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检索条件"任意字段=3rd International Conference on Computing and Data Science, CONF-CDS 2021"

共 907 条记录，以下是91-100 订阅

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Towards a Modular IOT Simulation System for Industry 3rd

Towards a Modular IOT Simulation System for Industry

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3rd international conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2023

作者： Coelho, Tiago Rodrigues, Ricardo Miranda, Rita Portela, Filipe University of Minho Guimarães Portugal IOTECH – Innovation on Technology Trofa Portugal

ISBN: (纸本)9783031489297

In this article, there is approached the simulation of Industry 4.0 environments, being used as an example, the textile industry. There will be explained the concept of simulating Industry 4.0 factory floors, approaching many topics such as the benefits of this solution, how it can be used in every industry sector, and most importantly the work and research that has been done around the subject area. The textile industry will be used as an example and, therefore, there will be explained later in the document more specific details regarding the appliance of the solution to the industry sector that is being approached. This explanation will include the required materials for the development of a prototype, as well as the used methods and expected results. In terms of results, this study created a scalable architecture and three data models that can config and store the simulation data. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing Cyber-physical systems Industry 4.0 Internet of Things Textile Industry

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Predicting of the physique performance and accuracy of college students based on multiple linear regression and BP neural network 3

Predicting of the physique performance and accuracy of colle...

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3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big data Forum, AIBDF 2023

作者： Huang, Xinxin Sulaiman, Mohd Suffian Bin Rosli, Marshima Mohd Binchun, Jiang College of Computing Informatics & Mathematics Universiti Teknologi MARA SHAH ALAM Malaysia College of Mechanical & Electrical Engineering Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798400716362

A multiple linear regression model and a BP neural network model are established to predict the physique performance of college students and analyze its accuracy based on the physical fitness test data of college students. The results of the study show that the average error of the multiple linear regression model and the BP neural network model for predicting the physique performance of college students are 1.23 and 0.51 points, respectively, and the average error percentage is 1.84% and 0.74%, respectively. Both models can effectively predict the physique performance of college students, but the BP neural network model has higher prediction accuracy than the multiple linear regression model. © 2023 Copyright held by the owner/author(s).

关键词： Multiple linear regression

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PR-GB-NET: Probabilistic Graph Bridging Network with data Resampling for Scene Graph Generation 3rd

PR-GB-NET: Probabilistic Graph Bridging Network with Data Re...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Lu, Hongyu Liu, Guoliang Tian, Guohui Jiang, Jian Wang, Shanmei Shandong University Jinan China Shandong Massdatas Development Co. Ltd. Jinan China Inspur Intelligent Technology Co. Ltd. Jinan China

ISBN: (纸本)9789819624676

Scene graphs are structured and powerful representations of images that extract semantic information from images to enhance visual understanding and image reasoning. To improve the utilization of predicate prior knowledge in scene graphs, we use a probability mass function (PMF) for the predicate, given a subject-object pair during the initialization of the graph neural network. The PMFs are updated through a gating function during message passing of the graph neural network. To mitigate the effects of long-tailed distributions in datasets, we employ a data resampling strategy. During each training iteration, we try to balance the head, body and tail predicates as much as possible. Experiments show that the proposed methods achieve competitive performance compared to previous methods on dataset we created for home environment, demonstrating their effectiveness and generality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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Resource Allocation for Cloud-Edge-End Simulation System 3

Resource Allocation for Cloud-Edge-End Simulation System

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3rd international conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2021

作者： Yan, Guoqiang Mao, Shaojie Li, Yuping Science and Technology on Information System Engineering Laboratory Jiangsu Nanjing China

Aiming at the problem of simulating service requests and optimal scheduling during the operation of the cloud computing/edge computing environment, the real-time optimization scheduling technology of computing resources is studied, and elastic resource optimization scheduling is realized through data feature mining analysis, service load prediction, and collaborative resource management. Ensure that the simulation service quality meets the mission requirements and provide support. © Published under licence by IOP Publishing Ltd.

关键词： Resource allocation

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Digital Twins for Daily Operation Management of Railway Station 3

Digital Twins for Daily Operation Management of Railway Stat...

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3rd IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Wang, Xiaoshu Shi, Tianyun Lv, Xiaojun Li, Chao Yang, Guoyuan Dong, Depeng China Academy of Railway Sciences Postgraduate Department Beijing China China Academy of Railway Sciences Co. Ltd. Technology and Information Technology Beijing China China Academy of Railway Sciences Co. Ltd. Institute of Computing Technology Beijing China Beijing Jingwei Information Tec. Co. Ltd. Mechanical and Electrical Division Beijing China

ISBN: (纸本)9798350318470

Station daily operation management is an essential task in the railway industry. To enhance the efficiency of this process, we propose a digital twins four-dimensional model for daily station operations (DTSDO) in this study. The DTSDO model consists of four distinct components: Passenger Station Physical Entity (PSPE), Passenger Station Virtual Entity (PSVE), Digital Twins Connections (DTCS), and Daily Operation Twins Services (DOTS). The DTSDO is driven by the fundamental data of passengers and trains, and it uses the eEPCD to link and control the Six essential production elements within the station based on a time sequence. The communication between the PSPE and PSVE is established via the HTTP protocol. The DOTS accurately portrays the virtual representation of the reality passenger station. Furthermore, we have validated the DTSDO model using Qinghe station as a specific case study. We created a one-to-one model of the station and tested its comprehensive functionalities. © 2023 IEEE.

关键词： Railroads

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A Method Based on Multi-Graph Convolutional Network with Graph Fusion for Rolling Bearing Fault Diagnosis 3

A Method Based on Multi-Graph Convolutional Network with Gra...

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3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： Wang, Qichen Li, Chaofan Han, Yufei Li, Tianrui Zhang, Fan Tangshan Institute Southwest Jiaotong University Tangshan063000 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798350331387

para>Graph data based on non-Euclidean space possesses more comprehensive information representation capabilities, which is of significant importance for fault diagnosis. Nonetheless, graph convolutional networks (GCNs) that perform well in non-Euclidean spaces have certain limitations. In this paper, we propose a multi-graph convolutional network with graph fusion called Multi-Graph Convolutional Network with Gated Recurrent Unit (MGGCN-GRU). In our method, we devise a multi-graph fusion approach that complements the single-graph features to enrich the feature information. Additionally, we design a multi-graph convolutional module that extracts features from vibration signals, including two single graphs and one fusion graph feature extraction, enabling the exploration of various relationships between signals. In the multi-graph convolution, we employ a multi receptive fields graph convolutional module as the convolutional unit, which integrates information from multi receptive fields. Furthermore, we incorporate a gated recurrent unit after the graph convolutional module to extract time-dependent features. Through experiments on a collected truck bench dataset, it is demonstrated that the proposed method exhibits excellent performance. © 2023 IEEE.

关键词： Convolution

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Research on slurm scheduling optimization based on multiqueue scenario 3

Research on slurm scheduling optimization based on multiqueu...

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3rd international conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Wu, Guannan Zhang, Tao Qin, Yunhui Jiang, Jinliang Zhengzhou University 100 Science Avenue Gaoxin District Henan Zhengzhou450001 China Co. Ltd. Sugon Building No.36 Zhongguancun Software Park No.8 Dongbeiwang West Road Haidian District Beijing100193 China

ISBN: (纸本)9781510680449

Slurm (Simple Linux Utility for Resource Management) is a popular open-source cluster job scheduling system. In scenarios involving multiple queues and a large number of scientific computing tasks, the Slurm scheduling system faces challenges such as improper queue depth settings and uneven job loads in queues. This research aims to optimize the scheduling performance of the Slurm scheduling system in scenarios where resources are shared among multiple queues. By forecasting future CPU load increases based on the historical CPU utilization of queues, a window period is identified. Using the forecasted results, a dynamic adjustment method for queue priorities is introduced. This method aims to elevate job priorities in specific queues when a significant number of jobs are queued, ensuring prompt execution. Additionally, this study involves a dynamic adjustment strategy for queue depth to address issues arising from inappropriate queue depth settings, where resources in the queue are idle, but queued jobs face delays in scheduling. Experimental results demonstrate that this approach enhances the scheduling performance of the Slurm system in multi-queue scenarios, improving cluster resource utilization and better meeting the diverse demands of users. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Queueing theory

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Prediction method for college student's mental health state based on association rules 23

Prediction method for college student's mental health state ...

引用

3rd international conference on Bioinformatics and Intelligent computing, BIC 2023

作者： Wang, Zhengli Chen, Zhenxiang Jiang, Xiaoqing Wang, Peicheng Wang, Hucheng School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9798400700200

Student's mental health problem has been more prevalent in recent years. The common method for predicting mental health involves using machine learning algorithms to dig student's psychological traits based on their network behavior. However, there is a risk that data about network behavior could be exposed. In this paper, we propose a prediction method for college student's mental health based on association rules, whereby, following the privacy calculation of psychological assessment data, the internal and external factors affecting student's mental health are digged with the aid of the improved FP-growth algorithm, and the prediction model of mental health status is constructed. The experimental results show that under the premise of satisfying the privacy protection, the model constructed by the improved association rules algorithm can predict the student's mental health state more accurate. © 2023 ACM.

关键词： Students

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Developing MCDM-Based Technique to Calculate Trustworthiness of Advertised QoE Parameters in Fog computing Environment 3rd

Developing MCDM-Based Technique to Calculate Trustworthines...

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3rd international conference on Machine Learning, Image Processing, Network Security and data sciences, MIND 2021

作者： Varshney, Shefali Sandhu, Rajinder Gupta, P.K. Department of Computer Science and Engineering Jaypee University of Information Technology Solan India

ISBN: (纸本)9789811958670

The Fog computing environment appeared as a beneficial platform for providing services like low latency and storage requirements at the network edge. Fog computing helps us access delay-sensitive applications, but there are some operational challenges. These challenges include the constrained nature of resources in Fog nodes, and the heterogeneity of smart applications that makes the trust evaluation of the QoE parameters more critical. The smart applications requesting services from the FSP cannot satisfy the end users for trust of QoE parameters. Therefore, trust evaluation of smart application QoE parameters are critical to providing a reliable service. In this paper, a MCDM-based trust evaluation framework has been proposed that calculates the trust value of smart applications’ QoE parameters. Here, we have applied the technique for order of preference by similarity to ideal solution (TOPSIS) to calculate the trust of smart application QoE parameter values. The obtained experimental results show that smart application SA15 has a high relative closeness of 0.36602 with a rank of 7 among the other smart applications placed in the Fog environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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Competitive Sports data Analyzing Model Based on Machine Learning 3

Competitive Sports Data Analyzing Model Based on Machine Lea...

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3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Luo, Junyao Wang, Zihan Zhang, Junyi Franklin & Marshall College 17603 United States School of Engineering The University of Mississippi Oxford38655 United States University of Electronic Science and Technology of China 610000 China

ISBN: (纸本)9781510661301

Machine learning is an important research area belonging to artificial intelligence technology. Machine learning has potent data processing and prediction ability. This paper uses machine learning to build a data analysis model and validate it with the NBA. This paper consists of two parts in total. The first part uses a clustering algorithm to analyze all teams' high-order data from 2000 to 2021 and classify the teams' styles. The main effect of using the k-means algorithm is to put the data in very different clusters, and in this paper the different clusters are considered to be the different team styles. At the same time, we can determine whether the style of the team has changed. We used the Euclidean distance as the distance formula for k-means. By the test of different k numbers, the result showed that between the numbers 2 to 9, the Silhouette Coefficient performed best when and second when. This paper used to measure the method that can be used to evaluate the influence of different algorithms or operating modes of algorithms on clustering results based on the same original data. For example, when k=2 and k=4. The experimental results show that the team styles in the NBA can be broadly classified into several categories over a long development period. The second part constructs a win prediction model based on the Elo method for the matchup data from 2020 to 2022. To sum up, in this paper, Elo ranking system, the Logistic regression model and three data sets are used to get a detailed prediction of the winning rate of NBA teams in 21-22 seasons. © 2023 SPIE.

关键词： Machine learning

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