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Computation and Experimental Validation of the Reachable Workspace of a 6-6 Stewart Platform Manipulator 1

7th IFToMM Asian conference on Mechanism and Machine Science, Asian MMS 2024

作者： Kolte, Aditya Mahesh Patra, Bibekananda Dhanush, S. Bandyopadhyay, Sandipan Department of Engineering Design Indian Institute of Technology Madras Tamil Nadu Chennai India Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India

ISBN: (数字)9783031675690

ISBN: (纸本)9783031675683

The workspace of a Stewart platform manipulator is obtained as an intersection of the workspaces of the individual SPS linkages connecting the fixed base to the moving platform. The workspace thus has a complicated geometric shape. In this paper, a semi-analytical approach is used to identify the reachable workspace of a semi-regular Stewart platform manipulator, considering the physical actuator limits for a given range of orientations of the moving platform. A convex description of the workspace is obtained in the form of the largest sphere which is centred at a chosen location and is fully contained inside the workspace. The dimension of the sphere is subsequently validated experimentally using a desktop prototype of the manipulator. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Flexible manipulators

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Thermal Vision RC Tank for Human Detection in Disaster Area Using IoT 5

Thermal Vision RC Tank for Human Detection in Disaster Area ...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Jyotsna, P. Sanjay Surendar, B. Sharan, B. Dhanaselvam, J. Saravanakumar, K. Rajesh, R. Sri Krishna College of Technology Dept of Instrumentation and Control Engineering Coimbatore India Sri Krishna College of Technology Dept. of Electrical and Electronics Engineering Coimbatore India Indian Institute of Technology Department of Engineering Design Chennai India

ISBN: (纸本)9798350385649

In this modern era of technology, still conventional methods (human and trained dogs) are being used to find and rescue the victims, who are buried under the rubble after natural or human-made destruction. Offering a revolutionary solution, this robot outperforms traditional methods of searching for individuals during rescue operations. Another key initiative is the integration of IoT technologies, which will facilitate communications and data exchange. These rescue efforts come with significant risks for both rescuers and the victims aim to save. Compared to current methods, this robot performs exceptionally well in locating people during rescue missions. Its main advantage lies in its incorporation of advanced sensors, particularly those capable of detecting body heat and infrared signals. This technological enhancement allows the robot to identify individuals even in challenging conditions, such as darkness, smoke, or clutter, where regular cameras struggle. © 2024 IEEE.

关键词： Cameras

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NANOHEXCOOL: Advanced Thermal control for Lithium-Ion Batteries 8

NANOHEXCOOL: Advanced Thermal Control for Lithium-Ion Batter...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Ragul, S. Rajeshwari, M. Meganthan, P. Dhanaselvam, J. Saravanakumar, K. Rajesh, R. Sri Krishna College of Technology Dept of Instrumentation and Control Engineering Coimbatore India Sri Krishna College of Technology Dept. of Electrical and Electronics Engineering Coimbatore India Indian Institute of Technology Madras Department of Engineering Design Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

This study evaluates thermal management strategies for lithium-ion batteries in electric vehicles, addressing safety concerns like thermal runaway and performance degradation under extreme temperatures. A comprehensive assessment considers factors such as production costs, maintenance needs, system complexity, and internal temperature control. Among various techniques explored, a hexagon-structured cooling chamber utilizing nano-fluids emerges as a promising solution. Liquid cooling systems, categorized into direct and indirect systems, are assessed based on fluid interaction with the battery surface. Hexagonal cooling chambers offer innovative thermal regulation, demonstrating efficient mitigation of temperature-related challenges. Numerical simulations confirm the effectiveness of the devised thermal management mechanism in maintaining battery cell temperatures within specified ranges. Implementation of such strategies is crucial for enhancing battery safety and performance in electric vehicles, ensuring reliable operation across diverse temperature conditions while mitigating risks associated with thermal runaway events. © 2024 IEEE.

关键词： Temperature control

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Traffic Speed Limit Sign Recognition using Deep Learning 7

Traffic Speed Limit Sign Recognition using Deep Learning

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kamali, B. Kishore, A. Rajkumar, S. Saravanakumar, K. Dhanaselvam, J. Rajesh, R. Sri Krishna College of Technology Dept of Instrumentation And Control Engineering Tamilnadu Coimbatore India Sri Krishna College of Technology Dept. of Electrical And Electronics Engineering Tamilnadu Coimbatore India Indian Institute of Technology Madras Department of Engineering Design Tamilnadu Chennai600036 India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Automated interpretation of data from roadside traffic signs is extremely helpful for road surveying, autonomous vehicle applications, and enhancing road infrastructure and safety. However due to the high degree of sign similarity, low visibility, and unfavorable nature condition of the road environment, this is a very difficult challenge. In the present research, an approach for automatic detection and recognition of speed limit based on an improved version of YOLOv5n was developed to address the issues in the traffic speed limit sign recognition system, the typical target detection algorithm has low recognition accuracy, sluggish rate, disregarded detection, and inaccurate detection. Deep learning algorithms can extract speed limit data from Roadside automatically. There are two approaches to introduce this method. First of all, it is suggested to partition speed limit signs into one class based on their pixels rather than grouping each sign into a different class. A second proposal is a classifier that can differentiate between speed signs and non-speed signs by evolving its weights, utilizing a false alert class. Images of all of India's state highways were used for developing the data set. The suggested method is very accurate in identifying and detecting speed limit signs, according to the comparative study of the data. The suggested method, with a processing time of 0.040 seconds per frame, produced an average recognition accuracy of 90% using YOLOv5n. This quick calculation time improves system reliability and stability in real-time applications. © 2024 IEEE.

关键词： Roadsides

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Validity of Temporal Encoded Structured Light RGB-D Handheld Scanner for Digital Anthropometric Measurement 8

Validity of Temporal Encoded Structured Light RGB-D Handheld...

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8th International Conference on instrumentation, control, and Automation, ICA 2023

作者： Fajra, Muhammad Alfiyya Khagi, Harmein Suprijanto, Suprijanto Putra, Narendra Kurnia Institut Teknologi Bandung Engineering Physics Department Bandung Indonesia Institut Teknologi Bandung Human and Industrial Product Design Research Group Bandung Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia

ISBN: (纸本)9798350301274

With the development of commercially affordable 3D optical imaging technologies, digital anthropometric measurement method which utilizes RGB-D handheld scanner emerges as a possible alternative to conventional anthropometric measurement method which utilizes analog instruments, offering various advantages such as quicker, cheaper, and non-invasive measurements in addition to yielding 3D body dimension data. While it promises many advantages over the conventional method, the validation of the digital method's performance is needed before its widespread use as an alternative to the conventional method. In this study, the digital method measurement result's intra-measurer intra-subject inter-dimension accuracy and precision was analyzed and compared to that of the conventional method, to determine whether or not the digital method is acceptable as an alternative. Structured light RGB-D handheld scanner was used in the digital method. Absolute mean difference and coefficient of variation comparison were used to determine whether or not the digital method's accuracy and precision is acceptable. It was found that the digital method's accuracy was unacceptable based on the allowable observer error criterion for dimensions adapted from ANSUR II. Its precision was marginally acceptable based on the 1.5% and 5% coefficient of variation threshold adapted from various references. Overall, the digital method's performance was lower than that of the conventional method, but may still be acceptable on various applications with larger room for errors and with proper compensation applied to deal with the systematic errors causing inaccuracies. © 2023 IEEE.

关键词： Systematic errors

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Enhancing Traffic Flow Forecasting with Spatio-Temporal Information: A Case Study on Highway Networks in Belgium 8

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

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8th International Conference on instrumentation, control, and Automation, ICA 2023

作者： Fathurrahman, Muhammad Farhan Catteeuw, Arne Gautama, Sidharta Aguirre, Angel L. Joelianto, Endra Sutarto, Herman Y. Ghent University Engineering and Product Design Department of Industrial Systems Ghent Belgium Department of Intelligent System Bandung Indonesia FlandersMake@UGent-Corelab ISyE Lommel Belgium Institut Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-Temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-Temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-Temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-Temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant. © 2023 IEEE.

关键词： Long short-term memory

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Auto-Text Completion Framework to Improve Online Conversation

Auto-Text Completion Framework to Improve Online Conversatio...

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Congress on Advances in Materials Science and engineering, CAMSE 2020

作者： Rajput, Vishal Verma, Om Prakash Ansari, Irshad Ahmad Department of Electronics and Communication Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India Department of Instrumentation and Control Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

ISBN: (纸本)9789811609411

There are many abilities that make human intelligent. One test which often separates machines and human is how well a machine is able to understand the text and its meaning. The task of sentence completion is still one of the hardest problems in natural language processing. Making a machine understand what is written is a tremendous task. Traditionally, machines were good at performing mathematical calculations like addition, subtraction, etc. only;but with time, new techniques to map language to some mathematical functions emerged. In this work, we discuss why and how to use long short-term memory (LSTM) to create a basic auto-text completion model on the Hindi written in English script (Hinglish) dataset. This study aims at showing that LSTM works very well with Hinglish dataset as it does with English dataset. A Hinglish dataset is prepared and cleaned before processing on LSTM. The obtained result proves the usefulness of LSTM networks for auto-text completion. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory (LSTM) Natural language processing Sentence completion

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Validity of Temporal Encoded Structured Light RGB-D Handheld Scanner for Digital Anthropometric Measurement

Validity of Temporal Encoded Structured Light RGB-D Handheld...

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International Conference on instrumentation control and Automation (ICA)

作者： Muhammad Alfiyya Fajra Harmein Khagi Suprijanto Suprijanto Narendra Kurnia Putra Engineering Physics Department Institut Teknologi Bandung Bandung Indonesia Human and Industrial Product Design Research Group Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Institut Teknologi Bandung Bandung Indonesia

With the development of commercially affordable 3D optical imaging technologies, digital anthropometric measurement method which utilizes RGB-D handheld scanner emerges as a possible alternative to conventional anthropometric measurement method which utilizes analog instruments, offering various advantages such as quicker, cheaper, and non-invasive measurements in addition to yielding 3D body dimension data. While it promises many advantages over the conventional method, the validation of the digital method's performance is needed before its widespread use as an alternative to the conventional method. In this study, the digital method measurement result’s intra-measurer intra-subject inter-dimension accuracy and precision was analyzed and compared to that of the conventional method, to determine whether or not the digital method is acceptable as an alternative. Structured light RGB-D handheld scanner was used in the digital method. Absolute mean difference and coefficient of variation comparison were used to determine whether or not the digital method’s accuracy and precision is acceptable. It was found that the digital method’s accuracy was unacceptable based on the allowable observer error criterion for dimensions adapted from ANSUR II. Its precision was marginally acceptable based on the 1.5% and 5% coefficient of variation threshold adapted from various references. Overall, the digital method’s performance was lower than that of the conventional method, but may still be acceptable on various applications with larger room for errors and with proper compensation applied to deal with the systematic errors causing inaccuracies.

关键词：

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Enhancing Traffic Flow Forecasting with Spatio-Temporal Information: A Case Study on Highway Networks in Belgium

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

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International Conference on instrumentation control and Automation (ICA)

作者： Muhammad Farhan Fathurrahman Arne Catteeuw Sidharta Gautama Angel Lopez Aguirre Endra Joelianto Herman Y. Sutarto Department Of Industrial Systems Engineering and Product Design Ghent University Ghent Belgium Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia FlandersMake@UGent–Corelab ISyE Lommel Belgium Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant.

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2024年 1140 LNEE卷 v-vi页

作者： George, V.I. Santhosh, K.V. Lakshminarayanan, Samavedham Department of Electrical and Electronics Engineering Mar Baselios Christian College of Engineering and Technology Kerala Peermade India Department of Instrumentation and Control Engineering Manipal University Manipal India College of Design and Engineering National University of Singapore Singapore Singapore

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