Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this p...Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI models.However,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT innovation.Notably,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security.This adversely influences interand intra-organization collaborative use of IIoT data.To tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape.Moreover,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture.Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.展开更多
The time-dependence bilinear mixed-regression deformation model and time-dependence bilinear dynamic system deformation model are established for deformation observation series. According to the multi- level recursive...The time-dependence bilinear mixed-regression deformation model and time-dependence bilinear dynamic system deformation model are established for deformation observation series. According to the multi- level recursive method, the time-dependence parameters are first traced and predicted, and then the dynamic system states. Due to the method considering time-dependence of deformation and having stronger adaptability to time-dependence system, it can improve forecast’s precision. It is very effective for data processing of nonlinear dynamic deformation monitoring to make multi-step forecasting.展开更多
Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across d...Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy regulations.The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations.The second technical challenge is handling the dynamic expansion of the federation without model retraining.To address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance.Based on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical challenge.FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally.The experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the federation.This opens up opportunities for cooperation between different organizations in wearable health monitoring.展开更多
Federated learning(FL)allows data owners to train neural networks together without sharing local data,allowing the industrial Internet of Things(IIoT)to share a variety of data.However,traditional FL frameworks suffer...Federated learning(FL)allows data owners to train neural networks together without sharing local data,allowing the industrial Internet of Things(IIoT)to share a variety of data.However,traditional FL frameworks suffer from data heterogeneity and outdated models.To address these issues,this paper proposes a dualblockchain based multi-layer grouping federated learning(BMFL)architecture.BMFL divides the participant groups based on the training tasks,then realizes the model training by combining synchronous and asynchronous FL through the multi-layer grouping structure,and uses the model blockchain to record the characteristic tags of the global model,allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity.In addition,to protect the privacy of the model gradient parameters and manage the key,the global model is stored in ciphertext,and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged.Finally,we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault datasets.The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm,and the key revocation overhead time is reasonable.展开更多
Since softswitch is the kernel of the Next Generation Network （NGN）, it is practically significant to improve the availability of the softswitch system. This paper expatiates upon the methods of realizing the high-a...Since softswitch is the kernel of the Next Generation Network （NGN）, it is practically significant to improve the availability of the softswitch system. This paper expatiates upon the methods of realizing the high-availability of softswitch system. It gives the methods from a multi-level viewpoint ： software-level high-availability design, platformlevel high-availability of softswitch kernel components, network-level high-availability. Additonally, it gives certain analysis on obtaining network high-availability.展开更多
文摘Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI models.However,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT innovation.Notably,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security.This adversely influences interand intra-organization collaborative use of IIoT data.To tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape.Moreover,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture.Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.
文摘The time-dependence bilinear mixed-regression deformation model and time-dependence bilinear dynamic system deformation model are established for deformation observation series. According to the multi- level recursive method, the time-dependence parameters are first traced and predicted, and then the dynamic system states. Due to the method considering time-dependence of deformation and having stronger adaptability to time-dependence system, it can improve forecast’s precision. It is very effective for data processing of nonlinear dynamic deformation monitoring to make multi-step forecasting.
基金supported by the National Natural Science Foundation of China under Grant Nos.62002187,62002189,61972383,61972237 and 61976127the Science Research Project of Hebei Education Department of China under Grant No.QN2023184。
文摘Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy regulations.The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations.The second technical challenge is handling the dynamic expansion of the federation without model retraining.To address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance.Based on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical challenge.FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally.The experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the federation.This opens up opportunities for cooperation between different organizations in wearable health monitoring.
基金supported in part by Natural Science Basic Research Program of Shaanxi under Grant No.2022JM-346.
文摘Federated learning(FL)allows data owners to train neural networks together without sharing local data,allowing the industrial Internet of Things(IIoT)to share a variety of data.However,traditional FL frameworks suffer from data heterogeneity and outdated models.To address these issues,this paper proposes a dualblockchain based multi-layer grouping federated learning(BMFL)architecture.BMFL divides the participant groups based on the training tasks,then realizes the model training by combining synchronous and asynchronous FL through the multi-layer grouping structure,and uses the model blockchain to record the characteristic tags of the global model,allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity.In addition,to protect the privacy of the model gradient parameters and manage the key,the global model is stored in ciphertext,and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged.Finally,we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault datasets.The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm,and the key revocation overhead time is reasonable.
文摘Since softswitch is the kernel of the Next Generation Network （NGN）, it is practically significant to improve the availability of the softswitch system. This paper expatiates upon the methods of realizing the high-availability of softswitch system. It gives the methods from a multi-level viewpoint ： software-level high-availability design, platformlevel high-availability of softswitch kernel components, network-level high-availability. Additonally, it gives certain analysis on obtaining network high-availability.
