北京1841年以来均一化最高和最低气温日值序列的构建

长期连续的日值观测资料是研究百年来极端气候事件及其变化特征的重要基础支撑.目前中外由于缺乏可靠的逐日百年尺度气候资料,使得20世纪50年代以前的极端气候变化规律仍然没有得到很好的认识.基于国家气象信息中心收集整理的日最高和最低气温观测资料,构建北京1841—2019年气温日值序列.首先,通过数据质量控制剔除原始基础资料...     

水中溴化物的测定方法改进

介绍了酚红分光光度法测定水中溴化物的改进方法。通过调整氧化剂的浓度使反应速度加快,在可接受的时间内测定显色反应产生的最大吸光度,使其在不同的显色环境温度下都能得到最大的灵敏度。显色温度、反应时间、氧化剂氯胺T的浓度是影响显色反应的主要因素。通过实验条件对比,建立了一种可在不同季节都能采用的检测方法。     

末次盛冰期以来南海南部天然气水合物储库变化及其对环境的影响

末次盛冰期以来南海南部海平面及海洋底水温度均发生了很大的变化. 为了研究南海南部天然气水合物稳定带厚度在这个过程中的变化情况及其对环境的影响,我们利用相关的计算公式,并编制了计算稳定带厚度的程序,在南海南部的南沙海槽、曾母盆地、巴拉望盆地和苏禄海等四个重点海域选取了35个点进行末次盛冰期及目前稳定带厚度的计算. 计算结果表明,南海南部末次盛冰期和目前的天然气水合物稳定带厚度分别为262m和233m;甲烷资源量分别为269×1013m3和239×1013m3;水合物资源量分别为164×1011m3和146×1011m3. 这说明自末次盛冰期以来,南海南部稳定带厚度平均减薄了29m,平均减薄百分比为12%,同时释放了大约30×1012m3的甲烷,这些甲烷对环境产生了较大影响,对末次冰期的结束起了较大作用.     

青藏铁路片石护坡路堤温度特性分析

通过对青藏铁路清水河段片石护坡路堤和普通路堤实体工程进行的地温监测,对比分析了两种路堤体内及基底的温度特性,分析结果表明采用片石护坡措施的路堤,与普通路堤相比,降温效果好,负积温量值大,最大融化深度抬升幅度也较大,因此,片石护坡能够有效发挥降低地温、保护多年冻土的作用。     

Statistical analysis of signal measurement in time-of-flight cameras

Three-dimensional imaging systems have evolved significantly in the last two decades due to increase in demand for tasks in the field of close range photogrammetry. The fast and growing need of 3D imaging devices has given rise to range image technology, especially time-of-flight (TOF) cameras, that provide direct measurement of distance between the camera and the targeted surface. A significant advantage of TOF devices over traditional range data sensors is their capability to provide frame rate range data over a full image array. In phase shift TOF cameras, phase shift sampling of the received signal is used to measure amplitude, phase and the offset (intensity) of the received signal. As a result, the quality of the measurement of these sensors depends heavily on signal-to-noise (SNR) of the incoming signal and the subsequent processing algorithms. A detailed understanding of the statistical distributions of the measurement parameters is crucial for accurate distance measurement analysis especially in low SNR scenarios. In this paper, we provide explicit noise models for the three parameters of amplitude, phase and intensity. The proposed stochastic model helps in investigating the effect of noise on signal and classifying range data reliability in TOF cameras. The model is used for prediction of errors in a TOF camera under various SNR conditions. Experimental verification confirms the validity of the model using real data for range error classification under different noise conditions.     

Identification of hazelnut fields using spectral and Gabor textural features

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F_β values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F_β values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards.     

Building more realistic reservoir optimization models using data mining - A case study of Shelbyville Reservoir

In this paper, we promote a novel approach to develop reservoir operation routines by learning from historical hydrologic information and reservoir operations. The proposed framework involves a knowledge discovery step to learn the real drivers of reservoir decision making and to subsequently build a more realistic (enhanced) model formulation using stochastic dynamic programming (SDP). The enhanced SDP model is compared to two classic SDP formulations using Lake Shelbyville, a reservoir on the Kaskaskia River in Illinois, as a case study. From a data mining procedure with monthly data, the past month’s inflow (Q_t−1), current month’s inflow (Q_t), past month’s release (R_t−1), and past month’s Palmer drought severity index (PDSI_t−1) are identified as important state variables in the enhanced SDP model for Shelbyville Reservoir. When compared to a weekly enhanced SDP model of the same case study, a different set of state variables and constraints are extracted. Thus different time scales for the model require different information. We demonstrate that adding additional state variables improves the solution by shifting the Pareto front as expected while using new constraints and the correct objective function can significantly reduce the difference between derived policies and historical practices. The study indicates that the monthly enhanced SDP model resembles historical records more closely and yet provides lower expected average annual costs than either of the two classic formulations (25.4% and 4.5% reductions, respectively). The weekly enhanced SDP model is compared to the monthly enhanced SDP, and it shows that acquiring the correct temporal scale is crucial to model reservoir operation for particular objectives.     

自重湿陷性黄土下限深度判定方法探讨

以郑西高速铁路沿线6个现场试坑浸水试验资料和大量室内土工试验资料为基础,对自重湿陷性黄土下限深度的判定问题进行探讨.研究表明:自重湿陷性黄土下限深度是一个明确且唯一的值,但由于受应力条件、变形条件、饱和状态、试样尺寸、边界效应等因素的影响,运用现场取样进行室内压缩试验判定自重湿陷性黄土下限深度的方法还存在一定的局限性,...