Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network

Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes. The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience, which have problems such as inaccurate recognition time, time-consuming and energy sapping. Therefore, this paper proposes a neural network-based method for predicting the flowering phase of pear tree. Firstly, based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019, three principal components (the temperature factor, weather factor, and humidity factor) with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method. Then, the three components are used as input factors for the BP neural network. A BP neural network prediction model is constructed based on genetic algorithm optimization. The crossover operator and mutation operator in the adaptive genetic algorithm are improved. Finally, the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper. The results demonstrate that, the model can solve the local optimization problem of the BP neural network model. The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy. Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method. This method can provide more reliable forecast information for the blooming period, which can provide decision-making reference for improving the development of tourism industry.     

SPOT立体像对的DEM恢复及质量评价

DEM是遥感技术应用中非常重要的数据源，它不仅可以生成等高线，而且可以产生正射影像，本论述了SPOT立体像对的DEM恢复技术流程；对不同参数模型恢复结果进行了质量评价，提出最优的参数模型；并分析了引起局部错误匹配的原因及解决方法。     

Über das Flüssigkeits-Grenzflächenpotential am Diaphragma von Referenzelektroden

The Liquid Junction Potential on the Diaphragm of Reference Electrodes The electrolyte solution flow through porous ceramic diaphragms is used for evaluating measurements of streaming potentials. With porous magnesia silicate diaphragms, by salt concentrations from 1 × 10⁻⁵ to 1 M KCl, the values of zeta potentials are negative. Increases in salt concentrations lower the zeta potentials through a decrease in double layer thickness. In presence of concentrated salt solutions the streaming potential has a very small effect on liquid junction potential of reference electrodes.     

MITTEILUNGEN DER SCHWEIZER INFORMATIKER GESELLSCHAFT · 4/2003

MITTEILUNGEN

MITTEILUNGEN DER SCHWEIZER INFORMATIKER GESELLSCHAFT · 4/2003     

Towards a more effective and reliable salt crystallization test for porous building materials: state of the art

The durability of building materials with respect to salt crystallization is commonly determined by accelerated weathering tests, carried out in the laboratory. An effective laboratory weathering test should assess the durability and, in the case of conservation of historic buildings, the compatibility of repair materials with those existing. Besides, the test should provide reliable results within a reasonable period of time, accelerating the deterioration process without however altering its mechanism. Despite several national and international standards, recommendations and guidelines, a commonly accepted testing protocol does not yet exist. Researchers often develop and apply their own procedure, a fact that complicates comparison between different studies. The RILEM Technical Committee 271 ASC has been set up with the scope of developing improved test procedures for the assessment of the behaviour of materials under the influence of salt crystallization, which should overcome the limitations of existing standards and recommendations. This paper constitutes one of the first results of the work of the Technical Committee. It critically reviews the literature on salt crystallization tests, identifies advantages and limitations of the several test protocols and provides new ideas for the development of improved salt crystallization procedures.     

Temporal Upsampling of Point Cloud Sequences by Optimal Transport for Plant Growth Visualization

Plant growth visualization from a series of 3D scanner measurements is a challenging task. Time intervals between successive measurements are typically too large to allow a smooth animation of the growth process. Therefore, obtaining a smooth animation of the plant growth process requires a temporal upsampling of the point cloud sequence in order to obtain approximations of the intermediate states between successive measurements. Additionally, there are suddenly arising structural changes due to the occurrence of new plant parts such as new branches or leaves. We present a novel method that addresses these challenges via semantic segmentation and the generation of a segment hierarchy per scan, the matching of the hierarchical representations of successive scans and the segment-wise computation of optimal transport. The transport problems' solutions yield the information required for a realistic temporal upsampling, which is generated in real time. Thereby, our method does not require shape templates, good correspondences or huge databases of examples. Newly grown and decayed parts of the plant are detected as unmatched segments and are handled by identifying corresponding bifurcation points and introducing virtual segments in the previous, respectively successive time step. Our method allows the generation of realistic upsampled growth animations with moderate computational effort.