浅谈BIM模式下设施管理的应用价值

本文主要从国内大型现代商业建筑的角度,结合国内BIM和设施管理发展现状,探讨和分析BIM模式对于设施管理的应用价值。     

Modeling Regression Quantile Process Using Monotone B-Splines

Quantile regression as an alternative to conditional mean regression (i.e., least-square regression) is widely used in many areas. It can be used to study the covariate effects on the entire response distribution by fitting quantile regression models at multiple different quantiles or even fitting the entire regression quantile process. However, estimating the regression quantile process is inherently difficult because the induced conditional quantile function needs to be monotone at all covariate values. In this article, we proposed a regression quantile process estimation method based on monotone B-splines. The proposed method can easily ensure the validity of the regression quantile process and offers a concise framework for variable selection and adaptive complexity control. We thoroughly investigated the properties of the proposed procedure, both theoretically and numerically. We also used a case study on wind power generation to demonstrate its use and effectiveness in real problems. Supplementary materials for this article are available online.     

Fuzzy MCDM framework for locating a nuclear power plant in Turkey

Turkey has recently initiated a project to revise its nuclear policy. The revised nuclear energy policy considers searching for possible alternative locations for future nuclear power plants in Turkey. At the most basic level, the public cannot accurately evaluate whether it is willing to support nuclear energy unless it has an idea about where the power plants are likely to be located. It is argued that the selection of a facility location is a multi-criteria decision-making problem including both quantitative and qualitative criteria. In this research, given the multi-criteria nature of the nuclear facility location selection problem, a new decision tool is proposed to rank the alternative nuclear power plant sites in Turkey. The proposed tool is based on fuzzy Entropy and t norm based fuzzy compromise programming to deal with the vagueness of human judgments. Finally, a discussion and some concluding remarks are provided.     

A tribe competition-based genetic algorithm for feature selection in pattern classification

Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification.     

Intelligent acoustic rotor speed estimation for an autonomous helicopter

Acoustic sensing to gather information about a machine can be highly beneficial, but processing the data can be difficult. In this work, a variety of methodologies have been studied to extract rotor speed information from the sound signature of an autonomous helicopter, with no a-priori knowledge of its underlying acoustic properties.     

Holistic environmental soil-landscape modeling of soil organic carbon

In environmental soil-landscape modeling (ESLM), the selection of predictive variables is commonly contingent on the researchers' domain expertise on soil–environment processes. This variable selection strategy may suffer bias or even fail in regions where the process knowledge is insufficient. To overcome this problem, this study demonstrates a holistic ESLM framework which consists of five components: model conceptualization, data compilation, process identification, parsimonious model calibration, and model validation. Based on the STEP-AWBH conceptual model, a comprehensive pool of 210 potential environmental variables that exhaustively cover pedogenic and environmental factors was constructed. This was followed by strategic variable selection and development of parsimonious prediction models using machine learning techniques. The all-relevant variable selection successfully identified the major and minor factors relevant to the SOC variation, showing that the major factors important for explaining SOC variation in Florida were vegetation and soil water gradient. Topography and climate showed moderate effects on SOC variation. Parsimonious SOC models developed using four minimal-optimal variable selection techniques and simulated annealing yielded optimal predictive performance with minimal model complexity. The holistic ESLM framework not only provides a new view of selecting and utilizing variables for predicting soil properties but can also assist in identifying the underlying processes of soil-environment systems of interest. Due to the flexibility of the framework to incorporate various types of variable selection and modeling techniques, the holistic environmental modeling strategy can be generalized to other environmental modeling domains for both prediction and process identification.     

供热电厂蒸汽流量仪表的改造探讨

本文探讨了浙江浙能长兴发电有限公司蒸汽流量仪表的应用现状、存在问题及原因分析,并从两种主要流量仪表的选型比较中得到改造思路,实施后降低了热网流量损耗,提高了收益.     

国外EPC项目中暖通设计与国内设计的差异

以笔者参与设计且现已建成投入使用的阿尔及利亚康斯坦丁3 000座剧院为主要例子,阐述海外EPC项目的暖通设计中冷热源及空调方案选择、设计参数选取、图纸设计深度、设备的配合选型招标等问题,以及在这些方面国外做法与国内常规做法的异同。同时介绍在设计过程中,设计师需要持有的心态及自我定位,设计需要准备的前期工作等其他相关注意事项。     

数控加工中零件淬火后阵列精孔的加工方法

淬火后的材料硬度很高,加工很难到位,对刀具的磨损较大,尤其对于精度高,数量多的阵列孔,要想保证孔径和孔间距达到公差要求需要在加工工艺和刀具的选择上合理安排。     

Channel and feature selection for a surface electromyographic pattern recognition task

The objective of this research is to select a reduced group of surface electromyographic (sEMG) channels and signal-features that is able to provide an accurate classification rate in a myoelectric control system for any user. To that end, the location of 32 sEMG electrodes placed around-along the forearm and 86 signal-features are evaluated simultaneously in a static-hand gesture classification task (14 different gestures). A novel multivariate variable selection filter method named mRMR-FCO is presented as part of the selection process. This process finds the most informative and least redundant combination of sEMG channels and signal-features among all the possible ones. The performance of the selected set of channels and signal-features is evaluated with a Support Vector Machine classifier.