农村电网改造规划方法的研究

农村电网的合理规划对节约优化农村有限的资金和能源、减轻农民负担、推动经济发展具有十分重要的意义。新理论和新技术的研究与应用是我国农村电网规划的大势所趋。为此,针对我国农村电网的实际情况,介绍了农网改造的缘由、意义和目的,阐述了电网规划的模型、采用的技术方法及其发展趋势。研究结果对我国现在和将来的农村电网规划与改造具有指导意义和参考价值。     

基于点云数据的NURBS曲面重建研究

提出一种基于点云数据的NURBS曲面重建方法,针对数据点的分布不均匀性,进行非均匀B样条曲线控制点的反算,通过累积弦长法构造非均匀节点矢量,保证了曲面的插值精度和曲面重建的质量。     

黄河下游影响带地下水系统动力学特征

以黄河下游影响带地下水系统为例,介绍了地下水系统的动力学特征分析方法,计算了其动力学特征指标:Hurst指数为0.91~0.97、Kolmogorov熵为0.054~0.081、最大Lyapunov指数均大于零、分维数为2.50~3.36。分析结果表明:黄河下游影响带地下水系统的各子系统都具有混沌性;Hurst指数刻划了系统的开放程度和受外界干扰程度;分形维描述了非线性系统的复杂程度。     

Some Ideas on Reconstruction of Villages inside the City

In this paper, the historical reasons of formation of the village inside the city are analyzed. It is indicated that such situation for the villages inside the city are the result of joint action of both the villagers and the government. The foundation of villages inside the city laid on the simple compensation for farmers in confiscating the rural land and lack of intelligence aids. In this paper, the ideas and measures for their reconstruction are put forward based on consideration of the reasons of their formation and difficulties in the reconstruction.     

试论农业机械化在社会主义新农村建设中的作用

近几年,随着政策导向和资源配置通过市场杠杆促进农业机械化,使农村拥有的农业机械不断增加,农机化综合水平不断提高,推动了农业耕作制度的革新,加速了农业的产业化步伐和农村劳动力的转移,因此,农业机械化在社会主义新农村建设中发挥着愈来愈重要的作用。     

论农业机械化在新农村建设中的重要作用

以新农村建设带给农机化新的发展机遇为主线,结合武汉市以实施农村"家园建设行动计划"为抓手,建设社会主义新农村的实际,论述农业机械化在新农村建设中发挥的重要作用。     

基于激光雷达扫描的料仓原料储料量测量方法

为提高仓内原料储料量测量自动化和智能化水平,设计了一种基于二维激光雷达扫描的储料量测量装置与系统。本系统采用RPLIDAR S1型二维激光雷达扫描获取不同储料量物料的原始点云数据,通过坐标变换、重叠点提取、滤波、分割等方法对原始点云进行预处理,采用贪婪投影三角化算法将预处理后的点云进行三维重建,获得仓内原料的三维模型,结合物料三维模型和物料的容重获得仓内原料的储料量,从而实现储料量的自动测量。以玉米为试验对象,测量小型储料塔内玉米储料量并对玉米不同储料量进行扫描测量,验证模型的准确性,结果显示：测量结果的平均绝对误差为8.05 kg,平均相对误差为1.52%。研究结果表明,基于二维激光雷达扫描的储料量测量方法是可行的,具有较好的稳定性和测量精度,能够满足实际生产需求。     

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.     

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics,vision, and plant phenotyping

This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants (modeling) and 2) creating models from real-world plants (reconstruction). This paper also discusses the limitations of current methods and possible future directions.