Multi-scale object-based measurement of arid plant community structure

The measurement of plant community structure provides an extensive understanding of its function, succession and ecological process. The detection of plant community boundary is rather a challenge despite in situ work. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address this challenge. This study presents a multi-scale segmentation approach to accurately identify the boundaries of each vegetation and plant community for mapping plant community structure. Initially, a very high resolution (VHR) Worldview-2 image of a desert area is hierarchically segmented from scale parameter 2 to 500. Afterward, the peak values of the standard deviation of brightness and normalized difference vegetation index (NDVI) across the segmentation scales are detected to determine the optimal segmentation scales of homogeneous single plant and plant community boundaries. A multi-scale classification of vegetation characterization with features of multiple bands, NDVI, grey-level co-occurrence matrix (GLCM) entropy and shape index is performed to identify dryland vegetation types. Finally, the four vegetation structural features on the type, diversity, object size and shape are calculated within the plant community boundaries and composed to plant community structure categories. Comparing the results with the object fitting index (FI) of the reference data, the validation indicates that the optimal segmentations of tree, shrub and plant communities are consistent with the identified peak values.     

Evolutionary level set method for structural topology optimization

This paper proposes an evolutionary accelerated computational level set algorithm for structure topology optimization. It integrates the merits of evolutionary structure optimization (ESO) and level set method (LSM). Traditional LSM algorithm is largely dependent on the initial guess topology. The proposed method combines the merits of ESO techniques with those of LSM algorithm, while allowing new holes to be automatically generated in low strain energy within the nodal neighboring region during optimization. The validity and robustness of the new algorithm are supported by some widely used benchmark examples in topology optimization. Numerical computations show that optimization convergence is accelerated effectively.     

Node trust evaluation in mobile ad hoc networks based on multi-dimensional fuzzy and Markov SCGM(1, 1) model

Due to the nature of distribution and self-organization, mobile ad hoc networks rely on cooperation between nodes to transfer information. One of the key factors to ensure high communication quality is an efficient assessment scheme for risks and trust of choosing next potential cooperative nodes. Trust model, an abstract psychological cognitive process, is one of the most complex concepts in social relationships, involving factors such as assumptions, expectations and behaviors. All of the above make it difficult to quantify and forecast trust accurately. In this paper, based on the theories of fuzzy recognition with feedback, SCGM(1, 1) model and Markov chain, we present a pattern of prediction making. The analysis and experimental computation show that this scheme is efficient in trust prediction for ad hoc networks.     

Buckling of carbon fiber composite pyramidal truss core sandwich columns

This paper deals with the theoretical prediction of global buckling loads for carbon fiber composite pyramidal truss core sandwich columns. Different from thin plate structures, transverse shear effect can not be neglected for sandwich structures. In addition, the attributes of the laminated face sheets are considered in the present paper. A zig-zag displacement approximation is made. Based on the principle of minimum potential energy, equilibrium equations and boundary conditions are derived via the variational method. The critical buckling loads under various boundary conditions are presented. In order to validate the reasonableness of the equivalent-core method, the strain energies stored in the actual discrete truss members and the equivalent continuous homogenous core layer are calculated respectively and compared, and a good agreement is obtained. The proposed analytical method is verified by comparing with the published theoretical predictions and experimental results.     

基于消除地方高校成长上限基模的实证研究——以江西M学校为例

本文基于系统动力学反馈基模分析方法,在分析江西地方高校发展的成长上限基模和对策基模的基础上进行实证研究,引入相关数据,构建消除成长上限基模的顶点赋权模型,并提出综合对策思考。     

对应用投资项目财务评价指标的探讨

投资项目财务效果评价指标，按决策目的可分为绝对效果评价指标和相对效果评价指标两大类；按是否考虑资金时间价值可分为静态评价指标和动态评价指标两大类。本文将按后一种分类对项目评价中使用的各种指标的应用作以下的探讨。     

基于Internet的空分设备远程监视与故障诊断专家系统的研究

简介了远程监视与故障诊断专家系统在国内空分设备中应用的实际意义，着重分析了大型空分设备远程监视与故障诊断专家系统中远程监视模块和故障诊断模块的开发。     

Solaris下综合教务系统的管理和维护

对于Solaris下综合教务系统的管理和维护,本文从系统的优化调整,备份、恢复策略,系统安全保护措施等三个方面进行了探讨。     

Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network

Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D>200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.