Variability in rheology of cemented paste backfill with hydration age,binder and superplasticizer dosages

The use of (CPB) material to ameliorate geotechnical stability of underground mine is in nascent stage in India. Rheological properties of CPB change with travelling time as it is transported to underground mine stope through pipeline reticulation. In this paper, rheological properties of CPB based on mill tailings of a carbonate rich mineral processing waste are evaluated for different dosages of polycarboxylate (PC) based (SP). Each CPB sample having 78?wt% solids is mixed separately with 4%, 6% or 8% of binder dosages (ratio of the weight of dry binder to the weight of dry tailings) and, 0%, 0.5%, or 1.0% of SP dosages as weight of dry binder. The paper presents a methodology for determining yield stress, plastic viscosity and thixotropic behaviour of CPB mixture as a function of hydration age, binder and SP dosages. Results from the experimental campaigns indicate that SP content has significant influence on rheological behaviour of CPB and can be suitably exploited to enhance the flow characteristics of the carbonate rich process tailings. The study also develops multivariate linear regression models of yield stress, plastic viscosity and thixotropy of CPB depending on the hydration age, binder and SP dosages.     

A Unified Analysis of Structured Sonar-Terrain Data Using Bayesian Functional Mixed Models

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. Although existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this article, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design. Supplementary materials for this article are available online.     

New analytical approximate solution of the generalised temperature integral for kinetic reactions

Simple closed-form expression of the generalised temperature integral in the basic equation to describe kinetic reactions for solid materials in linear heating process is always suitable for use in determining parameters. Many developed solutions only can give high accuracies on the general conditions. A new analytical approximate solution was deduced in this work. The deviations of this solution from the true value are fully analysed. This solution takes advantage in broader application conditions than other known solutions. The application of the new analytical approximate solution in austenite kinetic reaction in this work reinforces that austenite reaction rate is feasible to be given a priori.     

超精密平面飞切机床关键技术研究

研制了一种用于KDP晶体加工的平面飞切机床,该机床直线轴基于直线电动机驱动、液体静压导轨支承;刀具旋转轴采用高刚度气浮主轴+高刚度主轴旋转机构。基于高精度分辨率位置反馈+线性驱动器+PID控制算法,直线轴获得了1 mm/min速度下,0.018 mm/min的低速波动以及±0.01μm的位置精度;刀具微进给装置采用差动螺纹进给来实现,最终获得了进给分辨率1μm、锁紧后位置移动量小于1μm高精度进给。在优化工艺参数后,金刚石飞切机床加工100 mm×100 mm×10mm的KDP晶体后获得表面粗糙度Rq优于2 nm高精度指标;加工400 mm×400 mm×30 mm的铝镜后获得面形PV值优于3μm的高精度指标。     

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.     

Multi-product continuous plant scheduling: combination of decomposition,genetic algorithm,and constructive heuristic

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.     

基于线性自抗扰控制技术的永磁同步电机控制策略研究

永磁同步电机(PMSM)具有体积小、高速度、高精度等优势,广泛应用于现代工程实际当中。在多数情况下其控制策略采用经典的PID控制,但较多的实际复杂环境当中,传统的PID所暴露的固有缺陷已经无法满足PMSM的控制高效性。采用先进的线性自抗扰控制策略,其具有不依赖具体对象模型,控制器自身可以估计补偿作用于被控对象的所有扰动的优势,实验仿真结果表明,该方案有效抑制了扰动,进而提高了系统的控制性能。     

开关磁阻直线电机的设计及其推力优化

设计了一种开关磁阻直线电机,使用JMAG软件对其进行了仿真和优化。进行了推力公式的推导,研究了导通顺序和初始位置对电机推力的影响,并分析了三种典型情况的磁力线分布。对电机的定子和动子的齿部和轭部高度、宽度及铁耗进行仿真优化,得出了最优参数。将有取向硅钢应用于该电机,研究了推力与轧制角的关系,并与使用普通硅钢的开关磁阻直线电机进行了对比,推力有一定提升。提出在动子齿部开切向槽的方案,结果表明,开切向槽对推力的影响较小,并能显著减小推力波动。     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.     

A numerical filtering method for linear state-space models with Markov switching

A class of discrete-time random processes arising in engineering and econometrics applications consists of a linear state-space model whose parameters are modulated by the state of a finite-state Markov chain. Typical filtering approaches are collapsing methods, which approximate filtered distributions by mixtures of Gaussians, each Gaussian corresponding to one possibility of the recent history of the Markov chain, and particle methods. This article presents an alternative approach to filtering these processes based on keeping track of the values of the underlying filtered density and its characteristic function on grids. We prove that it has favorable convergence properties under certain assumptions. On the other hand, as a grid method, it suffers from the curse of dimensionality, and so is only suitable for low-dimensional systems. We compare our method to collapsing filters and a particle filter with examples, and find that it can outperform them on 1- and 2-dimensional problems, but loses its speed advantage on 3-dimensional systems. Meanwhile, our method has a proven theoretical convergence rate that is probably not achieved by collapsing and particle methods.