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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68823 条记录，以下是1061-1070 订阅

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Development and Evaluation of a Passive Upper Limb Exoskeleton System for Overhead Operations 20

Development and Evaluation of a Passive Upper Limb Exoskelet...

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20th IEEE international conference on Mechatronics and automation, ICMA 2023

作者： Chen, Lufeng Zhou, Xianchao Zheng, Liang Xie, Hongqin Cheng, Hong Qiu, Jing University of Electronic Science and Technology of China School of Automation Engineering Chengdu China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu China

ISBN: (纸本)9798350320831

Work-related musculoskeletal disorders (WMSDs) have become a significant health concern, impacting the healthy and efficiency of workers. The advent of exoskeleton robotic technology offers a potential solution to enhance human physical performance and reduce muscle fatigue. In this study, we develop a passive upper limb exoskeleton designed to assist during overhead tasks. The passive structure, employing a constant force spring, is devised based on the motion analysis. Design parameters are optimized with respect to the specific requirements of overhead tasks, leading to the development of a prototype exoskeleton. An experimental platform is also constructed to assess the performance of the system. Results demonstrate a considerable reduction in muscle activation intensity, indicating that the system effectively provides adequate assistance during several overhead scenarios. © 2023 IEEE.

关键词： Exoskeleton (Robotics)

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Machine and Deep Learning Classifications for IoT-Enabled Healthcare Devices 4

Machine and Deep Learning Classifications for IoT-Enabled He...

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4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Priya, S.Shanmuga Al-Fatlawy, Mohammed Hussien Khare, Neha Mahalakshmi, V. Ganesh, S. Sankar Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India College of Technical Engineering The Islamic University Najaf Iraq Ies Institute of Technology and Management Ies University Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Kommuri Pratap Reddy Institute of Technology Department of Computer Science and Engineering Ghanpur India

ISBN: (纸本)9798350393248

The emergence of the Internet of Things (IoT) is profoundly influencing academic research and will have longterm consequences on several industries, chief among them the healthcare sector. The healthcare industry has changed as a result of the Internet of Things (IoT), which has replaced old, centralized processes with decentralized networks of smart devices. Systems of customized healthcare have been developed as a result of this (PHS). This change has been made possible by recent advancements in fields like wearables, sensor networks, and cloud computing, which have simplified the use of IoT in healthcare settings on a large scale. Notwithstanding a few significant disadvantages, IoT has the potential to significantly transform the medical industry. Keeping up with a large number of devices, rising costs, and the requirement for more data storage space are the challenges. This paper offers a thorough examination of IoT as a flexible and robust technology, emphasizing its applications in the healthcare industry. Research is being done on the interaction between machine learning and deep learning. In addition to examining the various advantages and real-world applications of the technology, this article covers the architecture of an Internet of Things (IoT)-enabled healthcare system. It draws attention to the challenges posed by the IoT healthcare environment and emphasizes the need for academics to come up with novel solutions to these problems. The present work of several researchers in the fields of machine learning and deep learning, which are extensively used in IoT-enabled healthcare systems, is highlighted in this study. © 2023 IEEE.

关键词： Internet of things

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Development of Product Quality with Enhanced Productivity in Industry 4.0 with AI Driven automation and Robotic technology 2

Development of Product Quality with Enhanced Productivity in...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Lal, Bechoo Vishnu Sakravarthy, N. Kumar, Marrapu Aswini Chinthamu, Narender Pokhriyal, Sumit Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram Guntur India Sri Eshwar College of Engineering Department of Mechanical Engineering Tamilnadu Coimbatore India Centurion University of Technology and Management Department of Computer Science and Engineering Andhra Pradesh Vizianagaram India DallasTX United States Graphic Era Hill University Department of Physics Dehradun India

ISBN: (纸本)9798350325799

automation and robotics have advanced significantly with the introduction of Industry 4.0, revolutionizing the manufacturing environment. This study examines the evolution of product quality in the context of Industry 4.0, concentrating on how robotic and AI-driven automation technologies help to increase productivity. automation systems can efficiently analyze enormous volumes of data by integrating AI algorithms and machine learning capabilities, allowing for real-time monitoring and production process optimization. Utilizing AI-driven automation, manufacturers may increase task precision and accuracy, improving the quality of the final product. By enhancing human capacities and doing monotonous or physically taxing activities, robotic technology plays a significant role in Industry 4.0. AI-enabled advanced robots can collaborate with human workers, adapt to changing production conditions, and carry out intricate tasks quickly and accurately. Together, humans and robots are more productive and make fewer mistakes, which enhances the quality of the final output. Additionally, real-time defect or deviation detection and correction is possible with AI-driven quality control systems. These systems can detect quality problems at an early stage and take quick corrective action by analyzing sensor data and machine vision. Manufacturers can achieve uniform and standardized product quality while minimizing human error by automating quality control operations. Furthermore, proactive equipment monitoring and a lower chance of unforeseen breakdowns are made possible by the use of AI in predictive maintenance. AI algorithms can forecast maintenance requirements by examining sensor data and historical performance, enabling prompt repairs or replacements. In addition to increasing production by reducing downtime, this predictive strategy also helps to improve product quality by ensuring that machinery is running at peak efficiency. © 2023 IEEE.

关键词： Industry 4.0

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Quality Assurance Awareness in Open Source Software Projects on GitHub 23

Quality Assurance Awareness in Open Source Software Projects...

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23rd IEEE international Working conference on Source Code Analysis and Manipulation, SCAM 2023

作者： Khatami, Ali Zaidman, Andy Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350305067

Software engineers employ a variety of approaches to ensure the quality of software systems, including software testing, modern code review, automated static analysis, build automation, and continuous integration. To make effective decisions regarding quality assurance (QA), software engineers need to have an awareness of (1) the QA approaches that are in use in a project, and (2) how they are used. Through an exploratory, mixed-methods investigation we set out to better understand the awareness of software engineers in open-source software (OSS) development with regard to QA practices. This involved a largescale survey of 471 maintainers and contributors on GitHub. Our findings indicate that a high-level awareness among the respondents is common, but also that the respondents are less certain about how the practices are adopted;we further consider the perspective of both the contributor and the maintainer. © 2023 IEEE.

关键词： automation

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Improving performance of B-Sb-Ba-Nd glass by doping with different concentrations of Sb ions 9

Improving performance of B-Sb-Ba-Nd glass by doping with dif...

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9th international conference on Mechanical Engineering, Materials, and automation technology, MMEAT 2023

作者： Zhou, Zidong Zeng, Fanming School of Materials Science and Engineering Changchun University of Science and Technology Changchun130022 China

ISBN: (纸本)9781510668584

With the rapid development of optical fiber and solid-state laser, more and more researchers began to add Nd3+ to the borate glass for tuning for commercial application. In this paper, the glass with different concentration of Sb3+ was prepared. Nd3+ and Sb3+ were successfully doped into the glass network. Subsequently, the absorption properties of boric acid glass samples were measured. The results show that increasing the concentration of Sb3+ doping can adjust the absorption properties of boric acid glass. © 2023 SPIE.

关键词： Glass

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Improved Genetic Algorithm-Based Task Assignment Algorithm of Cooperative Patrol for Manned/Unmanned Surface Vehicles

Improved Genetic Algorithm-Based Task Assignment Algorithm o...

引用

2023 IEEE international conference on Electrical, automation and computer Engineering, ICEACE 2023

作者： Zhang, Wen Zhao, Fenxue Weng, Qing Wang, Jing Lv, Shengrong Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin China

ISBN: (纸本)9798350309614

To solve the collaborative task planning problem of manned/unmanned surface vehicles (MSV/USV), a task planning model of MSV/USV is proposed, the organic integration of MSV/USV task planning in the task planning algorithm and the function division of task decision makers are realized at the same time. On this basis, an MSV/USV task allocation algorithm based on a genetic algorithm is proposed. The algorithm adopts a dual population genetic structure, and proposes information contrast crossover and mutation operators to increase the searchability of the algorithm. The experiment shows that the algorithm can effectively optimize the MSV/USV task allocation results and improve task allocation efficiency. © 2023 IEEE.

关键词： genetic algorithm manned/unmanned surface vehicles task assignment

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Colorful Commuting Journey: Non-driving Related Tasks that Drivers Willing to Perform Across Vehicles of Various automation Levels and the Reasons 25th

Colorful Commuting Journey: Non-driving Related Tasks that D...

引用

25th international conference on Human-computer Interaction (HCI international)

作者： Pang, Jingyu Zhou, Xingchen Beijing Normal Univ Fac Psychol Beijing 100875 Peoples R China

ISBN: (纸本)9783031480461;9783031480478

Automated vehicles relieve drivers' physical and cognitive load from driving and enable them to freely perform non-driving related tasks (NDRTs), which is promising to free modern citizens from the cost of the daily car-driving commute. Today, what NDRTs drivers are willing to perform and the reasons why they are willing to perform remain unclear, hindering the design of in-vehicle services. To fill this gap, we interviewed 15 drivers with driving experience in L2 automated cars to explore their preferred NDRTs during commutes in various levels of automation and why these tasks were performed. We classified four typical groups of NDRTs that can be performed during a commute, and the results indicated that drivers' preferred NDRTs change with the automation level of cars and the digital devices they use. We further revealed 11 reasons why drivers are willing to perform certain NDRTs, and these reasons were categorized into the drivers' needs and habits, the NDRT features, and the driving conditions. The findings of this study extend the understanding of user behaviors when commuting in automated cars, which will guide the design of non-driving related services for autonomous driving.

关键词： Non-driving Related Tasks Automated Vehicles Commuting Interview study

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Prediction based Auto-Pilot Interface for Drone to Object Chasing using Historical TSPI Data 23

Prediction based Auto-Pilot Interface for Drone to Object Ch...

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23rd international conference on Control, automation and Systems, ICCAS 2023

作者： Jang, Shinhyoung Park, Byeonghwi Jeong, Juheon Mahedy, Jack Gebreslassie, Nebey Lee, Minji Matson, Eric T. Chungnam National University Department of Computer Science and Engineering Daejeon34134 Korea Republic of Purdue University Department of Computer and Information Technology West LafayetteIN47906 United States Purdue University Department of Technology West LafayetteIN47906 United States

ISBN: (纸本)9788993215274

Replacing military aircraft with Unmanned Aerial Vehicles (UAV) offers advantages such as reducing costs and risks associated with conventional military aircraft. To defend against enemy UAV attacks, anti-drone systems are used. While most technologies focus on drone detection, this paper proposes a process of tracking targets using arbitrary UAV to enhance camera data quality or directly engage and neutralize targets. The system comprises drone detection, movement prediction, and chasing. Devices such as radar are used to detect drones, and they provide Time-Space-Position Information (TSPI) using suitable data formats. Using Machine Learning (ML) techniques make it feasible to predict an object's future location, represented with equations. Utilizing trained machine learning models, the Auto-pilot Application Programming Interface (API) of the drone is employed to track based on the predicted results. Through experiments, the feasibility of predicting an object's future location using various ML techniques has been confirmed. Furthermore, verification has been conducted to establish the attainability of implementing the Auto-pilot API through the utilization of DJI's API. © 2023 ICROS.

关键词： Drones

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Flood Detection from Satellite Images Using Self-Attention Empowered EfficientNetV2 Mechanism 7

Flood Detection from Satellite Images Using Self-Attention E...

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2023 7th international conference On Computing, Communication, Control And automation, ICCUBEA 2023

作者： Selva Jeba, G. Chitra, P. Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Applications Madurai India

ISBN: (纸本)9798350304268

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%. © 2023 IEEE.

关键词： attention mechanism deep learning EfficientNetV2 flood detection remote sensing

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Software Fault Locating technology Based on Propagation Mechanism Analysis 5

Software Fault Locating Technology Based on Propagation Mech...

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5th international Academic Exchange conference on science and technology Innovation, IAECST 2023

作者： Chen, Danyang Jiangsu Automation Research Institute Lianyungang China

ISBN: (纸本)9798350357738

With the increasing size and complexity of software, the incidence and uncertainty of potential software failures increase, which poses new challenges to software testing and quality assurance. This paper proposes a software fault locating technology based on propagation mechanism analysis, which fully utilizes fault case data, expert knowledge, and software architecture to analyze fault correlations. It realizes the representation and calculation of multi-level and multidimensional propagation modes of faults, and improves the speed and accuracy of software fault localization. © 2023 IEEE.

关键词： Software testing

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