Improving GPGPU Energy-Efficiency through Concurrent Kernel Execution and DVFS

Master'sMASTER OF SCIENC

Layout optimization for multi-bi-modulus materials system under multiple load cases

Financial support from the National Natural Science Foundation of China (Grant No. 51179164) and the Australian Research Council (Grant No. DP140103137) is acknowledged

Fast micro-differential evolution for topological active net optimization

This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a ``best improvement local search'' (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm

A method for measuring rotation of a thermal carbon nanomotor using centrifugal effect

A thermal nanomotor is relatively easy to fabricate and regulate as it contains just a few or even no accessory devices. Since the double-wall carbon nanotube (CNT)-based rotary nanomotor was established in a thermostat, assessment of the rotation of the rotor (inner tube) in the stator (outer tube) of the nanomotor has been critical, but remains challenging due to two factors: the small size of the rotor (only a few nanometers) and the high rotational frequency (»1 GHz). To measure the rotation of the nanomotor, in the present study, a probe test method is proposed. Briefly, the rotor is connected to an end-tube (CNT) through a graphene (GN) nanoribbon. As the CNT-probe is on the trajectory of the end-tube which rotates with the rotor, it will collide with the end-tube. The sharp fluctuation indicating the probe tip deflection can be observed and recorded. As a curly GN by hydrogenation is adopted for connecting the rotor and the end-tube, collision between the end-tube and the probe tip occurs only when the centrifugal force is higher than a threshold which can be considered as the rotational frequency of the rotor being measured by the present method.The authors are grateful for financial support from the National Natural-Science-Foundation of China (Grant No. 11372100) and the Australian Research Council (Grant No. DP140103137)

A new station-enabled multi-sensor integrated index for drought monitoring

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available