恒温供水装置的自动控制

恒温水是能效标定的重要组成部分.针对目前国内外的研究现状,提出了一种新型的自动恒温供水系统.系统采用制冷半导体和电加热器实现对温度的调节,同时利用积分分离PID抑制系统超调,并应用固态继电器构建开关功放,最后通过ARM LPC2210控制器对系统进行了闭环自动控制.实验结果表明,系统出水温度精确、稳定、可靠;系统在暖水炉、空调等能效标定中将得到广泛应用.     

时间序列分析在轨道交通客流预报中的应用研究

时间序列分析是动态数据分析的重要方法,在多学科领域中得到广泛的研究和运用.研究时间序列分析在城市轨道交通自动售检票系统数据分析中的应用方法,并结合某线路AFC系统的历史沉淀数据,给出两个完整的时间序列分析案例,其分析方法能够为AFC运营管理提供有效决策的手段.     

基于粒子系统和音频系统的烟花模拟

针对模糊物体的模拟方法存在着粒子数量绘制巨大、粒子运动状态复杂、计算耗时、实时性及逼真性不够等问题,提出了一种基于粒子系统和音频系统的烟花模拟方法,以三角形格作为绘制烟花粒子的基本图元,结合纹理映射,色彩融合技术来绘制烟花粒子,在显示列表中生成纹理,再结合FMOD引擎技术,加入音频系统,实验结果表明提出的方法增强了模拟的逼真性.     

Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content

There are two main parameters describing the amount of water in vegetation: the gravimetric water content (GWC) and the equivalent water thickness (EWT). In this study, we investigated the applicability of hyperspectral water-sensitive indices from canopy spectra for estimating canopy EWT (CEWT) and GWC. First, the spectral reflectance’s response to different levels of canopy water content was analysed and a noticeable increase in the slope of the near-infrared (NIR) shoulder of the canopy spectrum was observed. Next, the correlation between the CEWT and various hyperspectral water-sensitive indices was investigated. It was found that all of the indices could retrieve the CEWT of winter wheat well, with the coefficients of determination (R²) all being higher than 0.80. Finally, the retrieval performance of these indices for canopy GWC was evaluated and no significant correlation was observed between canopy GWC and the water-sensitive indices except for the spectral ratio index in the NIR shoulder region (NSRI). These results showed that the traditional water-sensitive vegetation indices are more sensitive to CEWT than to GWC, especially when the LAI is not highly correlated with the GWC, and that the NSRI is a potential vegetation index for use in the retrieval of GWC.     

Structure‐adaptive Shape Editing for Man‐made Objects

One of the challenging problems for shape editing is to adapt shapes with diversified structures for various editing needs. In this paper we introduce a shape editing approach that automatically adapts the structure of a shape being edited with respect to user inputs. Given a category of shapes, our approach first classifies them into groups based on the constituent parts. The group‐sensitive priors, including both inter‐group and intra‐group priors, are then learned through statistical structure analysis and multivariate regression. By using these priors, the inherent characteristics and typical variations of shape structures can be well captured. Based on such group‐sensitive priors, we propose a framework for real‐time shape editing, which adapts the structure of shape to continuous user editing operations. Experimental results show that the proposed approach is capable of both structure‐preserving and structure‐varying shape editing.     

A graph-based approach for assessing storm-induced coastal changes

Hurricanes and tropical storms are severe threats to coastal properties, settlements, and infrastructure. Airborne light detection and ranging (lidar) surveys conducted before and after storm events allow detailed analysis of coastal geomorphologic and sediment volumetric changes and have been proved very useful in the study of coastal changes. Traditionally, most studies use the pixel-based differencing method to quantify the spatial extent and magnitude of coastal changes based on sequential lidar surveys. This research presents a graph theory-based approach and associated software tools for representing and quantifying storm-induced damages to buildings, beaches and sand dunes, coastal vegetation canopy, and infrastructure. Generation of elevation difference grids, construction of local contour trees, and derivation of semantic properties are key components of the new algorithm for change object detection and extraction. An ontology and taxonomy are proposed to classify change objects into different types of coastal damages in terms of their semantic properties. This method has been successfully applied to assess damages of Hurricane Ike to the Bolivar Peninsula on the Texas Gulf Coast based on pre- and post-storm airborne lidar data and colour infrared aerial photographs.     

Experimental Analysis of Variable Collective-pitch Rotor Systems for Multirotor Helicopter Applications

This paper presents an experimental study of variable collective-pitch rotor systems for multirotor helicopter applications. An experimental research facility has been established to conduct this research. The facility enables the high-resolution measurement of forces and torques produced by rotor systems. The power consumption of the rotor system during experimentation can also be recorded. The experimental research facility also allows for the characterisation of the effect of rotor systems on multirotor helicopter performance. It is shown that the variable collective-pitch rotors have a significant performance advantage over fixed-pitch rotors when comparing thrust response, and multirotor helicopter step input response performance. Further, it is observed that variable collective-pitch rotors are more efficient in terms of energy consumption than comparable fixed-pitch rotors under similar operating conditions.     

Output feedback regulation of upper triangular nonlinear systems with uncertain time‐varying delays in states and input

In this paper, an output feedback controller is studied to regulate a class of upper triangular nonlinear systems with uncertain time‐varying delays. The key features of our considered system are that there are uncertain time‐varying delays in both states and input and the high‐order nonlinearity is in a more relaxed form over the previous results. Theoretical analysis and numerical example are presented to show the benefits of our controller. Copyright © 2017 John Wiley & Sons, Ltd.     

Detecting ground control points via convolutional neural network for stereo matching

In this paper, we present a novel approach to detect ground control points (GCPs) for stereo matching problem. First of all, we train a convolutional neural network (CNN) on a large stereo set, and compute the matching confidence of each pixel by using the trained CNN model. Secondly, we present a ground control points selection scheme according to the maximum matching confidence of each pixel. Finally, the selected GCPs are used to refine the matching costs, then we apply the new matching costs to perform optimization with semi-global matching algorithm for improving the final disparity maps. We evaluate our approach on the KITTI 2012 stereo benchmark dataset. Our experiments show that the proposed approach significantly improves the accuracy of disparity maps.