Geographic Characterization of Leccinum rugosiceps by Ultraviolet and Infrared Spectral Fusion

Leccinum rugosiceps is an edible mushroom belonging to genus Leccinum of Boletaceae. Its fruiting bodies are richer in nutrients than many vegetables and fruit. The model of support vector machine was established for the discrimination of L. rugosiceps from regions based on rapid and low-cost ultraviolet and infrared spectroscopies. The mid-level data fusion was performed by support vector machine. Compared to a single spectroscopic technique, mid-level data fusion provided higher accuracy by selecting the most significant variance from data matrixes based on partial least squares discriminant analysis. The accuracy of the classification of samples in the calibration and test sets were 85.00 and 94.74%, higher than separate measurements by ultraviolet or infrared spectroscopy. This approach has applications for authentication and quality assessment of L. rugosiceps.     

Data assimilation and pollution forecasting in Burgers' equation with model error function

This article presents a correction method for a better resolution of the problem of estimating and predicting pollution, governed by Burgers' equations. The originality of the method consists in the introduction of an error function into the system's equations of state to model uncertainty in the model. The initial conditions and diffusion coefficients, present in the equations for pollution and concentration, and also those in the model error equations, are estimated by solving a data assimilation problem. The efficiency of the correction method is compared with that produced by the traditional method without introduction of an error function.Three test cases are presented in this study in order to compare the performances of the proposed methods. In the first two tests, the reference is the analytical solution and the last test is formulated as part of the “twin experiment”.The numerical results obtained confirm the important role of the model error equation for improving the prediction capability of the system, in terms of both accuracy and speed of convergence.     

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.     

Multi-Model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts

Models for weather and climate prediction are complex, and each model typi-cally has at least a small number of phenomena that are poorly represented, such as perhaps the Madden-Julian Oscillation (MJO for short) or El Ni\~{n}o-Southern Oscillation (ENSO for short) or sea ice. Furthermore, it is often a very challenging task to modify and improve a complex model without creating new deficiencies. On the other hand, it is sometimes possible to design a low-dimensional model for a particular phenomenon, such as the MJO or ENSO, with significant skill, although the model may not represent the dynamics of the full weather-climate system. Here a strategy is proposed to mitigate these model errors by taking advantage of each model''s strengths. The strategy involves inter-model data assimilation, during a forecast simulation, whereby models can exchange information in order to obtain more faithful representations of the full weather-climate system. As an initial investigation, the method is examined here using a simplified scenario of linear models, involving a system of stochastic partial differential equations (SPDEs for short) as an imperfect tropical climate model and stochastic differential equations (SDEs for short) as a low-dimensional model for the MJO. It is shown that the MJO prediction skill of the imperfect climate model can be enhanced to equal the predictive skill of the low-dimensional model. Such an approach could provide a route to improving global model forecasts in a minimally invasive way, with modifications to the prediction system but without modifying the complex global physical model itself.     

3D HYBRID DEPTH MIGRATION AND FOUR-WAY SPLITTING SCHEMES

The alternately directional implicit （ADI） scheme is usually used in 3D depth migration. It splits the 3D square-root operator along crossline and inline directions alternately. In this paper, based on the ideal of data line, the four-way splitting schemes and their splitting errors for the finite-difference （FD） method and the hybrid method are investigated. The wavefield extrapolation of four-way splitting scheme is accomplished on a data line and is stable unconditionally. Numerical analysis of splitting errors show that the two-way FD migration have visible numerical anisotropic errors, and that four-way FD migration has much less splitting errors than two-way FD migration has. For the hybrid method, the differences of numerical anisotropic errors between two-way scheme and four-way scheme are small in the case of lower lateral velocity variations. The schemes presented in this paper can be used in 3D post-stack or prestack depth migration. Two numerical calculations of 3D depth migration are completed. One is the four-way FD and hybrid 3D post-stack depth migration for an impulse response, which shows that the anisotropic errors can be eliminated effectively in the cases of constant and variable velocity variations. The other is the 3D shot-profile prestack depth migration for SEG/EAEG benchmark model with two-way hybrid splitting scheme, which presents good imaging results. The Message Passing Interface （MPI） programme based on shot number is adopted.     

基于LabWindows/CVI的导弹舵机测控系统设计

舵机是导弹控制系统的重要执行机构,为实现对电动及气动多种型号导弹舵机性能的测试,设计开发了一种基于LabWindows/CVI虚拟仪器的测控系统。介绍了该测控系统硬件组成和软件结构流程,软件设计过程中充分利用多线程、数据库等技术,准确快速完成舵机自动化性能测试。应用结果表明:该舵机测控系统工作稳定,测试精度和自动化程度高,满足舵机试验测试精度和技术指标要求,为舵机的性能研究和维修维护提供了良好的测试环境。     

����ͨ���豸��ר�ó������X�ź����ݲɼ��������

采用了通用高速A/D采集器采集信号数据,用软件的方法开发出了具备多道能谱脉冲识别和计数分析能力的软件.用此软件实现了每次托卡马克放电后,在较短的时间内从庞大的原始采集数据中提取出能谱脉冲数据,计算和显示能谱图,这为下一步测量电子温度打下了基础,并同时实现了数据共享.     

Derivative temporal clustering analysis: detecting prolonged neuronal activity

Temporal clustering analysis (TCA) and independent component analysis (ICA) are promising data-driven techniques in functional magnetic resonance imaging (fMRI) experiments to obtain brain activation maps in conditions with unknown temporal information regarding the neuronal activity. Although comparable to ICA in detecting transient neuronal activities, TCA fails to detect prolonged plateau brain activations. To eliminate this pitfall, a novel derivative TCA (DTCA) method was introduced and its algorithms with different subtraction intervals were tested on simulated data with a pattern of prolonged plateau brain activation. It was found that the best performance of DTCA method in generating functional maps could be obtained if the subtraction interval is equal to or larger than the length of the rising time of the fMRI response. The DTCA method and its theoretical predication were further investigated and validated using in vivo fMRI data sets. By removing the limitations in the previous TCA, DTCA has shown its powerful capability in detecting prolonged plateau neuronal activities.     

利用面板数据模型分析湖南城镇居民的收入与消费之间的关系

本文利用面板数据模型研究湖南省城镇居民1999-2005年收入与消费之间的关系,分析湖南居民的收入差异对居民消费的影响,为今后调整湖南的消费结构,扩大内需,促进经济发展提供理论依据.     

基于子波变换的光谱信息数据压缩方法

本文介绍一种基于子波变换的光谱信息数据压缩方法，利用子波变换的多尺度分析原理，将原始光谱数据分解成集中源信号绝大部分能量的模糊信号和反映源信号变化特性的锐化信号。由于锐化信号只有源信号变化梯度大的区域系数值才较大，其他区域都接近零，只需保存少量的系数，就可以实现数据压缩，用本文方法，对２１种典型地物光谱数据进行了数据压缩实验，在１．０～１．７均方根误差情况下，若压缩结果不编码，压缩比一般为４：１～