POWER MANAGEMENT IN THE CLUSTER SYSTEM

With growing cost of electricity, the power management of server clusters has become an important problem. However, most previous researchers have only addressed the challenge in traditional homogeneous environments. Considering the increasing popularity of heterogeneous and virtualized systems, this thesis develops a series of efficient algorithms respectively for power management of heterogeneous soft real-time clusters and a virtualized cluster system. It is built on simple but effective mathematical models. When deployed to a new platform, the software incurs low configuration cost because no extensive performance measurements and profiling are required. Built upon optimization, queuing theory and control theory techniques, our approach achieves the design goal, where QoS is provided to a larger number of requests with a smaller amount of power consumption. To strive for efficiency, a threshold based approach is adopted in the first part of the thesis. Then we systematically study this approach and its design decisions. To deploy our mechanisms on the virtualized clusters, we extend the work by developing a novel power-efficient workload distribution algorithm. Adviser: Ying L

Evaluation of methods to decrease the discharge temperature of R32 scroll compressor

Recently, R32 has been considered as an important alternative in application of small to middle capacity air conditioner by many countries due to its advantages such as low global warming potential (GWP), favorable thermal properties, less refrigerant charge and low cost. However, the much increased discharge temperature of R32 compressor, as compared with the R22, becomes the main barrier affecting the wide and quick alternation. Refrigerant injection has proven to be effective in decreasing discharge temperature. In this work, three kinds of refrigerant injection technology used to decrease the discharge temperature of R32 scroll compressor are discussed, namely, two-phase suction, liquid injection and two-phase/gas injection. The detailed scroll compressor model proposed in previous work is modified and validated by experimental data of R32 scroll compressor. The potentials in decreasing discharge temperature of the three methods are investigated. The detailed performance comparisons are presented. The results indicate that the two-phase/gas injection achieves the best performance with the enhancement of cooling capacity by 14.2% and increase in COP by 8.1%

Non-line-of-sight imaging with arbitrary illumination and detection pattern

Non-line-of-sight (NLOS) imaging aims at reconstructing targets obscured from the direct line of sight. Existing NLOS imaging algorithms require dense measurements at rectangular grid points in a large area of the relay surface, which severely hinders their availability to variable relay scenarios in practical applications such as robotic vision, autonomous driving, rescue operations and remote sensing. In this work, we propose a Bayesian framework for NLOS imaging with no specific requirements on the spatial pattern of illumination and detection points. By introducing virtual confocal signals, we design a confocal complemented signal-object collaborative regularization (CC-SOCR) algorithm for high quality reconstructions. Our approach is capable of reconstructing both albedo and surface normal of the hidden objects with fine details under the most general relay setting. Moreover, with a regular relay surface, coarse rather than dense measurements are enough for our approach such that the acquisition time can be reduced significantly. As demonstrated in multiple experiments, the new framework substantially enhances the applicability of NLOS imaging.Comment: main article: 32 pages with 8 figures; supplementary information: 49 pages with 26 figure

Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization

The non-line-of-sight imaging technique aims to reconstruct targets from multiply reflected light. For most existing methods, dense points on the relay surface are raster scanned to obtain high-quality reconstructions, which requires a long acquisition time. In this work, we propose a signal-surface collaborative regularization (SSCR) framework that provides noise-robust reconstructions with a minimal number of measurements. Using Bayesian inference, we design joint regularizations of the estimated signal, the 3D voxel-based representation of the objects, and the 2D surface-based description of the targets. To our best knowledge, this is the first work that combines regularizations in mixed dimensions for hidden targets. Experiments on synthetic and experimental datasets illustrated the efficiency and robustness of the proposed method under both confocal and non-confocal settings. We report the reconstruction of the hidden targets with complex geometric structures with only $5 \times 5$ confocal measurements from public datasets, indicating an acceleration of the conventional measurement process by a factor of 10000. Besides, the proposed method enjoys low time and memory complexities with sparse measurements. Our approach has great potential in real-time non-line-of-sight imaging applications such as rescue operations and autonomous driving.Comment: main article: 10 pages, 7 figures supplement: 11 pages, 24 figure

Temporal Aggregation and Risk-Return Relation

The function form of a linear intertemporal relation between risk and return is suggested by Merton’s (1973) analytical work for instantaneous returns, whereas empirical studies have examined the nature of this relation using temporally aggregated data, i.e., daily, monthly, quarterly, or even yearly returns. Our paper carefully examines the temporal aggregation effect on the validity of the linear specification of the risk-return relation at discrete horizons, and on its implications on the reliablility of the resulting inference about the risk-return relation based on different observation intervals. Surprisingly, we show that, based on the standard Heston’s (1993) dynamics, the linear relation between risk and return will not be distorted by the temporal aggregation at all. Neither will the sign of this relation be flipped by the temporal aggregation, even at the yearly horizon. This finding excludes the temporal aggregation issue as a potential source for the conflicting empirical evidence about the risk-return relation in the earlier studies. ∗We would like to thank Michael Brandt for helpful comments

Radio Sources Segmentation and Classification with Deep Learning

Modern large radio continuum surveys have high sensitivity and resolution, and can resolve previously undetected extended and diffuse emissions, which brings great challenges for the detection and morphological classification of extended sources. We present HeTu-v2, a deep learning-based source detector that uses the combined networks of Mask Region-based Convolutional Neural Networks (Mask R-CNN) and a Transformer block to achieve high-quality radio sources segmentation and classification. The sources are classified into 5 categories: Compact or point-like sources (CS), Fanaroff-Riley Type I (FRI), Fanaroff-Riley Type II (FRII), Head-Tail (HT), and Core-Jet (CJ) sources. HeTu-v2 has been trained and validated with the data from the Faint Images of the Radio Sky at Twenty-one centimeters (FIRST). We found that HeTu-v2 has a high accuracy with a mean average precision ($AP_{\rm @50:5:95}$) of 77.8%, which is 15.6 points and 11.3 points higher than that of HeTu-v1 and the original Mask R-CNN respectively. We produced a FIRST morphological catalog (FIRST-HeTu) using HeTu-v2, which contains 835,435 sources and achieves 98.6% of completeness and up to 98.5% of accuracy compared to the latest 2014 data release of the FIRST survey. HeTu-v2 could also be employed for other astronomical tasks like building sky models, associating radio components, and classifying radio galaxies

Impacts of CaO solid particles in carbon dioxide absorption process from ship emission with NaOH solution

CO2 emitted from ship exhaust is one of the major sources of atmospheric pollution. In order to reduce ship CO2 emissions, this paper comes up with the idea of recovering CO2 from ship exhaust by NaOH solution and improves the absorption rate by adding CaO solid particles. The effect mechanism of CaO solid particles on CO2 absorption efficiency is analyzed in detail, and the mathematical model is deduced and the CaO enhancement factor is calculated through experiments. Experiment result demonstrates that the effect of CaO solid particles on the absorption of CO2 in alkali solution is significant. The absorption rate of pure CO2 gas, the simulated ship exhaust gas and 6135AZG marine diesel engine emission can be increased by 10%, 15.85% and 10.30%, respectively. So it can be seen that CaO solid particles play an important role in improving the absorption efficiency of ship CO2 emission