Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

A structure-preserved local matching approach for face recognition

In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

基于连通域的模板匹配法用于字符识别的算法研究

文章介绍了一种用于字符识别的基于连通域的模板匹配算法.该算法是在传统的模板匹配法的基础上,运用了重心重叠等处理方法,并且对字符块进行连通域处理后再实施匹配,最后利用最大相似法得出识别结果.该算法在光学仪器读数图象的动态识别中取得了良好的识别效果.     

Block-based neural networks

This paper presents a novel block-based neural network (BBNN) model and the optimization of its structure and weights based on a genetic algorithm. The architecture of the BBNN consists of a 2D array of fundamental blocks with four variable input/output nodes and connection weights. Each block can have one of four different internal configurations depending on the structure settings, The BBNN model includes some restrictions such as 2D array and integer weights in order to allow easier implementation with reconfigurable hardware such as field programmable logic arrays (FPGA). The structure and weights of the BBNN are encoded with bit strings which correspond to the configuration bits of FPGA. The configuration bits are optimized globally using a genetic algorithm with 2D encoding and modified genetic operators. Simulations show that the optimized BBNN can solve engineering problems such as pattern classification and mobile robot control.     

PLC在高浓度冲稀自动控制系统的应用

本文在介绍高浓度冲稀自动控制系统的工作原理、特点、系统调校及日常维护要点的基础上,探讨了PLC在这种类型的系统中的应用。     

一个基于CORBA和Java技术的分布式并行有限元计算环境

该文结合ＣＯＲＢＡ（Ｃｏｍｍｏｎ Ｏｂｊｅｃｔ Ｂｒｏｋｅｒ Ａｒｃｈｉｔｅｃｔｕｒｅ）和Ｊａｖａ技术,提出了一个能在支持ＴＣＰ／ＩＰ协议的异构网络环境下实现分布式有限元计算环境的具体方案,为了验证方案的可行性,在西南交通大学应用力学与工程系计算中心的局域网上实现了分布式并行计算环境的基本框架,并进行了１０００阶矩阵相乘的计算。     

MATLAB 7.3与.NET新接口技术

MATLAB r2006(7.3)下利用COM组件实现VB.net与MATLAB混合编程的新方法。并用实例介绍了这种方法的具体应用过程。     

Rosetta: semantic support for model-centered systems-level design

With the advent of system-on-chip (SoC) technology, the design of small electrical systems has become a systems engineering task. Such tasks require working at high levels of abstraction to build systems by either integrating information from various design domains or modeling different aspects of the same component. Designers of traditional computer-based systems usually rely on component-based techniques that parallel the physical architecture. The model-based approach encompasses the component-centered approach, treating structural decomposition as a single model in the overall hierarchy. Model-centered semantics and languages let designers concentrate on the data, computation, or communication models that describe complex computer-based SoC requirements. Appropriate design semantics specify each system aspect, and the designer assembles those aspects into models to define complete systems and components. The developers of Rosetta, a heterogeneous systems-level modeling language that supports predictive design analysis, have identified mechanisms for defining and composing models that specify multiple domains of interest from many perspectives. Rosetta requirements and domains define test cases and generate abstract vectors for each test scenario. Several analysis tools are under development for transforming Rosetta into existing analysis environments, and the authors describe their authors are also developing native analysis tools for symbolic verification and simulation environments. The authors use Rosetta to provide computation models for customized digital and mixed-signal system-specification environments. Other domains to support, optical and microelectrical mechanical systems specifications are under consideration     

若干无机盐对聚丙烯酰胺溶液粘度的影响

探讨了无机盐溶液中阴阳离子对聚丙烯酰胺(PAM)溶液粘度的影响,确定了影响PAM溶液粘度的主要因素是二价阳离子(如ca2 及Mg2 离子)。