LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

Reducing numerical dissipation in smoke simulation

Numerical dissipation acts as artificial viscosity to make smoke viscous. Reducing numerical dissipation is able to recover visual details smeared out by the numerical dissipation. Great efforts have been devoted to suppress the numerical dissipation in smoke simulation in the past few years. In this paper we investigate methods of combating the numerical dissipation. We describe visual consequences of the numerical dissipation and explore sources that introduce the numerical dissipation into course of smoke simulation. Methods are investigated from various aspects including grid variation, high-order advection, sub-grid compensation, invariant conservation, and particle-based improvement, followed by discussion and comparison in terms of visual quality, computational overhead, ease of implementation, adaptivity, and scalability, which leads to their different applicability to various application scenarios.     

蒸汽减压站自控系统的巧设计

蒸汽是石油化工装置中最重要的物料。随着石油化工工业的迅猛发展 ,蒸汽在其中扮演着越来越重要的多重角色。因此 ,对蒸汽系统的平稳控制 ,对石油化工生产装置平稳、高效地运行是至关重要的。传统的蒸汽减压站自控系统基建投资大 ,紧急情况下靠人工手动操作稳定蒸汽压力系统 ,往往造成生产的波动或大面积的停车。针对传统蒸汽减压站自控系统的弱点 ,提出蒸汽减压站自控系统的优化设计方案 ,该方案得以在大型生产装置成功应用。新方案不但节省了大量的基建投资 ,而且取得了良好的生产效益     

挖金湾矿近距离煤层联合布置开采可行性研究

提出挖金湾矿近距离煤层联合布置开采方案，并对开采方案的技术难点进行了分析，找出了解决方法。     

A Modified Variational-Gradient Method for Quasilinear Equations

A modified variational-gradient method is proposed and substantiated for quasilinear operator equations in a Hilbert space.     

一种复合型数值方法的改进与算法实现

文中对原有的复合方法进行了改进,提出了一种新的复合型数值方法,这种方法解决了原方法求解含源项的问题时存在数值震荡的缺点。为了验证算法的有效性,对计算流体力学中一阶浅水波问题的两个典型的算例进行了数值模拟,在解的光滑性、锐利性等几个方面做了分析,结果表明改进后的方法既消除了数值震荡现象,又能准确地逼近真解,求出锐利的间断,是一种行之有效的数值方法。     

基于预前控制异步电机直接转矩控制方案的研究

在传统异步电动机直接转矩控制方案中，由于负载的变化规律不可预测，因此其常会带来较大的开关频率的变化，为此提出了一种基于预前控制的异步电动机直接转矩控制的方法。该方法依据前一个周期的磁链和转矩误差，对下一个开关周期所应施加到异步电动机的定子电压矢量进行预测，然后借助空间矢量PWM的方法，合成此开关电压矢量。样机实验结果表明，该方案不但能维持逆变器的开关频率基本恒定，而且还具有比传统直接转矩控制更为优良的动静态特性。     

SPECTRAL DENSITY ESTIMATION FROM NONLINEARLY OBSERVED DATA

Abstract. The estimation of the spectral density function of a stationary Gaussian process at the input of an instantaneous nonlinearity is considered when the nonlinearity is known and a finite set of observations of the output process is given. A class of spectral estimates is considered and their quadratic-mean consistency is established; precise asymptotic expressions for their bias and covariance are derived and their asymptotic normality is obtained.     

1 000 t/d湿磨干烧生产线工艺特点和调试

湿法生产线的技术更新改造有异地新建新型干法生产线和原地改成湿磨干法生产线二种。浙江虎山集团(?)3.0 m×88.68 m湿法线的改造,经方案比较和生产原料的状况,采用了湿磨干烧方案。其中,料浆脱水采用了真空吸滤法,浇成系统采用了南京院专有技术。改造投产后,获得了优质、高产、低耗和安全生产的技改效果。     

常门铺水库溢洪道设计方案比较

在常门铺水库溢洪道改造中，通过不工模型试验，对其数据成果进行分析并进行方案比较，最终采用即经济又合理的方案二，为国家节省投资50.8万元。