检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Distributed Systems and Informatic Devices Silesian University of Technology Poland

In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design dense and sparse feature tensors to deal with the sparsity of data sources. Heterogeneous federated learning is developed to share asynchronous parts of networks and select appropriate models for knowledge transfer. Experimental results show that the proposed HFL achieves the lowest prediction error among all benchmark systems on eight out of ten prediction tasks, with MSE reduction of 94.8%, 48.3%, and 52.1% compared to the benchmark systems. These results demonstrate the effectiveness of HFL in transferring knowledge from heterogeneous domains, especially in the smaller target domain. Ablation studies then demonstrate the effectiveness of the designed mechanisms for heterogeneous domain selection and switching in predicting healthcare time series with privacy, model security, and heterogeneous knowledge transfer. © 2025, CC BY-NC-SA.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Federated Learning systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism

arXiv

引用

arXiv 2025年

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Srivastava, Gautam Yun, Unil Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Distributed Systems and Informatic Devices Silesian University of Technology Poland Department of Math and Computer Science Brandon University Brandon Canada Department of Computer Science and Engineering Sejong University Korea Republic of

Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict power consumption, but existing models lack discussion of collaborative learning and privacy issues among multiple clients. To address these issues, we propose Multi-Head Heterogeneous Federated Learning (MHHFL) systems that consist of multiple head networks, which independently act as carriers for federated learning. In the federated period, each head network is embedded into 2-dimensional vectors and shared with the centralized source pool. MHHFL then selects appropriate source networks and blends the head networks as knowledge transfer in federated learning. The experimental results show that the proposed MHHFL systems significantly outperform the benchmark and state-of-the-art systems and reduce the prediction error by 24.9% to 94.1%. The ablation studies demonstrate the effectiveness of the proposed mechanisms in the MHHFL (head network embedding and selection mechanisms), which significantly outperforms traditional federated average and random transfer. © 2025, CC BY-NC-SA.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

General Concepts of a Simulation Method for Automated Guided Vehicle in Industry 4.0

General Concepts of a Simulation Method for Automated Guided...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Marek, Dariusz Biernacki, Piotr Szygula, Jakub Domanski, Adam Silesian University of Technology Department of Distributed Systems and Informatic Devices Poland

ISBN: (纸本)9781665480451

This paper presents an environment simulator dedicated to assess the behavior of Automated Guided Vehicles in industry. The concept proposed in this framework makes it possible to evaluate the hazards that can occur in an industrial plant. Conducting all of the necessary research in a natural environment requires a great deal of time and is very expensive. A simulation environment reduces the costs and also reduces the burden of the verification process of an AGV operation. This study presents an assessment of simulation environments based on the most proven solutions. The analysis were conducted from the point of view of the requirements that have been set by industry. As a result, the authors built the evaluation environment that was based on the Gazebo simulator. © 2022 IEEE.

关键词： Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

Analysis of web-based geo-visualization methods applied for Automated Guided Vehicle using Satellite Navigation systems

Analysis of web-based geo-visualization methods applied for ...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Szygula, Jakub Biernacki, Piotr Marek, Dariusz Domanski, Adam Sobczak, Lukasz Flak, Jaroslaw Caban, Dariusz Pawlas, Piotr Silesian University of Technology Department of Distributed Systems and Informatic Devices Poland

ISBN: (纸本)9781665480451

This article presents the comparative analysis of various JavaScript libraries, providing the possibilities of web-based geo-visualization, that can be applied in the case of AGV (Automated Guided Vehicle) working in the open area using GNSS (Global Navigation Satellite systems) localization systems. A representative group of JS libraries has been selected for analysis. We focused on libraries that enable map generation with the use of the Scalable Vector Graphics format. Their performance issues and development potential are compared. © 2022 IEEE.

关键词： Libraries

来源：评论

学校读者我要写书评

暂无评论

Testing Quality of Service of communication system for AGV fleet with Software-Defined Network

Testing Quality of Service of communication system for AGV f...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Marszalek, Karol Domanski, Adam Cupek, Rafal Drewniak, Marek Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Gliwice Poland

ISBN: (纸本)9798350324457

Software-Defined Network (SDN) is the new paradigm in the computer network architecture. The concept is based on the decoupling of the data plane from the control plane. Such decoupling creates the possibility for central traffic management, hence offering the potential to improve the network's performance and monitoring capabilities. We use those capabilities to perform extensive testing of the communication system for the fleet of Automated Guided Vehicles (AGV). First, we present the configuration of our testbed, the methodology of performing such testing, and a framework for experiment design. Finally, we present our results based on the presented approach. © 2023 IEEE.

关键词： AGV quality SDN service testing

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

引用

IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

Predicting AGV Battery Cell Voltage Using a Neural Network Approach with Preliminary Data Analysis and Processing

Predicting AGV Battery Cell Voltage Using a Neural Network A...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Pavliuk, Olena Medykovskyy, Mykola Steclik, Tomasz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Lviv Polytechnic National University Automated Control Systems Department Lviv Ukraine

ISBN: (纸本)9798350324457

Modern methods for solving the AGV battery cell voltage prediction problem include a symbiosis of probabilistic and machine learning methods. In this study, we propose to use two RNN-based approaches with preliminary data analysis and processing. Five filters were selected for smoothing: Moving average;Weighted moving average;Exponential smoothing;Smoothing with local regression;Lowess smoothing. RNN forecasting was performed on the basis of a centered stationary signal obtained by subtracting the value obtained with the five filters from the original *** forecast accuracy was evaluated using the following errors: MAPE, RMSE, MSPE, RMSPE, MFE, MBE, SEBIAS, ME. Adequacy of the proposed neural network model based on the determination coefficient R2. The coefficient of determination for all the centered signals obtained using the above five methods yielded adequate predictive models. Exponential smoothing (MAPE=0.002;RMSE=141.481;R2=0.94) and LS (MAPE=0.002;RMSE=124.239;R2:0.96) proved to be the most effective methods for short-term forecasts. Moving average (MAPE=0.00207;RMSE=146.589;R2=0.94) is recommended for long-term forecasts with additional smoothing of the forecasted values because of the noise in them. © 2023 IEEE.

关键词： AGV battery cell voltage data pre-processing forecast Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Smartwatch-Based Human Staff Activity Classification: A Use-Case Study in Internal Logistics systems Utilizing AGVs

Smartwatch-Based Human Staff Activity Classification: A Use-...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Pavliuk, Olena Mishchuk, Myroslav Lviv Polytechnic National University Department of Automated Control Systems Lviv Ukraine Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland

ISBN: (纸本)9798350362480

Recent advancements in the domain of human activity recognition (HAR) are increasingly aimed at developing methodologies, approaches, and models for real-time, multi-step activity recognition and analysis. This work presents a smartwatch-based approach for complex, real-time HAR that is applicable but not limited to internal logistics systems that use autonomous guided vehicles. A distributed smartwatch-based data collection system was developed, and a dataset was gathered and published, containing readings from human staff representatives executing activity sequences representing typical internal logistics tasks. A HAR-specific, pre-trained DenseNet121 was used for basic activity classification, achieving an F1-score of 91.01%. For multi-step activity classification, we compared models based on CNN, LSTM, BiLSTM, GRU, and BiGRU as meta-classifiers, employing different dataset-splitting strategies and models' configurations. The best-performing CNN-based model achieved an F1-score of 87.44% using the shared dataset utilization approach. Despite the challenges faced, the adaptability of the proposed approach suggests that it can be integrated into an intelligent enterprise management system to provide a robust and flexible HAR framework that enhances production efficiency. © 2024 IEEE.

关键词： Enterprise resource management

来源：评论

学校读者我要写书评

暂无评论

An approach towards AGV battery cell voltage prediction using DL and data pre-processing 18

An approach towards AGV battery cell voltage prediction usin...

引用

18th IEEE International Conference on Computer Science and Information Technologies, CSIT 2023

作者： Medykovskyy, Mykola Cupek, Rafal Pavliuk, Olena Mishchuk, Myroslav Lviv Polytechnic National University ACS Department Lviv Ukraine Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland

ISBN: (纸本)9798350360462

Modern methods for solving the AGV battery cell voltage prediction problem include a symbiosis of probabilistic and machine learning methods. In this study, we propose to use two RNN-based approaches with preliminary data analysis and processing. In the first approach, we make predictions based on smoothed data. Three filters were chosen for smoothing: exponential smoothing;moving average;weighted moving average. The moving average method showed the best results, but according to the Dickey-Fuller test, the predicted signal was still not stationary. The second approach was based on a centered stationary signal obtained by subtracting the value of the moving average from the original *** forecast accuracy was evaluated using the following errors: MAPE, RMSE, MSPE, RMSPE, MFE, SEBIAS, ME. Adequacy of the proposed neural network model based on the coefficient of determination R2. As can be seen from Table 2, a significant improvement in prediction accuracy is observed for the centered signal. The maximum error was reduced by 5.6 times R2 increasing by 0.0596. At the same time, both models are adequate. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

ZigBee Network for AGV Communication in Industrial Environments 10

ZigBee Network for AGV Communication in Industrial Environme...

引用

10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

作者： Flak, Jaroslaw Skowron, Tomasz Cupek, Rafal Fojcik, Marcin Caban, Dariusz Domanski, Adam Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering Bergen Norway

ISBN: (纸本)9798350345032

Automated Guided Vehicles (AGVs) are a key component of many modern industrial systems. AGVs are supposed to communicate with each other in real time using wireless networks. In this article, the advantages and disadvantages of the ZigBee wireless network related to the control of AGVs are considered. We analyze the performance of the ZigBee network programmed with both C# and Python libraries to control ZigBee devices. The throughput and signal strength are presented and discussed depending on the transmission speed of the serial port, the payload size, and the presence and distance from the obstacles. The results of the experiments show the effective values of these parameters, the methods of using C# and Python, and the reliability of the throughput up to a certain point in network devices. © 2023 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：