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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971-980 订阅

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An automatic system for picking up table tennis balls based on machine vision 4

An automatic system for picking up table tennis balls based ...

引用

4th international conference on Signal Processing and computer science, SPCS 2023

作者： Tan, Linyue School of Mechanical Engineering Dalian University of Technology No.2 Ling Gong Road Liaoning Da Lian116000 China

ISBN: (纸本)9781510672505

This article presents the establishment of a machine vision system intended for the retrieval of table tennis balls within a training hall. Capturing images of table tennis balls of various colors in the RGB color format was achieved through the utilization of a high-resolution camera affixed to the pick-up robot. To counteract the influence of environmental lighting conditions in the training hall, the acquired RGB images were converted into the HSI color format. Subsequently, image segmentation was executed based on predefined hue and saturation thresholds, enabling the precise identification and categorization of table tennis balls. The utilization of Canny edge detection and Hough circle detection methodologies was employed to designate the positions of the table tennis balls. Through analysis of circle centers and quantities, the specific locations of the table tennis balls were ascertained for the pick-up robot. This automated system, grounded in machine vision, holds the potential to significantly enhance the level of automation involved in the process of collecting table tennis balls. © 2023 SPIE.

关键词： computer vision

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Exploring Machine Learning in IoT Smart Home automation 8

Exploring Machine Learning in IoT Smart Home Automation

引用

8th IEEE international conference on Software Engineering and computer Systems, ICSECS 2023

作者： Waseem, Quadri Wan Din, Wan Isni Sofiah Bin Ab Rahman, Azamuddin Nisar, Kashif Faculty of Computing Pekan Malaysia AnalytiCray Kuala Lumpur55100 Malaysia Swinburne University of Technology Sydney Professional Computing Data Science ParramattaNSW2150 Australia

ISBN: (纸本)9798350310931

The Internet of Things (IoT) has evolved in these years. Various types of organizations, industries, research domains and almost all types of intelligent future applications are utilizing the advantages of IoT. These applications include smart homes, smart cities, smart infrastructure smart communities, smart healthcare, smart agriculture and many more. 'Smart Homes' has emerged as one the latest Internet of Things (IoT) applications known to automate household equipment's using remote or automated functioning from remote locations to improve the quality of life for its inhabitants. For a smart home system to function effectively, the machine learning (ML) implementation must go beyond basic remote control and simple automation. To fully realize its potential and provide homeowners with tremendous and unexpected benefits, more research and development in the fields of machine intelligence and smart home automation are required. In this research work, we aim to traverse ML in IoT smart home automation by classifying the home automation applications. We propose a taxonomy of machine learning (ML) for smart homes based on its application. This research also includes related surveys and literature reviews along with open challenges and issues as well as future directions in detail. © 2023 IEEE.

关键词： automation

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CFENet: Leveraging CLIP Text Features for Enhanced Few-Shot Semantic Segmentation 6

CFENet: Leveraging CLIP Text Features for Enhanced Few-Shot ...

引用

6th IEEE international conference on automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Zhao, Zeyu Chen, Zhong Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350305623

Few-shot semantic segmentation (FSS) confronts the task of constructing class-agnostic models capable of effectively segmenting novel classes with minimal annotations. Our paper introduces a novel approach to FSS by integrating a text feature module, specifically leveraging the CLIP's text feature module (CFM). Our primary objective is to augment segmentation model performance on novel classes with handful annotated images through the strategic inclusion of textual information. Simultaneously, we propose a multi-scale prior correlation module (MPM) that thoroughly exploits support-query image features at different scales, effectively supplementing and enhancing the prototype features in the images. The experimental results on the PASCAL-5i and COCO-20i dataset demonstrate a substantial performance boost in FSS task. The findings showcase the efficacy of our method, highlighting the excellent performance of the CFM and the MPM. This underscores the robustness and adaptability of our approach, establishing it as a promising solution for addressing the challenges posed by FSS task. © 2023 IEEE.

关键词： computer vision deep neural network few-shot segmentation visual-language model

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Design of wall painting robot based on machine vision 4

Design of wall painting robot based on machine vision

引用

4th international conference on Artificial Intelligence and Electromechanical automation, AIEA 2023

作者： Ma, Dandan You, Fahai Electrical and Information Engineering Beihua University Jilin132021 China Power generation Operation Department of Jilin Songhuajiang Thermal Power Co. LTD Jilin132021 China

ISBN: (纸本)9781510666412

In recent years, with more and more high-rises developing, the area of public buildings in our country is growing continuously. Each commercial space needs to decorate independently. The demand for the wall spraying robots is becoming more and more large, and the requirement is becoming more and more high. The spraying method proposed in this paper is based on the normal vector of the wall. The spray gun adjusts its attitude according to the normal vector of the wall, making the axis of the spray gun coincide with the normal vector of the wall, which can achieve better spraying effect. A sliding mode control algorithm based on adaptive adjustment is proposed, which combines adaptive motion control with sliding mode variable structure control to achieve the optimal trajectory tracking effect. © 2023 SPIE.

关键词： computer vision

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Meteoroid recognition algorithm of civil astronomical meteor monitoring system based on meteor trajectory recognition and light curve 5

Meteoroid recognition algorithm of civil astronomical meteor...

引用

5th international conference on Robotics and computer Vision, ICRCV 2023

作者： Shao, Qi Han, Xiao Lei Fan, Jia Nan Zhang, Xin Long Wang, Nan Internet of Things Engineering Department Shanghai Business School Shanghai China Beigene Ltd Shanghai China Computer Science and Technology Shanghai Business School Shanghai China Digital Publishing、Academic Resources Import and Export China Science Publishing & Media Ltd Beijing China

ISBN: (纸本)9798350326369

Due to the decentralized nature of civil meteor monitoring and the lack of conditions for large-scale observations like in scientific astronomical meteor monitoring, the identification algorithms for scientific astronomical meteor monitoring cannot be applied to civil meteor monitoring. In response to the problem that the civil meteor monitoring system cannot effectively distinguish meteors from other moving objects during meteor recognition, a meteor candidate recognition algorithm has been designed and implemented. This algorithm mainly includes two parts: meteor trajectory recognition and light change curve shape analysis. By using this meteor recognition algorithm to process two months' worth of images from the civil meteor monitoring system, 7400 meteor trajectories were extracted, of which 25% were non-meteor objects. The algorithm classifies the observed moving objects into target sets, and by considering the features of the light variation curve's single-peak structure and multi-frame features, the final algorithm feature accuracy is 86%. © 2023 IEEE.

关键词： Trajectories

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A swin transformer-based asymmetrical network for light field salient object detection 5

A swin transformer-based asymmetrical network for light fiel...

引用

2023 5th international conference on Information science, Electrical, and automation Engineering, ISEAE 2023

作者： Li, Yunqing Ma, Jiquan Department of Computer Science and Technology Heilongjiang University Harbin China

ISBN: (纸本)9781510667440

In this paper, we propose a Swin Transformer-based Asymmetrical Network (SwinAN) for light field salient object detection (SOD). SwinAN is a bifurcated backbone network composed of a 2D branch and a 3D branch. The 2D branch is driven by Swin Transformer blocks for all-in-focus images, while the 3D branch is constructed by a 3D convolutional neural network (CNN) for focus stacks. Then, the high-level features extracted by the asymmetric backbone network are integrated using cascaded Multi-Level feature Fusion (MLF) modules, which consist of channel attention mechanisms and a residual block. Finally, the Multi-Attention Fusion (MAF) module is used to fuse multi-modal features to generate the predicted saliency maps. Experimental results show that after comparing 10 different SOD models, our SwinAN achieves excellent performance on 3 datasets, confirming the superiority as an efficient and accurate method for detecting saliency © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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Servo Drive Control System 11

Servo Drive Control System

引用

11th international Scientific conference on computer science, COMSCI 2023

作者： Hristov, Kamen Hristov, Vladimir Technical University of Sofia Electrical Motion Automation Systems Sofia Bulgaria

ISBN: (纸本)9798350325256

In the present article, a modern drive system for research the characteristics of servo drives is presented. The drive system is designed for researching different operating modes of a servo drive. The drive system is intended for implementation in the learning process at the Faculty of automation. Various types of exercises have been developed for students to explore. Servo drives are electric drives for automatic control of torque, position, speed and possibly its derivatives using feedback. Feedback differentiates servo systems from other types of electric drives. In the case of servomechanisms, the command (coordinates, speed, moment, etc.) is fed to the input of the servo amplifier, which determines the current value, compares it with the set value and produces a control effect reducing their difference. With good reason, servo drives are accepted as the most high-tech electric drives. © 2023 IEEE.

关键词： Electric drives

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Impact of Cloud Computing on Agricultural Advancement using Data Mining Algorithms 2

Impact of Cloud Computing on Agricultural Advancement using ...

引用

2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Saravanan, S.K. Nisha, F. Robin Rohit, V. Lenin, J. Selvam, P.D. Rajmohan, M. Department of BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India School of Engineering and Technology Computer Science & Engineering CMR University Karnataka Bengaluru India Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Department: Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Incorporating Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) into the Reinforcement Learning framework provides a comprehensive solution. The framework's dynamic paradigm for precision agriculture increases output while decreasing resource consumption. The system adapts to its environment in real-time based on what it has learned via its previous experiences, thanks to Reinforcement Learning. By incorporating IoT algorithms, agricultural machinery may more easily gather and exchange data with cloud servers, improving precision and flexibility. By merging many models to provide reliable results, Ensemble Learning allows us to significantly enhance our predictive abilities. The usage of XAI, which provides context and justification for computations, has the potential to increase public confidence in AI-powered agricultural systems. It demystifies decision-making and arms participants with relevant information. In order to take use of the scalability and processing capacity of cloud resources, agricultural algorithms may now be readily installed and controlled with the help of this framework. Data mining algorithms have the potential to change agriculture by fostering sustainable practices and addressing challenges of global food security, as this research elucidates. © 2023 IEEE.

关键词： Agricultural Innovation Cloud Computing Data Mining Algorithms Ensemble Learning Explainable AI (XAI) Internet of Things (IoT) Precision Agriculture Reinforcement Learning Resource Optimization Sustainable Practices

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Fruits and Pest Diseases Detection using Deep Learning-Based Approach

Fruits and Pest Diseases Detection using Deep Learning-Based...

引用

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

作者： Adolphine Shyni, S. Sugunedham, S.K. Rajeswari, M. Anandhi, S. Manikandan, J. Manakula Vinayagar Institute of Technology Computer Science and Engineering Puducherry India Manakula Vinayagar Institute of Technology Artificial Intelligence and Machine Learning Puducherry India Information Technology Sri Sai Ram Engineering College Chennai India Computer Science and Engineering Rajalakshmi Engineering College Chennai India Information Technology St. Joseph's College of Engineering Chennai India

ISBN: (纸本)9798350315127

Current agricultural environments present threats from fungal and bacterial pathogens which pose grave threats to both crop production and food security, necessitating timely detection of plant pathogens as a priority. computerbased techniques that employ deep learning methodologies have been devised to effectively detect plant diseases by analysing indicators on stems and leaves. We focus on three primary detection systems that include YOLO, region-based Fully Convolutional Networks (R-FCNs) and faster Region-based Convolutional Neural Networks (Faster R-CNNs) as 'deep learning meta-architectures, including Residual Network and VGG Net. To enhance precision and minimize false positives in training time, we have devised a technique which combines global and local class tagging as well as feature extraction. To do this, we used an immense database called Pests and Diseases of Fruit Information that contains various images of pests and diseases as well as details like inflammation severity and root location - this data served to train and assess our systems extensively;its results demonstrate its ability to precisely identify nine pests/diseases even in challenging soils. At the core of it all lies our research: deep-learning meta-architectures and attribute concentrators demonstrate their value in disease detection. By employing cutting edge technologies, we hope to enhance plant health management while optimizing crop yield. © 2023 IEEE.

关键词： Fruits

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Research on Key Technologies for Bird's Nest Detection in High-Speed Railway Catenary Systems Based on Deep Learning 4

Research on Key Technologies for Bird's Nest Detection in Hi...

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4th international conference on Electronic Information Engineering and computer technology, EIECT 2024

作者： Wang, Rong Chen, Shenglan Le, Qiqing Wang, Jun Hunan Automotive Engineering Vocational University Zhuzhou China The College of Electrical Engineering and Automation Fuzhou University Fuzhou China The College of Automation Central South University Changsha China Crrc Times Co. Ltd Zhuzhou China

ISBN: (纸本)9798331528850

With the rapid development of high-speed railway technology, ensuring its operational safety has become a critical issue. In particular, real-time monitoring of the high-speed railway pantograph system is of utmost importance for preventing failures and reducing the incidence of accidents. The aim of this research is to enhance the intelligent detection performance of the high-speed railway pantograph network status, especially in the detection of bird nests, through an improved MobileNetV3 algorithm. The research methodology includes the application of deep learning and image processing technologies, focusing on optimizing the MobileNetV3 algorithm to increase its detection accuracy in complex environments, particularly its ability to identify bird nests in complex scenes and adapt to dynamic environments. It is expected that these improvements will enable more accurate and efficient monitoring of the status of high-speed railway pantographs, thereby enhancing the safe operation level of high-speed railways. © 2024 IEEE.

关键词： Birds

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