Holography Measurement for Crossed-Dragone Type Telescope & its Application to the Fred Young Submm Telescope

Microwave Holography is an accurate and efficient method for measuring the surface shape of large reflector antennas. The method is based on the Fourier transform relationship between the antenna's far-field diffraction beam pattern and its aperture field. Measuring the antenna's far-field beam both in amplitude and phase can deduce the aperture field distribution. The phase deviations of the aperture field are directly related to the antenna's surface shape. This technique has become a well-established method for surface metrology of large radio telescopes because of its high efficiency and measurement accuracy. However, employing the traditional holography cannot identify the surface deformity in a 'two-reflector' antenna system. This thesis investigates a new multi-map holography metrology to overcome this limitation. The new method is developed to align the Fred Young Sub-millimeter telescope (FYST), a coma-corrected Crossed-Dragone antenna with two 6-m off-axis reflectors. The surfaces of the two reflectors must be aligned to be better than 10.7um. The multi-map holography identifies the surface errors between the two reflectors by taking five holographic beam measurements by placing the receiver at well-separated points in the focal plane. The parallactic shift of the surface errors allows assigning them to either one of the two mirrors. A new data processing technique is developed using an inference technique to simultaneously analyze the five beams and convert them to two surface error maps. Extensive numerical simulations have been carried out to check the feasibility, measurement accuracy, and optimum set-up of the new holographic system by modeling the systematic errors in the system, such as random instrument noise and fluctuation of performance of the instruments. These indicate that a measurement accuracy of ~2um is achievable. The critical part of the data processing technique of the 'Multi-map' holography is to develop a fast and accurate beam simulation algorithm. The conventional physical optics method is very time-consuming for analyzing the FYST antenna. A new 'two-step' Kirchhoff-Fresnel diffraction method is developed, which, compared to the conventional physical optics analysis, can reduce the computational time by four orders of magnitude without noticeable accuracy degradation. The new multi-map holography and its data processing technique are implemented to measure the reflector errors for a 0.4-m diameter Crossed-Dragone antenna in the laboratory. The experiments prove that the errors on the two reflectors can be discriminated and accurately measured with a statistic error lower than 1um. The holographic measurements and reflector corrections also indicate that the large spatial errors existing on the two reflectors also can be measured

Numerical investigation of airborne contaminant transport under different vortex structures in the aircraft cabin.

Airborne contaminants such as pathogens, odors and CO2 released from an individual passenger could spread via air flow in an aircraft cabin and make other passengers unhealthy and uncomfortable. In this study, we introduced the airflow vortex structure to analyze how airflow patterns affected contaminant transport in an aircraft cabin. Experimental data regarding airflow patterns were used to validate a computational fluid dynamics (CFD) model. Using the validated CFD model, we investigated the effects of the airflow vortex structure on contaminant transmission based on quantitative analysis. It was found that the contaminant source located in a vorticity-dominated region was more likely to be "locked" in the vortex, resulting in higher 62% higher average concentration and 14% longer residual time than that when the source was on a deformation dominated location. The contaminant concentrations also differed between the front and rear parts of the cabin because of different airflow structures. Contaminant released close to the heated manikin face was likely to be transported backward according to its distribution mean position. Based on these results, the air flow patterns inside aircraft cabins can potentially be improved to better control the spread of airborne contaminant

Transitions and Conflicts: Reexamining Impacts of Migration on Young Women’s Status and Gender Practice in Rural Shanxi

This article explores impacts of migration on young women’s status and gender practice in rural northern China. Based on ethnographic fieldwork in a village in Shanxi Province, it suggests that rural-urban migration has served partially to reconstruct the traditional gender-based roles and norms in migration families. This reconstructive force arises mainly from the changes of the patrilocal residence pattern and rural women’s acquisition of subjectivity during the course of migration. However, after migrant women return to their home villages, they usually reassume their roles as care providers and homemakers, which is vividly expressed by a phrase referring to one’s wife as ‘the person inside my home’ (wo jiali de). Meanwhile, although migrant women’s capacity and confidence have greatly increased consequent upon working out of the countryside, their participation in village governance and in the public sphere has been decreasing. Further examination suggests that the reinforcement of gender inequality and the transformation of gender relations result from the continuous interplay of local power relations, market dominance, and unchallenged patrilocal institutions. Through adopting a life course perspective, it challenges too strict a differentiation between migrant and left behind women in existing literature

Supervised Sparsity Preserving Projections for Face Recognition

Recently feature extraction methods have commonly been used as a principled approach to understand the intrinsic structure hidden in high-dimensional data. In this paper, a novel supervised learning method, called Supervised Sparsity Preserving Projections (SSPP), is proposed. SSPP attempts to preserve the sparse representation structure of the data when identifying an efficient discriminant subspace. First, SSPP creates a concatenated dictionary by class-wise PCA decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least squares method. Second, by maximizing the ratio of non-local scatter to local scatter, a Laplacian discriminant function is defined to characterize the separability of the samples in the different sub-manifolds. Then, to achieve improved recognition results, SSPP integrates the learned sparse representation structure as a regular term into the Laplacian discriminant function. Finally, the proposed method is converted into a generalized eigenvalue problem. The extensive and promising experimental results on several popular face databases validate the feasibility and effectiveness of the proposed approach

A Chattering Free Discrete-Time Global Sliding Mode Controller for Optoelectronic Tracking System

Aiming at the uncertainties including parameter variations and external disturbances in optoelectronic tracking system, a discrete-time global sliding mode controller (DGSMC) is proposed. By the design of nonlinear switching function, the initial state of control system is set on the switching surface. An adaptive discrete-time reaching law is introduced to suppress the high-frequency chattering at control input, and a linear extrapolation method is employed to estimate the unknown uncertainties and commands. The global reachability for sliding mode and the chattering-free property are proven by means of mathematical derivation. Numerical simulation presents that the proposed DGSMC scheme not only ensures strong robustness against system uncertainties and small tracking error, but also suppresses the high-frequency chattering at control input effectively, compared with the SMC scheme using conventional discrete-time reaching law

Reduced Order Modeling of Diffusively Coupled Network Systems:An Optimal Edge Weighting Approach

This article studies reduced-order modeling of dynamic networks with strongly connected topology. Given a graph clustering of an original large-scale network, we construct a quotient graph with less number of vertices, where the edge weights are parameters to be determined. The model of a reduced network is thereby obtained with parameterized system matrices, and then, an edge weighting procedure is devised, aiming to select an optimal set of edge weights to minimize the approximation error between the original and the reduced-order network models in terms of \mathcal {H}-{2}-norm. The effectiveness of the proposed method is illustrated by a numerical example.</p

Spatio-Temporal Dynamics of Global Potential Vegetation Distributions Simulated by CSCS Approach

The study of Potential Natural Vegetation (PNV) has been proposed as a way to examine the impact of changes in climate on the distribution of vegetation. This study analyzes the influence of climate change in the potential vegetation distribution at global scale, using the Comprehensive Sequential Classification System (CSCS) approach to explore the changes of area, shift distance and direction for each broad vegetation category

GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation

We present a learning-based method, namely GeoUDF,to tackle the long-standing and challenging problem of reconstructing a discrete surface from a sparse point cloud.To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation that explicitly formulates the unsigned distance of a query point as the learnable affine averaging of its distances to the tangent planes of neighboring points on the surface. Besides,we model the local geometric structure of the input point clouds by explicitly learning a quadratic polynomial for each point. This not only facilitates upsampling the input sparse point cloud but also naturally induces unoriented normal, which further augments UDF estimation. Finally, to extract triangle meshes from the predicted UDF we propose a customized edge-based marching cube module. We conduct extensive experiments and ablation studies to demonstrate the significant advantages of our method over state-of-the-art methods in terms of reconstruction accuracy, efficiency, and generality. The source code is publicly available at https://github.com/rsy6318/GeoUDF