布尔算子Fuzzy逻辑公式的λ－恒假性

在布尔算子Ｆｕｚｚｙ逻辑中证明了任意公式Ｇ是λ－恒假的，当且仅当Ｇ所对应的子句集Ｓ是λ－恒假的。进而有子句集Ｓ是λ－恒假的，当且仅当Ｓ在其所有Ｈｅｒｂｒａｎｄ解释下是λ－恒假的，从而保证了在布尔算子Ｆｕｚｚｙ逻辑中施行归结的正确性．

Accurate estimation of soil shear strength parameters

The cost and safety of geotechnical engineering are highly depending on the accuracy of soil shear strength parameters.There are three methods often used to estimate soil shear strength parameters,i.e.,moment method,3-sigma rule and linear regression method.In this study,the accuracy of these three methods is compared.Traditional linear regression method(LRM)can only offer the mean of shear strength parameters.Some engineers misuse the standard error of shear strength indexes as the standard deviations.Such misuse may highly underestimate the uncertainty and induce high risk to the geotechnical design.A modified LRM is proposed to determine both the mean and variance of shear strength parameters.The moment method,three-sigma rule and LRM are used to analyze the tri-axial test data in Xiaolangdi Hydraulic Project and three numerical shear strength tests.The results demonstrate that:1)The modified LRM can offer the most accurate estimation to shear strength parameters;2)A dimensionless formula is much preferred in LRM rather than a dimensional formula.The stress ratio formula is much better than stress relation in the shear strength parameter analysis.The proposed method is applicable to shear strength parameter analysis for tri-axial test data,direct shear test and the un-drained shear strength test of stratified clay.

Feasibility investigation on deep ocean compact autonomous Raman spectrometer developed for in-situ detection of acid radical ions

A newly developed Deep Ocean Compact Autonomous Raman Spectrometer （DOCARS） system is introduced and used for in-situ detection of acid radical ions in this paper. To evaluate the feasibility and capability of DOCARS for quantitative analysis of the acid radical ions in the deep ocean, extensive investigations have been carried out both in laboratory and sea trials during the development phase. In the laboratory investigations, Raman spectra of the prepared samples （acid radical ions solutions） were obtained, and analyzed using the method of internal standard normalization in data processing. The Raman signal of acid radical ions was normalized by that of water molecules. The calibration curve showed that the normalized Raman signal intensity of SO4^2-, NO3^-, and HCO^-3 increases linearly as the concentration rises with correlation coefficient R^2 of 0.99, 0.99, and 0.98 respectively. The linear function obtained from the calibration curve was then used for the analysis of the spectra ,data acquired in the sea trial under a simulating chemical field in the deep-sea environment. It was found that the detected concentration of NO3 according to the linear function can reflect the concentration changes of NO~ after the sample was released, and the detection accuracy of the DOCARS system for SO^2-_4 is 8%. All the results showed that the DOCARS system has great potential in quantitative detection of acid radical ions under the deep-sea environment, while the sensitivity of the DOCARS system is expected to be improved.

Correlations between direct and indirect strength test methods

The difficulties associated with performing direct compression strength tests on rocks lead to the development of indirect test methods for the rock strength assessment. Indirect test methods are simple, more economical, less time-consuming, and easily adaptable to the field. The main aim of this study was to derive correlations between direct and indirect test methods for basalt and rhyolite rock types from Carlin trend deposits in Nevada. In the destructive methods, point load index, block punch index, and splitting tensile strength tests are performed. In the non-destructive methods, Schmidt hammer and ultrasonic pulse velocity tests are performed. Correlations between the direct and indirect compression strength tests are developed using linear and nonlinear regression analysis methods. The results show that the splitting tensile strength has the best correlation with the uniaxial compression strength.Furthermore, the Poisson's ratio has no correlation with any of the direct and indirect test results.

Dry sliding wear behavior of stir cast AA6061-T6/AlN_p composite

The dry sliding wear behavior of AA6061 matrix composite reinforced with aluminium nitride particles（AlN） produced by stir casting process was investigated. A regression model was developed to predict the wear rate of the prepared composite. A four-factor, five-level central composite rotatable design matrix was used to minimize the number of experimental runs. The factors considered in this study were sliding velocity, sliding distance, normal load and mass fraction of AlN reinforcement in the matrix. The developed regression model was validated by statistical software SYSTAT 12 and statistical tools such as analysis of variance（ANOVA） and student＇s t test. It was found that the developed regression model could be effectively used to predict the wear rate at 95% confidence level. The influence of these factors on wear rate of AA6061/AlNp composite was analyzed using the developed regression model and predicted trends were discussed with the aid of worn surface morphologies. The regression model indicated that the wear rate of cast AA6061/AlNp composite decreased with an increase in the mass fraction of AlN and increased with an increase of the sliding velocity, sliding distance and normal load acting on the composite specimen.

Regression analysis of major parameters affecting the intensity of coal and gas outbursts in laboratory

Estimating the intensity of outbursts of coal and gas is important as the intensity and frequency of outbursts of coal and gas tend to increase in deep mining. Fully understanding the major factors contributing to coal and gas outbursts is significant in the evaluation of the intensity of the outburst. In this paper, we discuss the correlation between these major factors and the intensity of the outburst using Analysis of Variance(ANOVA) and Contingency Table Analysis(CTA). Regression analysis is used to evaluate the impact of these major factors on the intensity of outbursts based on physical experiments. Based on the evaluation, two simple models in terms of multiple linear and nonlinear regression were constructed for the prediction of the intensity of the outburst. The results show that the gas pressure and initial moisture in the coal mass could be the most significant factors compared to the weakest factor-porosity. The P values from Fisher's exact test in CTA are: moisture(0.019), geostress(0.290), porosity(0.650), and gas pressure(0.031). P values from ANOVA are moisture(0.094), geostress(0.077), porosity(0.420), and gas pressure(0.051). Furthermore, the multiple nonlinear regression model(RMSE: 3.870) is more accurate than the linear regression model(RMSE: 4.091).

Moderating variables in the relationship between mental toughness and performance in basketball

Purpose： This study explored the relationship between mental toughness and college basketball performance, specifically examining possible moderating variables （gender and starting status）. Methods： Male and female （n = 197） college basketball players completed the Psychological Performance Inventory-Alternative （PPI-A）, a measure of characteristics and skills consistent with mental toughness, and the PERK an objective measure of basketball performance. Results： Findings suggest that basketball performance can be partially predicted by mental toughness and starting status. Males reported greater mental toughness than females. Starters and nonstarters did not differ in mental toughness. Moderated hierarchical regression analysis indicated that mental toughness was related to performance for male players as both a main effect and interaction with starter status. For female players, in contrast, starter status was the only significant predictor of performance. Practitioners are encouraged to foster the psychological skills associated with mental toughness in females and non-starters in basketball. Conclusion： Discussion of the PPI-A as a measure of mental toughness and suggestions for its improvement are explored. A need exists for additional research on mental toughness and objective performance, as performance enhancement is a major impetus for research on mental toughness. Copyright @ 2012, Shanghai University of Sport. Production and hosting by Elsevier B.V. All rights reserved.

Handover improvement based on cost function and user fairness for cellular relay enhanced LTE networks

In order to optimize handover target selection and achieve better handover performance in cellular relay networks under the long term evolution （LTE） system, a novel handover measurement and decision scheme based on cost function is proposed in the paper. The relay-enhanced network provides user equipment （UE） multiple handover choices from neighbor base stations or relays. This may result in different overhead, resource utility and traffic load which attaches importance to proper handover schemes. Compared with traditional handover measurement and decision schemes, the proposed scheme adopts reference signal receiving quality （RSRQ） in the measurement stage and uses a modified signal cost function considering signal overhead in different handover types and expected long-term throughput of UE. Besides, UE fairness and sector resource utilization are taken into account as well. Theory analysis and simulation results prove that the proposed scheme can enhance cell throughput, decrease handover delay and signal overhead, and improve UE fairness.

The construction of normalized enhanced water index and the extraction of supra-glacial water based on WorldView-2 imagery

As an important part of the mass balance of the Ice Sheet,Supra-glacial Water not only reflects the diversity of polar environmental changes,but also plays an important role in the study of global climate and environmental changes.In this paper,we chose northern Greenland as the research area,and constructed a Normalized Enhanced Water Index(NEWI)based on the high-precision WorldView-2 images of different phases during the ablation period in northern Greenland,followed by a statistical analysis on the spectral characteristics of the images were for the typical features in the study area.Then the fuzzy areas with similar gray values of thin sea ice and shallow ice water bodies were located,according to the distribution rules of ground objects and histogram graphic features of the images,so as to enhance the contrast of ground objects between the regions,and finally the extraction of the fine range of water bodies on the ice surface.Experimental results showed that the proposed index effectively highlighted the ice water with the water of the reflectivity difference,compared with the commonly used water index NDWI,etc.,especially in shallow water,which contributes to differentiation from other objects.The precision evaluation showed that the applied method of extraction has higher degree of refinement compared with other methods,by which the ice water can get complete ice water effectively.

Rule Generation Based on Dominance Matrices and Functions

Rough set theory has proved to be a useful tool for rule induction. But, the theory based on indiscernibility relation or similarity relation cannot induce rules from decision tables with criteria. Greco et al have proposed a new rough set approach based on dominance relation to handle the problems. The concept of dominance matrix is put forward and the dominance function is constructed to compute the minimal decision rules which are more general and applicable than the ones induced by the classical rough set theory. In addition, the methodology of simplification is presented to eliminate the redundancy in the rule set.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

No-Reference Quality Assessment of Enhanced Images

Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remains an open problem,which may hinder further development of enhancement techniques.In this paper,a no-reference quality metric for digitally enhanced images is proposed.Three kinds of features are extracted for characterizing the quality of enhanced images,including non-structural information,sharpness and naturalness.Specifically,a total of 42 perceptual features are extracted and used to train a support vector regression(SVR) model.Finally,the trained SVR model is used for predicting the quality of enhanced images.The performance of the proposed method is evaluated on several enhancement-related databases,including a new enhanced image database built by the authors.The experimental results demonstrate the efficiency and advantage of the proposed metric.

Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.

Estimation of tensile strength of ductile iron friction welded joints using hybrid intelligent methods

A hybrid intelligent method for evaluation of near optimal settings of friction welding process parameters of ductile iron was presented, The optimization of welding parameters was carried out in automatic cycle with the use of support vector regression （SVR）, genetic algorithm （GA） and imperialist competitive algorithm （ICA）. The method suggested was used to determine welding process parameters by which the desired tensile strength was obtained in the friction welding of ductile iron. The highest tensile strength （TS） of 256.93 MPa was obtained using SVR plus GA method for the following friction welding parameters： heating force 40 kN, heating time 300 s and upsetting force 10.12 kN. The samples were welded by friction and subjected to the tensile strength test. The optimized values obtained by means of these hybrid techniques were compared with the experimental results. The application of hybrid intelligent methods allowed to increase the tensile strength joints from 211 to 258 MPa for the friction welder ZT-14 type.

Attribution of the Present-Day Aerosol Direct Radiative Forcing to Anthropogenic Emission Sectors

In this study,a general circulation model coupled with a gas-phase module and an aerosol chemistry module was employed to investigate the impacts of anthropogenic emission sectors on aerosol direct radiative forcing at the top of atmosphere （TOA） in the present-day climate.The predictions were based on the emission inventories developed in support of the Intergovernmental Panel on Climate Change （IPCC） Fifth Assessment Report （AR5）.Six emission sectors-agriculture,open biomass burning,domestic activities,industry,energy generation,and transport-were considered,with a special focus on nitrate aerosol that shows large uncertainties in current models.The results show that the energy sector accounts for the largest contribution （-222 mW m-2） to global aerosol radiative forcing,with substantial negative forcing from sulfate.Inclusion of nitrate results in the transport sector yielding a global nitrate radiative forcing of-92 mW rm-2 and an internally mixed aerosol radiative forcing of-85 mW m-2,which is opposite to the positive radiative forcing predicted in the past,indicating that the transport emissions could not be a potential control target to counteract climate warming as expected before.The maximum change in nitrate burden is found to be associated with agricultural emissions,which accounts for about 75％ of global ammonia gas （NH3） emissions.Agricultural emissions account for global nitrate radiative forcing of-186 mW m-2 and internally mixed aerosols direct radiative forcing of-149 mW m-2.Such agricultural radiative forcing exceeds the radiative forcing of the industrial sector and is responsible for a large portion of negative radiative forcing over the Northern Hemisphere.

Adjacent Strong Edge Chromatic Number of Series-Parallel Graphs

In this paper, we will study the adjacent strong edge coloring of series-parallel graphs, and prove that series-parallel graphs of △(G) = 3 and 4 satisfy the conjecture of adjacent strong edge coloring using the double inductions and the method of exchanging colors from the aspect of configuration property. For series-parallel graphs of △(G) ≥ 5, △(G) ≤ x'as(G) ≤ △(G) + 1. Moreover, x'as(G) = △(G) + 1 if and only if it has two adjacent vertices of maximum degree, where △(G) and X'as(G) denote the maximum degree and the adjacent strong edge chromatic number of graph G respectively.

Convergence Rates of Wavelet Estimators in Semiparametric Regression Models Under NA Samples

Consider the following heteroscedastic semiparametric regression model：where {Xi, 1 〈 i 〈 n} are random design points, errors {ei, 1 〈 i 〈 n} are negatively associated （NA） random variables, （r2 = h（ui）, and {ui} and {ti} are two nonrandom sequences on [0, 1]. Some wavelet estimators of the parametric component β, the non- parametric component g（t） and the variance function h（u） are given. Under some general conditions, the strong convergence rate of these wavelet estimators is O（n- 1 log n）. Hence our results are extensions of those re, sults on independent random error settings.

Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau

Accurate quantification of aboveground biomass of grasslands in alpine regions plays an important role in accurate quantification of global carbon cycling.The monthly normalized difference vegetation index（NDVI）,enhanced vegetation index（EVI）,mean air temperature（Ta）,≥5℃ accumulated air temperature（AccT）,total precipitation（TP）,and the ratio of TP to AccT（TP/AccT） were used to model aboveground biomass（AGB） in grasslands on the Tibetan Plateau.Three stepwise multiple regression methods,including stepwise multiple regression of AGB with NDVI and EVI,stepwise multiple regression of AGB with Ta,AccT,TP and TP/AccT,and stepwise multiple regression of AGB with NDVI,EVI,Ta,AccT,TP and TP/Acc T were compared.The mean absolute error（MAE） and root mean squared error（RMSE） values between estimated AGB by the NDVI and measured AGB were 31.05 g m^（-2） and 44.12 g m^（-2）,and 95.43 g m^（-2） and 131.58 g m^（-2） in the meadow and steppe,respectively.The MAE and RMSE values between estimated AGB by the AccT and measured AGB were 33.61 g m^（-2） and 48.04 g m^（-2） in the steppe,respectively.The MAE and RMSE values between estimated AGB by the vegetation index and climatic data and measured AGB were 28.09 g m^（-2） and 42.71 g m^（-2）,and 35.86 g m^（-2） and 47.94 g m^（-2）,in the meadow and steppe,respectively.The study finds that a combination of vegetation index and climatic data can improve the accuracy of estimates of AGB that are arrived at using the vegetation index or climatic data.The accuracy of estimates varied depending on the type of grassland.

Cooperation in Two-Stage Games on Undirected Networks

In the paper, cooperative two-stage network games are studied. At the first stage of the game, players form a network, while at the second stage players choose their behaviors according to the network realized at the first stage. As a cooperative solution concept in the game, the core is considered.It is proved that some imputations from the core are time inconsistent, whereas one can design for them a time-consistent imputation distribution procedure. Moreover, the strong time consistency problem is also investigated.

Some uniform convergence results for kernel estimators

This paper derives some uniform convergence rates for kernel regression of some index functions that may depend on infinite dimensional parameter. The rates of convergence are computed for independent, strongly mixing and weakly dependent data respectively. These results extend the existing literature and are useful for the derivation of large sample properties of the estimators in some semiparametric and nonparametric models.