The performance of an integrated packet voice/data multiplexer using a stop-and-wait(SW)automatic repeat request(ARQ)protocol is discussed.We assume that the input for the data traffic is exponentially distributed in ...The performance of an integrated packet voice/data multiplexer using a stop-and-wait(SW)automatic repeat request(ARQ)protocol is discussed.We assume that the input for the data traffic is exponentially distributed in increments via the Poisson process,with each data packet transmitted within an individual slot time.Another assumption is that there is only a single voice signal,which has a higher priority over the data packet,and whose traffic is given via an on-off Markov process.Whenever the voice signal is active,the output link is used and will be blocked for the data packet.We introduce the concept of buffer occupancy to simplify the analysis,and discover that data multiplexers using the SW ARQ protocol exhibit a behavior of queueing delay and buffering when the interruption signal is given via a Markov process.Simulation results verify the validity of the analytical results.展开更多
Dynamic sliding window is a novel ARQ protocol for half-duplex short-wave channel.This algorithm ex-tracts the virtue of Stop-and Wait and Sliding Window protocols,uses acknowledgements and timeouts to implement relia...Dynamic sliding window is a novel ARQ protocol for half-duplex short-wave channel.This algorithm ex-tracts the virtue of Stop-and Wait and Sliding Window protocols,uses acknowledgements and timeouts to implement reliability,and changes sliding window size according to channel quality.The paper puts forward the idea and the model of this protocol,The paper also analyzes the performance of this protocol,compared to the case using Wait-and-Stop protocol.展开更多
随着农作物病虫害研究文献的快速增长,对农作物病虫害领域文献进行文本挖掘变得越来越重要。开发有效、准确的农作物病虫害命名实体识别系统有助于在农作物病虫害相关研究报告中提取研究成果,为农作物病虫害的治理提供有效建议。本文针...随着农作物病虫害研究文献的快速增长,对农作物病虫害领域文献进行文本挖掘变得越来越重要。开发有效、准确的农作物病虫害命名实体识别系统有助于在农作物病虫害相关研究报告中提取研究成果,为农作物病虫害的治理提供有效建议。本文针对中文农作物病虫害数据集缺失问题,提出了基于半远程监督的停等算法,利用该算法构建中文农作物病虫害领域语料库,大幅度减少标注过程的人工成本和时间成本;同时,提出了中文农作物病虫害命名实体识别模型(Agricultural information extraction,Agr-IE),该模型基于BERT-BILSTM-CRF,辅以多源信息融合(多源分词信息和全局词汇嵌入信息)丰富字符向量,使其充分结合字符级与词汇级的信息,以提高模型捕捉上下文信息的能力。实验表明,该模型可以有效地识别病害、虫害、药剂、作物等实体,F1值分别为96.56%、95.12%、94.48%、95.54%,并对识别难度较大的病原实体具有较好的识别效果,F1值为81.48%,高于BERT-BILSTM-CRF、BERT等模型的相应值。本文所提模型在MSRA和Weibo等其他领域数据集上与CAN-NER、Lattice-LSTM-CRF等模型进行了对比实验,并取得最佳的识别效果,F1值分别为95.80%、94.57%,表明该算法具有一定的泛化能力。展开更多
文摘The performance of an integrated packet voice/data multiplexer using a stop-and-wait(SW)automatic repeat request(ARQ)protocol is discussed.We assume that the input for the data traffic is exponentially distributed in increments via the Poisson process,with each data packet transmitted within an individual slot time.Another assumption is that there is only a single voice signal,which has a higher priority over the data packet,and whose traffic is given via an on-off Markov process.Whenever the voice signal is active,the output link is used and will be blocked for the data packet.We introduce the concept of buffer occupancy to simplify the analysis,and discover that data multiplexers using the SW ARQ protocol exhibit a behavior of queueing delay and buffering when the interruption signal is given via a Markov process.Simulation results verify the validity of the analytical results.
文摘Dynamic sliding window is a novel ARQ protocol for half-duplex short-wave channel.This algorithm ex-tracts the virtue of Stop-and Wait and Sliding Window protocols,uses acknowledgements and timeouts to implement reliability,and changes sliding window size according to channel quality.The paper puts forward the idea and the model of this protocol,The paper also analyzes the performance of this protocol,compared to the case using Wait-and-Stop protocol.
文摘随着农作物病虫害研究文献的快速增长,对农作物病虫害领域文献进行文本挖掘变得越来越重要。开发有效、准确的农作物病虫害命名实体识别系统有助于在农作物病虫害相关研究报告中提取研究成果,为农作物病虫害的治理提供有效建议。本文针对中文农作物病虫害数据集缺失问题,提出了基于半远程监督的停等算法,利用该算法构建中文农作物病虫害领域语料库,大幅度减少标注过程的人工成本和时间成本;同时,提出了中文农作物病虫害命名实体识别模型(Agricultural information extraction,Agr-IE),该模型基于BERT-BILSTM-CRF,辅以多源信息融合(多源分词信息和全局词汇嵌入信息)丰富字符向量,使其充分结合字符级与词汇级的信息,以提高模型捕捉上下文信息的能力。实验表明,该模型可以有效地识别病害、虫害、药剂、作物等实体,F1值分别为96.56%、95.12%、94.48%、95.54%,并对识别难度较大的病原实体具有较好的识别效果,F1值为81.48%,高于BERT-BILSTM-CRF、BERT等模型的相应值。本文所提模型在MSRA和Weibo等其他领域数据集上与CAN-NER、Lattice-LSTM-CRF等模型进行了对比实验,并取得最佳的识别效果,F1值分别为95.80%、94.57%,表明该算法具有一定的泛化能力。
