[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural languag...Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.展开更多
首先归纳了AADL(architecture analysis and design language)的发展历程及其主要建模元素.其次,从模型驱动设计与实现的角度综述了AADL在不同阶段的研究与应用,总结了研究热点,分析了现有研究的不足,并对AADL的建模与分析工具、应用实...首先归纳了AADL(architecture analysis and design language)的发展历程及其主要建模元素.其次,从模型驱动设计与实现的角度综述了AADL在不同阶段的研究与应用,总结了研究热点,分析了现有研究的不足,并对AADL的建模与分析工具、应用实践进行了概述.最后,探讨了AADL的发展与研究方向.展开更多
关于舆情事件的新闻数据是纷繁复杂的.即便是关于同一舆情事件的新闻数据,往往包含有不同的子话题(事件的不同侧面).因此,如何生成能够准确描述事件子话题含义的标签对深入分析舆情事件(包括掌握事件热点、监测发展走向等)具有重要意义...关于舆情事件的新闻数据是纷繁复杂的.即便是关于同一舆情事件的新闻数据,往往包含有不同的子话题(事件的不同侧面).因此,如何生成能够准确描述事件子话题含义的标签对深入分析舆情事件(包括掌握事件热点、监测发展走向等)具有重要意义.事件子话题标签的生成通常包括两个关键步骤:首先发现子话题,然后依据每个子话题的关键词或文档内容生成描述该子话题的有效标签.传统方法在发现话题时多采用聚类或分类的方法,它们将同一个话题的文档整合到一个簇中.然而,由于隶属同一事件的文档具有很强的相似性,现有方法难以度量他们之间的距离,因此无法应用于发现事件子话题这一任务.此外,在为子话题生成标签时,传统的方法通常通过抽取来实现.此类方法所生成标签的准确性无法保证.为此,该文提出了一种基于PLSA with Background Language并结合关键词聚类发现事件内部子话题,进而基于维基百科等知识库生成事件子话题标签的模型ET-TAG.在多类舆情事件数据集上的实验结果表明,ET-TAG算法相比K-means和LDA等已有子话题发现方法具有更好的性能;从子话题标签生成角度而言,ET-TAG生成的标签相对于传统方法也具有更好的准确性和概括性.该文最后将ET-TAG算法生成的子话题标签用于事件的对比和追踪,结果表明通过子话题标签可以发现事件共性,并反映事件子话题热度的变化趋势.展开更多
提出了一种基于时间抽象状态机(timed abstract state machine,简称TASM)的AADL(architecture analysis and design language)模型验证方法.分别给出了AADL子集和TASM的抽象语法,并基于语义函数和类ML的元语言形式定义转换规则.在此基础...提出了一种基于时间抽象状态机(timed abstract state machine,简称TASM)的AADL(architecture analysis and design language)模型验证方法.分别给出了AADL子集和TASM的抽象语法,并基于语义函数和类ML的元语言形式定义转换规则.在此基础上,基于AADL开源建模环境OSATE(open source AADL tool environment)设计并实现了AADL模型验证与分析工具AADL2TASM,并基于航天器导航、制导与控制系统(guidance,navigation and control)进行了实例性验证.展开更多
能够提供更强计算能力的多核处理器将在安全关键系统中得到广泛应用,但是由于现代处理器所使用的流水线、乱序执行、动态分支预测、Cache等性能提高机制以及多核之间的资源共享,使得系统的最坏执行时间分析变得非常困难.为此,国际学术...能够提供更强计算能力的多核处理器将在安全关键系统中得到广泛应用,但是由于现代处理器所使用的流水线、乱序执行、动态分支预测、Cache等性能提高机制以及多核之间的资源共享,使得系统的最坏执行时间分析变得非常困难.为此,国际学术界提出时间可预测系统设计的思想,以降低系统的最坏执行时间分析难度.已有研究主要关注硬件层次及其编译方法的调整和优化,而较少关注软件层次,即,时间可预测多线程代码的构造方法以及到多核硬件平台的映射.提出一种基于同步语言模型驱动的时间可预测多线程代码生成方法,并对代码生成器的语义保持进行证明;提出一种基于AADL(architecture analysis and design language)的时间可预测多核体系结构模型,作为研究的目标平台;最后,给出多线程代码到多核体系结构模型的映射方法,并给出系统性质的分析框架.展开更多
基于位置的服务被认为是继短信之后电信增值业务发展的下一次高潮,在前期所提出的一种面向电信增值业务领域的流程描述语言XPL(extended-calling process language)的基础上,进一步提出了一种描述地理信息服务的语言GDL(geography descr...基于位置的服务被认为是继短信之后电信增值业务发展的下一次高潮,在前期所提出的一种面向电信增值业务领域的流程描述语言XPL(extended-calling process language)的基础上,进一步提出了一种描述地理信息服务的语言GDL(geography description language),GDL可以和XPL配合使用,共同描述基于位置的电信服务.XPL和GDL具有抽象层次高,使用灵活简单,开发业务速度快的特点.还介绍了支持XPL和GDL的业务生成系统.该业务生成系统基于SOA(services-oriented architecture,面向服务的构架),适用于网络融合条件下的业务生成.展开更多
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
基金Supported by the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.
文摘首先归纳了AADL(architecture analysis and design language)的发展历程及其主要建模元素.其次,从模型驱动设计与实现的角度综述了AADL在不同阶段的研究与应用,总结了研究热点,分析了现有研究的不足,并对AADL的建模与分析工具、应用实践进行了概述.最后,探讨了AADL的发展与研究方向.
基金Supported by the National Natural Science Foundation of China under Grant No.60473064(国家自然科学基金)the National High-Tech Research and Development Plan of China under Grant Nos.2007AA010301,2005AA112030(国家高技术研究发展计划(863))+2 种基金the National Basic Research Program of China under Grant No.2005CB321805(国家重点基础研究发展计划(973))the Key Technologies R&D Program of China under Grant No.2003BA904B02 (国家科技攻关计划)the National Key Technology R&D Program of China under Grant No.2006BAH02A02(国家科技支撑计划)
文摘关于舆情事件的新闻数据是纷繁复杂的.即便是关于同一舆情事件的新闻数据,往往包含有不同的子话题(事件的不同侧面).因此,如何生成能够准确描述事件子话题含义的标签对深入分析舆情事件(包括掌握事件热点、监测发展走向等)具有重要意义.事件子话题标签的生成通常包括两个关键步骤:首先发现子话题,然后依据每个子话题的关键词或文档内容生成描述该子话题的有效标签.传统方法在发现话题时多采用聚类或分类的方法,它们将同一个话题的文档整合到一个簇中.然而,由于隶属同一事件的文档具有很强的相似性,现有方法难以度量他们之间的距离,因此无法应用于发现事件子话题这一任务.此外,在为子话题生成标签时,传统的方法通常通过抽取来实现.此类方法所生成标签的准确性无法保证.为此,该文提出了一种基于PLSA with Background Language并结合关键词聚类发现事件内部子话题,进而基于维基百科等知识库生成事件子话题标签的模型ET-TAG.在多类舆情事件数据集上的实验结果表明,ET-TAG算法相比K-means和LDA等已有子话题发现方法具有更好的性能;从子话题标签生成角度而言,ET-TAG生成的标签相对于传统方法也具有更好的准确性和概括性.该文最后将ET-TAG算法生成的子话题标签用于事件的对比和追踪,结果表明通过子话题标签可以发现事件共性,并反映事件子话题热度的变化趋势.
文摘提出了一种基于时间抽象状态机(timed abstract state machine,简称TASM)的AADL(architecture analysis and design language)模型验证方法.分别给出了AADL子集和TASM的抽象语法,并基于语义函数和类ML的元语言形式定义转换规则.在此基础上,基于AADL开源建模环境OSATE(open source AADL tool environment)设计并实现了AADL模型验证与分析工具AADL2TASM,并基于航天器导航、制导与控制系统(guidance,navigation and control)进行了实例性验证.
文摘能够提供更强计算能力的多核处理器将在安全关键系统中得到广泛应用,但是由于现代处理器所使用的流水线、乱序执行、动态分支预测、Cache等性能提高机制以及多核之间的资源共享,使得系统的最坏执行时间分析变得非常困难.为此,国际学术界提出时间可预测系统设计的思想,以降低系统的最坏执行时间分析难度.已有研究主要关注硬件层次及其编译方法的调整和优化,而较少关注软件层次,即,时间可预测多线程代码的构造方法以及到多核硬件平台的映射.提出一种基于同步语言模型驱动的时间可预测多线程代码生成方法,并对代码生成器的语义保持进行证明;提出一种基于AADL(architecture analysis and design language)的时间可预测多核体系结构模型,作为研究的目标平台;最后,给出多线程代码到多核体系结构模型的映射方法,并给出系统性质的分析框架.
