Study on Loading Ability of Straw Bale

The loading ability of straw bale was tested by Electronical Testing Machine. The linear regression equations were proposed between failure density and loading ability, and failure density and compressing energy. Based on an exponent model, the testing coefficients of straw bale were estimated using Levenberg-Marquardt Method. The results of test showed that the relation between failure density and loading ability and compressing energy was linear in the phase of high density. The loading ability of straw bale could meet the building bill.

Non-Darcy flow seepage characteristics of saturated broken rocks under compression with lateral constraint

Using an MTS816.03 test system and self-designed seepage apparatus, seepage tests of saturated broken rocks were conducted, and the influence of lithology, axial stress, grain size distribution and loading rate on seepage characteristics was analyzed. The results show that： （1） Under the same axial stress （12 MPa）, the permeability of different lithologic samples increases in the order： gangue 〈 mudstone 〈 sandstone 〈 limestone. The permeability of gangue is 3 magnitudes lower than that of limestone. The absolute value of the non-Darcy coefficient β increases in the order： limestone 〈 sandstone 〈 mudstone 〈 gangue. The non-Darcy coefficient β of limestone, which is positive, is 5 magnitudes lower than that of gangue. （2） With increasing axial stress, the permeability of saturated broken sandstone decreases, and the absolute value of the non-Darcy coefficient β increases. After the axial stress exceeds 12 MPa, the curves of permeability and non-Darcy coefficient β all tend to be stable. （3） With increasing Talbol power exponent, the permeability increases, and the absolute value of the non-Darcy coefficient β decreases. （4） With increasing loading, the permeability increases, and the absolute value of the non-Darcy coefficient β decreases. When the loading rate is 0.5 kN/s, the non-Darcy coefficient β is positive.

Efficient Activation Method of Hardware Trojan Based on Greedy Algorithm

To generate test vector sets that can efficiently activate hardware Trojans and improve probability of the hardware Trojan activation,an efficient hardware Trojan activation method is proposed based on greedy algorithm for combinatorial hardware Trojans. Based on the greedy algorithm and the recursive construction method in the combination test,the method formulates appropriate and useful greedy strategy and generates test vector sets with different combinatorial correlation coefficients to activate hardware Trojans in target circuits. The experiment was carried out based on advanced encryption standard（ AES） hardware encryption circuit,different combinatorial hardware Trojans were implanted in AES as target circuits,the experiment of detecting hardware Trojans in target circuits was performed by applying the proposed method and different combinatorial hardware Trojans in target circuits were activated successfully many times in the experiment. The experimental results showthat the test vector sets generated using the proposed method could effectively activate combinatorial hardware Trojans,improve the probability of the hardware Trojan being activated,and also be applied to practice.

EFFECT OF FRICTIONAL FORCE ON GEARING CONTACT FATIGUE STRENGTH

The model for computing frictional coefficient between two teeth faces at the state of mixed elastohydrodynamic lubrication is established. And then more than 80 sets of numerical calculations and six sets of disc fatigue tests are completed. The results show that when the film thickness ratio λ 〈1.6, frictional coefficient μ is drastically decreased as λ. rises; Thereafter it decreases smoothly until λ=4.5. When λ〉4.5, however, it goes up again with λ, which indicates that the excessive film thickness ratio will deteriorate gearing contact fatigue strength. At the end, the formulae for determining the frictional coefficients are formed.

Method study and uncertainty analysis of calibration coefficients validation based on the Inner Mongolia test site

Calibration coefficients validation is the foundation for ascertaining the sensor performance and carrying out the quantitative application.Based on the analysis of the differences between the calibration and validation,two calibration coefficients validation methods were introduced in this paper.Taking the HJ-1A satellite CCD1 camera as an example,the uncertainties of calibration coefficients validation were analyzed.The calibration coefficients validation errors were simulated based on the measured data at an Inner Mongolia test site.The result showed that in the large view angle,the ground directional reflectance variation and the atmospheric path variation were the main error sources in calibration coefficients validation.The ground directional reflectance correction and atmospheric observation angle normalization should be carried out to improve the validation accuracy of calibration coefficients.

Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010

To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index（NDVI） time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, Theil–Sen median trend analysis and the Mann–Kendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 2000–2005 than in 2006–2010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 20–30% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive： regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.