRC)

作者： M. Hassan Tanveer Zainab Fatima Tanazzah Rehman Moaaz Siddique Arbaz Shah Razvan Cristian Voicu M. Farrukh Shahid Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta USA Department of Software Engineering NED University of Engineering and Technology Karachi Pakistan Department of AI and Data Science FAST University of computing and Emerging Science Karachi Pakistan

ISBN: (数字)9798331517489

ISBN: (纸本)9798331517496

Road cavities, often called potholes, pose a major problem for our transportation infrastructure, causing accidents, costly repairs, and traffic jamming. This study shows using a computer vision technique called You Only Look Once (YOLO) to find potholes on roads. YOLO is proficient and efficient for realtime object detection. The researchers want to see which version of YOLO is more effective in object analysis in pictures and videos. We'll be comparing YOLOv5, YOLOv7, and YOLOv8, thereby also setting up the system in Gazebo simulated environment. By carefully analyzing the results, we aim to understand the advantages and disadvantages of each YOLO version. This will help us see the most useful ones for finding potholes when managing and maintaining roads.

关键词： YOLO Location awareness Roads Transportation Maintenance engineering Prediction algorithms Classification algorithms Object recognition Jamming Videos

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The Role of YOLOv8 in Enhancing Strategic Military Equipment Detection

The Role of YOLOv8 in Enhancing Strategic Military Equipment...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdulfattah E. Ba Alawi Hussein M. A. Mohammed Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Energy and Cooling Systems Department Turk Telekom Ankara Turkey Mechatronics and Robotics Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The accurate and fast identification of military equipment on the battlefield is a crucial problem of various military missions and tasks. First, it affects the operation success of the modern armed forces. Second, decision-making is considerably affected by such a capability. In this paper, we address this issue using the potential of the powerful YOLOv8 technique. This allows us to offer an accurate model for identifying various components related to the military and armed forces. The model is tested on the publicly available dataset which contains 11,800 images by evaluating the detection task with YOLOv8-based models. This dataset consists of eleven classes on military-related equipment, which covers different types of military assets. Finally, the YOLOv8x model shows encouraging results in detecting military equipment as demonstrated by a value of 99.1% in terms of mAP50. Through the findings of this study, there has been significant improvement in using AI in military missions, decision-making, and mission readiness which are better than those obtained in the previous studies. According to the obtained findings, this study demonstrates substantial progress in the application of artificial intelligence to military missions and decision-making and operational development.

关键词： Accuracy Decision making Task analysis Artificial intelligence Military equipment

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CASolarNet: Channel Attention EfficientNet-based Model for Solar Panel Dust Detection

CASolarNet: Channel Attention EfficientNet-based Model for S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Hussein M. A. Mohammed Abdulfattah E. Ba Alawi Energy and Cooling Systems Department Turk Telekom Ankara Turkey Mechatronics and Robotics Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The accumulation of dust on solar panels dramatically reduces the efficiency of solar energy systems, an issue that is often overlooked in both urban and rural settings. This work proposes a novel solution to this widespread problem of Solar Dust Detection. Utilizing EfficientNet, which is known for its balanced approach to model depth, width, and resolution, our proposed model accurately identifies dust on solar panels. The suggested model (CASolarNet) employs Channel Attention, EfficientNet, and focal loss to enhance the performance of detecting dust on solar cells. By dynamically modifying feature weights, channel attention techniques enhance dust detection by emphasizing the locations of dust in an image. This capability is crucial for maintaining optimal energy production and extending the lifespan of solar cells. Our model has been extensively tested and validated, resulting in a high accuracy rate of more than 98%. The obtained performance demonstrates the model's effectiveness in various environmental conditions, establishing a new standard in solar maintenance technology. The use of the proposed CASolarNet model has several advantages, including increased energy efficiency, lower maintenance costs, and greater sustainability of solar power systems.

关键词： Image resolution Photovoltaic cells Solar energy Production Feature extraction Maintenance Power systems

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