零航速减摇鳍鳍型参数估算方法

零航速减摇鳍具有双重工作模式,不同生力机理使其流体动力特性存在本质差异,从而导致二者对鳍型参数要求截然不同,为满足零航速模式需求,零航速减摇鳍大多选用小展弦比鳍型,但亦给常规减摇模式带来诸多不利影响。通过分析展弦比、翼梢形状等因素,从双模式对鳍型参数的实际要求出发,应用线性系统理论、对抗控制原理,提出适用于零航速减摇鳍的尺寸参数估算及综合评定方法,得出不可收放式零航速减摇鳍的最佳展弦比范围,为工程化设计提供了必要的理论依据。

离子吸附型稀土矿储量动态估算方法(RiRee)及其拓展运用

我国南方离子吸附型稀土矿勘查中一般采用传统的地质块段法估算资源储量,存在着探矿工程量大、投入高、效率低的缺点。而稀土资源包含17种不同的稀土元素,不同元素精矿的市场价格差别又很大,现有的基于稀土氧化物总量的工业指标难以快速对接实际矿山生产,造成表外矿实际在产、而表内矿不经济可采甚至可能无法利用的反常现象。本文基于离子吸附型稀土矿床的特点,借鉴土壤化探样品处理的克里格法,建立了离子吸附型稀土矿资源储量估算的三维模型及其相应评价方法,简称RiRee。该模型和方法不但可以对风化壳中的"样品点"进行体积和品位的估算,而且与市场价格直接关联、可根据每天市场价格的动态变化确定边界品位和工业品位,进行实现矿体的实时圈定、资源储量的日常更新与矿山经济效益的动态评价。运用该模型,作者在赣南、滇西、浙江等地不同离子吸附型稀土矿区开展了典型研究和试点示范,取得了初步成效。结果显示,利用克立格法计算的矿体体积与传统手工圈定的块段法相比较,前者计算的结果是合理的,且具有快速、准确、方便的特点。利用自主开发的以克里格法为基础的数字矿山经济评价系统(主要是其中的价格-边界品位敏感性分析模块),动态设置边界品位,灵活圈定不同稀土单元素价格下经济可采的矿体边界,一旦稀土精矿的市场价格发生变化,即可通过软件系统快速修正盈亏平衡点,并随时变更边界品位,动态确定经济可采的矿体的空间范围,为合理布置采矿工程提供科学依据,有助于促进离子吸附型稀土矿产资源(尤其是重稀土)的合理、高效的开发利用。

基因型估算法对全基因组关联荟萃分析的影响

目的：评估基因型估算技术对全基因组关联荟萃分析的影响，通过模拟量化基因型估算法关联分析的效率，为实践应用基因型估算法进行全基因组关联荟萃分析提供参考依据。方法：利用实际的Framingham全基因组关联研究构建子样本，在不同荟萃组合情况下，进行了大量的模拟实验，考察基因型估算技术对荟萃分析的统计功效的影响。结果：基因型估算引起显著的研究群体间异质性，由此导致关联荟萃分析的功效下降约20％～40％；使用随机效应模型控制因基因型估算引起的研究间异质性，关联荟萃分析的功效有所提高。结论：全基因组关联研究荟萃分析，若部分研究需要进行基因型估算，采用随机效应模型更为合适。

用小型蒸发量估算E-601B型蒸发量研究

为延长单站小型蒸发量历史资料序列的使用价值,依据射阳站4年平行观测资料采用小型蒸发量线性回归方法和选用气温、湿度及日照时数等气候因子多元线性回归方法估算E-601B型蒸发器蒸发量。试验结果表明两者误差与折算系数方法相当:前者年相对误差?1.4%到+2.6%;后者年相对误差?0.9%到+1.6%。气候要素回归方法由于部分月份的多元方程中有个别因子不能通过0.1水平显著性检验,显示其模拟E-601B型蒸发量的可靠性存疑,可能增大年合成误差,计算繁杂,不建议使用该方法换算E-601B型蒸发量数据。

散热强化型覆晶球栅数组组合体之锡球可靠度研究

本文探讨之散热强化型覆晶球栅数组组合体是基本型覆晶球栅数组封装体包封着封胶,接着在封胶上面黏附着一片铝质散热板,最后以锡球构装在印刷电路板上。凸块与锡球是锡铅合金属于黏塑材料,在文中的数值模拟中,是以亚兰德模型来定义并描述其弹性、塑性及潜变行为,其余组件是假设为弹性材料。本文使用有限元素分析软件ANSYS建立一精确之三维有限元素模型,于温度循环负载下,针对此组合体并搭配修正型Coffin-Manson疲劳寿命估算式进行锡球可靠度分析。本文报告了锡球应力、应变及疲劳寿命等机械行为。最后,进行參数化分析,探讨组合体组件的设计参数对锡球疲劳寿命的影响。结果表明:增加基板基材的热膨胀系数、杨氏系数与厚度可以提高锡球疲劳寿命。减少散热板膨胀系数、杨氏模数、厚度与宽度也会造成锡球寿命增加。增加封胶热膨胀系数或减少封胶杨氏模数会造成锡球疲劳寿命增加。黏着剂所占的体积小对锡球疲劳寿命的影响很小。在本文之研究范围内,减少热膨胀系数、减少杨氏模数及增大厚度有助于提升锡球疲劳寿命。

IT企业员工绩效考核量化管理研究

信息技术的发展推动着社会生产方式的转变,当今世界正处于信息技术飞速发展的阶段,我国IT企业也迎来了机遇和挑战。为了促进IT企业持续稳定发展,对IT企业员工的绩效考核成为企业战略目标实现的重点。针对IT企业日常工作进行分类,并通过专业计量方法计算员工每月的考核得分,为IT企业员工绩效考核提供了一套科学有效的量化办法。

Yield Estimation Model of Citrus Based on Spectral Data and Agronomic Parameters

With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.

Empirical-Statistical Models Based on Remote Sensing for Estimating the Volume of Landslides Induced by the Wenchuan Earthquake

The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - barrier lake - bursts flood disaster chain. The number and the area of landslides in a wide region can be easily obtained by remote sensing technique, while the volume is relatively difficult to obtain because it requires some detailed geometric information of slope failure surface and sub-surface. Different empirical models for estimating landslide volume were discussed based on the data of 107 landslides in the earthquake-stricken area. The volume data of these landslides were collected by field survey. Their areas were obtained by interpreting remote sensing images while their apparent friction coefficients and height were extracted from the images unifying DEM (digital elevation model). By analyzing the relationships between the volume and the area, apparent friction coefficients, and the height, two models were established, one for the adaptation of a magnitude scale landslide events in a wide range of region, another for the adaptation in a small scope. The correlation coefficients (R2) are 0.7977 and 0.8913, respectively. The results estimated by the two models agree well with the measurement data.

Rice Yield Estimation by Integrating Remote Sensing with Rice Growth Simulation Model

Since remote sensing can provide information on the actual status of an agricultural crop, the integration between remote sensing data and crop growth simulation models has become an important trend for yield estimation and prediction.The main objective of this research was to combine a rice growth simulation model with remote sensing data to estimate rice grain yield for different growing seasons leading to an assessment of rice yield at regional levels. Integration between NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data and the rice growth simulation model ORYZA1 to develop a new software, which was named as Rice-SRS Model, resulted in accurate estimates for rice yield in Shaoxing, China, with an estimation error reduced to 1.03% and 0.79% over-estimation and 0.79% under-estimation for early, single and late season rice, respectively. Selecting suitable dates for remote sensing images was an important factor which could influence estimation accuracy. Thus, given the different growing periods for each rice season, four images were needed for early and late rice, while five images were preferable for single season rice.Estimating rice yield using two or three images was possible, however, if images were obtained during the panicle initiation and heading stages.

Video Scene Invariant Crowd Density Estimation Using Geographic Information Systems

Crowd density is an important factor of crowd stability.Previous crowd density estimation methods are highly dependent on the specific video scene.This paper presented a video scene invariant crowd density estimation method using Geographic Information Systems(GIS) to monitor crowd size for large areas.The proposed method mapped crowd images to GIS.Then we can estimate crowd density for each camera in GIS using an estimation model obtained by one camera.Test results show that one model obtained by one camera in GIS can be adaptively applied to other cameras in outdoor video scenes.A real-time monitoring system for crowd size in large areas based on scene invariant model has been successfully used in 'Jiangsu Qinhuai Lantern Festival,2012'.It can provide early warning information and scientific basis for safety and security decision making.

An estimation model for the fragmentation properties of brittle rock block due to the impacts against an obstruction

Mountain hazards with large masses of rock blocks in motion – such as rock falls, avalanches and landslides – threaten human lives and structures. Dynamic fragmentation is a common phenomenon during the movement process of rock blocks in rock avalanche, due to the high velocity and impacts against obstructions. In view of the energy consumption theory for brittle rock fragmentation proposed by Bond, which relates energy to size reduction, a theoretical model is proposed to estimate the average fragment size for a moving rock block when it impacts against an obstruction. Then, different forms of motion are studied, with various drop heights and slope angles for the moving rock block. The calculated results reveal that the average fragment size decreases as the drop height increases, whether for free-fall or for a sliding or rolling rock block, and the decline in size is rapid for low heights and slow for increasing heights in the corresponding curves. Moreover, the average fragment size also decreases as the slope angle increases for a slidingrock block. In addition, a rolling rock block has a higher degree of fragmentation than a sliding rock block, even for the same slope angle and block volume. Finally, to compare with others' results, the approximate number of fragments is estimated for each calculated example, and the results show that the proposed model is applicable to a relatively isotropic moving rock block.

Support Vector Machine Cost Estimation Model for Road Projects

A cost estimate is one of the most important steps in road project management. There are ranges of factors that mostly affect the final project cost. Many approaches were used to estimate project cost, which took into consideration probable project performance and risks. The aim is to improve the ability of construction managers to predict a parametric cost estimate for road projects using SVM （support vector machine）. The work is based on collecting historical road executed cases. The 12 factors were identified to be the most important factors affecting the cost-estimating model. A total of 70 case studies from historical data were divided randomly into three sets： training set includes 60 cases, cross validation set includes three cases and testing set includes seven cases. The built model was successfully able to predict project cost to the AP （accuracy performance） of 95%.

Comments on three comparative analyses of stem taper models published in Journal of Mountain Science in 2014-2016

Recently the Journal of Mountain Science published three papers（Lumbres et al.2014;Jung et al.2015;Lumbres et al.2016）that compared selected taper models for bias and precision when estimating upper stem diameters for various tree species.

Simulation of second-order RC equivalent circuit model of lithium battery based on variable resistance and capacitance

With the rise of the electric vehicle industry,as the power source of electric vehicles,lithium battery has become a research hotspot.The state of charge(SOC)estimation and modelling of lithium battery are studied in this paper.The ampere-hour(Ah)integration method based on external characteristics is analyzed,and the open-circuit voltage(OCV)method is studied.The two methods are combined to estimate SOC.Considering the accuracy and complexity of the model,the second-order RC equivalent circuit model of lithium battery is selected.Pulse discharge and exponential fitting of lithium battery are used to obtain corresponding parameters.The simulation is carried out by using fixed resistance capacitance and variable resistance capacitor respectively.The accuracy of variable resistance and capacitance model is 2.9%,which verifies the validity of the proposed model.

Factors Acquisition and Content Estimation of Farmland Soil Organic Carbon Based upon Internet of Things

Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.

Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.

Application of fuzzy theory on earthquake damage rate estimation of buildings

Variations between earthquakes result in many factors that influence post-earthquake building damage(e.g.,ground motion parameters,building structure,site information,and quality of construction).Consequently,it is necessary to develop an appropriate building damage-rate estimation model.The building damage survey data were recorded and constructed into files by the Architecture and Building Research Institute(ABRI),Taiwan for the 1999 Chi-Chi earthquake in the Nantou region as a basis for developing a building damage rate estimation model by applying fuzzy theory to express the fragility curves of buildings as a membership function.Empirical verification was performed using post-earthquake building damage data in the Taichung city that suffered relatively severe damage.Results indicate that fuzzy theory can be applied to predict building damage rates and that the estimated results are similar to actual disaster figures.Prediction of disaster damage using building damage rates can provide a reference for immediate disaster response during earthquakes and for regular disaster prevention and rescue planning.

Stratospheric Aerosol Extinction Profile Retrieval from SCIAMACHY Limb Measurements

Stratospheric aerosol extinction profiles are retrieved from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography(SCIAMACHY) limb scatter measurements.In the process of retrieval,the SCIATRAN radiative transfer model is used to simulate the limb scattering radiation received by the SCIAMACHY instrument,and an optimal estimation algorithm is used to calculate the aerosol extinction profiles.Sensitivity analyses are performed to investigate the impact of the surface albedo on the accuracy of the retrieved aerosol extinction profiles in the northern midlatitudes.It is found that the errors resulting from the bias of the assumed surface albedo in the retrieval are generally below 6%.The retrieved SCIAMACHY aerosol extinction profiles are compared with corresponding Stratospheric Aerosol and Gas Experiment(SAGE) II measurements,and the results indicate that for the zonal mean profiles,the SCIAMACHY retrievals show good agreement with SAGE II measurements,with the absolute differences being less than 2.3×10-5 km-1 from 14–25 km,and less than 5.9×10-6 km-1 from 25–35 km;and the relative differences being within 20% over the latitude range of 14–35 km.

Measuring Effectiveness of COCOMO I and COCOMO II Using a Case Study

There are several software estimation models such as Line of Code, Function Point and COnstructive COst MOdel （COCOMO）. The original COCOMO model is one of the most widely practiced and popular among the software development community because of its flexible usage. It is a suite of models i.e., COnstructive Cost MOdel I and COnstructive Cost MOdel II. in this paper, we are evaluating the both models, to find out the level of efficiency they present and how they can be tailored to the needs of modem software development projects. We are applying COCOMO models on a case study of an e-commerce application that is built using Hyper Text Markup Language （HTML） and JavaScript. We will also shed light on the different components of each model, and how their Cost Drivers effect on the accuracy of cost estimations for software development projects.

An improved activity-based costing model for product cost estimation applied in a complex manufacturing environment

In order to evaluate and estimate me cosEs oi prouuct~ plt, uu^u e vironment properly, an improved activity-based costing （ABC） model is presented. By utilizing the input-output analysis method, the complex consumption relationships in a complex manufacturing environment are first expressed. The consumption characteristics （mainly presented by the activity rates） of all production activities are extracted by solving these relationships. Then with the con- sumption characteristics and operating parameters of these activities, the detailed cost consumption of a product in its manufacturing process is estimated. A case study is finally given based on the compressor products of a manufacturing company, and its effectiveness is shown. As the cost influ- ence of complex consumption relationships is fully considered, the limitation of traditional ABC method is overcome, and therefore a high accuracy in product cost estimation under the complex manufacturing environment can be achieved.