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

共 68829 条记录，以下是831-840 订阅

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Design of Table Tennis Collection System Based on STM32 and μC/OS-III 3

Design of Table Tennis Collection System Based on STM32 and ...

引用

3rd IEEE international conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Huang, Jiashuai Yang, Chunshan Guilin University of Aerospace Technology College of Electronic Information and Automation Guilin China

ISBN: (纸本)9781665490788

Aiming at the problem of frequent manual picking up ping-pong balls during training, an automatic system for picking up ball is designed. The system combines single-chip computer technology, sensor technology and embedded technology to achieve the recognition, tracking, and picking up of ping-pong balls. The OpenMV visual module is used to identify and track table tennis balls, and the roller structure is used to pick up balls. The μC/OS-III embedded real-time operating system is transplanted to STM32 to achieve multitask management and task scheduling of the system, which improves the performance and stability of the system at a certain extent. The practice of the automatic ping-pong ball collection system shows that the rate of success picking up ball is high and its stability is good, which can reduce the burden of manual ball picking at a certain extent. © 2023 IEEE.

关键词： Sports

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Modeling House Price Prediction Model using XG Boost and Machine Learning Algorithms

Modeling House Price Prediction Model using XG Boost and Mac...

引用

2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Kiran, Ajmeera Khan, Mudassir Babu, J. Chinna Santosh Kumar, B.P. Ahmed, Sheik Jamil Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad500043 India King Khalid University College of Science & Arts Tanumah Department of Computer Science Abha Saudi Arabia Annamacharya Institute of Technology and Sciences Department of Ece AP Rajampet India Yv University Ysr Engineering College Department of Ece AP Proddatur India Presidency University School of Computer Science and Information Science India

ISBN: (纸本)9798350317060

Every year, the cost of homes rises, necessitating the development of a system that can forecast future home prices. The ability to estimate house prices can be useful to both the developer and the customer in planning the best time to buy a home. The physical state, design, and location of a house are the three variables that affect its cost. This paper discusses using Python modules to estimate housing prices using regression approaches. The proposed method utilized more precise house price calculations and provided more accurate predictions. It also discusses graphical and numerical methods for housing price prediction. This paper describes our machine learning-based house price model and its dataset. Real estate data mining is frequent. Data mining can anticipate house values, essential property features, and more by extracting meaningful information from raw data. Research shows that property price variations worry homeowners and the real estate market. To find the best house price prediction models, a literature review is done. This investigation showed that Artificial Neural Networks, Support Linear Regression, and were the most efficient models. We also found that locational and structural features predict property prices. This study will help housing developers and academics identify the most important factors affecting house prices and identify the best machine learning model for this subject. Machine Learning Model, Linear Regression, House Price Prediction. © 2023 IEEE.

关键词： Forecasting

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End-to-End Robotics Processing Management

End-to-End Robotics Processing Management

引用

2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Savita Sowmya, C.S. Gopalakrishna, K. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

Using software robots that communicate with systems via their user interfaces, robotic process automation (RPA) attempts to automate corporate operations while increasing efficiency and cutting costs. To prevent inefficiencies,choosing the appropriate procedures for RPA automation is essential. This study offers a method for examining RPA development in corporate settings. Design, methodology, and approach: To pinpoint the key ideas behind RPA, this research undertakes a thorough literature assessment. It suggests a model linking these ideas and assesses its viability using Design science Research (DSR) analysis of previous RPA case studies. Findings- The study shows that several of the key RPA ideas outlined in the literature analysis are absent from a number of case studies. © 2023 IEEE.

关键词： automation business process management case studies disadvantages future challenges integration literature review opportunities process assessment process selection, advantages Robotic Process automation RPA strategic goals tactical evaluation

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AI-Based Hand Gesture Recognition Through Camera on Robot 20

AI-Based Hand Gesture Recognition Through Camera on Robot

引用

20th international conference on Frontiers of Information technology, FIT 2023

作者： Csonka, Gergo Khalid, Muhammad Rafiq, Husnain Ali, Yasir University of Hull School of Computer Science HullHU6 7RX United Kingdom Edge Hill University Department of Computer Science United Kingdom Shahzeb Shaheed Government Degree College Razzar Swabi Higher Education Department Peshawar Pakistan

ISBN: (纸本)9798350395785

This paper presents an innovative approach to real-time hand gesture recognition for robot control using Artificial Intelligence (AI). The core of this project is a machine learning model trained on a custom data set of hand gestures, which was meticulously hand-annotated to ensure accuracy. To enhance the model's performance and generalization, data augmentation techniques were employed. Furthermore, the model leverages the power of transfer learning, with a ResNet backbone serving as the foundation, to efficiently learn from the data set. In addition to the development of the AI model, a custom robot was designed and built using Arduino and Raspberry Pi. This robot is equipped with a camera to capture images of hand gestures, which are then transmitted to the machine learning model for real-time analysis. The hardware of the robot was meticulously optimized to ensure smooth operation and accurate data capture. The resulting system enables real-time hand gesture recognition on the robot, opening up a plethora of applications, from industrial automation to smart home technology. By synergistically combining AI, computer vision, and robotics, this project not only demonstrates the potential for innovative solutions to real-world problems but also significantly enhances the functionality and usability of robots. It paves the way for improved human-computer interaction through the practical implementation of advanced AI and computer vision techniques. © 2023 IEEE.

关键词： Human computer interaction

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A CNN-Based Framework for Detection and Recognition of Objects Using Transfer Learning: A Case Study on Flower Recognition 1

A CNN-Based Framework for Detection and Recognition of Objec...

引用

2023 1st DMIHER international conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Tiwari, Shivam Sharma, Vikrant Rathore, Yogesh Singh Gandhi, Parul Srivastava, Durgesh Sharda University Department of Computer Science & Engineering Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Sharda University Computer Science and Application UP Greater Noida India Manav Rachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350338423

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species. © 2023 IEEE.

关键词： Feature extraction

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Machine Vision Applied to Backscattered Electron Imaging 6

Machine Vision Applied to Backscattered Electron Imaging

引用

6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Huang, Junyuan Zhang, Wei Chen, Jingyun Beihang University School of Mechanical Engineering and Automation Beijing China Beijing Institute of Space Launch Technology Beijing China

ISBN: (纸本)9798350325485

Electron beam processing usually needs to be carried out in a vacuum environment. The influence of high temperature and high concentration of metal vapor makes it difficult for traditional machine vision observation systems to perform long-Term observation. In this paper, a machine vision algorithm was used, a quadrupole plate sensor and image fusion algorithm were adopted, and the combination of software and hardware was realized. Finally, we solved the problems of scale imbalance and slice model scanning order in the imaging process and successfully realized clear observation of the surface in all directions. The backscattered electron signal imaging technology has been successfully applied to the electron beam processing process, which meets the online observation requirements of the electron beam processing process. © 2023 IEEE.

关键词： computer vision

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Multi-template-based Object Tracking via Occlusion Detection 3

Multi-template-based Object Tracking via Occlusion Detection

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3rd international conference on computer science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

作者： Guo, Ziliang Bai, Zanzhou Qiao, Yu Li, Zihao Zhang, Xiwen Fang, Xingqi Tian, Yi Zhang, Xiaowei Shanghai Jiao Tong University Department of Automation Shanghai China Shanghai Electro-mechanical Engineering Institute Simulation Department Shanghai China

ISBN: (纸本)9798350372052

Although object tracking methods are developing rapidly recently, their robustness to occlusion scenarios remains a fundamental problem. In this paper, we focus on single object tracking based on the widely-adopted Siamese network. In order to reduce the frames of lost target, we propose a multi-template based object tracking framework by detecting occlusions. Firstly, a multi-template design is introduced, which stores long-term target appearance in libraries, and updates current template by fusing the similarity-oriented short-term knowledge. Secondly, occlusion detected is applied to guide template updating. Both occlusion and template memories contribute to the insensitivity to the learning rate. We conduct extensive experiments on VOT2018 and VOT2019. The significant reduction of frames of lost target validates the overall effectiveness of our approach. © 2023 IEEE.

关键词： Object detection

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A new Interleaved Full-Bridge Converter for AC battery 12

A new Interleaved Full-Bridge Converter for AC battery

引用

12th IEEE international conference on Control, automation and Information sciences, ICCAIS 2023

作者： Do, Tuan Anh Le, Quang Nghia Nguyen, Thang Long Dich, Nguyen Quang Hoang, Phuong Vu Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Hanoi University of Science and Technology Institute for Control Engineering and Automation Hanoi Viet Nam

ISBN: (纸本)9798350328783

Home battery storage systems using Second-life time batteries (SLBs) are of recent research interest for resilient homes. The AC Battery is chosen for this application due to its flexibility, in which the Interleaved Full Bridge (IFB) converter is studied in this paper for the Power Correction Factor (PFC) stage. The pulse width modulation and control strategy are introduced for the IFB converter in this research. To verify the operation and the feasibility of the proposed structure in the grid-tied mode, a real-time simulation is carried out with the results meeting a good agreement with the theoretical analyst. © 2023 IEEE.

关键词： Electric power factor correction

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Passive Sonar Multi-Target Frequency Feature-Aided Data Association Algorithm Based On DPGMM 11

Passive Sonar Multi-Target Frequency Feature-Aided Data Asso...

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11th IEEE international conference on computer science and Network technology, ICCSNT 2023

作者： Wang, Zechong Teng, Tingting Harbin Engineering University Acoustic Science and Technology Laboratory Harbin150001 China Harbin Engineering University Ministry of Industry and Information Technology Key Laboratory of Marine Information Acquisition and Security Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350311594

In passive sonar system, it's always a challenge to accomplish multi-target data association. We propose a remedy for this problem by using a frequency feature-aided data association algorithm to track targets' frequency and bearing information simultaneously. Frequency and bearing information are extracted by Dirichlet Process Gaussian Mixture Model(DPGMM). Simulation results are presented to illustrate the tracking performance of the proposed algorithm in terms of incorrect association, association interrupt times and tracking accuracy aspect. © 2023 IEEE.

关键词： Gaussian distribution

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Enhancing Biomedical Literature Analysis to Predict Drug Interactions Using Multi Criteria Decision Making and LSTM-CNN Model

Enhancing Biomedical Literature Analysis to Predict Drug Int...

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2023 international conference on System, Computation, automation and Networking, ICSCAN 2023

作者： Sankaran, A. Sathiyamurthy, K. Puducherry Technological University Dept. of Computer Science and Engineering India Puducherry Technological University Faculty of Computer Science and Engineering India

ISBN: (纸本)9798350315127

The extraction of information from unstructured text has become a crucial task in the biomedical literature field. Specifically, the analysis of various relationships such as Drug Food, Drug Disease, Drug non-prescription and Drug Drug interactions has gained significant importance. To accomplish this, a combination of Multi Criteria Decision Making (MCDM) and machine learning techniques has been employed. A dataset has been curated and utilized to train a model based on Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). The trained model enables accurate predictions and identification of relevant information pertaining to drug interactions in biomedical literature. This approach enhances the understanding and knowledge extraction from vast amounts of unstructured text data, leading to improved insights and potential advancements in the field of biomedical research. © 2023 IEEE.

关键词： Long short-term memory

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