炉排-循环床复合垃圾焚烧炉内干燥和燃烧过程

本文从实验和数学模型两方面，系统研究了具有自主知识产权的炉排-循环床复合垃圾焚烧炉内干燥、燃烧过程．利用马弗炉模拟垃圾在固定炉排干燥床上的干燥情况，研究了垃圾种类、垃圾厚度、温度等对干燥过程的影响．实验结果表明：吸水性强的垃圾比吸水性弱的垃圾难干燥；温度越高干燥越迅速，垃圾层厚度越小越易干燥．干燥炉排上的热解实验表明，温度越高CO／C转化率越低，干燥床内的温度影响流化床密相区的温度分布．建立了炉排．循环床复合垃圾焚烧炉的燃烧过程的数学模型，主要包括物料分布模型、挥发分释放模型、炭颗粒燃烧模型等．用本文提出的模型对实际运行的垃圾焚烧炉进行了计算，并将计算结果与工业测试数据比较，表明炉膛内的压力分布、物料浓度分布与工业测试结果吻合良好，O2，CO2，CO浓度分布特征在趋势上与工业测试数据吻合．模型所用到的经验参数根据实验或者工业数据确定，具有半经验特性，模型可帮助分析、指导焚烧炉的设计与运行．     

基于K-means聚类的快递企业客户细分方法

为了实现对快递企业客户的科学划分，制定差异化的客户营销策略，建立了一种基于K-means聚类的客户细分模型。对快递企业呼叫中心的客户相关数据特征进行了分析与预处理，确定了合理的客户细分变量，并建立了基于呼叫中心数据挖掘的客户细分流程。以某快递企业为例对客户细分方法进行了验证。结果表明该方法能够有效区分快递客户为敏感客户、节俭客户、高端客户、潜在客户与优质客户等五类，为进一步营销方案的设计提供决策支持。     

基于决策树的超市客户数据挖掘与仿真

FoodMart超市市场部想提高客户满意度和客户保有率，于是计划对会员卡方案重新定义，以便更好地为客户提供服务并且使提供的服务能够更加密切地满足客户的期望。本文基于对FoodMart超市客户数据的分析，适当选取某些客户类型特征作为决策属性，并利用ID3算法从FoodMart超市客户数据中挖掘客户分类规则。并利用java语言编程实现了ID3算法。仿真的结果不仅为FoodMart超市会员卡重新定义制定最佳的指导策略，而且在理论研究和工程实践中都具有重要的意义。     

冲泻区水动力特性的数值分析

基于三维非稳态的Navier．Stokes方程，采用有限体积法进行数值离散，在构造高分辨率STACS格式的VOF方法的基础上，建立基于气液两相流的三维自由面流动模型，并基于该模型对三维剪切流场和圆柱水体坍塌进行了三维模拟，检验其数值精度．应用该模型数值研究冲泻区内涌波（bore）在均匀斜坡上的动态传播过程．对上爬水流自由面水位高度与实验数值进行对比，结果显示数值解与实验解吻合较好，模型能很好描述水流的掺气运动．数值分析了涌波崩塌（BoreCollapse）、上冲流（Uprush）和回落流（Backwash）等过程中的自由水面、瞬时流速及床面最大剪切应力的时空分布．结果表明，冲泻区水动力结构时空变化非常复杂，模型能捕捉到高速薄层水流结构，优于前人的数值结果，研究有利于进一步了解冲泻区内的泥沙输运规律及岸滩演变机制．     

基于BB—SIR方法的结肠癌特征基因提取

结合复杂数据分析（Complex Data Analysis）理论和充分降维的思想，在有效提取结肠癌特征基因研究的基础上，建立结肠癌特征基因提取的BB-SIR模型。该模型方法简洁易懂且有较高的识别率。依据BB-SIR模型和所给数据找到了两个结肠癌亚型，并确定了该亚型的特征基因。实验结果表明，BB—SIR方法选出的特征基因能够识别出结肠癌亚型，并且识别正确率达到96％以上。     

基于知识发现创新技术的专家系统新构造

为解决“知识匮乏”这一专家系统中的瓶颈问题，提出了基于数据库与知识库协同机制的综合过程模型KD（D＆K）及其相关的创新技术，进而提出了基于知识发现的专家系统ESKD新构造．作为ESKD的核心知识获取构件KD（D＆K）由KDD＊和KDK＊两部分组成，对基于双库协同机制的KDD＊过程模型和基于双基融合机制的KDK＊过程模型分别做了介绍；给出了ESKD的总体框架；并讨论了ESKD的一些子系统和动态知识库系统；最后，在农业真实数据库上验证了ESKD的有效性和先进性．ESKD有望把专家系统推向新阶段．     

高温层的横向生长对异质外延GaN结构性质的影响

通过改变MOCVD生长GaN反应的V／III比来改变横／纵向生长速度比，以此来研究两步法中高温GaN层的横向生长对材料结构性质的影响．透射电子显微镜（TEM）和x射线衍射（XRD）实验的研究表明，高温GaN层的横向生长速度越快，位错的传播方向更易于偏离c轴，弯向晶粒内部，且弯曲的位置越靠近缓冲层，但位错密度并不随横向生长的加速而单调变化．提出了一个关于GaN生长动力学过程和位错弯曲机制的模型以解释横向生长与GaN结构的对应关系．     

基于VDM的复杂装备故障数据分析

在历史故障数据基础上，分别掏建故障信息、装备信息、装备使用状况的数据集市，通过数据挖掘模型和可视化交互平台，形成基于可视化数据挖掘（Visual Data Mining，VDM）故障分析架构。以此架构为基础，探讨了故障数据仓库的建立以及故障数据挖掘算法，对实际故障数据进行预处理，并分析故障责任、故障趋势、故障分布等特性，得到图形化的分析结论，为装备的设计、制造、维护提供支持。     

集成多个传感器网络数据的网站

美国橡树岭国家实验室正在开发一个用户可写的网站-SensorPedia（www．sensorpedia．com），可帮助突发事件应对人员和决策者以近实时的速度获取不同传感网络采集到的数据。     

线性定常系统特征模型的证明

研究线性定常系统的特征建模问题，证明了多输入多输出线性定常系统的特征模型可以用二阶时变差分方程组描述，并且刻画了特征模型的建模误差，在系统不含零实部极点的情形下，在稳态和暂态时建模误差分别为0和O（h），一般情形下，分别为O（h^2）和D（h）．该证明表明特征建模与一般模型降阶方法是本质不同的，特征模型是把高阶模型的有关信息压缩到几个特征参量之中，并没有丢失系统信息．     

An Inhomogeneous Hidden Markov Model for Efficient Virtual Machine Placement in Cloud Computing Environments

In a cloud environment virtual machines are created with different purposes, such as providing users with computers or handling web traffic. A virtual machine is created in such a way that a user will not notice any difference from working on a physical computer. A challenging problem in cloud computing is how to distribute the virtual machines on a set of physical servers. An optimal solution will provide each virtual machine with enough resources and at the same time not using more physical services (energy/electricity) than necessary to achieve this. In this paper we investigate how forecasting of future resource requirements (CPU consumption) for each virtual machine can be used to improve the virtual machine placement on the physical servers. We demonstrate that a time‐dependent hidden Markov model with an autoregressive observation process replicates the properties of the CPU consumption data in a realistic way and forecasts future CPU consumption efficiently. Copyright © 2016 John Wiley & Sons, Ltd.     

物联网服务建模：一种基于环境建模的方法

物联网服务是传统Web服务通过传感器网络向物理环境的延伸,它通过传感器网络感知物理环境中的实体,也向物理环境实体施加作用.与传统Web服务相比,由于物联网服务受到所依赖的物理环境的时间受限性、资源受限性和设备潜在故障概率的影响,物联网服务的响应速度、服务能耗和容错能力等特性成为影响物联网系统整体特性的重要因素.因此,对物联网服务进行全面建模,对物联网服务所处的外部环境进行形式化描述,并结合物理环境模型对物联网服务的性质进行分析,对于确保物联网系统的正确性、稳定性非常必要.本文针对物联网服务的特点,结合基于环境建模的需求工程思想,提出一种基于环境的物联网服务三元问题域建模框架,给出了物联网服务建模本体以及相应的建模原则.在此基础上,提出了一种物联网服务行为建模方法,该方法将物联网服务和物理环境建模为概率时间自动机,将用户期望的服务特性描述为时序逻辑公式,为物联网服务功能行为正确性验证和非功能性约束可满足性验证奠定了基础.     

Evidence from trade cards for the scientific instrument industry

Trade cards were a means of advertising products or services and thereby attracting customers to the owner's shop. They often included a variety of details about the proprietor and his business, and illustrated his wares. Cards for the scientific instrument industry depicted all classes of instrument and the products from which they were made. A careful study of the cards can reveal much supplementary information about the way the industry worked, so their use, and limitations, as a source of historical evidence is discussed in detail. Over sixty English trade cards from London and the provinces, covering the period 1670 to 1900, are illustrated (most of them previously unpublished). By careful analysis, it is shown that aesthetic style and type-face design, combined with written and pictorial content, may be used to deduce dates and business practices. Many small pieces of information gradually build a complex picture of an important and fascinating industry.     

An integrated bayesian vector auto regression and error correction model for forecasting electricity consumption and prices

The analysis and forecasting of electricity consumption and prices has received considerable attention over the past forty years. In the 1950s and 1960s most of these forecasts and analyses were generated by simultaneous equation econometric models. Beginning in the 1970s, there was a shift in the modeling of economic variables from the structural equations approach with strong identifying restrictions towards a joint time-series model with very few restrictions. One such model is the vector auto regression (VAR) model. It was soon discovered that the unrestricted VAR models do not forecast well. The Bayesian vector auto regression (BVAR) approach as well the error correction model (ECM) and models based on the theory of co integration have been offered as alternatives to the simple VAR model. This paper argues that the BVAF., ECM, and co integration models are simply VAR models with various restrictions placed on the coefficients. Based on this notion of a restricted VAR model, a four-step procedure for specifying VAR forecasting models is presented and then applied to monthly data on US electricity consumption and prices.     

Forecasting the Italian industrial production index in real time

Daily electricity consumption data, available almost in real time, can be used in Italy to estimate the level of industrial production in any given month before the month is over. We present a number of procedures that do this using electricity consumption in the first 14 days of the month. (This is an extension of a previous model that used monthly electricity data.) We show that, with a number of adjustments, a model using half-monthly electricity data generates acceptable estimates of the monthly production index. More precisely, these estimates are more accurate than univariate forecasts but less accurate than estimates based on monthly electricity data. A further improvement can be obtained by combining ‘half-monthly’ electricity-based estimates with univariate forecasts. We also present quarterly estimates and discuss confidence intervals for various types of forecasts.     

基于投影寻踪模型的水资源利用效率评价

本文根据13个省市的水资源相关指标，包括农业万元GDP用水量、工业万元GDP用水量、人均COD排放量、人均生活用水量等，建立评估指标体系并利用投影寻踪模型对上述各省的水资源利用效率进行综合评价。该方法可以依据样本自身的数据特性寻求最佳投影方向，利用最佳投影方向可以判断各评价指标对综合评价目标的贡献大小和方向，进而得到最终的评价结论。     

Environmental impacts of household appliances in Europe and scenarios for their impact reduction

Assessing the environmental impact due to consumption of goods and services is a pivotal step towards achieving the sustainable development goal related to responsible production and consumption (i.e. SDG 12). Household appliances plays a crucial role and should be assessed in a systemic manner, namely considering all life cycle stages, technological efficiency, and affluence aspects. The present study assess the impact of such household appliances used in Europe, and tests scenarios of potential impact reduction at various scales. Life cycle assessment is applied to 14 different household appliances (ranging from dishwashers to television devices) selected to build a set of representative products, based on their economic value and diffusion in households in Europe. Related impacts are calculated with the Environmental Footprint method for calculating a Consumer Footprint “appliances” for the baseline year 2010. A number of scenarios encompassing eco-solutions on a technical level, changes in consumption pattern, behavioral changes, as well as the combination of all these aspects are run to estimate the Consumer Footprint related to household appliances for the year 2030, compared against this baseline scenario. The baseline Consumer Footprint is confirming the importance of the use phase in leading the impacts in almost all impact categories. Testing different scenarios concludes that there is a reduction of the impact for most of the categories (with up to 67% for the ozone depletion potential, and still around 35% for the global warming potential), while two of the here examined impact categories (i.e. land-use and mineral resource depletion) show an overall potential that is even negative – i.e. the results of all scenarios are higher than the ones of the 2010 baseline scenario. The increase in purchase and use of such appliances may offset energy efficiency benefits in some of the examined categories. Hence, the assessment of sustainability of appliances consumption should always include several scales, from the efficiency of the products (micro scale), to the improvement of the energy mix (meso scale), up to accounting for socio-economic drivers and patterns of consumption affecting the overall appliances stock (macro scale).     

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure.     

Environmental impacts of household appliances in Europe and scenarios for their impact reduction

Assessing the environmental impact due to consumption of goods and services is a pivotal step towards achieving the sustainable development goal related to responsible production and consumption (i.e. SDG 12). Household appliances plays a crucial role and should be assessed in a systemic manner, namely considering all life cycle stages, technological efficiency, and affluence aspects. The present study assess the impact of such household appliances used in Europe, and tests scenarios of potential impact reduction at various scales. Life cycle assessment is applied to 14 different household appliances (ranging from dishwashers to television devices) selected to build a set of representative products, based on their economic value and diffusion in households in Europe. Related impacts are calculated with the Environmental Footprint method for calculating a Consumer Footprint “appliances” for the baseline year 2010. A number of scenarios encompassing eco-solutions on a technical level, changes in consumption pattern, behavioral changes, as well as the combination of all these aspects are run to estimate the Consumer Footprint related to household appliances for the year 2030, compared against this baseline scenario. The baseline Consumer Footprint is confirming the importance of the use phase in leading the impacts in almost all impact categories. Testing different scenarios concludes that there is a reduction of the impact for most of the categories (with up to 67% for the ozone depletion potential, and still around 35% for the global warming potential), while two of the here examined impact categories (i.e. land-use and mineral resource depletion) show an overall potential that is even negative – i.e. the results of all scenarios are higher than the ones of the 2010 baseline scenario. The increase in purchase and use of such appliances may offset energy efficiency benefits in some of the examined categories. Hence, the assessment of sustainability of appliances consumption should always include several scales, from the efficiency of the products (micro scale), to the improvement of the energy mix (meso scale), up to accounting for socio-economic drivers and patterns of consumption affecting the overall appliances stock (macro scale).