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

共 68437 条记录，以下是381-390 订阅

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Design of Subpixel computer Vision Detection Algorithm Based on Texture Image

Design of Subpixel Computer Vision Detection Algorithm Based...

引用

2023 international conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Wang, Junxian Mao, Kaihui Zhang, Xiaolong Lv, Wei Gao, Junhan Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Zhuhai519041 China

ISBN: (纸本)9798350313314

Testing technology based on computer vision is a new testing technology. It uses the image as a means or carrier to detect and transmit information, extracts useful signals from the image, and obtains various required parameters by processing the measured image. It has the characteristics of non-contact, full-field measurement, high precision and high degree of automation. In this paper, an optimal design scheme of sub-pixel computer vision detection algorithm based on texture image is proposed. The sub-pixel image texture is extracted by texture database, and then the sub-pixel accuracy is grayed by computer vision detection to achieve the optimal design of sub-pixel accuracy. Finally, the sub-pixel of texture image is analyzed experimentally. Finally, the experiment shows that the sub-pixel gray level is improved by 8.45% compared with the traditional algorithm in the case of texture primitives. It is of great practical significance to carry out the research of computer vision inspection design with sub-pixel as the core to speed up the improvement of vision inspection technology. © 2023 IEEE.

关键词： Image texture

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Low-dimensional Feature Enhanced Camouflaged Object Detection

Low-dimensional Feature Enhanced Camouflaged Object Detectio...

引用

2023 international conference on automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Wang, Xin Zhang, Zhao Xu, Junfeng School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9781510667662

Camouflaged Object Detection (COD) is a challenging vision task that aims at locating objects having high similarity in appearance with its background. Existing COD methods are highly dependent on the high-dimensional feature of camouflaged object while neglecting details within the low-dimensional feature. In this paper, we highlight low-dimensional information to enlarge the discrepancies between objects and their surroundings. We propose a CNN-based network ShallowNet with enhanced low-dimensional feature to retain more differences in the process of encoding. The proposed approach shows enhanced effectiveness both qualitatively and quantitatively compared with existing COD methods regarding various datasets. © 2023 SPIE.

关键词： Object detection

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Lag Consensus for Second-Order Nonlinear Multi-Agent Systems on the Basis of Passivity 13

Lag Consensus for Second-Order Nonlinear Multi-Agent Systems...

引用

13th IEEE international conference on CYBER technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Ru-Yue Han, Xiao Wang, Jin-Liang School of Mathematical Sciences Tiangong University Tianjin300387 China School of Mechanical Engineering Tiangong University Tianjin300387 China School of Computer Science and Technology Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tiangong University Tianjin300387 China

ISBN: (纸本)9798350315196

This paper investigates lag consensus of second-order nonlinear multi-agent systems (MASs) on the basis of passivity. on one hand, the passivity of single agent is addressed by utilizing the appropriate controller. On the other hand, a criterion is derived for assuring lag consensus of MASs with the help of the passivity of single agent. Finally, the designed strategies are verified through a numerical example. © 2023 IEEE.

关键词： Multi agent systems

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Which performs better, linear scanning or the KNN searching algorithm based on 2D grids? 3

Which performs better, linear scanning or the KNN searching ...

引用

3rd international conference on Robotics, automation and Intelligent Control, ICRAIC 2023

作者： Ji, Tong Lin, Yifeng Yang, Yuer Liu, Hui Jinan University College of Information Science and Technology Guangzhou China Jinan University College of Cyber Security Guangzhou China The University of Hong Kong Department of Computer Science Hong Kong Hong Kong University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China

ISBN: (纸本)9798350360813

The K-nearest neighbor (KNN) algorithm is widely used in navigation, such as traffic management, driverless vehicles, and logistics planning. While it offers powerful instance-based learning, its performance can be influenced by data distribution and distance metric choice, and scalability can be an issue with large datasets. In this paper, we compare KNN with linear scanning in 2D grid-based search algorithms. KNN excels in accuracy, multidimensional feature processing, flexibility, and stepwise optimization, making it a strong choice for efficient and accurate 2D grid data search. Especially when the dimension of the grid is 200 ∗ 200 and the grade is 1, the performance of KNN is 99.70% better than linear scanning. However, we acknowledge that KNN's performance hinges on distance metric selection, and different metrics may perform differently in various applications. KNN is also sensitive to noise and outliers, which highlights the need for future enhancements and optimizations. © 2023 IEEE.

关键词： Large datasets

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FAS-reid: Fair Architecture Search for Person Re-IDentification 3

FAS-reid: Fair Architecture Search for Person Re-IDentificat...

引用

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

作者： Chen, Shengbo Chen, Shijie Lei, Zhou Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665490788

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust. © 2023 IEEE.

关键词： automation

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Applications of visual navigation and intelligent recognition technology 4

Applications of visual navigation and intelligent recognitio...

引用

4th international conference on Testing technology and automation Engineering, TTAE 2024

作者： Jin, Zhuang Zhuang, Zhao Yuan, Bo College of Computer Guangdong University of Science and Technology Dongguan China

ISBN: (数字)9781510686656

ISBN: (纸本)9781510686649

This paper presents the design and implementation of an intelligent car system based on Raspberry Pi CM4, focusing on the camera module in the intelligent car. The camera module incorporates functionalities such as camera calibration, visual line following, color tracking, facial recognition, and label recognition. Utilizing image acquisition and processing techniques, this module can accurately perform various visual tasks. The system utilizes hardware components including the Raspberry Pi CM4 core board, relevant expansion boards, and corresponding visual processing equipment. The software portion is developed based on the Raspberry Pi Linux operating system, establishing a development environment and control interface suitable for the camera module's functions. System demonstrations and tests validate the stability and reliability of the camera module in autonomous driving and visual recognition tasks. © 2024 SPIE.

关键词： Image acquisition

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Bioacoustics Signal Classification Using Hybrid Feature Space with Machine Learning 15

Bioacoustics Signal Classification Using Hybrid Feature Spac...

引用

15th international conference on computer and automation Engineering, ICCAE 2023

作者： Haider, Usman Hanif, Muhammad Kobayashi, Hiroki Parajuli, Laxmi Kumar Shimotoku, Daisuke Rashid, Ahmar Safeer, Sonia Gik Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Topi Pakistan The University of Tokyo Information Technology Center Tokyo Japan

ISBN: (纸本)9798350396225

The vocal sounds emitted by animals and birds possess distinctive signatures. They are vital for acoustic monitoring to extract useful ecological data and track biodiversity. Recently, automated bioacoustics classification drew attention from the research community due to its diverse application. To that aim, we present a novel classification method for acoustic data by fusing optimally selected signal features. The proposed method extracts the distinctive statistical features, calculated using coefficients of Discrete Wavelet Transform (DWT), and fuses them with the descriptive features estimated using the Mel-Frequency Cepstral Coefficients (MFCC). The combined feature set is then passed to different machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) for sound classification of different animals and birds. The evaluation results show that the proposed method improves the classification accuracy and achieved high precision on all classifiers. © 2023 IEEE.

关键词： Birds

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Enhancing Poultry Farm Productivity Using IoT-Based Smart Farming automation System 26

Enhancing Poultry Farm Productivity Using IoT-Based Smart Fa...

引用

26th international conference on computer and Information technology, ICCIT 2023

作者： Rahman, Mahbubur Kohinoor, Md Saidur Rahman Sami, Aftar Ahmad Leading University Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350359015

The swift expansion of smart farming is mainly attributable to the industry's noteworthy impact of the Internet of Things (IoT) and various sensors have revolutionized data collection across different farming aspects, encompassing machine performance and supply chain operations. Addressing this technological evolution, our paper introduces a comprehensive smart farming automation system, apt for multiple agricultural domains. Its primary focus is to bolster farm productivity through precise environmental monitoring and built-in analytics to interpret data trends over time, offering actionable insights and recommendations for optimal production growth. To illustrate its practical application, we specifically delve into its implementation in poultry farming, emphasizing sensors that track temperature and humidity, which are paramount for poultry well-being. Integrated features include Wi-Fi-based real-time monitoring, automation for food and water distribution, rain-protective curtain controls, and a dedicated mobile application paired with a web server, utilizing data scrapping APIs to discern and suggest optimal farming patterns based on accumulated and real-time data. This system also offers precise tracking of environmental factors and an alert mechanism, leveraging IR sensors, to signal food storage deficits and extreme conditions. As such, this project presents itself as a feasible alternative to traditional farming practices, which typically pose environmental challenges and crave considerable labor through data-driven feedback and streamlined automation. © 2023 IEEE.

关键词： agricultural automation Agriculture data analysis farm management IoT sensors smart poultry farming

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Motion Planning of Warehouse automation Robot Based on Recurrent Neural Network

Motion Planning of Warehouse Automation Robot Based on Recur...

引用

2023 international conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2023

作者： Li, Xiangmei Yantai Sci. Tech. Innovation Promotion Centre Yantai Shandong264003 China

ISBN: (纸本)9798350301397

The global economy is developing rapidly and the promotion of economic globalization is also steadily advancing, which makes the market competition at home and abroad more and more cruel. In order to be in an advantageous position in the fierce economic market competition, enterprises have no choice but to fully realize industrial automation. With the vigorous development of e-commerce and logistics industry, warehouse automation has become a hot research direction in the field of robotics. Mobile robotics is a comprehensive system that integrates sensing, dynamic decision-making and planning, behavior control and execution, etc. Its research content involves sensor technology, computer engineering, automation control, mechanics and other multidisciplinary theories and applications. It reflects the latest achievements of information science and artificial intelligence technology. It is one of the most active fields of scientific and technological development. This paper uses a cyclic neural network, which introduces traditional signal analysis methods into the cyclic neural network. By replacing the state vector of the hidden layer of the traditional cyclic neural network with a state-frequency matrix, the time-domain characteristic pattern and frequency domain of the time series are realized. Joint modeling of feature patterns. © 2023 IEEE.

关键词： Competition

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An AI Model For Recognization Of Raagas In Indian Classical, Carnatic Music – A Review Article 11

An AI Model For Recognization Of Raagas In Indian Classical,...

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11th IEEE international conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Mothi, Shubhada M. Tesfahun, Abebe Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

Music is a sound based form of art. From medieval periods there has been a constant effort to create great melodies for the purpose of entertainment, propaganda and healing. There are many formal music systems worldwide that provide a proper method and framework to create great melodies. Music is created by voice and hundreds of musical instruments available worldwide. The two popular classical music systems available in India are Hindustani Classical Music and Carnatic Classical Music. Although there are a few similarities between both the systems such as swaras and Raagas, both have different basic frame work. The basic aim of this review is to collect and analyze all the previous works and research done in the field of Raaga Recognition of Classical Music using automation and AI. It also involves review of few research journals which are purely based on classification and framework of Carnatic Classical Music in the traditional method without involving automation or Artificial Intelligence. The analysis is in the pursuit of finding the scope for further research and to create a complete model to recognize maximum possible Raagas with minimal errors. ©2023 IEEE.

关键词： automation

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