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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

共 38171 条记录，以下是671-680 订阅

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Modeling of the cooperative operation process of aircraft ground centralized deicing 4

Modeling of the cooperative operation process of aircraft gr...

引用

2024 4th International conference on mechatronics Technology and Aerospace engineering, ICMTAE 2024

作者： Xing, Zhiwei Wang, Yuning Yan, Chunyu Sun, Ke Zhang, Tao Zhao, Liang College of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China College of Safety Science and Engineering Civil Aviation University of China Tianjin300300 China Engineering Technology Research Center Second Research Institute of Civil Aviation Administration of China Chengdu610041 China Sichuan Branch of China Eastern Airlines Technology Company Limited Chengdu610041 China

Aiming at the problems of insufficient refinement and low efficiency of aircraft ground centralized deicing operation under snow and ice weather, a description method of aircraft ground centralized deicing operation process based on a spatiotemporal correlation network was proposed. Based on the allocation and spatiotemporal distribution of airport deicing resources, a colored spatiotemporal network model was established to describe the dynamic process of aircraft ground deicing through the evolution of a colored spatiotemporal network. A resource optimization method was designed. The traditional genetic algorithm was improved by Sine chaotic mapping technology and differential evolution, and verified by experiment. The results show that the root-mean-square error of the idle time of each deicing site is 18.29 after optimization, which is 4.88 less than before optimization, suggesting that the utilization rate of the deicing site is increased by 21.06%. The improved NSGA-Ⅱ algorithm can increase the completion rate of deicing potential resource allocation by 7.62% and has a faster convergence rate compared with the traditional algorithm, which proves the feasibility of the proposed method. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Mean square error

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A Novel Approach for Specification and Verification of Symmetric Distributed Algorithms Using SPIN 3

A Novel Approach for Specification and Verification of Symme...

引用

3rd International conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Namvaritazehkand, Leila Ebnenasir, Ali University of Tabriz Faculty of Electrical and Computer Engineering Department of Computer Engineering East Azerbaijan Tabriz Iran Michigan Technological University Department of Computer Science HoughtonMI49931 United States

ISBN: (纸本)9798350381580

This paper focuses on the challenges of modeling and verifying symmetric distributed algorithms with point-to-point and bidirectional communications using the SPIN model checker. In this paper, we first state the problems of implementing the model of such algorithms in the Promela language. Then, we present a solution for each of these problems, where these solutions present a hybrid data structure that facilitates the definition of any set of variables or channels as a matrix. Also, we model and verify one of the valuable and practical algorithms called the causal broadcast (CB) algorithm as a case study using the proposed method. In addition, we specify the main properties of the CB algorithm, namely validity, integrity, causal delivery, and termination, in Linear Temporal Logic (LTL) and then verify them using the SPIN model checker. © 2024 IEEE.

关键词： Model checking

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A Gesture Recognition and Drone Control System based on Residual Neural Network 20

A Gesture Recognition and Drone Control System based on Resi...

引用

20th IEEE International conference on mechatronics and Automation, ICMA 2023

作者： Wang, Rui Sun, Chengjiao Hubei University of Arts and Science School of Computer Engineering 296 Longzhong Road Hubei Xiangyang China

ISBN: (纸本)9798350320831

To achieve visual communication between Drone and operator, a static gesture recognition and Drone control system based on residual neural network is designed. The system segments static gestures through YCrCb colour space and elliptical skin colour detection model, extracts gesture features using the residual neural network ResNet34 and uses deep learning techniques to realise the combination of gesture recognition and UAV control. The system uses the gesture images taken from the UAV viewpoint as the dataset and will send the corresponding control signals to the UAV side according to the calibrated control commands to achieve stable control of the UAV, and the classification accuracy is used as an evaluation index to assess the performance of the model of this system in gesture recognition. Finally, the control performance of the system was verified in a real flight in a Tello vehicle platform, and the experimental results showed that the average accuracy rate could reach 90.43%. © 2023 IEEE.

关键词： Deep learning

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Research on Value Budgeting Model of science and Technology Innovation Resource Allocation 24

Research on Value Budgeting Model of Science and Technology ...

引用

5th International conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Wang, Ying Liu, Mengzhe Qiu, Shijin Zhang, Yue Zhang, Zhiyuan State Grid Fujian Economic Research Institute Fujian Fuzhou China

ISBN: (纸本)9798400718007

This study aims to analyze the allocation mechanism of S&T innovation resources under the value budgeting model and assess the practical application of this mechanism on the optimization of resource allocation efficiency and S&T innovation effectiveness, and discusses how S&T projects can achieve a balance between short-term economic gains and long-term social value through the value budgeting model in the process of implementation, and the study shows that the adoption of the value budgeting model improves S&T innovation through the internal cross-subsidization mechanism of Economic and social benefits, the study points out that the value budget model provides scientific resource allocation methods for S&T management departments and R&D entities, and promotes the optimal allocation of S&T resources and the rapid transformation of scientific and technological achievements. © 2024 Copyright held by the owner/author(s).

关键词： cross-subsidy mechanism

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Design of Smart Tourism Visual Analysis Platform Based on Digital Twin 3

Design of Smart Tourism Visual Analysis Platform Based on Di...

引用

3rd IEEE International conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Li, Suiqun Feng, Yidan Chu, Qikai Chen, Guangjian Sichuan University of Science and Engineering Sichuan Key Provincial Research Base of Intelligent Tourism Zigong China School of Computer Science Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798350380989

With the continuous development and application of new technologies such as big data, Internet of Things, cloud computing, virtual reality and artificial intelligence, intelligent tourism has ushered in new development opportunities. More and more cities begin to pay attention to the information construction of scenic spots, and look forward to new technologies to provide scientific and effective analytical data for the tourism industry. In view of the above problems, the smart tourism visual analysis platform based on digital twins is designed to conduct a comprehensive and detailed analysis of the tourist destination's traffic situation, security supervision, resource information, weather environment monitoring and epidemic prevention and control through the method of data visualization and visual analysis. The results indicate that the platform presents information to users in a digital twin and visualized manner, with characteristics such as scientific information management and intelligent processing and analysis. © 2024 IEEE.

关键词： Digital twin Smart tourism Visual analysis Visualization

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Machine Learning–Based Pre-Stroke Detection System

Machine Learning–Based Pre-Stroke Detection System

引用

2024 International conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Maheshwaran, M. Devi, Kiruthiga V. Gunapriya, D. Pushpalatha, N. Sam Karthik, S. Selvi, A. Department of Mechatronics Engineering Nehru Institute of Engineering and Technology Thirumalayampalayam Tamilnadu Coimbatore641105 India Department of Electrical and Electronics Engineering K.S.R College of Engineering Tamilnadu India Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore641 202 India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering Tamilnadu Coimbatore India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur639113 India

ISBN: (纸本)9798350376913

Stroke remains a significant health concern worldwide, often leading to severe disabilities and mortality. Early detection and intervention are crucial for preventing strokes and mitigating their impacts. In this study, the proposed machine learning-based pre-stroke detection system aimed at identifying individuals at risk of experiencing a stroke before the onset of symptoms. The proposed system utilizes a diverse dataset containing demographic information, medical history, lifestyle factors, and biomarkers relevant to stroke risk. Various machine learning algorithms, including Decision Tree Algorithm, Random Forest Algorithm, Naïve Bayes Algorithm, Multilayer Perceptron Algorithm, and J Rip Algorithm, were trained, and their evaluation was conducted separately using the dataset. The proposed system uses a cloud-based stroke warning system that utilizes machine learning technology to predict impending strokes. The peculiar scrutiny of many machine learning methods led to the development of an effective machine learning strategy for the precise detection of strokes. A microcontroller-based system was used to establish and exhibit an actual patient monitoring system that can sense a variety of real-time indicators, including core temperature, blood pressure, blood flow, heart rhythm, and oxygen level, to let caregivers and doctors keep an eye on stroke patients around the clock. Decisions can be made quickly and easily with the aid of various decision-making algorithms, and anyone can access the database according to their needs. © 2024 IEEE.

关键词： Blood pressure

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The Importance of System engineering Competences and Knowledge for Big Data science and Research Infrastructure Projects

The Importance of System Engineering Competences and Knowled...

引用

2024 IEEE International conference on Big Data, BigData 2024

作者： Demchenko, Yuri University of Amsterdam Complex Cyber Infrastructure Lab Amsterdam Netherlands

ISBN: (纸本)9798350362480

Big Data science projects are an essential part of the modern data driven science and require extensive use of digital technologies and are typically built using large scale Research Infrastructures (RI) that combine computational, storage and data management facilities to support data collection, processing and scientific workflow management. The success of realising Big Data science projects and effective use of Big Data Infrastructures (BDI) will depend on adopting modern technologies and well-defined architectures that support the sustainability and long term evolution of technical solutions. Building and operating modern BDIs and data driven scientific instruments require a wide spectrum of competences and knowledge related to modern technologies, system and software engineering, including also Big Data infrastructure engineering. The paper provides background information on large European RI and e-Infrastructure projects and analyses different cases/scenarios where the availability of necessary system and software engineering competences and knowledge are critical for the success of RI. The paper summarises the Sustainable Architecture Design Principles (SADP) that can provide both guidance for the technical design of RI/BDI and a basis for targeted training for engineering and scientific personnel. The paper also refers to ongoing research and developments on ensuring the environmental sustainability of RIs where the proposed SADP plays an important role. The presented work is based on the author's experiences of developing and teaching courses on Big Data Infrastructure Technologies for Data Analytics (BDIT4DA) and DevOps for Software engineering (DevOps4SE) that include necessary competences and learning outcomes required for building modern data-driven infrastructures and applications. The paper shares the experience of how both courses have evolved and been adapted to the continuous technology development and specific needs of the developer teams. © 2024

关键词： Data science

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A Sustainable Smart Irrigation and Crop Protection System for Bangladesh 8

A Sustainable Smart Irrigation and Crop Protection System fo...

引用

8th IEEE International conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ahmed, Khandakar Rabbi Salam, Towfika Miraz, Shafiun Jahan, Israt Hoque, Aminul Al Noman, Abdullah Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Kazla Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Uttara Model Town Dhaka Bangladesh Asian University of Bangladesh Dept. of Computer Science and Engineering Bangabandhu Road Tongabari Ashulia Dhaka Bangladesh Jahangirnagar University Dept. of Institute of Information Technology Savar Dhaka Bangladesh

ISBN: (纸本)9798331505462

Bangladesh's most crucial sector for development is agriculture. Rainfall has been irregular in recent years due to climate change. In our country, the untimely rain and hailstorms harm crops and vegetables, especially winter vegetables. Surface water is mainly used for agriculture, but its availability constantly declines. Our current key challenge is to save surface water while protecting crops from rain and hail. IoT is cutting-edge technology in many applications, such as smart homes, smart cities, and intelligent traffic management. The IoT may be implemented in so many different industries and sectors. This research work is about smart irrigation with crop protection systems in agriculture. The system's primary parts are the sensors, water pump, servo motors, embedded systems, and internet connectivity. IoT sensors like soil moisture sensors and rain sensors are used in the suggested methodology. The fields of crops have received water from a pump. A motor has covered the crops. The proposed solution is a straightforward Internet of Things setup that sends data to the Arduino after detecting rainfall and soil moisture levels. Depending on the sensor data, the Arduino sends signals to the motor and pump to irrigate the crops and cover crops as necessary, revealing the crops after rain. The system is constructed economically using bamboo, and poly and plastic nets have been utilized to protect the crops from rain and fog. The system's primary effects are increased productivity, decreased crop damage rate, and saved surface water. © 2024 IEEE.

关键词： Soil moisture

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Simulation Analysis of Pulsation Characteristics of Axial Piston Pump

Simulation Analysis of Pulsation Characteristics of Axial Pi...

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2023 International conference on mechatronics, Control and Robotics, ICMCR 2023

作者： Zhang, Xuhui Tian, Tian Xi'An University of Science and Technology School of Mechanical Engineering Key Labs. for Intelligent Monitoring of Mechanical and Electrical Equipment in Shaanxi Province Shaanxi Xi'an710054 China Xi'An University of Science and Technology School of Mechanical Engineering Shaanxi Xi'an710054 China

ISBN: (纸本)9798350321555

The emergence of 'digital hydraulic' makes many industrial fields put forward higher requirements for the performance of piston water pump, and realizes the control of piston water pump, and realizes the digital control of it. But because the efficiency of hydraulic drive is not high, to carry out a wide range of adjustment, need to go through the complex mechanical energy-hydraulic energy conversion, so the efficiency is very high, pressure loss is also very large. Therefore, taking a new type of double row piston pump with flow distribution as an example, the pulsation characteristics of axial piston pump are theoretically analyzed, and the flow pulsation and capacity utilization are simulated by computer. The simulation results show that the closed-loop and integration of components continue to improve, components can be modular assembly, easy maintenance, clean appearance, smaller volume, less pipeline connection, can effectively prevent leakage and pipeline loss caused by too many pipeline interfaces. It can be seen that the control method based on fast switching valve controlled pump effectively promotes the improvement of flow pulsation and capacity utilization. © 2023 IEEE.

关键词： Pipelines

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Big Data Clustering Algorithms: Improving Efficiency and Scalability for Massive Data Mining and Pattern Recognition

Big Data Clustering Algorithms: Improving Efficiency and Sca...

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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Muthu, Shanmugam Kalyan, G Ramesh Samuthira Pandi, V. Shobana, D. Lakshmi Priya, J. Jaishika, S. Shipt Inc 420 20th St N # 100 BirminghamAL35203 United States Sns College of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India Saveetha Engineering College Department of Ece Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798331508685

The emergence of big data has completely changed the way data analysis is done. As a result, there is a pressing need for effective clustering algorithms that can handle large datasets from different fields. Efficiency, scalability, and finding meaningful patterns in enormous data are some of the important difficulties that this research tackles in relation to big data clustering. We thoroughly examine and evaluate a range of clustering algorithms, from more conventional ones like hierarchical and K-Means clustering to more cutting-edge ones like density-based spatial clustering (DBSCAN) and model-based clustering. To further improve the performance and scalability of clustering algorithms, we investigate new methods that make use of distributed computing frameworks like Apache Spark and MapReduce. We offer a thorough assessment of their efficacy in terms of clustering quality, computational efficiency, and resource use by using and evaluating these methods on large-scale datasets. While conventional clustering algorithms may fail miserably when faced with extremely big data sets, our research shows that newer methods that make use of optimization techniques and distributed processing greatly outperform their predecessors. The effect of feature selection and dimensionality reduction techniques on improving clustering results is also shown. By shedding light on how to choose and implement clustering algorithms for effective data mining and pattern identification, this study adds to the continuing endeavors in the domain of big data analytics. Progress in areas like customer segmentation, anomaly detection, and social network analysis can be achieved with the help of adaptive algorithms that can handle large datasets while keeping clustering results accurate and dependable, according to the study. © 2025 IEEE.

关键词： Data accuracy

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