A LARGE SAMPLE ESTIMATE IN MEDIAN LINEAR REGRESSION MODEL Ⅰ: NONTRUNCATED CASE

This paper uses a grouping-adjusting procedure to the data from a median linear regression model, and estimtes the regression coefficients by the method of weighted least squares. This method simplifies computation and in the meantime, preserves the same asymptotic normal distribution for the estimator, as in the ordinary minimum L_1-norm estimates.

Method to generate training samples for neural network used in target recognition

Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.

On Spatial Distribution Pattern of Broccoli Clubroot Disease and and the Sampling Technique

[Objective] To further improve the prediction and forecast and continuous control ability of broccoli clubroot disease. [Methods] The spatial distribution pattern of diseased or infected plants was analyzed using the least square method, fre- quency distribution, aggregation index, m＊-m regression analysis and Taylor＇s pow- er law model. [Result] The field distribution of broccoli plants with clubroot disease tended to be aggregated distribution, m＇-m regression analysis showed that the el- ementary composition of the spatial distribution of diseased or infected plants was individual colony, the individuals attracted each other; the disease had obvious dis- ease focus in the field, and the individual colony showed uniform distribution pattern in the field. Taylor＇s power law showed that the spatial pattern of individual dis- eased or infected plant with clubroot disease tended to be uniform distribution with the increase of the density. On the basis of this, Iwao optimal theoretical sampling model and sequential sampling model were established, namely N =273.954 1/m- 59.698 5, To （N）=0.368 4N±1.926 8√N, respectively, it meant that when surveying N plants, if the accumulative incidence rate exceeded upper bound, the field can be set as control object; if the accumulative incidence rate didn＇t reach lower bound, it can be set as uncontrol field; if the accumulative incidence rate was between upper bound and lower bound, it should be surveyed continuously until the maximum sample size （mo=0.368 4） appeared, that was, the disease incidence was 15%, so the sampling number should be 684 plants. [Conclusion] The research results had very important instructive meaning for disease control.

Indentation size effect of germanium single crystal with different crystal orientations

In order to study the indentation size effect(ISE)of germanium single crystals,nano-indentation experiments were carried out on the(100),(110)and(111)plane-orientated germanium single crystals.The true hardness of each crystal plane of germanium single crystals was calculated based on the Meyer equation,proportional sample resistance(PSR)model and Nix-Gao model,and the indentation size effect(ISE)factor of each crystal plane was calculated.Results show that,the germanium single crystals experience elastic deformation,plastic deformation and brittle fracture during the loading process,and the three crystal planes all show obvious ISE phenomenon.All three models can effectively describe the ISE of germanium single crystals,and the calculated value of Nix-Gao model is the most accurate.Compared with the other two crystal planes,Ge(110)has the highest size effect factor m and the highest hardness,which indicates that Ge(110)has the worst plasticity.

Management factors affecting densities of common grassland birds of high elevation grasslands of eastern South Africa:Ingula as a case study

Background: World?wide grassland birds are in decline due to habitat loss and degradation resulting from inten?sive agricultural practices. Understanding how key grassland habitat attributes determine grassland bird densities is required to make appropriate conservation decisions. We examine drivers of bird densities in a South African grass?land area that has been managed for biodiversity conservation with reduced grazing pressure.Methods: We estimated the density of the eight most common grassland bird species encountered in our area to evaluate the effects of recent grassland management changes on the avifauna. We collected data on birds and habitat from the austral summers of 2006/2007, 2007/2008 and 2010/2011. We used hierarchical distance sampling methods to estimate density of birds relative to two main habitat variables, i.e., grass cover and height. In addition, we used regression splines within these distance sampling models as a more flexible description of suitable ranges of grass height and cover for each species.Results: For most species, density is related to grass height and cover as expected. The African Quailfinch(Ortygospiza atricollis) and Common Quail(Coturnix coturnix) preferred relatively short and open grass. The Yellow?breasted Pipit(Anthus chloris), African Pipit(Anthus cinnamomeus) and Red?capped Lark(Calandrella cinerea) preferred short and relatively dense grass, while the Wing?snapping Cisticola(Cisticola ayresii) preferred grass of intermediate height and cover. The Cape Longclaw(Macronyx capensis) and Zitting Cisticola(Cisticola juncidis) preferred tall and dense grass. Our results agree with previous studies that grass height combined with grass cover are the most important habitat features that managers should manipulate in order to increase the density of target species. The regression splines show that the effect of these two habitat variables on density is well described by linear relationships for most species.Conclusions: This study supports previous studies suggesting that grazing and fire are important tools for manage?ment to use in order to create a mosaic of grass height and cover that would support high densities of desired spe?cies. We suggest that conservation managers of these grasslands combine fire and grazing as management tools to create suitable habitats for grassland birds in general.

Dynamic Global-Principal Component Analysis Sparse Representation for Distributed Compressive Video Sampling

Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.

Improved allometric equations for tree aboveground biomass estimation in tropical dipterocarp forests of Kalimantan,Indonesia

Background： Currently, the common and feasible way to estimate the most accurate forest biomass requires ground measurements and allometric models.Previous studies have been conducted on allometric equations development for estimating tree aboveground biomass（AGB） of tropical dipterocarp forests（TDFs） in Kalimantan（Indonesian Borneo）.However, before the use of existing equations, a validation for the selection of the best allometric equation is required to assess the model bias and precision.This study aims at evaluating the validity of local and pantropical equations; developing new allometric equations for estimating tree AGB in TDFs of Kalimantan; and validating the new equations using independent datasets.Methods： We used 108 tree samples from destructive sampling to develop the allometric equations, with maximum tree diameter of 175 cm and another 109 samples from previous studies for validating our equations.We performed ordinary least squares linear regression to explore the relationship between the AGB and the predictor variables in the natural logarithmic form.Results： This study found that most of the existing local equations tended to be biased and imprecise, with mean relative error and mean absolute relative error more than 0.1 and 0.3, respectively.We developed new allometric equations for tree AGB estimation in the TDFs of Kalimantan.Through a validation using an independent dataset,we found that our equations were reliable in estimating tree AGB in TDF.The pantropical equation, which includes tree diameter, wood density and total height as predictor variables performed only slightly worse than our new models.Conclusions： Our equations improve the precision and reduce the bias of AGB estimates of TDFs.Local models developed from small samples tend to systematically bias.A validation of existing AGB models is essential before the use of the models.

Static Frame Model Validation with Small Samples Solution Using Improved Kernel Density Estimation and Confidence Level Method

An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.

Optimal decision-making model of spatial sampling for survey of China's land with remotely sensed data

In the remote sensing survey of the country land, cost and accuracy are a pair of conflicts, for which spatial sampling is a preferable solution with the aim of an optimal balance between economic input and accuracy of results, or in other words, acquirement of higher ac-curacy at less cost. Counter to drawbacks of previous application models, e.g. lack of compre-hensive and quantitative-comparison, the optimal decision-making model of spatial sampling is proposed. This model first acquires the possible accuracy-cost diagrams of multiple schemes through initial spatial exploration, then regresses them and standardizes them into a unified ref-erence frame, and finally produces the relatively optimal sampling scheme by using the discrete decision-making function (built by this paper) and comparing them in combination with the dia-grams. According to the test result in the survey of the arable land using remotely sensed data, the Sandwich model, while applied in the survey of the thin-feature and cultivated land areas with aerial photos, can better realize the goal of the best balance between investment and accuracy. With this case and other cases, it is shown that the optimal decision-making model of spatial sampling is a good choice in the survey of the farm areas using remote sensing, with its distin-guished benefit of higher precision at less cost or vice versa. In order to extensively apply the model in the surveys of natural resources, including arable farm areas, this paper proposes the prototype of development using the component technology, that could considerably improve the analysis efficiency by insetting program components within the software environment of GIS and RS.

Farmers’perception of climate change and adaptation strategies in the Dabus watershed,North-West Ethiopia

Introduction:This study is aimed at analyzing farmers’perception and adaptation to climate change in the Dabus watershed.It is based on analysis of data collected from 734 randomly selected farm household heads substantiated with Focus Group Discussions and field observations.Methods:The study employed descriptive methods to assess farmers’perception of climate change,local indicators of climate change and types of adaptation measures exercised to cope up with the risk of the change in climate.The study also employed the Heckman sample selection model to analyze the two-step process of adaptation to climate change which initially requires farmers’perception that climate is changing prior to responding to the changes through adaptation measures.Results:Based on the model result educational attainment,the age of the head of the household,the number of crop failures in the past,changes in temperature and precipitation significantly influenced farmers’perception of climate change in wet lowland parts of the study area.In dry lowland condition,farming experience,climate information,duration of food shortage,and the number of crop failures experienced determined farmers’perception of climate change.Farmers’adaptation decision in both the wet and dry lowland conditions is influenced by household size,the gender of household head,cultivated land size,education,farm experience,non-farm income,income from livestock,climate information,extension advice,farm-home distance and number of parcels.However,the direction of influence and significance level of most of the explanatory variables vary between the two parts of the study area.Conclusions:In line with the results,any intervention that promotes the use of adaptation measures to climate change may account for location-specific factors that determine farmers'perception of climate change and adaptive responses thereof.

The influence of wave surge force on surf-riding/broaching vulnerability criteria check

This study focuses on the influence of the wave surge force on the assessments of the surf-riding/broaching vulnerability criteria according to the new proposal of the IMO Second Generation Intact Stability Criteria. A code is developed for the criteria check and the sample ship calculations show that the accuracy of the wave surge force estimation has a significant influence on the assessment result. For further investigation, the wave surge force measurement through a captive model test is made for a purse seiner to validate the numerical model, the effects of the wave steepness and the ship forward speed on the wave surge force responses are also discussed. It is demonstrated that the diffraction effect is important for the correct estimation of the wave surge force. Therefore, it is recommended to include this effect in the assessment procedure.

Nontarget screening using passive air and water sampling with a level Ⅱ fugacity model to identify unregulated environmental contaminants

It is thought that there are many unregulated anthropogenic chemicals in the environment.For risk assessment of chemicals, it is essential to estimate the predicted environmental concentrations. As an effort of identifying residual organic contaminants in air and water in Korea, nontarget screening using two-dimensional gas chromatography time-of-flight mass spectrometry（GC × GC-TOFMS） was conducted at 10 sites using polyurethane foam passive air sampler and at 6 sites using polydimethyl siloxane（PDMS） passive water sampler in three different seasons in 2014. More than 600 chemical peaks were identified satisfying the identification criteria in air and water samples, respectively, providing a list for further investigation. Chemical substances with reported national emission rates in2014（n = 149） were also screened for potential existence in the environment using a level Ⅱ fugacity model. Most of chemical substances classified as not detectable were not identified with detection frequency greater than 20% by nontarget screening, indicating that a simple equilibrium model has a strong potential to be used to exclude chemicals that are not likely to remain in the environment after emissions from targeted monitoring.