Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

Multi-resource scheduling for FPGA systems

In modern cloud data centers, reconfigurable devices (FPGAs) are used as an alternative to Graphics Processing Units to accelerate data-intensive computations (e.g., machine learning, image and signal processing). Currently, FPGAs are configured to execute fixed workloads, repeatedly over long periods of time. This conflicts with the needs, proper to cloud computing, to flexibly allocate different workloads and to offer the use of physical devices to multiple users. This raises the need for novel, efficient FPGA scheduling algorithms that can decide execution orders close to the optimum in a short time. In this context, we propose a novel scheduling heuristic where groups of tasks that execute together are interposed by hardware reconfigurations. Our contribution is based on gathering tasks around a high-latency task that hides the latency of tasks, within the same group, that run in parallel and have shorter latencies. We evaluated our solution on a benchmark of 37500 random workloads, synthesized from realistic designs (i.e., topology, resource occupancy). For this testbench, on average, our heuristic produces optimum makespan solutions in 47.4% of the cases. It produces acceptable solutions for moderately constrained systems (i.e., the deadline falls within 10% of the optimum makespan) in 90.1% of the cases.     

油气集输站场生活污水土地处理技术

通过对土地处理系统的介绍，结合油气站场污水的特点，分析了不同土地处理技术在实际中的设置情况和方式，给出一般的面积计算方法和结果，总结了土地处理的技术特点和应用前景。     

网格资源管理系统模型研究

网格资源管理系统是网格计算系统的重要组成部分。文章分析了网格资源管理系统的功能需求，具体论述了该模型的原型系统，最后介绍了相关研究工作。     

利用TM资料测量毛乌素沙地面积的方法和结果

利用1:25万陆地卫星TM资料和地形图，在GIS支持下，建立毛乌素沙地图形数据库，对毛乌素沙地目前流沙分布的范围进行了调查研究。重.资、介绍了利用陆地卫星TM资料对毛乌素沙地的面积进行测童计算的技术和方法，给出了最新测贡得到的毛乌素沙地全区总面积和分布于各旗县的沙地面积数据。     

基于Struts框架的科研资源计划系统的应用研究

Struts是基于模型视图控制体系(MVC)的Web应用框架。详细介绍了基于MVC体系的Struts工作原理，探讨应用Struts框架构建企业级应用系统的步骤及方法。给出了基于Struts的科研资源计划系(SRRPS)的设计实现，结果表明Struts框架能极大地提高系统的可扩展性、可重用性及可维护性。     

核磁共振谱的岩石孔喉结构分析

刘堂宴 ,王绍民 ,傅容珊 ,周明顺 ,李艳华 ,罗曼 .核磁共振谱的岩石孔喉结构分析 .石油地球物理勘探 ,2 0 0 3,38(3) :32 8～ 333岩心核磁共振 T2 谱和压汞分析数据均在一定程度上反映了岩石的孔喉结构 ,理论分析表明 ,这两组数据具有相关性。应用岩心核磁共振 T2 谱研究岩石孔喉结构 ,关键是确定 T2 与 Pc的转换系数。但以前的方法在 T2与 Pc 的转换过程中 ,需要涉及某些岩石特性参数 ,实用中有一定困难。本文直接利用岩心核磁共振 T2 谱和压汞分析数据之间的相关性 ,客观地确定 T2 与 Pc之间的转换系数 ,避免了确定岩石特性参数的困难。应用本文方法 ,对 6块岩心的多种核磁共振分析数据进行了对比分析 ,做出了 NMR (Nuclear Magnetic Resonance,简称NMR) T2 毛管力曲线和孔喉半径分布 ,并将这些结果与压汞分析的结果进行了对比。结果表明 ,岩心 NMR T2谱在实用性和评价精度上均略显优势 ;至少是在饱含油的条件下 ,岩心的 NMR T2 谱可以用于研究孔喉结构分布 ,油气的弛豫特性作为影响背景值存在 ,对于评价结果没有明显的影响 ;在全部 6块岩心中 ,T2 与 Pc 的转换系数位于 2 5 0 0～ 4 0 0 0μs· MPa之间。     

Production Equations for Unsteady-State Construction Processes

The production equation called Little’s law has been applied to construction data recently. However, Little’s law was derived for steady-state conditions assuming constant input and output rates and long production runs. Production in construction is inherently temporary, and learning curves and environmental influences often render input and output rates unequal and nonlinear. Starting with a conservation of mass formulation, general equations for work-in-process and cycle time for unsteady-state conditions and limited run production are developed. The motivation behind these equations is to explain common trends in production variables seen on construction projects. Previous studies have shown that when output from a construction production system is drastically increased, a significant upward impact is also seen on cycle time and work-in-process, and this work provides underlying theory and equations to explain these trends. Cycle time and work-in-process equations are presented as functions of time and on average. Data from construction activities are used to show that unsteady-state conditions commonly occur. Reasonable simplifications of the general equations provide guidelines for buffer sizing and resource allocation decisions.     

Selecting a waste management option using a life-cycle analysis approach

Solid-waste management, and in particular the disposal of used packaging, is currently the subject of much topical debate. This is driven by both consumer and legislative pressures. Consumers see used packaging as a highly visible element of municipal solid waste, complaining of excessive packaging and low levels of recycling. Legislators, perhaps in pandering to the views expressed by consumer bodies, have been active within the CEC and individual Member States by introducing (or proposing) legislation or similar regulatory tools and targets to facilitate a greater diversion of used packaging from disposal by landfill to alternative solid-waste management practices, in particular recycling, which are widely accepted to have a lower impact on the environment. In this paper the relative environmental profiles of pursuing alternative solid-waste management practices to disposal by landfill are explored, focusing not just on solid waste per se but also on associated considerations of energy consumption and emissions, which are invariably overlooked as factors contributing to the environmental impact of solid-waste management practices.