Physicochemical and thermophysical properties of CaO–TiO2–SiO2–Na3AlF6 system based electrode coating for AUSC power plant

The aim of this study is to investigate the physicochemical and thermophysical properties of CaO–TiO₂–SiO₂–Na₃AlF₆ based electrode coating developed for the welding of advanced ultra-supercritical (AUSC) thermal power plant components. The extreme vertices approach has been used to create twenty-six electrode coating compositions. Various physicochemical and thermophysical properties were characterized using experiments. The coating properties are essential in ensuring sound weld with desirable in-service performance. The developed coating's structural behavior was analyzed using Fourier transformation infrared spectroscopy (FTIR) and X-ray diffraction (XRD). In addition, regression analysis was carried out to evaluate the influence of coating constituents. Individual constituents and their interaction were found to have a prominent role in influencing the physicochemical and thermophysical properties.     

融合重检测机制的卷积回归网络目标跟踪算法

针对基于人工特征的背景感知相关滤波（CACF）算法在形变、运动模糊、低分辨率情形跟踪效果较差以及跟踪器遇到严重遮挡等情形容易陷入局部最优而导致跟踪失败的问题，提出一种融合重检测机制的卷积回归网络（CRN）目标跟踪算法。在训练阶段，将相关滤波作为CRN层融入进深度神经网络，使网络成为一个整体进行端到端训练；在跟踪阶段，通过残差连接融合不同网络层及其响应值，同时引入重检测机制使算法从潜在的跟踪失败中恢复，当响应值低于给定阈值时激活检测器。在数据集OTB-2013上的实验表明，所提算法在50个视频序列上精确度达到88.1%，相比原始CACF算法提高9.7个百分点，在具有形变、运动模糊等属性的视频序列上相比原始算法表现更优秀。     

基于RFID标签阵列的睡眠期间呼吸量连续监测系统

睡眠期间连续且准确的呼吸量监测有助于推断用户的睡眠阶段以及提供一些慢性疾病的线索。现有工作主要针对呼吸频率进行感知和监测,缺乏对呼吸量进行连续监测的手段。针对上述问题提出了一种基于商用无线射频识别(RFID)标签的无线感知用户睡眠期间呼吸量的系统——RF-SLEEP。RF-SLEEP通过阅读器连续收集附着在胸部表面的标签阵列返回的相位值及时间戳数据,计算出呼吸引起的胸部不同点的位移量,基于广义回归神经网络(GRNN)构建胸部不同点的位移量与呼吸量之间的关系模型,从而实现对用户睡眠期间呼吸量的评估。RF-SLEEP通过在用户肩膀处附着双参考标签,消除用户睡眠期间翻转身体对胸部位移计算造成的误差。实验结果表明,RFSLEEP对不同用户睡眠期间的呼吸量连续监测的平均精确度为92.49%。     

Prediction of sediment,particulate nutrient and dissolved nutrient concentrations in a dry tropical river to provide input to a mechanistic coastal water quality model

A Generalised Additive Modelling (GAM) approach is applied to prediction of both particulate and dissolved nutrient concentrations in a wet-tropical river (the Fitzroy River, Queensland, Australia). In addition to covariant terms considered in previous work (i.e. flow, discounted flow and a rising-falling limb term), we considered several new potential covariates: meteorological and hydrological variables that are routinely monitored, available in near-real time, and were considered to have potential predictive power. Of the additional terms considered, only flows from three tributaries of the Fitzroy River (namely, the Nogoa, Comet and Isaac Rivers) were found to significantly improve the model. Inclusion of one or more of these additional flow terms greatly improved results for dissolved nitrogen and dissolved phosphorus concentrations, which were not otherwise amenable to prediction. In particular, the Nogoa sub-catchment, dominated by pasture for cattle, was found to be important in determining dissolved inorganic nitrogen and phosphorus concentrations reaching the river mouth. This insight may direct further research, including future refinement of processed-based catchment models. The GAMs described here are used to provide near real-time river boundary conditions for a complex coupled hydrodynamic and biogeochemical model of the Great Barrier Reef Lagoon, and can be coupled with a forecasting hydrological model to allow integrated forecasting simulations of the catchment to coast system.     

Performance analysis of empirical models for predicting rock mass deformation modulus using regression and Bayesian methods

Deformation modulus of rock mass is one of the input parameters to most rock engineering designs and constructions. The field tests for determination of deformation modulus are cumbersome, expensive and time-consuming. This has prompted the development of various regression equations to estimate deformation modulus from results of rock mass classifications, with rock mass rating (RMR) being one of the frequently used classifications. The regression equations are of different types ranging from linear to nonlinear functions like power and exponential. Bayesian method has recently been developed to incorporate regression equations into a Bayesian framework to provide better estimates of geotechnical properties. The question of whether Bayesian method improves the estimation of geotechnical properties in all circumstances remains open. Therefore, a comparative study was conducted to assess the performances of regression and Bayesian methods when they are used to characterize deformation modulus from the same set of RMR data obtained from two project sites. The study also investigated the performance of different types of regression equations in estimation of the deformation modulus. Statistics, probability distributions and prediction indicators were used to assess the performances of regression and Bayesian methods and different types of regression equations. It was found that power and exponential types of regression equations provide a better estimate than linear regression equations. In addition, it was discovered that the ability of the Bayesian method to provide better estimates of deformation modulus than regression method depends on the quality and quantity of input data as well as the type of the regression equation.     

Statistical Data Analysis in the Era of Big Data

Big data is on everyone's lips and often raises emotions. On the one hand, the notion is a basis for much technological optimism, mostly directed towards new business models, or simplifications and optimizations in professional and private life. On the other hand, it is a basis for dystopic perspectives, which are targeted, e.g., at profiling of the individual and their privacy space, overarching optimization in daily life and intransparency of decision making. In this article, after a short historical prolog, it is discussed what distinguishes big data from traditional data analysis. The underlying mathematical methods are introduced and scientific successes are reported. Additionally, the risks and limits – especially regarding the derivation of causal relationships – of data analysis are discussed.     

基于GRA-GRNN模型的露天金属矿爆破后矿岩界线位移分析

为了避免露天金属矿爆破后导致爆堆边缘矿石品位贫化损失,需要根据最低品位阈值重新计算矿岩边界,而影响矿岩边界发生改变因素众多,需要确定主要影响因素。因此,利用爆堆爆破前地形方向和爆堆地质数据,构建灰色关联-广义回归神经网络模型(GRA-GRNN)分析爆堆矿岩边界变化主要影响因素。首先对爆堆钻孔品位数据使用析取克里金法进行空间插值,并根据矿山工艺最低品位阈值提取爆破前的矿岩边界;其次,将爆破前后的数字DEM模型进行求差,求得爆破后的爆堆数字DEM模型,并构建爆破前后爆堆数字DEM模型空间分布椭圆,从而确定爆堆爆破后的水平形变方向;对影响爆堆爆破后形变的可能因素进行提取,并应用GRA-GRNN模型选取主要影响因素及对其强度进行分析,并将其结果与BP神经网络模型预测结果进行了对比。从实验结果可知,影响爆堆爆破后形变强度排前三的因素为:爆破前地形方向、爆孔排距和列距,强度分别为0.880、0.760和0.755,预测结果优于BP模型。     

Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis

In this study, the separation of hydrogen from gas mixtures using a palladium membrane coupled with a vacuum environment on the permeate side was studied experimentally. The gas mixtures composed of H₂, N₂, and CO₂ were used as the feed. Hydrogen permeation fluxes were measured with membrane operating temperature in the range of 320–380 °C, pressures on the retentate side in the range of 2–5 atm, and vacuum pressures on the permeate side in the range of 15–51 kPa. The Taguchi method was used to design the operating conditions for the experiments based on an orthogonal array. Using the measured H₂ permeation fluxes from the Taguchi approach, the stepwise regression analysis was also employed for establishing the prediction models of H₂ permeation flux, followed by the analysis of variance (ANOVA) to identify the significance and suitability of operating conditions. Based on both the Taguchi approach and ANOVA, the H₂ permeation flux was mostly affected by the gas mixture composition, followed by the retentate side pressure, the vacuum degree, and the membrane temperature. The predicted optimal operating conditions were the gas mixture with 75% H₂ and 25% N₂, the membrane temperature of 320 °C, the retentate side pressure of 5 atm, and the vacuum degree of 51 kPa. Under these conditions, the H₂ permeation flux was 0.185 mol s^?1 m^?2. A second-order normalized regression model with a relative error of less than 7% was obtained based on the measured H₂ permeation flux.     

露天矿山电动轮自卸汽车装载监测系统研制开发

就露天矿山大型电动轮自卸汽车装载监测问题,研制开发了HY—2型附着式传感器和HYRI—2型装载监测系统。介绍了该系统的基本原理,并给出了现场实际试验结果。表明该系统适用于大型自卸汽车或大型电动轮自卸汽车的装载监测工作。     

矿产资源形势分析与预测的理论与方法

结合矿产资源形势分析与预测的基本理论，综合介绍了目前比较流行的几种分析与预测的方法，旨在为政府和企业提供预测方法和决策依据，以促进资源的合理开发和利用。