复数图像去噪算法的稀疏编码实现

针对复数图像系统所获取的图像具有噪声的问题,将稀疏编码理论扩展到复数域中,并且运用该理论提出一种复数据的降噪算法.该算法采用复数域的正交匹配追赶算法对图像进行稀疏编码,利用自适应方式从待训练含噪图像样本中获取表示词典,并指出最终图像的降噪效果与表示的稀疏度有关.实验结果表明,文中算法对于复数图像取得了很好的降噪效果,尤其是可以有效地提高复数相位的降噪信噪比.     

一种改进小波阈值函数的光纤光栅传感信号去噪方法

光纤布拉格光栅( FBG)传感信号中存在的噪声严重影响了其中心波长的检测精度,因此必须进行去噪处理。本文针对传统的小波软、硬阈值函数去噪的不足,提出了一种改进的对数阈值函数,并确定了最优的小波基和分解尺度。仿真表明,本文提出的改进阈值函数去噪后信噪比( SNR)较大,均方根误差( RMSE)较小,其中信噪比比传统的软、硬阈值方法提高了1.5 dB~4 dB。最后通过实验验证了本文方法的去噪效果。     

无线传感器网络的系统化自适应建模

无线传感器网络的系统能耗制约着全网络的综合应用能力,其中节点有限的能量从根本上影响着传感器网络效能。针对无线传感器网络的全局能耗问题,提出了基于径向基函数神经网络以及状态空间表达的系统化建模方法。考虑到无线传感器网络的拓扑结构与分级关系, 采用径向基函数神经网络自适应实时规划系统。鉴于各传感器节点对数据的不同处理方式与能耗密切相关, 对全系统能耗建立系统化矩阵模型。仿真分析表明该模型可根据实际应用背景调整设置完成全局优化。     

一种基于小波去噪的DFT信道估计改进算法

针对小波去噪与DFT插值相结合的信道估计算法没有对循环前缀内的噪声进行去噪的缺点,提出了一种基于小波去噪与改进的DFT插值相结合的信道估计新算法。该算法首先利用离散小波变换对最小二乘(LS)法估计出的结果进行阈值去噪处理,并根据循环前缀内、外噪声方差的均值在DFT插值的过程中设置相应门限,然后对循环前缀内的噪声再次处理,以进一步减小噪声的影响。仿真实验结果表明,在复杂度基本不变的前提下,该算法能够较好地减小加性高斯白噪声的影响,并有效提升信道估计的准确度,其总体性能较小波去噪与DFT插值相结合的信道估计算法更优。     

Globally Stable Adaptive Tracking Control for Uncertain Strict‐Feedback Systems Based on Neural Network Approximation

This paper addresses the problem of globally stable adaptive neural tracking control for a class of strict‐feedback nonlinear systems. Compared with the existing works, the salient properties of the proposed scheme are given as follows. First, a novel switching controller is developed, which consists of a traditional adaptive neural controller and an extra robust controller to pull back the transient outside of the approximation domain. Second, only two adaptive parameters need to be tuned online, and the computational burden is considerably alleviated in practice. Third, to design the desired switching controller via the backstepping technique, a novel switching function, which has continuous derivatives up to the nth order, is constructed. It is shown that the system output converges to a small neighborhood of the reference signal and the closed‐loop system is globally stable. Finally, an example is provided to verify the effectiveness of the proposed control method.     

A General Robust MPC Design for the State‐Space Model: Application to Paper Machine Process

Applying model predictive control (MPC) in some cases such as complicated process dynamics and/or rapid sampling leads us to poorly numerically conditioned solutions and heavy computational load. Furthermore, there is always mismatch in a model that describes a real process. Therefore, in this paper in order to prevail over the mentioned difficulties, we design a robust MPC using the Laguerre orthonormal basis in order to speed up the convergence at the same time with lower computation adding an extra parameter “a” in MPC. In addition, the Kalman state estimator is included in the prediction model and accordingly the MPC design is related to the Kalman estimator parameters as well as the error of estimations which helps the controller react faster against unmeasured disturbances. Tuning the parameters of the Kalman estimator as well as MPC is another achievement of this paper which guarantees the robustness of the system against the model mismatch and measurement noise. The sensitivity function at low frequency is minimized to tune the MPC parameters since the lower the magnitude of the sensitivity function at low frequency the better command tracking and disturbance rejection results. The integral absolute error (IAE) and peak of the sensitivity are used as constraints in optimization procedure to ensure the stability and robustness of the controlled process. The performance of the controller is examined via the controlling level of a Tank and paper machine processes.     

Multi layer ELM-RBF for multi-label learning

Many neural network methods such as ML-RBF and BP-MLL have been used for multi-label classification. Recently, extreme learning machine (ELM) is used as the basic elements to handle multi-label classification problem because of its fast training time. Extreme learning machine based auto encoder (ELM-AE) is a novel method of neural network which can reproduce the input signal as well as auto encoder, but it can not solve the over-fitting problem in neural networks elegantly. Introducing weight uncertainty into ELM-AE, we can treat the input weights as random variables following Gaussian distribution and propose weight uncertainty ELM-AE (WuELM-AE). In this paper, a neural network named multi layer ELM-RBF for multi-label learning (ML-ELM-RBF) is proposed. It is derived from radial basis function for multi-label learning (ML-RBF) and WuELM-AE. ML-ELM-RBF firstly stacks WuELM-AE to create a deep network, and then it conducts clustering analysis on samples features of each possible class to compose the last hidden layer. ML-ELM-RBF has achieved satisfactory results on single-label and multi-label data sets. Experimental results show that WuELM-AE and ML-ELM-RBF are effective learning algorithms.     

大学计算机基础课程教学的实践探讨

通过对大学计算机基础课程教学目的、课程特点、教学内容、课程模式的分析,总结了当前高校计算机基础课程教学中所存在的问题,并提出了该课程教学的新模式,为各地区大学计算机基础课程的教学实践提供了参考建议。     

水利工程造价管理与控制

分析了以设计阶段为控制重点的全过程造价管理及目前我国水利工程造价管理中存在的一些问题,并就水利工程造价管理与控制提出了一些建议。     

小波阈值与小波包对南水北调工程防渗墙监测数据去噪有效性研究

以南水北调北京段大宁水库防渗墙加高施工工程为背景,基于小波包变换去噪法和小波变换阀值去噪法,对现场监测数据进行了消噪处理,比较了两者在工程实际中的应用效果。研究表明,对防渗墙工程安全监测数据,小波包的处理结果更为理想,能够更有效地去除噪音突变信号,保留原始有用信号的突变点,使重构信号能够更光滑地重现原始信号,其去噪法性能比小波变换阀值去噪法更佳,具有更好的实际应用价值。研究结论为后期更加准确地评价防渗墙施工期间的变形和进行墙体长期稳定性分析提供重要依据,研究方法可为类似工程提供参考。