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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Computer Vision System for Determining the Reference Point 3

Computer Vision System for Determining the Reference Point

引用

3rd ieee KhPI Week on advanced Technology, KhPI Week 2022

作者： Sheremet, Oleksii Kovalchuk, Oleh Sheremet, Kateryna Sadovoi, Oleksandr Kiriienko, Tetiana Sokhina, Yuliia Donbass State Engineering Academy Department of Electromechanical Systems of Automation and Electric Drive Kramatorsk Ukraine Donbass State Engineering Academy Department of Intelligent Decision Support Systems Kramatorsk Ukraine Dnipro University of Technology Department of Electric Drive Dnipro Ukraine Dniprovsk State Technical University Department of Electrical Engineering and Machinery Kamianske Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

The stages of development and implementation of neural network methods for visual determination of the reference points coordinates of a specific technical object are considered. Publicly available information about artificial neural networks and computer vision is analyzed. The description of the proposed intellectual system is carried out, questions of data markup, as well as an artificial increase in their number, are raised. The resulting intelligent system has an average accuracy of 95.2%. © 2022 ieee.

关键词： Image classification

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Path planning of an autonomous mobile robot using adaptive network based fuzzy controller

Path planning of an autonomous mobile robot using adaptive n...

引用

ieee International Advance computing Conference, IACC

作者： Prases K. Mohanty Dayal R. Parhi Alok K. Jha Anish Pandey Department of Mechanical Engineering National Institute of Technology Rourkela Orissa India

ISBN: (纸本)9781467345279

In recent years intelligent soft computing techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are proven to be efficient and suitable when applied to a variety of systems. In this paper we intend to formulate an adaptive neuro-fuzzy inference system (ANFIS), a new sensor based navigation technique for mobile robot. The ANFIS controller uses different sensors based information such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD) and heading angle (HA) for choosing the optimal direction while moving towards target. The real time experiment has been carried out under different environmental scenarios to collect the data set for modeling ANFIS tool box. Using ANFIS tool box, the obtained mean of squared error (MSE) for training data set in the current paper is 0.031. We also have present the simulation experiments using MATLAB, showing that ANFIS consistently perform better results to navigate the mobile robot safely in a terrain populated by stationary obstacles.

关键词： Mobile robots Navigation Robot sensing systems Wheels Adaptive systems Firing

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Structure of the updated ANKE DAQ system

Structure of the updated ANKE DAQ system

引用

ieee NPSS Conference on Real Time

作者： P. Wustner A. Ackens W. Erven G. Kemmerling H. Kleines W. Loevenich R. Schleichert S. Mikirtytchiants S. Trusov Forschungszentrum Jülich GmbH Germany

The three big experiments at the cooler synchrotron COSY in Juelich (ANKE, WASA and TOF) permanently require an improvement of their data aquisition systems. We will use ANKE as an example for the overall structure of these data aquisition systems und the improvements in the last years. We differentiate between three generations of our DAQ systems. The 1st is not used any more. The 2nd uses intel-compatible computers running NetBSD and 'dumb' crate controllers. The synchronisation was done with special PCI modules in the computers with point-to-point connections building a ring. The 3rd generation was introduced with the transfer of the WASA detector from Uppsala) to Julich. It uses special LVDS crates witch can be read out without any software overhead. It required a new synchronisation system consisting of LVDS modules only. ANKE now uses a mixture of the 2nd and 3rd generation in order to make use of some of the new features but still has to deal with old systems like FASTBUS which make it impossible to use the pure 3rd generation.

关键词： data acquisition Fastbus Detectors CAMAC Real time systems Optical computing Field programmable gate arrays Synchrotrons Control systems Operating systems

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Research and Implementation of intelligent Interior Design Algorithms Based on Artificial Intelligence and Big data

Research and Implementation of Intelligent Interior Design A...

引用

Applied Intelligence and computing (AIC), ieee World Conference on

作者： Jing He School of Road Bridge and Architecture Chongqing Vocational College of Transportation Chongqing China

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In the past few decades, interior design has relied on manual experience and designer intuition, during which time was consumed and the design results often failed to meet customer needs. intelligent interior design is an emerging field based on artificial intelligence and big data technology, which aims to provide efficient and personalized interior design solutions through intelligent algorithms and data analysis. Therefore, this article's research on the application of intelligent design in indoor space has great practical significance and practical value. In this article, artificial intelligence and big data technology and their expansion of intelligent algorithms are analyzed with emphasis, and experiments and performance comparisons of several algorithms related to interior design are conducted. This article uses an interactive method to compare the running time of genetic algorithms, evolutionary algorithms and differential evolution algorithms. Experimental results show that the interactive evolutionary algorithm takes 56.2ms and the interactive genetic algorithm takes 33.6ms.

关键词： Machine learning algorithms Evolutionary computation Manuals Big data Real-time systems data models Artificial intelligence Recommender systems Optimization Genetic algorithms

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Towards the Practical Use of Qualitative Spatial Reasoning in Geographic Information Retrieval

Towards the Practical Use of Qualitative Spatial Reasoning i...

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International ieee Conference on intelligent systems, IS

作者： Alia I. Abdelmoty Philip Smart Baher A. El-Geresy Cardiff School of Computer Science Cardiff University UK Cardiff School of Computing University of Glamorgan UK

Geo-ontologies have a key role to play in the development of the geo-semantic Web, with regard to facilitating the search for geographical information and resources. They normally hold large amounts of geographic information and undergo a continuous process of revision and update to ensure their currency. Hence, means of ensuring their integrity are crucial and needed to allow them to serve their purpose. This paper proposes the use of qualitative spatial reasoning as a tool to support the development of a geo-ontology management system. Spatial integrity rules based on uniform and hybrid spatial reasoning are proposed for the automatic derivation of spatial relationships and for maintaining the spatial consistency of the geographic data. A framework for the representation of and reasoning over geo-ontologies is presented using the Web ontology language OWL and its associated reasoning tools. Spatial reasoning and integrity rules are represented using a spatial rule engine extension to the reasoning tools associated with OWL. To demonstrate the proposed approach, a case study showing a prototype geo-ontology and the implementation of the spatial reasoning engine is presented. This work is a step towards the realisation of a complete geo-ontology management system for the semantic Web

关键词： Information retrieval OWL Search engines Ontologies Error correction Geographic Information systems intelligent systems Maintenance Quality management Prototypes

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Application and Development of Shrimp Farming intelligent Monitoring System on Edge computing

Application and Development of Shrimp Farming Intelligent Mo...

引用

ieee International Conference on Architecture, Construction, Environment and Hydraulics (ICACEH)

作者： Tien-Sheng Lin Ta-Jen Chu Yi-Jia Shih Jung-Kuei Yang Jun Wan Xu-Yin Lin Quanzhou University of Information Engineering Fengze District Quanzhou Fujian China Fisheries College of Jimei University Jimei District Xiamen Fujian China School of Information Science and Technology Huizhou University Huicheng District Huizhou Guangdong China Xiamen Ocean Vocational college Xiangen District Xiamen Fujian China

ISBN: (纸本)9781665405317

Convolutional neural network (CNN) and machine deep learning are used to analyze the required static parameters of the growth environment of shrimp farming. Using the parameters, the intelligent monitoring system analyzes the needs of different shrimp larvae with different growth environments. Image recognition technology and the collection of sensor data are used to control various dynamic parameters, such as feeding, growth, movement, and accident warning. The edge computing of the proposed system can improve the efficiency of shrimp farming production and avoid accidental injuries caused by emergency accidents.

关键词： Deep learning Image recognition Conferences Production Hydraulic systems Convolutional neural networks Monitoring

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Construction of a Signal-class Experimental Platform Based on Python 3

Construction of a Signal-class Experimental Platform Based o...

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3rd International Conference on Computer Technologies, ICCTech 2024

作者： Lu, Ying Ming, Niankun Dai, Fei Wang, Mingyang Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350395419

There are many advanced concepts and abstractions involved in signal-class courses, which require complex mathematical formulas to be deduced and proven. The derivation process of these formulas is difficult to follow, and there is a challenge in correlating them to the experimental results, making it difficult for students to understand. In response to the non-open-source nature of Matlab, Python, as a free and open-source programming language, has powerful features and rich third-party library resources, supporting various signal processing methods and algorithms, showing unique advantages and effects. Currently, existing experimental simulation platforms for signal courses are often designed for a certain type of course, such as Digital Signal Processing, Communication Principle, Signals and systems, etc. These platforms only provide limited cases and experiments, and the course content they cover is relatively single and independent, lacking the connection and integration of knowledge points between different courses. To improve this situation, this article chose PyQt5 as the development tool library, with its excellent performance and rich resources, to design and develop an experimental simulation platform for signal-class courses. This platform closely follows the teaching syllabus and divides the platform into multiple modules according to the division of teaching content. Each module contains corresponding course knowledge points and experimental simulations. The platform also has various teaching service functions such as online self-study and teaching guidance, which can help teachers make teaching more vivid and interesting, and can also help students better understand abstract theories and concepts. The platform has been put into teaching and learning in our college and other brotherly universities, which receiving positive feedback and praise from teachers and students. © 2024 ieee.

关键词： Python

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Distributed computing and data Processing Technology under the Power data Middleware Architecture

Distributed Computing and Data Processing Technology under t...

引用

data Science and Computer Application (ICDSCA), ieee International Conference on

作者： Yuelin Zou Jia Shen Rui Yin Jiangtao Guo Yi Guo State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi China State Grid Xinjiang Electric Power Co. Ltd. Urumqi China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou China

With the establishment and development of integrated monitoring platforms and communication information platforms for intelligent substations, the data volume of the power system is showing explosive growth. However, existing data centers have poor processing capabilities for massive amounts of data, making it difficult to meet the multiple service quality requirements of users. How to effectively manage massive and complex data, fully utilize the value of data, improve service quality, reduce operational costs, and achieve rapid response to users is an urgent problem to be solved. Therefore, based on the actual needs of power enterprises, this article proposed a cloud computing power data middleware architecture based on distributed technology, which effectively managed, cleaned, converted, and evaluated power data sources through cloud computing platforms. This article mainly analyzed the shortcomings of traditional power data processing platforms and the feasibility of cloud computing based power data platforms. Finally, through load balancing bias comparison and pressure testing experiments under different task numbers, it was proven that cloud computing systems outperformed traditional power data processing systems in terms of utility value, completion time, and load balancing. Moreover, in the pressure testing experiment, under the condition of 10 concurrent users and 673 system request transactions, 91.3% of the interface response time in the cloud computing system was within 50ms. Therefore, cloud computing systems have certain advantages in processing power data.

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Update propagation based on tree structure in peer-to-peer networks

Update propagation based on tree structure in peer-to-peer n...

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ACS/ieee International Conference on Computer systems and Applications

作者： T. Hara M. Nakadori W. Uchida K. Maeda S. Nishio Department of Multimedia Engineering Graduate School of Information Science and Technology Osaka University Japan Network Laboratories NTT DoCoMo Inc. Singapore

Summary form only given. Recently, there has been increasing interest in research of data sharing in peer-to-peer networks. In this paper, we propose a new update propagation strategy that achieves not only load balancing but also delay reduction, where the update information is immediately notified to all peers holding the replicas when the update occurs. The proposed strategy creates an n-ary tree whose root is the owner of the original data and the other nodes are peers holding its replicas, and propagates the update information according to the trees. Moreover, we verify the effectiveness of the proposed strategy by simulation experiments.

关键词： Tree data structures intelligent networks Peer to peer computing Load management Information science Laboratories Propagation delay Context Network servers Control systems

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Wireless ad-hoc control networks

Wireless ad-hoc control networks

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ieee International Conference on Industrial Informatics (INDIN)

作者： S. Bu F. Naghdy School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong Australia

A new concept called wireless ad-hoc control networks (WACNets), designed for distributed and remote monitoring and control is suggested in this work. Such systems represent the next stage in the evolution of distributed control and monitoring. WACNet explores a framework for organic, evolutionary and scalable method of integrating a large number of nodes with sensing and/or actuation, local intelligence and control, data processing and communication capabilities. The concept is introduced and the design of the network is presented. As an essential element in the operation of WACNet, service discovery developed for WACNet will be described and progress made so far will be reported.

关键词： Communication system control intelligent sensors Distributed computing Remote monitoring Ethernet networks Automatic control IEC standards Peer to peer computing Telecommunication control Computer networks

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