Reconstructing fluvial bar surfaces from compound cross‐strata and the interpretation of bar accretion direction in large river deposits

The interpretation of fluvial styles from the rock record is based for a significant part on the identification of different types of fluvial bars, characterized by the geometric relationship between structures indicative of palaeocurrent and surfaces interpreted as indicative of bar form and bar accretion direction. These surfaces of bar accretion are the boundaries of flood‐related bar increment elements, which are typically less abundant in outcrops than what would be desirable, particularly in large river deposits in which each flood mobilizes large volumes of sediment, causing flood‐increment boundary surfaces to be widely spaced. Cross‐strata set boundaries, on the other hand, are abundant and indirectly reflect the process of unit bar accretion, inclined due to the combined effect of the unit bar surface inclination and the individual bedform climbing angle, in turn controlled by changes in flow structure caused by local bar‐scale morphology. This work presents a new method to deduce the geometry of unit bar surfaces from measured pairs of cross‐strata and cross‐strata set boundaries. The method can be used in the absence of abundant flood‐increment bounding surfaces; the study of real cases shows that, for both downstream and laterally accreting bars, the reconstructed planes are very similar to measured bar increment surfaces.     

基于ANUSPLIN的京津冀区域逐日气温格点数据集建立方法研究

为建立一个高精度、高空间分辨率的逐日气温格点数据集,满足公共气象服务对于精确信息及实时信息的需要,利用2018年6—8月京津冀区域以及临近省区共3 974个国家级及区域气象观测站质控后的逐日气温资料,采用ANUSPLIN软件对逐日气温数据进行空间内插,得到了京津冀区域逐日气温格点数据集(0.01°×0.01°),并分别利用反距离权重插值法、普通克里金插值法、样条函数法对逐日气温数据进行空间插值,采用相关系数(Corr)、平均绝对误差(MAE)、平均相对误差(MRE)等作为评估指标来检验插值精度。结果表明:1) ANUSPLIN软件满足了空间插值对精度及曲面平滑度的要求,能直观体现京津冀区域气温由北向南递增的空间分布特征; 2) 4种插值方法中,基于ANUSPLIN软件的插值结果最优,相关系数平均达0.97,其样本误差在1℃之内占比为90.59%,MAE为0.46℃,MRE为1.81%; 3)插值误差较大的区域位于冀北高原、燕山丘陵及太行山脉一带,高海拔、低站点密度等是造成插值误差的主要原因。基于ANUSPLIN插值方法建立的逐日气温格点数据集具有分辨率高、空间插值误差小的优势,ANUSPLIN对气温的空间分布具有较好的预测能力。     

寡照对温室黄瓜花果期生长及产量品质影响研究

为了探讨寡照对日光温室黄瓜花果期生长发育、产量和品质的影响,以黄瓜品种“德瑞特L108”(Cucumis sativusL. cv.Derit L108)为试材,通过不同遮阴日数（1 d、3 d、5 d、7 d、9 d）及恢复控制试验,研究寡照对日光温室黄瓜生长及果实品质的影响。结果表明：1）持续寡照日数超过5 d,黄瓜株高和茎粗生长受到显著影响,尤其是茎粗存在变细现象,7 d以上对植株生长影响不可逆。2）黄瓜果实横径、长度生长速率和果重受寡照影响程度存在一定差异,果实上下两端横径长势对寡照较为敏感,连续寡照3 d时影响即达显著水平,可导致上端横径生长速率降低0.87 mm·d-1,下端为0.73～0.99 mm·d-1;果实中部横径和长度增长速率在寡照5 d以上时影响达显著水平,生长速率降低0.88 mm·d-1和0.87～1.00 cm·d-1;与对照（以CK表示）相比,寡照3 d的单果重降低18.7 g,寡照日数每延长1 d,单果重降低2～3 g。3）果实的外观等级受寡照影响较大,1～5 d寡照,特级果比例降低9.4%～43.0%;持续寡照5 d以上二级果和坏果比例明显升高,连续寡照9 d,二级果和坏果比例达到20%和25%;寡照3 d以上,果实含水率与CK差异即达到显著性差异;维生素含量对寡照影响反映敏感,1～5 d寡照,果实维生素含量降低6%左右,7 d以上的寡照日数,则降低10%～13%;寡照持续日数超过5 d,花青素和有机酸含量影响较大;持续寡照日低于7 d时,可溶性糖含量对寡照影响敏感度相对较低,超过7 d其含量达到显著性差异,且寡照日每延长1 d,其含量降低4%。     

雹云识别的物元可拓模型及其效果检验

雹云识别是一个多参数的模式识别问题。应用物元可拓思想将单参数雹云识别的不相容性转化为相容性问题。通过构造雹云和雷雨云两类样本的经典物无降节域物元，根据计算出的待识别云体与两类样本云体之间的关联度大小，进行雹云识别。该方法用于成都地区雹云识别结果的正确率达85％以上。     

沙漠地区无人机影像连接点提取

提出了一种纹理自适应的影像连接点提取方法,有针对性地解决沙漠地区无人机影像对比度低、相似性强、纹理信息不均衡,经常会造成连接点过少甚至提取、匹配失败的突出问题。该方法利用影像金字塔进行粗匹配,建立影像之间近似单应变换关系;通过对原始影像分块计算灰度共生矩阵的熵来评估影像纹理指标,并据此实现特征点的自适应提取与匹配。实验表明,该方法能够得到数量较多、分布均匀、可靠性强的同名点。     

Spectral-type simulation of spatially correlated fracture set properties

Fracture set properties such as orientation, spacing, trace length, and waviness tend to be spatially correlated. These properties can be efficiently simulated by spectral analysis procedures that take advantage of the computational speed of the fast Fourier transform. The covariance function of each property to be simulated is obtained from the variogram function estimated from mapped fracture set data and is typically referenced to the mean vector of the set. Simulation procedures for normally and exponentially distributed data involve generating uncorrelated Fourier coefficients that are assigned proper variance according to the spectral density, which is the Fourier transform of the covariance function. These coefficients are then reverse Fourier transformed to produce simulated set properties that have the desired variance and variogram function.     

模糊信息熵与环境质量综合评价

本文从信息论观点出发,分析了环境评价中各评价指标(或因子)的模糊信息熵与权重之间的关系,认为可用模糊信息熵函数给各指标赋权。文中给出了实例,并利用模糊信息熵对评价结果的可靠性作了分析。     

区域环境质量的层次模糊综合评价模型及应用

本文在模糊综合评判方法中引入层次分析法,对区域环境进行综合评价。在综合评判中,利用层次分析原理给各因素赋权,具有定性与定量相结合的特点。实例表明,该模型为区域环境质量综合评价提供了一条新的途径。     

鲢鱼脆片的研制和质量评定

以鲢鱼肉和淀粉为主要原料,通过加工处理而制成鲢鱼脆片;并利用试制的产品资料和根据模糊数学综合评判原理,建立鲢鱼脆片质量评判数学模式,借助它可找出质量好的最佳生产工艺。     

The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan

Texture information offers an extensive solution for image classification by providing better accuracy of image information. However, huge amounts of improper additional texture information may result in a chaotic state, and this leads to uncertainty in the classification process. Considerable portion of earlier works have been carried out through the generally acknowledged procedure of Principal Components Analysis (PCA). However, the PCA method has flaws in the area of influenced and non- influenced attributes. On the whole, whether PCA provides an effective solution to determine the value of knowledge rule in image information still remains a question. This study proposes an innovative method, called Discrete Rough Set method, as a tool for image classification. This study focuses on two crucial issues: (1) The core attributes of the target categories in image classification are systematically analyzed while eliminating surplus attributes rationally; (2) The unique point of each attribute, which influenced the target categories, is successfully found. This is a crucial aspect that is very helpful for the construction of decision rule. Finally, in this study we utilized the expert knowledge classifier and the overall accuracy of Discrete Rough Set (96.67%) exceeds that of the conventional PCA (86.00%) of paddy rice area evaluation from Quickbird image. This result shows that the appropriate classification knowledge can be presented by Discrete Rough Set, and this information can effectively improve the accuracy of image classification. An erratum to this article can be found at