Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonl...Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly-Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.展开更多
Modeling and matching texts is a critical issue in natural language processing(NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features(MG-CMF) model was proposed. T...Modeling and matching texts is a critical issue in natural language processing(NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features(MG-CMF) model was proposed. The proposed model used convolution operations to construct the representation of text under multiple granularities, used max-pooling operations to filter more reasonable text representations and built a matching matrix at different granularities. Then, the convolution neural network(CNN) was used to capture the matching information in each granularity. Finally, the captured matching features were input into the fully connected neural network to obtain the matching similarity. By making some experiments, the results indicate that the MG-CMF model not only gets multiple granularity representations of sentences but also can obtain matching information from multiple granularities of sentences better than the other text matching models.展开更多
As the effective capture region of optical motion capture system is limited by quantity,installation mode,resolution and focus of infrared cameras,the reflective markers on certain body parts(such as wrists,elbows,etc...As the effective capture region of optical motion capture system is limited by quantity,installation mode,resolution and focus of infrared cameras,the reflective markers on certain body parts(such as wrists,elbows,etc.)of multi-actual trainees may be obscured when they perform the collaborative interactive operation.To address this issue,motion data compensation method based on the additional feature information provided by the electromagnetic spatial position tracking equipment is proposed in this paper.The main working principle and detailed realization process of the proposed method are introduced step by step,and the practical implementation is presented to illustrate its validity and efficiency.The results show that the missing capture data and motion information of relevant obscured markers on arms can be retrieved with the proposed method,which can avoid the simulation motions of corresponding virtual operators being interrupted and deformed during the collaborative interactive operation process performed by multiactual trainees with optical human motion capture system in a limited capture range.展开更多
【目的】为科学统筹综合能源系统运行经济性、稳定性和低碳性优化目标,采用何种技术手段以提升能源转化效率,减少系统能源浪费和区域环境污染,是当下综合能源系统合理优化的主要问题。为此,提出一种基于场景生成与信息间隙决策理论的含...【目的】为科学统筹综合能源系统运行经济性、稳定性和低碳性优化目标,采用何种技术手段以提升能源转化效率,减少系统能源浪费和区域环境污染,是当下综合能源系统合理优化的主要问题。为此,提出一种基于场景生成与信息间隙决策理论的含碳捕集与封存(carbon capture and storage,CCS)—两段式电转气(power to gas,P2G)综合能源系统低碳优化策略。【方法】在技术层面,通过对电P2G两阶段精细化建模,提高氢能利用效率,建立热电联产(combined heating and power,CHP)-CCS-P2G耦合模型;在市场机制层面,引入阶梯型碳交易模型以降低系统中CO_(2)排放量。最终,基于信息间隙决策理论(IGDT)构建不同风险偏好下的优化调度模型。【结果】以典型综合能源系统进行算例分析,仿真结果表明所提模型可提高风光消纳率,实现系统低碳、经济、稳定运行。【结论】该优化策略可有效帮助决策者根据其风险偏好制定风险规避与风险追求策略下的调度方案,实现系统不确定性与经济性的平衡。展开更多
为高效处理综合能源系统IES(integrated energy system)中因热电供需矛盾导致的弃风及碳排放问题,构建了考虑碳捕集与封存CCS(carbon capture and storage)技术以及光热CSP(concentrating solar power)电站的优化调度模型。首先,利用CC...为高效处理综合能源系统IES(integrated energy system)中因热电供需矛盾导致的弃风及碳排放问题,构建了考虑碳捕集与封存CCS(carbon capture and storage)技术以及光热CSP(concentrating solar power)电站的优化调度模型。首先,利用CCS技术对热电联产CHP(combined heat and power)机组进行低碳化改造,建立碳捕集热电联产机组的数学模型;然后,在此基础上引入CSP电站,构成CSP-CHP-CCS协同框架,并建立含CSP-CHPCCS的IES低碳经济调度模型;接着,针对系统中的源、荷不确定性,采用信息间隙决策理论进行模拟分析,构建风险规避鲁棒模型;最后,通过算例仿真对比,验证了所提模型在促进新能源消纳和降低碳排放方面的有效性。展开更多
为了更有效的利用已有数据资源,不造成科研设施的重复投资,数据共享越来越受到重视.NASA对地观测系统(EOS)提供了大量的包括MODIS在内的免费数据资源,为此,EOS Data Dumper(EDD)通过程序模拟EOS数据门户的正常下载流程,采用了先进的Web...为了更有效的利用已有数据资源,不造成科研设施的重复投资,数据共享越来越受到重视.NASA对地观测系统(EOS)提供了大量的包括MODIS在内的免费数据资源,为此,EOS Data Dumper(EDD)通过程序模拟EOS数据门户的正常下载流程,采用了先进的Web页面文本信息捕捉技术,实现定时自动下载研究区的全部EOS免费数据,并通过免费的DIAL系统,向互联网重新发布,实现复杂的基于时空的数据查询.从技术角度详细介绍了EDD的项目背景与意义、实现方案。展开更多
文摘Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly-Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.
文摘Modeling and matching texts is a critical issue in natural language processing(NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features(MG-CMF) model was proposed. The proposed model used convolution operations to construct the representation of text under multiple granularities, used max-pooling operations to filter more reasonable text representations and built a matching matrix at different granularities. Then, the convolution neural network(CNN) was used to capture the matching information in each granularity. Finally, the captured matching features were input into the fully connected neural network to obtain the matching similarity. By making some experiments, the results indicate that the MG-CMF model not only gets multiple granularity representations of sentences but also can obtain matching information from multiple granularities of sentences better than the other text matching models.
基金the project supported by the National Natural Science Foundation of China(Grant No.61702524)the Natural Science Foundation of Shaanxi Province(Grant No.2016JQ6052).
文摘As the effective capture region of optical motion capture system is limited by quantity,installation mode,resolution and focus of infrared cameras,the reflective markers on certain body parts(such as wrists,elbows,etc.)of multi-actual trainees may be obscured when they perform the collaborative interactive operation.To address this issue,motion data compensation method based on the additional feature information provided by the electromagnetic spatial position tracking equipment is proposed in this paper.The main working principle and detailed realization process of the proposed method are introduced step by step,and the practical implementation is presented to illustrate its validity and efficiency.The results show that the missing capture data and motion information of relevant obscured markers on arms can be retrieved with the proposed method,which can avoid the simulation motions of corresponding virtual operators being interrupted and deformed during the collaborative interactive operation process performed by multiactual trainees with optical human motion capture system in a limited capture range.
文摘【目的】为科学统筹综合能源系统运行经济性、稳定性和低碳性优化目标,采用何种技术手段以提升能源转化效率,减少系统能源浪费和区域环境污染,是当下综合能源系统合理优化的主要问题。为此,提出一种基于场景生成与信息间隙决策理论的含碳捕集与封存(carbon capture and storage,CCS)—两段式电转气(power to gas,P2G)综合能源系统低碳优化策略。【方法】在技术层面,通过对电P2G两阶段精细化建模,提高氢能利用效率,建立热电联产(combined heating and power,CHP)-CCS-P2G耦合模型;在市场机制层面,引入阶梯型碳交易模型以降低系统中CO_(2)排放量。最终,基于信息间隙决策理论(IGDT)构建不同风险偏好下的优化调度模型。【结果】以典型综合能源系统进行算例分析,仿真结果表明所提模型可提高风光消纳率,实现系统低碳、经济、稳定运行。【结论】该优化策略可有效帮助决策者根据其风险偏好制定风险规避与风险追求策略下的调度方案,实现系统不确定性与经济性的平衡。
文摘为高效处理综合能源系统IES(integrated energy system)中因热电供需矛盾导致的弃风及碳排放问题,构建了考虑碳捕集与封存CCS(carbon capture and storage)技术以及光热CSP(concentrating solar power)电站的优化调度模型。首先,利用CCS技术对热电联产CHP(combined heat and power)机组进行低碳化改造,建立碳捕集热电联产机组的数学模型;然后,在此基础上引入CSP电站,构成CSP-CHP-CCS协同框架,并建立含CSP-CHPCCS的IES低碳经济调度模型;接着,针对系统中的源、荷不确定性,采用信息间隙决策理论进行模拟分析,构建风险规避鲁棒模型;最后,通过算例仿真对比,验证了所提模型在促进新能源消纳和降低碳排放方面的有效性。
文摘为了更有效的利用已有数据资源,不造成科研设施的重复投资,数据共享越来越受到重视.NASA对地观测系统(EOS)提供了大量的包括MODIS在内的免费数据资源,为此,EOS Data Dumper(EDD)通过程序模拟EOS数据门户的正常下载流程,采用了先进的Web页面文本信息捕捉技术,实现定时自动下载研究区的全部EOS免费数据,并通过免费的DIAL系统,向互联网重新发布,实现复杂的基于时空的数据查询.从技术角度详细介绍了EDD的项目背景与意义、实现方案。
