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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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2021 ieee 23rd International Conference on High Performance computing and Communications, 7th International Conference on data Science and systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

2021 IEEE 23rd International Conference on High Performance ...

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23rd ieee International Conference on High Performance computing and Communications, 7th ieee International Conference on data Science and systems, 19th ieee International Conference on Smart City and 7th ieee International Conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

ISBN: (纸本)9781665494571

The proceedings contain 343 papers. The topics discussed include: three-level compact caching for search engines based on solid state drives;a fine-grained volley gesture recognition method with direction independence;universal adversarial attack against 3d object tracking;efficient hardware redo logging for secure persistent memory;a cost-efficient metadata scheme for high-performance deduplication systems;high speed true random number generator controlled by logistic map;characterization and implication of edge WebAssembly runtimes;frend for edge servers: reduce server number! keeping service quality!;on-the-fly servers placement for online multiplayer games in the fog;advanced architecture design of high-radix router based on chiplet integration and IP reusability;embrace the conflicts: exploring the integration of single port memory in systolic array-based accelerators;visual sensitivity aware rate adaptation for video streaming via deep reinforcement learning;semi-supervised federated learning with non-IID data: algorithm and system design;on-demand intelligent routing algorithms for the deterministic networks;and distributed service placement in ultra-dense edge computing: a game-theoretical approach.

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Verification Model for Agent Coordination of Distributed Information systems 11

Verification Model for Agent Coordination of Distributed Inf...

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11th ieee International Conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Martynyuk, Oleksandr Drozd, Oleksandr Sachenko, Anatoliy Stepova, Hanna Martynyuk, Dmitry Sugak, Lyudmila Turchenko, Iryna Odessa Polytech State Univ Shevchenko Ave 1 UA-65044 Odessa Ukraine West Ukrainian Natl Univ 3 Peremoga Sq UA-46009 Ternopol Ukraine Join Venture Nippon Auto Williams St 15 UA-65000 Odessa Ukraine

ISBN: (纸本)9781665426053

Modern distributed information systems, among many applied technologies, rely on multi-agent systems, which is due to the characteristic properties of the latter - autonomy, mobility, intelligence and cooperation. These properties, providing power, flexibility and adaptability in solving many distributed, dynamic, fuzzy problems, are gaining in importance. As in many cases, the features of these properties require a special analysis for them while ensuring the operability and reliability of the functioning of distributed information systems, especially in critical applications. The considered model of verification of the dynamic coordination property - one of the basic properties of agents' cooperation - is based on extended Petri nets and automata behavioral experiments of check and recognition. The model for verifying the coordination of agent groups is distinguished by the behavioral check of cause-and-effect coordination-synchronization of conditions, events, actions, functions, both agents and the resource environment of their placement with the individuality and independence of agent goals. The verification model of agent coordination is designed to determine the conditions for the check and recognition of group agent interactions in the methods of testing of distributed information systems with agent properties and allows to reduce the computational complexity of behavioral analysis, check, recognition and diagnosis in general.

关键词： distributed information system Petri net identifier behavioral testing agent coordination verification model

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Using data Analysis Methods for Predicting the Concentration of Toxic Elements in Soil

Using Data Analysis Methods for Predicting the Concentration...

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ieee International Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Lyazat Naizabayeva Gulnara Zakirova International Information Technology University Almaty Kazakhstan

This article is aimed at the selection of machine learning algorithms for the perception of elements in the composition of the soil. In our study, machine learning aims to track changes in the toxicity of contaminated soils in order to predict the level of human health hazard of a contaminated soil condition. For this, the following tasks were set: 1) collection and analysis of data on the types of soil toxicity; 2) study of a mathematical model of toxicity to assess the level of resistance; 3) testing the concentration of toxic elements in soils using four machine learning algorithms. Four machine learning algorithms were tested in the article to predict the content of heavy metals in the soil. The results show that the use of machine learning algorithms allows achieving a high degree of prediction of harmful elements concentrations in soils, which can be useful for making decisions on managing pollution sites. Further improvement of data accuracy and selection of the model and algorithm can also increase the accuracy of prediction. As a result of forecasting using the K-Nearest Neighbors algorithm, the accuracy of the model was achieved at the level of 68%, and using the Decision Tree - 69%. The results of the experiment make it possible to predict the measures of danger to human health from the state of contaminated soil. This will help reduce the risks of the relationship between soil pollution and human health. The obtained results can be useful for specialists in environmental protection, land resource management, and decision-making.

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Demonstration of a Novel AI-Based Approach for Digitalization Ladle Metallurgy Facility

Demonstration of a Novel AI-Based Approach for Digitalizatio...

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intelligent Reality (ICIR), ieee International Conference on

作者： Esteve Hassan Amir Eaman Jodrey School of Computer Science Acadia University Wolfville NS

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

In 2023, a major steel manufacturer initiated a pilot project aimed at the digital transformation of a Ladle Metallurgy Facility (LMF). The project, supported by governmental and technological partnerships, focused on addressing the digitalization challenges within heavy industries, which often lag behind other manufacturing sectors. This study explores the implementation of advanced digital technologies, including machine learning-based video analytics, novel sensing technologies, Industrial Internet of Things (IIoT) integration, real-time cognitive computing, big data analytics, and digital twins. The primary objective of this initiative was to enhance process efficiency by reducing manual interventions, minimizing process variability, and improving the final metallurgical properties of the steel. This work provides valuable insights into the digitalization process, technical execution, data standardization requirements, and workforce impact in a heavy manufacturing environment. The results demonstrate the potential of these technologies to advance operational efficiency in the steelmaking industry, laying the groundwork for future technology trials.

关键词： Industries Visual analytics Standardization Manuals Real-time systems Metallurgy Manufacturing Sensors Steel Industrial Internet of Things

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Federated Learning Empowered Resource Allocation in UAV-Assisted Edge intelligent systems

Federated Learning Empowered Resource Allocation in UAV-Assi...

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Computer Communication and Artificial Intelligence (CCAI), International Conference on

作者： Bintao Hu Matilda Isaac Olukunle Mobolaji Akinola Hadyan Hafizh Wenzhang Zhang School of Internet of Things Xi’an Jiaotong-Liverpool University suzhou china

Mobile edge computing (MEC) has been considered a promising advanced technology to support delay-sensitive tasks of user equipment (UE) in the internet of things (IoT) systems, it is necessary to allow multiple UEs to offload their computationally intensive tasks to a flexible edge computing server, such as an unmanned aerial vehicle (UAV)-assisted edge computing server. However, most existing works mainly focused on minimising energy consumption under the transmission and/or processing delay constraints while ignoring privacy-preserving, which will be challenging when dealing with large volumes of raw data. In this paper, we consider a federated learning (FL) empowered UAV-assisted edge intelligent system to minimise the maximum utility cost (which indicates the relationship between latency and energy consumption) to the selected UE for task processing. We propose to jointly optimise the FL task offloading decisions among all UEs and the communication resource allocation under each epoch. This is achieved by devising a federated learning-based edge intelligence offloading decision optimisation algorithm (FEOA). Simulation results show that our proposed schemes outperform the benchmarks in terms of the maximum cost efficiency among all UEs.

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Construction of a digital twin model for ships based on BeiDou communication 3

Construction of a digital twin model for ships based on BeiD...

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3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022

作者： Chen, Ruishu Lv, Zhiyong Xu, Jiahui National Engieering Research Center for Water Transport Safety Intelligent Transportation Systems Research Center Wuhan China Intelligent Transportation Systems Research Center Guangdong Inland Port and Shipping Industry Research Co. Ltd. Wuhan China Wuhan University of Technology School of Automation Wuhan China

ISBN: (纸本)9798350331974

In order to reduce the limitation of geographical area and network bandwidth on building real-time ship twin vision model, realize the construction of ship digital twin vision in full time and spatial domain, and improve the stability and safety of ship remote monitoring, this paper develops a construction method to build the ship digital twin vision through the Cesium framework with BeiDou satellite communication as the communication medium. The dynamic information such as ship position are first and image collected from the ship ***, the extended Kalman filter, Huffman algorithm, and LZW algorithm are used for noise reduction and lossless compression of the ship data. The data is send to the shore side serve via BeiDou satellie After that and using Mercator projection in the Cesium framework is deployed to realise the ship digital twin system. By extending the Kalman filter model to simulate the ship trajectory and comparing it with the twin vision built through 5G mobile network, the proposed technique is verified to be able to build a ship digital twin vision system more accurately by transmitting the ship's dynamic information through BeiDou in the region without network signal. © 2022 ieee.

关键词： Ships

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Research on Enterprise Information Transmission System under Computer Big data Technology

Research on Enterprise Information Transmission System under...

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Power Electronics, Computer Applications (ICPECA), ieee International Conference on

作者： Xiuyun Lu Zhenjiang Vocational Technical College Zhenjiang China

This paper studies and designs a standard networked data acquisition and transmission system based on OPC standard and Web Services technology. The system establishes master and slave stations for FMS equipment, and conducts communication test between master station and slave station. The OPCUA server is used to store the collected and converted real-time data and interact with the OPCUA client, and at the same time upload the converted data to the cloud storage system for further data analysis. The cloud combines Redis and MySQL to realize the storage of running data. Finally, the feasibility and integrity of the proposed system are verified by experiments.

关键词： Cloud computing Frequency modulation Web services data acquisition Information processing Maintenance engineering Real-time systems

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Adaptive Power Control with Improved Genetic Algorithm to Enhance QoS Performance in Vehicular Communication systems 3

Adaptive Power Control with Improved Genetic Algorithm to En...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Aranganathan, A. Al-Tarazi, Dalia Bharath, S. Devi, Durga Giri, Jayant Ravi, S. Sathyabama Institute of Science and Technology Department of Ece Chennai India Zarqa University Faculty of Engineering Department of Architecture Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Rajalakshmi Institute of Technology Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798331508456

Background: Vehicular Ad hoc Networks (VANETs) play a crucial role in intelligent transportation systems since they enable vehicles to communicate instantaneously. Elevated mobility and dynamic settings create difficulties for dependable data transmission, resulting in longer delays and packet loss. Method used: An advanced Genetic Algorithm-based Vehicular Ad hoc Routing (AIGAV) model that leverages meta-heuristic optimization to enhance routing decisions in VANETs. It utilizes GPS for live navigation and modifies routes as required. This optimization reduces congestion and distributes network load more evenly for improved packet forwarding. Results: The AIGAV model exceeds the performance in crucial efficacy aspects. It enhanced data success rates (95.25%), remarkable throughput (521.41 Kbps), and minimal routing overhead (254.18 packets), and decreased data loss rates. Conclusion: The AIGAV model improves the efficiency of VANET routing through dependable and adaptable communication in mobile settings. It employs optimization to enhance safer, more intelligent transportation systems for upcoming uses. © 2025 ieee.

关键词： advanced driver assistance systems

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data-Driven Control of a Weakly-Instrumented Excavator with Deep Learning

Data-Driven Control of a Weakly-Instrumented Excavator with ...

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intelligent Reality (ICIR), ieee International Conference on

作者： N. Hoffmann M. Cohen L. Roullier M. Preda T. Zaharia

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

This paper presents a data-driven approach for controlling a weakly-instrumented excavator within a Virtual Reality (VR) supervision environment. We address challenges related to non-linear dynamics and limited sensor data by focusing on arm movement control using both traditional and advanced strategies, including Proportional-Integral-Derivative (PID) controllers, Model Predictive Control (MPC), and Deep Reinforcement Learning (DRL). Our results demonstrate the effectiveness of these methods in achieving precise control despite non-linearities and limited instrumentation, contributing to the broader field of intelligent machine control.

关键词： Machine control Instruments Focusing Virtual reality Excavation Robot sensing systems Deep reinforcement learning Digital twins PD control Predictive control

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Method of Selecting and Determining the Free Parameters of Swarm intelligent Algorithms for Optimizing Solutions in GIS 11

Method of Selecting and Determining the Free Parameters of S...

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11th ieee International Conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Uhryn, Dmytro Lytvyn, Vasyl Lendiuk, Taras Natl Tech Univ Kharkiv Polytech Inst Chernivtsi Fac Dept Informat Syst Holovna Str 203A UA-58000 Chernovtsy Ukraine Lviv Polytech Natl Univ Dept Informat Syst & Networks Bandery Str 12 UA-79013 Lvov Ukraine West Ukrainian Natl Univ Dept Informat Comp Syst & Control 3 Peremoga Sq UA-46020 Ternopol Ukraine

ISBN: (纸本)9781665426053

The article proposes information technology for solving the discrete optimization problems in Geographic Information systems (GIS). The information model of geographic data (geodata) was further developed, which consists of a formalized combination of their spatial and attributive components. It takes into account relational, semantic and frame models for representing the knowledge of the attributive component in order to unify the branch geodata. The conceptual model of decision support in sectoral GIS has been further developed, which takes into account the information model of geodata and the dynamics of their change, the hierarchy of tasks of sectoral GIS, the input operational data, the function of preferences and the criterion of decision-making. A new method of decision support for GIS construction was developed on the base of the proposed conceptual model. The method of selection and determination of free parameters of swarm intelligence algorithms is developed to increase the effectiveness of GIS. It is suggested to determine the free parameters for individual swarm algorithms based on machine learning with reinforcement, namely the Q-Learning method. Based on this method Markov chains for the swarm algorithms were constructed. Reinforcement consisted in the expert analysis of the results obtained by a certain swarm algorithm. On the example of territorial administration, optimal values of the parameters for individual swarm algorithms were found.

关键词： swarm intelligence free parameters optimization problems Geographic Information systems geodata decision making

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