3D model retrieval using principal plane analysis and dynamic programming

Three dimensional models play an important role in many applications; the problem is how to select the appropriate models from a 3D database rapidly and accurately. In recent years, a variety of shape representations, statistical methods, and geometric algorithms have been proposed for matching 3D shapes or models. In this paper, we propose a 3D shape representation scheme based on a combination of principal plane analysis and dynamic programming. The proposed 3D shape representation scheme consists of three steps. First, a 3D model is transformed into a 2D image by projecting the vertices of the model onto its principal plane. Second, the convex hall of the 2D shape of the model is further segmented into multiple disjoint triangles using dynamic programming. Finally, for each triangle, a projection score histogram and moments are extracted as the feature vectors for similarity searching. Experimental results showed the robustness of the proposed scheme, which resists translation, rotation, scaling, noise, and destructive attacks. The proposed 3D model retrieval method performs fairly well in retrieving models having similar characteristics from a database of 3D models.     

Robust pole clustering in a good ride quality region of aircraft for matrices with structured uncertainties

This paper presents a general analysis of robust pole clustering in a good ride quality region (GRQR) of aircraft for matrices with structured uncertainties. This region is an intersection of a ring and a horizontal strip, located in the left half-plane, which is a specific non-Ω-transformable region providing good ride quality of aircraft. The paper applies the Rayleigh principle along the norm theory to analyze robust pole clustering within this region since the generalized Lyapunov theory is not valid for non-Ω-transformable regions. Concerned uncertainties are structured/parametric uncertainties, including interval matrices. The results are useful for robust control analysis and design, especially, of robust good ride quality of aircraft, shuttles, vehicles and space station, as well as some industrial systems. An example of the F-16 dynamics for which GRQR is suitable is included to illustrate the results.     

Robot control and parameter estimation with only joint position measurements

Most adaptive control schemes for rigid robots assume velocities measurements to be available. Although it is possible to measure velocities by using tachometers, this increases costs and the signals delivered may be contaminated with noise. Since the use of encoders allows to read joint position pretty accurately, it is desirable to estimate joint velocities through an observer. This paper presents an adaptive scheme designed in conjunction with a linear observer. Boundedness of the estimated parameters and uniform ultimate boundedness for the tracking and observation errors are guaranteed. Experimental results are included to support the developed theory.     

基于SWMM模型模拟的海绵城市建设效果评估——以北京市房山区为例

为定量评估房山区现状海绵建设情况,支撑2020年海绵城市达标考核工作,采用资料收集结合现场调研的方式获取各类海绵建设项目信息并进行校核,借助ArcGIS软件将海绵设施数字化,并以排水分区为单元完成落图.基于暴雨径流管理模型SWMM软件构建全区数值模型,将调研的海绵设施概化到模型中开展模拟评估.结果表明:截至2020年底,房山全区有效海绵项目418个,主要分布在拱辰及西潞街道、长阳镇、良乡镇、窦店镇、阎村镇、城关街道及燕山地区.模拟结果表明城市建成区达标面积为13.73 km2,达标比例20.46％.本项目研究方法可为全市其他区的海绵现状评估提供参考.     

基于GRACE的京津冀地下水储量变化

基于2003—2019年GRACE陆地水储量变化数据和全球陆地水文模型（global land data assimilation system,GLDAS）数据反演京津冀地下水储量变化,运用时空分析方法对地下水等效水高变化进行时空演变特征分析。在空间变化上,整个京津冀地下水等效水高变化速率约为-51.77 mm/a,其中：北京市变化速率最低,约为-38.15 mm/a;天津市变化速率最高,约为-62.85 mm/a;河北省变化速率与区域平均变化水平相当,约为-52.42 mm/a。基于Sen Slope和Mann-Kendall非参数检验法分析得出西部、西南部和中部地区地下水等效水高下降[JP]趋势最明显,东北部下降趋势最小。在时间变化上,地下水等效水高变化具有一定的季节性规律：夏季变化速率最大,约为-75.99 mm/a;冬季变化速率最小,约为-37.24 mm/a;春秋两季的变化速率大致相同,分别为-52.34 mm/a和-48.21 mm/a。在影响因素分析中,人类活动是引起京津冀地区地下水等效水高变化的主要因素。研究结果可为科学掌握京津冀地下水储量时空变化规律提供数据支撑。     

Blind estimation of the PN sequence in lower SNR DS-SS signals with residual carrier

This paper presents a method of singular value decomposition (SVD) plus digital phase lock loop (DPLL) to solve the difficult problem of blind pseudo-noise (PN) sequence estimation in low signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS, DS) signals with residual carrier. Of course, the method needs to know the parameters of DS signal, such as the period and code rate of PN sequence. Firstly, the received signal is sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. Then, an autocorrelation matrix is computed and accumulated by the signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of this autocorrelation matrix. Furthermore, a DPLL is used to deal with the estimated PN sequence with residual carrier, it estimates and tracks the residual carrier, removes the residual carrier in the end. Theory analysis and computer simulation results show that this method can effectively realize the PN sequence estimation from the input DS signals with residual carrier in lower SNR.