The core of"Long Forest"belief of the Dai nationality is to protect natural forests and forests for conservation of water supply as the home of ancestral gods of the nation and to maintain the ecological bal...The core of"Long Forest"belief of the Dai nationality is to protect natural forests and forests for conservation of water supply as the home of ancestral gods of the nation and to maintain the ecological balance by means of"Long Forest"worship,"Long Forest"taboo,the traditional customary law,and village regulation and non-governmental agreement.This paper takes Manjingchengzi Village in Xishuangbanna as an example to analyze the distribution of forest land in different periods over the past 60 years and explore the role of"Long Forest"belief in the conservation of local forest resources in a view to providing a frame of reference for regional ecological environment protection.展开更多
Forest biological disasters(FBD) seriously impact energy flow and material cycling in forest ecosystems,while the underlying causes of FBD are complex. These disasters involve large areas and cause tremendous losses. ...Forest biological disasters(FBD) seriously impact energy flow and material cycling in forest ecosystems,while the underlying causes of FBD are complex. These disasters involve large areas and cause tremendous losses. As a result,the occurrence of FBDs in China( CFBD) threatens the country's ability to realize its strategic target of increasing both forested area(40 million ha) and forest volume(1.3 billion m^3) from 2005 to 2020. Collectively,China has officially named this effort to increase forest area and volume the "Two Increases" as national goals related to forestry. Based on Hurst index analysis from fractal theory,we analyzed the time series of the occurrence area and related data of FBDs from 1950 to 2007 to quantitatively determine the patterns of the macro occurrence of FBDs in China. Results indicates that,the time series of( CFBD) areas is fractal( self-affinity fractal dimension D = 1. 3548),the fluctuation of( CFBD) areas is positively correlated( auto-correlation coefficient C = 0. 2170),and the occurrence of the time series of( CFBD) is long-range dependent( Hurst index H =0. 6416),showing considerably strong trend of increases in FBDC area. Three different methods were further carried out on the original time series,and its two surrogate series generated by function surrogate in library t series,and function Surrogate Data in library in Wavelet software R,so as to analyze the reliability of Hurst indexes. The results showed that the Hurst indices calculated using different estimation methods were greater than 0. 5,ranging from 0. 64 to 0. 97,which indicated that the change of occurrence area data of FBDs was positively autocorrelated.The long-range dependence in forest biological disasters in China is obvious,and the spatial extent of FBDs tended to increase during this study period indicating this trend should be expected to persistent and worsen in the future.展开更多
空调热交换器性能异常检测技术是快速判断民机空调系统运行状态并合理安排维修任务的关键,传统的异常检测方法难以有效处理高维时序数据,无法实现系统早期故障预警。为此,本文提出了一种基于长短期记忆网络(LSTM,long-short term memory...空调热交换器性能异常检测技术是快速判断民机空调系统运行状态并合理安排维修任务的关键,传统的异常检测方法难以有效处理高维时序数据,无法实现系统早期故障预警。为此,本文提出了一种基于长短期记忆网络(LSTM,long-short term memory)与自编码器(AE,autoencoder)模型的无监督异常检测方法,用以识别民机空调系统异常运行状态。首先,基于民机空调系统原始传感器参数构建表征空调热交换器性能的特征监测参数;其次,构建LSTM-AE模型进行数据特征重构并计算重构误差;最后,使用孤立森林(iForest, isolation forest)进行无监督异常监测。将本文构建的无监督异常检测方法与传统方法对比,并建立模型评估指标,验证结果表明,所构建的模型方法可以对民机空调热交换器性能异常状态进行有效检测。展开更多
含沙量预测对流域泥沙治理、水沙调控以及水质与水环境管理等具有重要意义。长江上游地区幅员广阔,支流众多,水沙来源复杂,对准确预测三峡入库含沙量过程构成了挑战。针对长江上游区间降雨和干支流来水来沙对寸滩站含沙量产生不同程度...含沙量预测对流域泥沙治理、水沙调控以及水质与水环境管理等具有重要意义。长江上游地区幅员广阔,支流众多,水沙来源复杂,对准确预测三峡入库含沙量过程构成了挑战。针对长江上游区间降雨和干支流来水来沙对寸滩站含沙量产生不同程度的影响,提出了一种基于随机森林(Random Forest,RF)与长短时记忆(Long Short Term Memory,LSTM)神经网络结合的日含沙量预测深度学习模型RF-LSTM。首先,该模型利用RF算法筛选出与寸滩站日含沙量相关性强的水沙因子,然后将这些因子作为LSTM神经网络的输入变量,进一步识别出优选水沙因子与寸滩含沙量之间的映射关系,最后以长江上游向家坝至寸滩区间为研究区域,应用该模型对不同预见期下的寸滩站汛期日含沙量进行了预测,结果表明:与LSTM模型相比,RF-LSTM模型能较好地考虑预测因子对含沙量影响的滞后效应,且有效捕获与寸滩站日含沙量相关性强的特征,四种预见期下其在预测精度和性能方面均有较好表现,其中无预见期和预见期1 d时两种模型预测精度均较高,验证期的纳什效率系数均大于0.82,无预见期下RF-LSTM模型的纳什效率系数可达到0.91,相应的均方根误差和平均绝对误差分别较LSTM模型降低了13%和8%,且两种预见期下RF-LSTM模型可以较为准确捕获沙峰及峰现时间;当预见期增加至2 d和3 d时两种模型精度均有明显下降,但RF-LSTM模型计算精度仍优于LSTM模型。研究结果可为长江上游含沙量预测提供参考。展开更多
依托济南市济泺路穿黄隧道东线工程,选取1130组掘进数据,按照施工顺序划分数据集,采用粗细程度、软硬程度、密实程度和渗透能力4个维度描述土体的物理力学状态,分别建立基于长短期记忆模型(Long-Short Term Memory,LSTM)、随机森林模型(...依托济南市济泺路穿黄隧道东线工程,选取1130组掘进数据,按照施工顺序划分数据集,采用粗细程度、软硬程度、密实程度和渗透能力4个维度描述土体的物理力学状态,分别建立基于长短期记忆模型(Long-Short Term Memory,LSTM)、随机森林模型(Random Forest)和BP神经网络的盾构隧道掘进参数预测模型,详细对比分析3种模型对总推力和掘进速度的预测效果。研究表明:(1)LSTM模型在按施工顺序预测盾构总推力和掘进速度时,平均相对误差仅为3.72%和7.41%,模型训练时间均在20 s以内,整体表现优于随机森林模型和BP神经网络;(2)在地形发生剧烈变化以及盾构掘进线路在直线与平曲线过渡时,总推力和掘进速度出现较大波动,LSTM模型预测结果相对误差偏大的组数仅占4%与10.2%,且总体误差满足施工要求;(3)随机森林模型预测结果的相对误差在总推力和掘进速度剧烈波动的环段处偏大,数量偏多,因此在按施工顺序预测时不是优选。展开更多
文摘The core of"Long Forest"belief of the Dai nationality is to protect natural forests and forests for conservation of water supply as the home of ancestral gods of the nation and to maintain the ecological balance by means of"Long Forest"worship,"Long Forest"taboo,the traditional customary law,and village regulation and non-governmental agreement.This paper takes Manjingchengzi Village in Xishuangbanna as an example to analyze the distribution of forest land in different periods over the past 60 years and explore the role of"Long Forest"belief in the conservation of local forest resources in a view to providing a frame of reference for regional ecological environment protection.
基金Supported by the Project "Researches of Southern China’s Forestry Strategy"(2013-R17) and "Improvement of the Forest Resources Monitoring System of China"(2011-R03) Funded by the State Forestry Administration of China
文摘Forest biological disasters(FBD) seriously impact energy flow and material cycling in forest ecosystems,while the underlying causes of FBD are complex. These disasters involve large areas and cause tremendous losses. As a result,the occurrence of FBDs in China( CFBD) threatens the country's ability to realize its strategic target of increasing both forested area(40 million ha) and forest volume(1.3 billion m^3) from 2005 to 2020. Collectively,China has officially named this effort to increase forest area and volume the "Two Increases" as national goals related to forestry. Based on Hurst index analysis from fractal theory,we analyzed the time series of the occurrence area and related data of FBDs from 1950 to 2007 to quantitatively determine the patterns of the macro occurrence of FBDs in China. Results indicates that,the time series of( CFBD) areas is fractal( self-affinity fractal dimension D = 1. 3548),the fluctuation of( CFBD) areas is positively correlated( auto-correlation coefficient C = 0. 2170),and the occurrence of the time series of( CFBD) is long-range dependent( Hurst index H =0. 6416),showing considerably strong trend of increases in FBDC area. Three different methods were further carried out on the original time series,and its two surrogate series generated by function surrogate in library t series,and function Surrogate Data in library in Wavelet software R,so as to analyze the reliability of Hurst indexes. The results showed that the Hurst indices calculated using different estimation methods were greater than 0. 5,ranging from 0. 64 to 0. 97,which indicated that the change of occurrence area data of FBDs was positively autocorrelated.The long-range dependence in forest biological disasters in China is obvious,and the spatial extent of FBDs tended to increase during this study period indicating this trend should be expected to persistent and worsen in the future.
文摘空调热交换器性能异常检测技术是快速判断民机空调系统运行状态并合理安排维修任务的关键,传统的异常检测方法难以有效处理高维时序数据,无法实现系统早期故障预警。为此,本文提出了一种基于长短期记忆网络(LSTM,long-short term memory)与自编码器(AE,autoencoder)模型的无监督异常检测方法,用以识别民机空调系统异常运行状态。首先,基于民机空调系统原始传感器参数构建表征空调热交换器性能的特征监测参数;其次,构建LSTM-AE模型进行数据特征重构并计算重构误差;最后,使用孤立森林(iForest, isolation forest)进行无监督异常监测。将本文构建的无监督异常检测方法与传统方法对比,并建立模型评估指标,验证结果表明,所构建的模型方法可以对民机空调热交换器性能异常状态进行有效检测。
文摘含沙量预测对流域泥沙治理、水沙调控以及水质与水环境管理等具有重要意义。长江上游地区幅员广阔,支流众多,水沙来源复杂,对准确预测三峡入库含沙量过程构成了挑战。针对长江上游区间降雨和干支流来水来沙对寸滩站含沙量产生不同程度的影响,提出了一种基于随机森林(Random Forest,RF)与长短时记忆(Long Short Term Memory,LSTM)神经网络结合的日含沙量预测深度学习模型RF-LSTM。首先,该模型利用RF算法筛选出与寸滩站日含沙量相关性强的水沙因子,然后将这些因子作为LSTM神经网络的输入变量,进一步识别出优选水沙因子与寸滩含沙量之间的映射关系,最后以长江上游向家坝至寸滩区间为研究区域,应用该模型对不同预见期下的寸滩站汛期日含沙量进行了预测,结果表明:与LSTM模型相比,RF-LSTM模型能较好地考虑预测因子对含沙量影响的滞后效应,且有效捕获与寸滩站日含沙量相关性强的特征,四种预见期下其在预测精度和性能方面均有较好表现,其中无预见期和预见期1 d时两种模型预测精度均较高,验证期的纳什效率系数均大于0.82,无预见期下RF-LSTM模型的纳什效率系数可达到0.91,相应的均方根误差和平均绝对误差分别较LSTM模型降低了13%和8%,且两种预见期下RF-LSTM模型可以较为准确捕获沙峰及峰现时间;当预见期增加至2 d和3 d时两种模型精度均有明显下降,但RF-LSTM模型计算精度仍优于LSTM模型。研究结果可为长江上游含沙量预测提供参考。
文摘依托济南市济泺路穿黄隧道东线工程,选取1130组掘进数据,按照施工顺序划分数据集,采用粗细程度、软硬程度、密实程度和渗透能力4个维度描述土体的物理力学状态,分别建立基于长短期记忆模型(Long-Short Term Memory,LSTM)、随机森林模型(Random Forest)和BP神经网络的盾构隧道掘进参数预测模型,详细对比分析3种模型对总推力和掘进速度的预测效果。研究表明:(1)LSTM模型在按施工顺序预测盾构总推力和掘进速度时,平均相对误差仅为3.72%和7.41%,模型训练时间均在20 s以内,整体表现优于随机森林模型和BP神经网络;(2)在地形发生剧烈变化以及盾构掘进线路在直线与平曲线过渡时,总推力和掘进速度出现较大波动,LSTM模型预测结果相对误差偏大的组数仅占4%与10.2%,且总体误差满足施工要求;(3)随机森林模型预测结果的相对误差在总推力和掘进速度剧烈波动的环段处偏大,数量偏多,因此在按施工顺序预测时不是优选。
