Edge computing is swiftly gaining traction and is being standardised by the European Telecommunications Standards Institute(ETSI)as Multi-access Edge Computing(MEC).Simultaneously,oneM2M has been actively developing s...Edge computing is swiftly gaining traction and is being standardised by the European Telecommunications Standards Institute(ETSI)as Multi-access Edge Computing(MEC).Simultaneously,oneM2M has been actively developing standards for dynamic data management and IoT services at the edge,particularly for applications that require real-time support and security.Integrating MEC and oneM2M offers a unique opportunity to maximize their individual strengths.Therefore,this article proposes a framework that integrates MEC and oneM2M standard platforms for IoT applications,demonstrating how the synergy of these architectures can leverage the geographically distributed computing resources at base stations,enabling efficient deployment and added value for time-sensitive IoT applications.In addition,this study offers a concept of potential interworking models between oneM2M and the MEC architectures.The adoption of these standard architectures can enable various IoT edge services,such as smart city mobility and real-time analytics functions,by leveraging the oneM2M common service layer instantiated on the MEC host.展开更多
In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need t...In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and control.This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices.One of the major protocols widely used today in industrial electronic devices is Modbus.However,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer platform.oneM2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial services.Therefore,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards.In the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this architecture.We analyze our solution and provide an evaluation by implementing it based on a solar power management use case.The results demonstrate that our model is feasible and can be applied to real case scenarios.展开更多
Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is ...Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.展开更多
介绍列车数字无线调度通信系统(Digital Radio communication system for Train Dispatching,DRTD)对既有450 MHz列调通信的兼容方案,包括地面和车载设备方案、多制式通话互通方案、操作台兼容操作方案、数据业务及C T C系统接口适配方...介绍列车数字无线调度通信系统(Digital Radio communication system for Train Dispatching,DRTD)对既有450 MHz列调通信的兼容方案,包括地面和车载设备方案、多制式通话互通方案、操作台兼容操作方案、数据业务及C T C系统接口适配方案等多个层面,以便满足普速铁路450 MHz列调系统数字化改造及其过渡期的运营需求。展开更多
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Information Technology Research Center(ITRC)grant funded by the Korea government(IITP-2025-RS-2021-II211816)supported by the National Research Foundation of Korea(NRF)grant(NRF-2023R1A2C1004453)+3 种基金funded by the European Union’s HORIZON-JUSNS-2023 HE research and innovation program(6G-Path project,Grant No.101139172)the Horizon 2020 Research and Innovation Program(aerOS project,Grant No.101069732)supported by the ESTIMED project,conducted by the ETSI Specialist Task Force 685(STF 685)funded by the European Commission(EC)and the European Free Trade Association(EFTA).
文摘Edge computing is swiftly gaining traction and is being standardised by the European Telecommunications Standards Institute(ETSI)as Multi-access Edge Computing(MEC).Simultaneously,oneM2M has been actively developing standards for dynamic data management and IoT services at the edge,particularly for applications that require real-time support and security.Integrating MEC and oneM2M offers a unique opportunity to maximize their individual strengths.Therefore,this article proposes a framework that integrates MEC and oneM2M standard platforms for IoT applications,demonstrating how the synergy of these architectures can leverage the geographically distributed computing resources at base stations,enabling efficient deployment and added value for time-sensitive IoT applications.In addition,this study offers a concept of potential interworking models between oneM2M and the MEC architectures.The adoption of these standard architectures can enable various IoT edge services,such as smart city mobility and real-time analytics functions,by leveraging the oneM2M common service layer instantiated on the MEC host.
基金the support of the Korea Research Foundation with the funding of the Ministry of Science and Information and Communication Technology(No.2018-0-88457,development of translucent solar cells and Internet of Things technology for Solar Signage).
文摘In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and control.This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices.One of the major protocols widely used today in industrial electronic devices is Modbus.However,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer platform.oneM2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial services.Therefore,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards.In the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this architecture.We analyze our solution and provide an evaluation by implementing it based on a solar power management use case.The results demonstrate that our model is feasible and can be applied to real case scenarios.
基金supported by the International Research Center of Big Data for Sustainable Development Goals under Grant No.CBAS2022GSP05the Open Fund of State Key Laboratory of Remote Sensing Science under Grant No.6142A01210404the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant No.KLIGIP-2022-B03.
文摘Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.
文摘介绍列车数字无线调度通信系统(Digital Radio communication system for Train Dispatching,DRTD)对既有450 MHz列调通信的兼容方案,包括地面和车载设备方案、多制式通话互通方案、操作台兼容操作方案、数据业务及C T C系统接口适配方案等多个层面,以便满足普速铁路450 MHz列调系统数字化改造及其过渡期的运营需求。
