一种用于A/D转换的量程自动控制电路

提出了一种不需ＣＰＵ干预的、用于Ａ／Ｄ转换的高速量程自动控制电路，并详细介绍了它的工作原理。

Mining Weights of Land Evaluation Factors Based on Cloud Model and Correlation Analysis

The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.

Active velocity tomography for assessing rock burst hazards in a kilometer deep mine

Active velocity tomography was used to determine the stress state and rock burst hazards in a deep coal mine. The deepest longwall face, number 3207 in the Xingcun colliery, was the location of the field trials. The positive correlation between stress and seismic velocity was used to link the velocity data with stratum stresses. A GeoPen SE2404NT data acquisition system was used to collect seismic data from 300 g explosive charges fired by instantaneous electric detonator and located in the tail entry. The geophones were installed on the rock bolts in the head entry of LW3207. Velocity inversion shows an inhomogeneous distribution of stress in the longvvall face that could not be obtained from theory or numerical simulations. Three abnormally high P-wave velocity regions were identified that were located on the corners of the two roadways and at the face end near the rail entry side. The maximum velocity gradient is located at the open cut off near the rail entry and is the area most dangerous for rock burst. Mining-induced tremors recorded by a micro-seismic monitoring system demonstrated that the position of energy release during mining coincides with the high velocity gradient area. This technology aids technicians in the coal mine as they design measures to weaken or eliminate potential danger during subsequent mining.

Assimilating ASAR Data for Estimating Soil Moisture Profile Using an Ensemble Kalman Filter

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas.In this study,Advanced Synthetic Aperture Radar(ASAR)observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin,Northwest China.A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter(EnKF),the forward radiative transfer model,crop model,and the Distributed Hydrology-Soil-Vegetation Model(DHSVM)was developed.The crop model,as a semi-empirical model,was used to estimate the surface backscattering of vegetated areas.The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape.Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June20 to July 15,2008.The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model.Compared with the simulation and in situ observations,the assimilated results were significantly improved in the surface layer and root layer,and the soil moisture varied slightly in the deep layer.Additionally,EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data.Moreover,to improve the assimilation results,further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed,also improving estimation accuracy of model operator is important.

Inverse Estimation on Trigger Factors of Simultaneous Slope Failures with Purification of Training Data Sets

This paper presents an procedure for purifying training data sets （i.e., past occurrences of slope failures） for inverse estimation on unobserved trigger factors of ＂different types of simultaneous slope failures＂. Due to difficulties in pixel-by-pixel observations of trigger factors, as one of the measures, the authors had proposed an inverse analysis algorithm on trigger factors based on SEM （structural equation modeling）. Through a measurement equation, the trigger factor is inversely estimated, and a TFI （trigger factor influence） map can be also produced. As a subsequence subject, a purification procedure of training data set should be constructed to improve the accuracy of TFI map which depends on the representativeness of given training data sets of different types of slope failures. The proposed procedure resamples the matched pixels between original groups of past slope failures （i.e., surface slope failures, deep-seated slope failures, landslides） and classified three groups by K-means clustering for all pixels corresponding to those slope failures. For all cases of three types of slope failures, the improvement of success rates with respect to resampled training data sets was confirmed. As a final outcome, the differences between TFI maps produced by using original and resampled training data sets, respectively, are delineated on a DIF map （difference map） which is useful for analyzing trigger factor influence in terms of ＂risky- and safe-side assessment＂ sub-areas with respect to ＂different types of simultaneous slope failures＂.

WATERiD＇s Novel Methodology for Condition Assessment Cost Data Collection and Visualization

A profound understanding of the costs to perform condition assessment on buried drinking water pipeline infrastructure is required for enhanced asset management. Toward this end, an automated and uniform method of collecting cost data can provide water utilities a means for viewing, understanding, interpreting and visualizing complex geographically referenced cost information to reveal data relationships, patterns and trends. However, there has been no standard data model that allows automated data collection and interoperability across platforms. The primary objective of this research is to develop a standard cost data model for drinking water pipeline condition assessment projects and to conflate disparate datasets from differing utilities. The capabilities of this model will be further demonstrated through performing trend analyses. Field mapping files will be generated from the standard data model and demonstrated in an interactive web map created using Google Maps API （application programming interface） for JavaScript that allows the user to toggle project examples and to perform regional comparisons. The aggregation of standardized data and further use in mapping applications will help in providing timely access to condition assessment cost information and resources that will lead to enhanced asset management and resource allocation for drinking water utilities.

Evaluating instrumented Charpy impact strain signals using curve fitting equations

An effective and simple way to develop equations from impact strain signals was proposed.Little research has been performed in this area,but this equation is very important for evaluating input signals in finite element analysis impact tests and for obtaining additional information on material deformation and fracture processes under impact loading.For this purpose,dynamic impact responses were examined through signals obtained from a strain gauge installed on an impact striker connected to a data acquisition system.Aluminium 6061-T6 was used to extract strain responses on the striker during Charpy impact testing.Statistical analysis was performed using the I-kaz method,and curve fitting equations based on the equation for vibration response under a non-periodic force were used to evaluate the Charpy impact signals.The I-kaz coefficients and curve fitting equations were then compared and discussed with related parameters,such as velocities and thicknesses.Velocity and thickness were found to be related to the strain signal patterns,curve fitting equations and I-kaz coefficients.The equations developed using this method had R2 values greater than 97.7%.Finally,the constructed equations were determined to be suitable for evaluating Charpy impact strain signal patterns and obtaining additional information on fracture processes under impact loading.

Dynamic evaluation of traffic flows on city roads

This paper presents an analysis of the random fluctuations, deferred conduction effect and periodicity of road traffic based on the basic features of road networks. It also discusses the limitations of road network evaluation theories based on road "V/C". In addition, it proposes a set of theoretical and technical methods for the real-time evaluation of traffic flows for entire road networks, and for solving key technical issues, such as real-time data collection and processing in areas with no blind zones, the spatial-temporal dynamic analysis of road network traffic, and the calibration of key performance index thresholds. It also provides new technical tools for the strategic transportation planning and real-time diagnosis of road traffic. The new tools and methodology presented in this paper are validated using a case study in Beijing.

Rural Impact Assessment of Agriculture Water Systems in a Climate Change Context

This study is a rapid appraisal procedure （RAP） of two forms of agriculture water delivery systems comprising two canal irrigation schemes and 26 Zimbabwean bush pumps in the Midlands and Masvingo Provinces. A longitudinal multiple data collection technique employed involved various primary and secondary sources including site visits, literature review, observation, interviews with key personnel and group discussions. General findings of this study indicate： （1） the available coping mechanisms in smallholder farming in a climate change context and （2） the challenges faced in the actual delivery of water in terms of design, management, physical and institutional factors. The study provides pragmatic recommendations for overall improvement and performance in a local, technical and socio-economic context through evaluation of the current situation, practices and processes. An integrated approach to addressing climate change impacts should include water management, rehabilitation, complete overhaul and introduction of other relevant water systems and water saving farming techniques. Yet, ownership of these technologies by communities remains instrumental. Rural development and agricultural policies that ensure maximum and full capacity utilisation of water systems to improve rural livelihoods, mitigation and adaptation to climate change are recommended.

A new algorithm of brain volume contours segmentation

This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.

Homegardens as a Source of Income for Rural Households A Case Study of Bieha District, Southern Burkina Faso

This exploratory research studied the contribution of homegardens to household income generation. In Burkina Faso, the Bieha department was purposively selected. The initial rapid rural appraisal was complemented by a household survey. Tools of data collection included observation, key informant interview, and focus group discussion. For the survey, eighty households were selected based on systematic sampling. Data was collected by individual interviews at household level, and for statistical calculations, the households were subsequently categorized as small, medium and commercial based on their homegarden size. It was found that all the surveyed households managed homegardens composed of trees, crops and animals. Majority of the farmers （43%） belonged to the small category due to land constraints. Generally, farms contributed more income than the plant component of homegardens. However, when the livestock component of homegardens was added, homegardens became far more important than farms and accounted for over 60% of income generation for all categories. It can be concluded that homegardens are important to rural people for food and cash income.

AN EVALUATION PLAN FOR A SET OF GENERAL ENGLISH COURSE BOOKS

The evaluation of an English course book is very important for a language teacher to gauge whether the book is effective or not in developing the learners' language skills in listening, speaking, reading and writing. There are a wide variety of English course books for non-English majors at present in China. In addition, there are various evaluations of English language teaching materials in foreign countries. There is also some evaluation of English course books available in China. However, the evaluation in China seem to be book reviews, which focus on the design of the course books, the content of the book and so on. This article proposes an evaluation plan based on Ellis (1997,1998), which involves the approach to be adopted, content, methods to collect data and data analysis, hoping to raise awareness of evaluation of teaching materials in China and to provide some rationale for the evaluation of a course book. The course book used as an example for the evaluation is the New College English (students' book) edited by the English Language Department of Zhejiang University and published by the Foreign Language Teaching and Research Press in Beijing, China. The evaluation focuses on the grammatical items, vocabulary, reading practice, listening practice and functional speaking in the book.

Scale Effects of Geographical Soil Datasets on Soil Carbon Estimation in Louisiana,USA:A Comparison of STATSGO and SSURGO

Estimation of soil organic carbon （SOC） pools and fluxes bears large uncertainties because SOC stocks vary greatly over geographical space and through time. Although development of the U.S. Soil Survey Geographic Database （SSURGO）, currently the most detailed level with a map scale ranging from 1：12 000 to 1：63 360, has involved substantial government funds and coordinated network efforts, very few studies have utilized it for soil carbon assessment at the large landscape scale. The objectives of this study were to 1） compare estimates in soil organic matter among SSURGO, the State Soil Geographic Database （STATSGO）, and referenced field measurements at the soil map unit; 2） examine the influence of missing data on SOC estimation by SSURGO and STATSGO; 3） quantify spatial differences in SOC estimation between SSURGO and STATSCO, specifically for the state of Louisiana; and 4） assess scale effects on soil organic carbon density （SOCD） estimates from a soil map unit to a watershed and a river basin scale. SOC was estimated using soil attributes of SSURGO and STATSGO including soil organic matter （SOM） content, soil layer depth, and bulk density. Paired t-test, correlation, and regression analyses were performed to investigate various relations of SOC and SOM among the datasets. There were positive relations of SOC estimates between SSURGO and STATSGO at the soil map unit （R2 = 0.56, n = 86, t = 1.65, P = 0.102; depth： 30 cm）. However, the SOC estimated by STATSGO were 9%, 33% and 36~ lower for the upper 30-cm, the upper l-m, and the maximal depth （up to 2.75 m） soils, respectively, than those from SSURGO. The difference tended to increase as the spatial scale changes from the soil map unit to the watershed and river basin scales. Compared with the referenced field measurements, the estimates in SOM by SSURGO showed a closer match than those of STATSCO, indicating that the former was more accurate than the latter in SOC estimation, both in spatial and temporal resolutions. Further applications of SSURGO in SOC estimation for the entire United States could improve the accuracy of soil carbon accounting in regional and national carbon balances.

Minimum Data Set for Assessing Soil Quality in Farmland of Northeast China

Soil quality assessment provides a tool for agriculture managers and policy makers to gain a better understanding of how various agricultural systems affect soil resources. Soil quality of Hailun County, a typical soybean （Glycine max L. Merill） growing area located in Northeast China, was evaluated using soil quality index （SQI） methods. Each SQI was computed using a minimum data set （MDS） selected using principal components analysis （PCA） as a data reduction technique. Eight MDS indicators were selected from 20 physical and chemical soil measurements. The MDS accounted for 74.9% of the total variance in the total data set （TDS）. The SQI values for 88 soil samples were evaluated with linear scoring techniques and various weight methods. The results showed that SQI values correlated well with soybean yield （r = 0.658＊＊） when indicators in MDS were weighted by the regression coefficient computed for each yield and index. Stepwise regression between yield and principal components （PCs） indicated that available boron （AvB）, available phosphorus （AvP）, available potassium （AvK）, available iron （AvFe） and texture were the main factors limiting soybean yield. The method used to select an MDS could not only appropriately assess soil quality but also be used as a powerful tool for soil nutrient diagnosis at the regional level.

Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data:A Case Study of Selected South African Soils

Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.

Predicting Soil Moisture Characteristic Curves from Continuous Particle-Size Distribution Data

Soil moisture characteristic curve （SMC） is a fundamental soil property and its direct measurement is tedious and time consuming. Therefore, various indirect methods have been developed to predict SMC from particle-size distribution （PSD）. However, the majority of these methods often yield intermittent SMC data because they involve estimating individual SMC points. The objectives of this study were 1） to develop a procedure to predict continuous SMC from a limited number of experimental PSD data points and 2） to evaluate model predictions through comparisons with measured values. In this study, an approach that allowed predicting SMC from the knowledge of PSD, parameterized by means of the closed-form van Genuchten model （VG）, was used. Through using Mohammadi and Vanclooster （MV） model, the parameters obtained from fitting of VG to PSD data were applied to predict SMC curves. Since the residual water content （Or） could not be obtained through fitting of VG-MV integrated model to PSD data, we also examined and compared four different methods estimating 0r. Results showed that the proposed equation （MV-VG integrated model） provided an excellent fit to all the PSD data and the model could adequately predict SMC as measured in forty-two soils sampled from different regions of Iran. For all soils, the method in which Or Was obtained through parameter optimization procedure provided the best overall predictions of SMC. The two methods estimating Or with Campbell and Shiozawa （CS） model resulted in less accuracy than the optimization procedure. Furthermore, the proposed model underestimated the moisture content in the dry range of SMC when the value of 0r was assumed to equal zero. 0r could be attributed to the incomplete desorption of water coated on soil particles and the accurate estimation of 0r was critical in prediction of SMC, especially for fine-textured soils at high suction heads. It could be concluded that the advantages of our approach were the continuity, robustness, and independency of model performance on soil type, allowing to improve predictions of SMC from PSD at the field and watershed scales.

Thermal contact conductance estimation and experimental validation in hot stamping process

The variation in temperature of the blank would make major contribution to the mechanical properties of final component in hot stamping of ultra high strength steel(UHSS).It is important to use accurate thermal contact conductance(TCC) to carry out finite element simulation of hot stamping for reliable caculation results.In this paper,a flat compression test was performanced on a servo press.A fast response temperature measurement and data acquisition system was designed to obtain the temperature history of blank and die under different pressures.The thermal contact conductance between blank and die was got using an optimization algorithm.The error between the temperature curves using calculated thermal contact conductance and the curves of measurement temperature was analyzed.Result shows that reliable simulation results of temperature can be got through accurate thermal contact conductance.

Asymptotic properties of plug-in level set estimators for right censored data

We assume T1,..., Tn are i.i.d. data sampled from distribution function F with density function f and C1,...,Cn are i.i.d. data sampled from distribution function G. Observed data consists of pairs （Xi, δi）, em= 1,..., n, where Xi = min{Ti,Ci}, δi = I（Ti 6 Ci）, I（A） denotes the indicator function of the set A. Based on the right censored data {Xi, δi}, em=1,..., n, we consider the problem of estimating the level set {f 〉 c} of an unknown one-dimensional density function f and study the asymptotic behavior of the plug-in level set estimators. Under some regularity conditions, we establish the asymptotic normality and the exact convergence rate of the λg-measure of the symmetric difference between the level set {f ≥ c} and its plug-in estimator {fn ≥ c}, where f is the density function of F, and fn is a kernel-type density estimator of f. Simulation studies demonstrate that the proposed method is feasible. Illustration with a real data example is also provided.

An Extended Weibull Model with Variable Periodicity

A new distribution for the fluctuation of materials＇ lifetime cumulative hazard rate is firstly proposed. The new distribution is extended from the Weibull distribution by adding a sine function. After that, the properties of its hazard rate function, cumulative hazard rate function, probability density function and cumulative distribution function are studied. The analysis result shows this distribution can well model the lifetime with variable and periodic hazard rate. Finally, the new distribution is verified with two real data sets as examples to demonstrate its capability.

k-step adaptive cluster sampling with Horvitz-Thompson estimator

Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a k-step ACS based on Horvitz-Thompson （HT） estimator was developed and an unbiased estimator was derived. The k-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz （ACS-HH） and ACS- HT estimators, and k-step ACS-HT estimator. The effectiveness of using different k- step sizes was also compared. The results showed that k-step ACS^HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species, k-step ACS without replacement was slightly more effective than that with replacement. In k-step ACS, the variance estimate of one-step ACS is much larger than other k-step ACS （k 〉 1）, but it is smaller than ACS. This implies that k-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.