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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Sekhar, C.H. Meenakshi Mousa, Saeed Sharma, M.K. Gobinath, V.M. Dharamvir Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam India Gd Goenka University HARYANA SOHNA India CLERSE University of Lille for Science and Technology France Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut250004 India Karpaga Vinayaga College of Engineering and Technology Robotics and Automation Tamil Nadu Chengalpattu India The Oxford College of Engineering Department of Mca Bangalore India

ISBN: (纸本)9781665437899

Internet of Things (IoT) and Machine Learning (ML) are recent trends in huge data management. Traditionally, data management was carried out by humans where big data collection, analysis and acquisition were solely done by humans. However, IoT and ML have enabled machines rather than humans to collect big data, analyse them and develop a new business model. It has shown huge success in industry 4.0;however, during the management of big data, large-scale companies encountered certain issues. These include, lack of effective training leads to less accuracy in data management, security issues during cloud computing and security issues in blockchain as well. Therefore, this study has explained the ways to improve big data management through ML and to enhance the security of blockchain. A primary survey was carried out with 50 respondents who provided their responses to certain questions related to big data management solutions and blockchain security. Findings showed that providing training to the workers and using guidelines/policies in blockchain can increase the data management accuracy and can improve the security in cloud computing, respectively. © 2022 IEEE.

关键词： Information management

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Low-Cost 3D Holographic Display With Gesture Control

Low-Cost 3D Holographic Display With Gesture Control

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Saima Bibi Ayesha Imran Safwan Saud Mukesh Kumar Maheshwari Bhawani Shankar Chowdhry Waleed Ejaz Department of Electrical Engineering Bahria University Karachi Campus Karachi Pakistan National Center of Robotics & Automation Condition Monitoring Systems Lab Mehran University of Engineering & Technology Jamshoro Pakistan Department of Electrical Engineering Lakehead University Barrie ON Canada

A way behind the past where teaching and learning were only by video, images, and 2D animations. Now we have live sessions where a classroom is bounded on just a laptop screen. What happened if that live session and 3D imaging are presented in the form of real sculpture by using Augmented Reality? In this paper, we are introducing a low-cost 3D display that presents both recorded and live sessions. Also, this display will be controlled with our fingertips. This application covers sectors of education, medical imaging, and advertisements with good effects and cheap cost.

关键词： Wireless communication Three-dimensional displays Costs Portable computers Education Glass Holography

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IoT in Health Care Study by Text Analysis 2

IoT in Health Care Study by Text Analysis

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Dhumal, Yashomati R Gobinath, V.M. Jangirala, Srinivas Vidhya, M. Prakash, A. Shinde, Arundhati College of Engineering Electronics Department Pune India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Chengalpattu India O. P. Jindal Global University Jindal Global Business School Sonipat India Panimalar Institute of Technology Department of Computer Science and Engineering Varadarajapuram India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India College of Engineering Electronics Department Pune India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The Internet of Things (IoT) should be a new buzzword that emerged little by little (IoT). Organizations, governments, and the scientific community have already understood the relevance of IoT in the past few years, with a growing number of publications. A key component of this progress was IoT in health, but little research has been conducted on aspirations and potential developments. Only as the solution, they integrate bibliometric analysis and data mining to evaluate the longitudinal patterns of the IoT devices to Medical in this study. From 2000 to 2021, 778 articles were derived from the Web of Science. Abstract textual data indicate that articles were divided into 30 groups, revealing eight IoT in health trends. The development of this field is worldwide, with distinctive patterns that should be noted to promote some areas. © 2022 IEEE.

关键词： Internet of things

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Evaluating the effect of magnesium oxide nanoparticles on the thermal energy storage characteristics of the inorganic PCM

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Materials Today: Proceedings 2023年

作者： Manoj Kumar, P. Karuna, M.S. Sureshkumar, M.S. Rinawa, Moti Lal Sakthivel, R. Muthukumar, K. Kathir Malavan, E. Department of Mechanical Engineering KPR Institute of Engineering and Technology Coimbatore 641407 Tamil Nadu India Department of Chemical Engineering M.J.P. Rohilkhand University Bareilly 243006 Uttar Pradesh India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore 641022 Tamil Nadu India Department of Mechanical Engineering Government Engineering College Jhalawar 326023 Rajasthan India

Thermal storage with phase changing materials (PCM) has got the attention of the researchers in recent days. Among the PCMs, inorganic salt hydrates are having high potential to act as thermal storing medium due to their large latent content. However, they lack large-degree supercooling and have issues with phase segregation. In the present study, the thermal characteristics of sodium acetate trihydrate (SAT) have been studied under the influence of different weight fractions (0.0%, 0.25%, 0.5%, 0.75%, and 1.0%) of MgO nanoparticles (nano-MgO). Carboxymethyl cellulose (CMC) has been used as the thickener, and nano-MgO has been tested as the nucleating medium. The results demonstrated that the use of 0.75% nano-MgO with SAT is suggested as the best composition to augment the thermal characteristics of SAT PCM. With 0.75% nano-MgO, the supercooling of the SAT was reduced to 0.32 °C, and the thermal conductivity of the SAT was enhanced by 40.94%. Nonetheless, the latent heat of the SAT PCM had not been significantly affected by the addition of nano-MgO until its fraction of 0.75%. © 2023 Elsevier Ltd. All rights reserved.

关键词： Magnesia

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A Real- Time Multi-Camera Auto-Adjustment Framework for Infrastructure Inspections

A Real- Time Multi-Camera Auto-Adjustment Framework for Infr...

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IEEE/SICE International Symposium on System Integration

作者： Chuong Phuoc Le Charles Ellison Steven Bunkley Hung La Anton Netchaev Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA

In this paper, we proposed a novel multi-camera auto-adjustment framework for infrastructure inspections, specifically 3D point cloud mapping via lidar-camera fusion. Our method uses a single controller for consensus target brightness and adaptive lighting. In addition, a vignette correction filter was created to apply to images outputted from cameras to avoid dark strips on the point cloud data. This approach is designed to be a versatile, real-time friendly, and platform agnostic software stack.

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Immune Optimization Algorithm for Traveling Salesman Problem Based on Clustering Analysis and Self-Circulation 3

Immune Optimization Algorithm for Traveling Salesman Problem...

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3rd International Conference on Advanced Information Science and System, AISS 2021

作者： Gao, Yunqiang Shen, Yan Yang, Zhe Chen, Dapeng Yuan, Mingxin School of Mechanical Engineering Jiangsu University of Science and Technology China School of Mechanical and Power Engineering Jiangsu University of Science and Technology China Department of Robotics Jiangsu Institute of Automation China

ISBN: (纸本)9781450385862

In order to improve the population diversity during immune optimization of high-dimensional TSP problem, avoid falling into local optimal solutions, and then improve the algorithm's global optimization capability and optimization efficiency, Immune Optimization Algorithm for TSP Based on Cluster Analysis and Self-Circulation, (IOATCS) is proposed. Inspired by cluster analysis, TSP population data is classified and evolved according to the relevance of the problem to improve the efficiency of population optimization;a self-circulation strategy is designed to obtain new offspring through the self-circulation and crossover of the parent body, and then inherit the gene fragments of excellent individuals to the offspring, which improves the global search ability of the algorithm. The test results of six groups of high-dimensional TSP problems show that compared with the basic genetic algorithm, immune genetic algorithm and ant colony algorithm, the planning performance of the algorithm in this paper is the best, and the average relative error and relative error standard deviation of the algorithm are reduced by 10.21% and 4.54% respectively, and the average convergence algebra and convergence algebra standard deviation are reduced by 31.2% and 38.7% respectively, which verifies the strong optimization ability, high optimization efficiency and good stability of the proposed algorithm. © 2021 ACM.

关键词： Genetic algorithms

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Enhanced Soil Property Estimations from Earth Observation Data with Differential Evolution-Based Multi-Objective TSK Model

Enhanced Soil Property Estimations from Earth Observation Da...

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Congress on Evolutionary Computation

作者： Nikolaos L. Tsakiridis Nikiforos Samarinas Eleni Kalopesa John B. Theocharis George C. Zalidis Department of Agriculture Spectra Lab Group Laboratory of Remote Sensing Spectroscopy and GIS Aristotle University of Thessaloniki Thessaloniki Greece Automation and Robotics Laboratory Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

This paper introduces a novel approach integrating Differential Evolution (DE) with multi-objective optimization techniques for enhancing Type-1 Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems to attain both fair predictive performance and enhanced interpretability within the context of explainable artificial intelligence. The developed approach encodes the whole knowledge base into one chromosome and endeavors to concurrently optimize the prediction accuracy, the number of rules, and the selection of relevant input features. The methodology was tested on Earth observation data for the estimation of soil health parameters via infrared spectroscopy, and more concretely i) on the 2015 LUCAS topsoil dataset to predict three key soil properties (namely, soil organic carbon, clay content, and pH) from laboratory spectra, and ii) on a set of PRISMA hyperspectral images to predict soil organic carbon in a region of northern Greece. Compared to the classical Random Forest (RF) algorithm, our proposed learning algorithm attains a fair balance between accuracy and interpretability, and is statistically equivalent to RF in terms of accuracy. The findings underscore the potential of the proposed methodology in refining TSK models and its applicability in Earth observation-driven predictions, paving the way for both enhanced modeling accuracy and sparse feature selection.

关键词： Earth Radio frequency Accuracy Soil properties Estimation Predictive models Prediction algorithms

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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BiRP: Learning Robot Generalized Bimanual Coordination Using Relative Parameterization Method on Human Demonstration

BiRP: Learning Robot Generalized Bimanual Coordination Using...

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IEEE Conference on Decision and Control

作者： Junjia Liu Hengyi Sim Chenzui Li Kay Chen Tan Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

Human bimanual manipulation can perform more complex tasks than a simple combination of two single arms, which is credited to the spatio-temporal coordination between the arms. However, the description of bimanual coordination is still an open topic in robotics. This makes it difficult to give an explainable coordination paradigm, let alone applied to robotics. In this work, we divide the main bimanual tasks in human daily activities into two types: leader-follower and synergistic coordination. Then we propose a relative parameterization method to learn these types of coordination from human demonstration. It represents coordination as Gaussian mixture models from bimanual demonstration to describe the change in the importance of coordination throughout the motions by probability. The learned coordinated representation can be generalized to new task parameters while ensuring spatio-temporal coordination. We demonstrate the method using synthetic motions and human demonstration data and deploy it to a humanoid robot to perform a generalized bimanual coordination motion. We believe that this easy-to-use bimanual learning from demonstration (LfD) method has the potential to be used as a data augmentation plugin for robot large manipulation model training. The corresponding codes are open-sourced in https://***/Skylark0924/Rofunc.

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Aggregating Single-Wheeled Mobile Robots for Omnidirectional Movements

Aggregating Single-Wheeled Mobile Robots for Omnidirectional...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Meng Wang Yao Su Hang Li Jiarui Li Jixiang Liang Hangxin Liu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BI-GAI) Department of Advanced Manufacturing and Robotics College of Engineering Peking University Department of Robot Engineering School of Automation Science and Electrical Engineering Beihang University

This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation settings. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs.

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