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6th International Conference on Energy Conservation and Efficiency, ICECE 2023

作者： Liaqat, Umair Sohaib, Muhammad Shaukat, Hamza Younis, Usman Hafiz, Rehan Tauqeer, Tauseef Information Technology University Faculty of Engineering Lahore Pakistan National Center of Robotics and Automation Industrial Monitoring and Automation Lab Pakistan

ISBN: (纸本)9798350332193

The purpose of this study is to analyze the health and power quality analysis of motors installed at different blocks of Johar Town Lahore installed by Water and Sanitation Authority (WASA) Lahore. Induction motors are the most important components of industries due to their cost-effectiveness, robustness and high efficiency. However, these motors are prone to some mechanical and electrical faults with time. Most of the faults are due to mismanagement of power and load variations. Usually, health monitoring and quality analysis of induction motors is laborious work. This study was conducted by keeping in mind the most widely used standards of the International Electro-technical Commission (IEC) and the Institute of Electrical and Electronics Engineer (IEEE). To enhance the effectiveness of the electrical systems, a detailed inspection and knowledge are needed for every power system. This technique involves taking measurements from motors in different states i.e. rest, transient and steady. The comparison of the results on different parameters with the help of graphical data is presented in this study for the safe and economical use of machines. The study covers only seven motors out of 596 installed motors at tube well sites in the city of Lahore covering almost 8 million people. There is no existing system to analyze the performance, efficiency and health of these motors. This study may be helpful in quality assurance and save from critical failure that tends to disturb the water supply system. It can also beneficial of saving the cost of repair and plant's down time. It may be recommended to analyze thoroughly the remaining tube well motors for the beneficial of WASA water supply system. © 2023 IEEE.

关键词： Induction motors

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Indoor Localization System by Using Bluetooth Low Energy with Mobile Application

Indoor Localization System by Using Bluetooth Low Energy wit...

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World Conference on Information Systems for Business Management, ISBM 2023

作者： Vongbunyong, Supachai Mekswang, Warut Thamrongaphichartkul, Kitti Hospital Automation Research Center Institute of Field Robotics King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9789819983483

Localization problems are occurred in various applications, indoor and outdoor. The location data of objects is one of the primary information that can be further used in many applications, especially in logistic management in hospitals. Beacon technology based on Bluetooth Low Energy (BLE) is one of the technologies used for indoor localization. With multiple BLE locators used as reference points, the receiver can sense the signal with various receive signal strength indicators (RSSI). The distance between each locator and the receiver can be estimated, so that the receiver can be located. However, a number of factors affect the accuracy of localization, e.g., poses of the receiver and the quantity of locator. This research focuses on these factors to identify the limitation and to achieve the best performance possible. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bluetooth

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LabView based Automated Motor Test Bench for Induction Motors 4

LabView based Automated Motor Test Bench for Induction Motor...

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4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Sohaib, Muhammad Shaukat, Hamza Tauqeer, Tauseef Shahid, Arslan Younis, Usman Hafiz, Rehan Information Technology University Faculty of Engineering Lahore Pakistan Industrial Monitoring and Automation Lab National Center of Robotics and Automation Pakistan

ISBN: (纸本)9798350335316

Induction motors comprise more than 90 percent of the industrial load. With time, Induction motors are prone to losses. To save energy consumption, predictive maintenance of motors must be carried out at regular intervals. The industrial monitoring and automation lab has developed state-of-the-art motor test bench facility which is completely automated using LabView - a widely used industrial software. The manual methods of motor testing are not only hectic but also unreliable. A systematic approach has been adopted to measure and analyze various parameters of the induction motor, which will help us to identify key performance factors. This work is towards the development of a high performance motor test bench facility for industrial load. In its current state it can measure up to 15 hp induction motors and perform the tests such as No-Load Test, Full-Load Test, Locked-Rotor Test, Temperature Rise Test, DC Winding Test etc. © 2023 IEEE.

关键词： Induction motors

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Real-Time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes

Real-Time Motion Generation and Data Augmentation for Graspi...

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

作者： Yamamoto, Kenjiro Ito, Hiroshi Ichiwara, Hideyuki Mori, Hiroki Ogata, Tetsuya Connective Automation Innovation Center R&d Group Hitachi Ltd. Robotics Research Department Japan Institute for Ai and Robotics Future Robotics Organization Japan Waseda University Faculty of Science and Engineering Japan

ISBN: (纸本)9798350312072

While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion teaching in a real environment. The manipulation of moving objects, due to their dynamic properties, requires learning a wide range of factors such as the object's position, movement speed, and grasping timing. We propose a data augmentation method for enabling a robot to grasp moving objects with different speeds and grasping timings at low cost. Specifically, the robot is taught to grasp an object moving at low speed using teleoperation, and multiple data with different speeds and grasping timings are generated by down-sampling and padding the robot sensor data in the time-series direction. By learning multiple sensor data in a time series, the robot can generate motions while adjusting the grasping timing for unlearned movement speeds and sudden speed changes. We have shown using a real robot that this data augmentation method facilitates learning the relationship between object position and velocity and enables the robot to perform robust grasping motions for unlearned positions and objects with dynamically changing positions and velocities. © 2024 IEEE.

关键词： Time series

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Correcting of the unexpected localization measurement for indoor automatic mobile robot transportation based on a neural network

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Transportation Safety and Environment 2024年第2期6卷 24-35页

作者： Jiahao Huang Steffen Jung inger Hui Liu Kerstin Thurow Center for Life Science Automation(CELISCA) University of RostockRostock 18119Germany Institute of Automation University of RostockRostock 18119Germany Institute of Artificial Intelligence&Robotics(IAIR) School of Traffic&Transportation EngineeringCentral South UniversityChangsha 410075HunanChina

The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory ***,when mobile robots move in laboratory environments,mechanical errors,environmental disturbances and signal interruptions are *** can compromise the accuracy of the robot’s localization,which is crucial for the safety of staff,robots and the laboratory.A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory *** proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile *** experimental results demonstrate the effectiveness of this proposed method.

关键词： mobile robots laboratory automation indoor localization neural network

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YOLO v5, v7 and v8: A Performance Comparison for Tobacco Detection in Field 3

YOLO v5, v7 and v8: A Performance Comparison for Tobacco Det...

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3rd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2023

作者： Mahboob, Zaid Zeb, Ayesha Khan, Umar S. National University of Science and Technology College of Eme Islamabad Pakistan National Center of Robotics and Automation Pakistan

ISBN: (纸本)9798350330816

Accurate tobacco plant detection is crucial for effective agricultural management strategies. Due to the challenges posed by high plant variability, adverse lighting conditions, and occlusions, existing machine learning algorithms and convolutional neural networks (CNNs) have struggled to achieve satisfactory detection accuracy under real-world scenarios. This paper employs YOLOv8, a state-of-the-art object detection algorithm, to reliably detect tobacco plants. A comprehensive evaluation and benchmark of 12 state-of-the-art YOLO object detection algorithms including the YOLOv8, is established for tobacco detection. YOLOv8 demonstrates remarkable test accuracy of 96.4compared to YOLOv5 and YOLOv7. Performance comparisons with YOLOv5 and YOLOv7, two ancestor algorithms, using test images further solidify YOLOv8's superior detection capabilities for tobacco plants. YOLOv8's real-time object detection ability renders it an ideal solution for mobile and embedded devices, opening new possibilities for on-the-go agricultural management. © 2023 IEEE.

关键词： Object detection

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Predictive Maintenance in Urban Railway Systems Using Machine Learning Models 7

Predictive Maintenance in Urban Railway Systems Using Machin...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Shaikh, Muhammad Zakir Ali, Sahil Ali, Sara Baro, Enrique Nava Baloch, Yaqoob Ali Chowdhry, Bhawani Shankar Jamshoro Pakistan School of Industrial Engineering University of Malaga Malaga Spain National Center of Robotics and Automation Cms Lab Jamshoro Pakistan Universidad de Malaga Departamento de Ingeniería de Comunicaciones Campus de Teatinos Malaga Spain National Center for Robotics Automation and Artificial Intelligence Jamshoro Pakistan

ISBN: (纸本)9798331534271

The Air Production Unit (APU) is responsible for managing the load in the metro train by making sure that the load is evenly distributed on the wheels despite passenger congestion. The failure of APU results in a complete halt of operations. Therefore, it is pivotal to timely maintain the APU system. In this paper, a predictive maintenance (PdM) approach has been suggested using multiple state-of-the-art machine learning algorithms to predict failure in the APU. Sensor parameters that lead to failures in APUs of the electric trains were identified from the MetroPT train dataset by employing a variety of baseline models including linear regression (LR), decision tree regressor (DTR), random forest regressor (RFR), gradient boosting regressor (GBR) and XGBoost Regressor (XGBR) models. This will significantly reduce both the operational cost as well as the downtime of the trains. It will also help in the identification of faulty parts at a faster rate and reduce the failure rate of urban transportation systems. © 2024 IEEE.

关键词： Predictive maintenance

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Fuzzy Inference based Pedestrian and Cyclist Tracking in a Compressed Video

Fuzzy Inference based Pedestrian and Cyclist Tracking in a C...

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2024 IEEE Conference on Engineering Informatics, ICEI 2024

作者： Praveenkumar, S.M. Patil, Prakashgouda Karadgi, Sachin Hiremath, P.S. KLE Technological University Department of Computer Applications Hubballi India KLE Technological University Center for Automation and Robotics Research Hubballi India

ISBN: (纸本)9798331505776

In recent times, the artificial intelligence (AI) based technologies are pervading the various human activities, for instance, smart manufacturing plants and autonomous driving vehicles (ADVs) on roads. With the advent of Industry 4.0, it is of common occurrence that human workers and autonomous mobile robots (AMRs) coexist and collaborate in the various tasks of smart manufacturing plants. The humans could be either walking or riding a bicycle in case of large area plants such as paper manufacturing industries. Thus, the industrial safety and traffic safety become cause of primary concern in the usage of AMRs and ADVs, respectively, which entails the need for equipping these intelligent devices with computer vision for detecting the humans, who are either walking or cycling, in their path of mobility, and thus avoid their collision. The integration of such detection systems is crucial for maintaining safe interactions between humans and machines. In the present study, a novel methodology based on fuzzy inferencing is designed for video analytics in the compressed domain, aimed at real-time detection and tracking of pedestrians and cyclists, by utilizing motion vectors (MV) and Transformer Prediction Heads-You Only Look Once version five (TPH-YOLOv5). The video compression formats MPEG-4 and H. 264 are considered. The proposed method can be adapted to any compression format and video camera settings without any prior setup. In the literature, many algorithms for tracking of pedestrians and cyclists in a compressed domain video are experimented, but these have some limitations such as (i) a fixed interval for keyframe occurrence, (ii) utilization of only P frames, and (iii) setup for only a single video codec. The proposed method is designed to address these limitations by employing keyframe intervals of variable length, utilizing P and B frames, and setting up two codecs MPEG-4 and H.264. Further the event box prediction in video frames is done by fuzzy inferencing.

关键词： Bicycles

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Data-Driven Control of Highly Deformable Robotic Endoscopes Using Proprioceptive Shape Feedback

Data-Driven Control of Highly Deformable Robotic Endoscopes ...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Lu, Yiang Zheng, Jixuan Liu, Yun-Hui T Stone Robotics Institute The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Hong Kong

ISBN: (纸本)9781665481090

In this paper, we introduce a versatile data-driven approach for servo-controlling the highly deformable robotic endoscope equipped with Draw Tower Gratings (DTGs). The advancement of perception and decision-making technologies is pivotal for enabling flexible robots to execute automatic procedures. However, modeling these robots presents a significant challenge due to their large deformations and susceptibility to environmental disturbances. To address this issue, we propose a robust adaptive control framework that leverages DTG-based proprioceptive shape feedback. This automatic paradigm can online estimate the unknown deformation model of the flexible robot based on function approximation (FA), while also accounting for unknown perturbations without prior identification. By employing the proposed FA-based adaptive algorithm, we can ensure the convergence of the shape control error. Experimental results validating a highly deformable flexible robot embedded with DTG sensors showcase the efficiency of the proposed control strategy in unknown scenarios. © 2024 IEEE.

关键词： Adaptive algorithms

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Natural Disaster Damage Assessment using Semantic Segmentation of UAV Imagery

Natural Disaster Damage Assessment using Semantic Segmentati...

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2023 International Conference on robotics and automation in Industry, ICRAI 2023

作者： Asad, Muhammad Haroon Asim, Malik Muhammad Awan, Muhammad Naeem Mumtaz Yousaf, Muhammad Haroon University of Engineering and Technology Computer Engineering Department Taxila Pakistan National Center of Robotics and Automation University of Engineering and Technology Swarm Robotics Lab Taxila Pakistan

ISBN: (纸本)9781665464727

Numerous natural disasters due to climate change pose major threats to the sustainability of public infrastructure and human lives. For emergency rescue and recovery during a disaster, a rapid and accurate evaluation of disaster damage is essential. In recent years, the Transformer has gained popularity in a number of tasks related to computer vision, which offers tremendous potential for improving the accuracy of disaster damage assessments. Our research aims to determine whether Vision Transformer (ViT) can be used to assess natural disaster damage on high-resolution Unmanned Aerial Vehicle (UAV) data in comparison with conventional deep-learning semantic segmentation techniques. We discuss if Transformer can perform better than CNNs in accurately assessing the damage caused in order to bridge the gap. Detailed performance comparison of state-of-art deep learning semantic segmentation models (UNET, Segnet, PSPNet, Deeplabv3+) and Transformer framework (SegFormer) for damage assessment is presented. The experimentation is performed on both natural disaster damage datasets (RescueNet, FloodNet). The study supported SegFormer as the most appropriate model for estimating disaster damage, with mIoUs of 96% on the RescueNet dataset and 82.22% on the FloodNet dataset, respectively. The Transformer is capable of outperforming conventional segmentation CNNs in understanding the entirety of the scene and assessing the severity of the damage, based on both quantitative evaluation and visual results. © 2023 IEEE.

关键词： Disasters

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