Measurements and modeling of absorptive partitioning of volatile organic compounds to painted surfaces

Partitioning to surfaces is an important sink for volatile organic compounds (VOCs) indoors, but the mechanisms are not well understood or quantified. Here, a mass spectrometer was coupled to a portable surface reactor and a flow tube to measure partitioning of VOCs into paint films coated onto glass or wallboard, and their subsequent diffusion. A model was developed to extract values of the effective absorbing organic mass concentration of the film, C_w, which is a measure of absorption capacity, and VOC diffusion coefficients, D_f, from VOC time profiles measured during film passivation and depassivation. Values of C_w agreed well with the value estimated from the paint film mass and flow tube air volume, and D_f values (also measured using attenuated total reflectance-Fourier transform infrared spectroscopy) correlated well with VOC vapor saturation concentrations, C*, estimated using a group contribution method. The value of these relationships for estimating key parameters that control VOC partitioning into paint and the fate of VOCs indoors was demonstrated using a house model, which indicated that >50% of VOCs with C* ≤10⁸ μg/m³ (C* of octane, hexanone, and propanol) that contacted a paint film of typical thickness fully permeated the film regardless of emission duration.     

在线超低温预浓缩-全二维气相色谱-飞行时间质谱联用系统在大气挥发性有机物监测中的应用

采用自主研制的在线超低温预浓缩设备，连续在线采集大气样品或者连续采集苏玛罐离线样品，对样品进行超低温捕集浓缩和二次超低温冷冻聚焦，采用全二维气相色谱-飞行时间质谱联用技术分离检测浓缩后的挥发性有机物，实现对大气中C2~C12共100多种挥发性有机物的浓缩富集和定性定量分析。本研究对该方法的线性、重复性、加标回收率、检出限等性能进行表征，并与常规一维气相色谱-四极杆质谱联用系统的定性定量结果进行比较。结果表明，该方法不仅满足大气中挥发性有机物检测的性能指标要求，且组分定性定量的准确性、灵敏度等指标均优于一维气相色谱-四极杆质谱联用技术，在挥发性有机物成分较复杂的大气样品检测中具有明显优势。     

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.     

Leader-following consensus of fractional-order multi-agent systems via adaptive control

This article focuses on the consensus problem of leader-following fractional-order multi-agent systems (MASs) with general linear and Lipschitz nonlinear dynamics. First, the distributed adaptive protocols for linear and nonlinear fractional-order MASs are constructed, respectively. We allow the control coupling gains to be time varying for each agent. Moreover, the adaptive modification schemes for the control gain are designed, which renders smaller control gains and thus requires smaller amplitude on the control input without sacrificing consensus convergence. Second, based on fractional-order Lyapunov stability theorem and Barbalat's lemma, two novel sufficient conditions in terms of linear matrix inequalities are provided to ensure that the leader-following consensus can be obtained in the case for any undirected connected communication graph. Furthermore, we show that the proposed algorithm also works for consensus of agents with intrinsic Lipschitz nonlinear dynamics. As a result, the proposed framework requires no global information and thus can be implemented in a fully distributed manner. Finally, the numerical simulations are given to demonstrate the effectiveness of obtained the theoretical results.