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Flexible Sparse Learning of Feature Subspaces
It is widely observed that the performances of many traditional statistical learning methods degenerate when confronted with high-dimensional data. One promising approach to prevent this downfall is to identify the intrinsic low-dimensional spaces where the true signals embed and to pursue the learning process on these informative feature subspaces. This thesis focuses on the development of flexible sparse learning methods of feature subspaces for classification. Motivated by the success of some existing methods, we aim at learning informative feature subspaces for high-dimensional data of complex nature with better flexibility, sparsity and scalability.
The first part of this thesis is inspired by the success of distance metric learning in casting flexible feature transformations by utilizing local information. We propose a nonlinear sparse metric learning algorithm using a boosting-based nonparametric solution to address metric learning problem for high-dimensional data, named as the sDist algorithm. Leveraged a rank-one decomposition of the symmetric positive semi-definite weight matrix of the Mahalanobis distance metric, we restructure a hard global optimization problem into a forward stage-wise learning of weak learners through a gradient boosting algorithm. In each step, the algorithm progressively learns a sparse rank-one update of the weight matrix by imposing an L-1 regularization. Nonlinear feature mappings are adaptively learned by a hierarchical expansion of interactions integrated within the boosting framework. Meanwhile, an early stopping rule is imposed to control the overall complexity of the learned metric. As a result, without relying on computationally intensive tools, our approach automatically guarantees three desirable properties of the final metric: positive semi-definiteness, low rank and element-wise sparsity. Numerical experiments show that our learning model compares favorably with the state-of-the-art methods in the current literature of metric learning.
The second problem arises from the observation of high instability and feature selection bias when applying online methods to highly sparse data of large dimensionality for sparse learning problem. Due to the heterogeneity in feature sparsity, existing truncation-based methods incur slow convergence and high variance. To mitigate this problem, we introduce a stabilized truncated stochastic gradient descent algorithm. We employ a soft-thresholding scheme on the weight vector where the imposed shrinkage is adaptive to the amount of information available in each feature. The variability in the resulted sparse weight vector is further controlled by stability selection integrated with the informative truncation. To facilitate better convergence, we adopt an annealing strategy on the truncation rate. We show that, when the true parameter space is of low dimension, the stabilization with annealing strategy helps to achieve lower regret bound in expectation
Study on Gravity Independence of Compressor Performance for Space-borne Vapor Compression Heat Pump
Aerospace technology plays an important role in the modern scientific research and engineering applications. Most energy consumed by equipment inside the spacecraft is converted into waste heat. Current thermal control and management technology research for international aerospace has made considerable progress. Vapor compression heat pump is an important aerospace thermal control means to lunar probe program and deep space exploration. Compressors are the most important components in vapor compression heat pump systems. How to improve the performance of aerospace refrigeration compressors is a key technology. Micro-gravity environment has a great impact on system performance. For the study of the performance of compressor and heat pump system under micro-gravity environment, a test bed for the gravity independence of space-borne vapor compression heat pump system is designed and built. The test system of gravity independence mainly consists of three parts: refrigerant cycle system, water cycle system and data acquisition system. The paper tests the gravity independence of rotary compressor performance, and evaluates the performance of compressor and heat pump system in micro-gravity environment. The results show that when the control voltage of compressor speed is 2 V, the maximum tilt angle is about 60° under the condition of low speed operation. The inclination angle of compressor has little effect on evaporation and condensation pressure. Evaporation and condensation pressure is 4.3 bar and 12 bar, respectively, and the pressure ratio is steady. Energy efficiency ratio and coefficient of performance are about 3.3 and 4.2, respectively. Under the condition of the maximum tilt angle of 60°, the system could run in the compressor speed control range of 1-3.5 V. There’s a balance point when the control voltage is 1-2 V, and the maximum of energy efficiency ratio and coefficient of performance are 3.1 and 4.0, respectively. When the voltage is 2.5-3.5 V, the compressor suction and exhaust temperature is difficult to stabilize, showing a periodical rise and fall. If the voltage is more than 3.5 V, the compressor stops to run for self-protection. Under the condition that the compressor works in full, the maximum tilt angle is 20°. When the tilt angle is less than 20°, the compressor is under normal operation; when the tilt angle is more than 20°, the compressor can not continue to maintain the operation, resulting in downtime. When the tilt angel is less than 20°, the performance parameters do not change with the increase of the inclination angle. Evaporation and condensation pressure stabilize at 4.2 and 13 bar, respectively. Energy efficiency ratio and coefficient of performance are about 2.7 and 3.6, respectively. Under the condition of the tilt angle of 15°, as the control voltage increases from 1.5 to 5 V, the energy efficiency ratio decreases from 3.5 to 2.7, and the coefficient of performance drops from 4.2 to 3.6
Optimized design for micro Wankel compressor used in space-borne vapor compression heat pump
For aerospace applications, vapor compression heat pump can be used as thermal control system to collect the heat from electronic devices and transport heat to radiator by which heat can be rejected to space. Heat pumps can be used in two cases. The first consists of raising the temperature of heat energy so that the amount of radiator surface required is reduced. The second involves situations where heat cannot be directly rejected by radiators, because the heat sink temperature is higher than that of the heat source. However, the key problem is to make a small and lightweight refrigeration compressor. In order to meet the need for aerospace applications, an innovative miniature hermetic Wankel compressor was proposed and designed in this paper. We fabricated the components such as shell, cylinder, rotor, piston gear, stationary gear, rotor and stator of motor. A compressor prototype was manufactured by integrating these components. The experimental system was built to test the performances of compressor prototype. The effects of condensing temperature, compressor rotation speed and refrigerant charge on the compressor performance were obtained. The influences of tilt angle on the performances of compressor were also investigated. The results indicated that the prototype have good performance, reliability and micro-gravity adaptability
Edge effect removal in Fourier ptychographic microscopy via perfect Fourier transformation (PFT)
Edge effect may degrade the imaging precision and is caused by the aperiodic
image extension of fast Fourier transform (FFT). In this letter, a perfect
Fourier transform algorithm termed PFT was reported to remove the artifacts
with comparable efficiency to FFT. Although we demonstrated the performance of
PFT in Fourier ptychographic microscopy (FPM) only, it can be expanded in any
occasion where the conventional FFT is used.Comment: 4 pages, 6 figure
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