恒温供水装置的自动控制

恒温水是能效标定的重要组成部分.针对目前国内外的研究现状,提出了一种新型的自动恒温供水系统.系统采用制冷半导体和电加热器实现对温度的调节,同时利用积分分离PID抑制系统超调,并应用固态继电器构建开关功放,最后通过ARM LPC2210控制器对系统进行了闭环自动控制.实验结果表明,系统出水温度精确、稳定、可靠;系统在暖水炉、空调等能效标定中将得到广泛应用.     

Image morphing with conformal welding

We address the issue of deforming an image of a source object to that of a target. Previous works including barycentric coordinates and functional maps can hardly enforce shape consistency, especially for the objects with complex nested shape components. We leverage the conformal welding theory that maps 2D shapes (planar contours) to the automorphisms of the unit circle, named shape signatures. Conformal welding enables us to apply the Laplacian constraint to deformations in the signature space (or unit circle domain), which renders efficiency and flexibility. Additionally, we are able to fully reconstruct complex shape contours from deformed signatures, and hence generate the morphed images for target shapes. The experiments on complex shape contours and facial images, where multiple components exist, validate the effectiveness of the proposed approach.     

Observer‐based fault estimators using iterative learning scheme for nonlinear time‐delay systems with intermittent faults

This paper deals with the intermittent fault estimation problem for a class of nonlinear time‐delay systems with measurement noise. The time delays are assumed to occur in state vector, nonlinear term as well as output vector, thus reflecting the time delays influence in reality more closely. The aim of the problem is to estimate the intermittent fault by using iterative learning scheme, with the property of index, hence attenuating the influence from measurement noise. Different from existing fault estimating schemes, the state error information and fault estimating information in the previous iteration are used in the current iteration to improve the estimating results. The stability and convergence of iterative learning observer and uniform boundedness of dynamic error system are achieved by using Lyapunov function and optimal function design. Simultaneously, an improved sufficient condition for the existence of such an estimator is established in terms of the linear matrix inequality by the Schur complements and Young relations. Furthermore, the results are both suited for the systems with time‐varying delay and the systems with constant delay. Finally, two numerical examples are proposed to illustrate the effectiveness of the proposed method, and a comparability example is presented to demonstrate its superiority. Copyright © 2017 John Wiley & Sons, Ltd.     

A fast chaos-based image encryption scheme with a novel plain image-related swapping block permutation and block diffusion

In the paper, a fast image encryption scheme with block permutation and block diffusion is introduced. Considering the increasing size of the image, encryption process is manipulated by block, and the encryption of every block image consists of block permutation and block diffusion. A novel plain image-related swapping block permutation strategy is presented, the swapping operation of the block image is controlled by the random chaotic sequences, and the initial value and parameter of the chaotic system are produced by SHA 256 hash value of the plain image, thus our algorithm is highly sensitive to changes of the plain image. Diffusion method within the block image (DMWBI) and diffusion method between two block images (DMBTBI) are presented to effectively eliminate the correlation of adjacent pixels in the plain image. The correlated chaos is employed to enhance the relationship of chaos, and take fully use of chaotic maps. One-dimensional Logistic-Sine System (LSS) is used to generate pseudo-random sequences during the whole encryption process. Experiment results and security analysis have proved the proposed image encryption algorithm is secure and effective.     

基于Chernoff上界的多类问题特征选择优化迭代算法

本文提出正态分布条件下面向不同分布的多类问题基于Chernoff上界的特征选择优化迭代算法.该算法由两重迭代组成首先在设定的原始空间和特征空间Chernoff参数s条件下,通过解矩阵方程的迭代算法求得变换矩阵的最优解;然后,在变换矩阵确定的特征空间中搜索最佳的参数s使错误概率上界最小;最后采用折半法修正设定的Chernoff参数s及其迭代步长.通过分析和实例可见基于Chernoff上界特征选择是面向不同分布的多类问题的最佳特征选择方法.     

基于壳聚糖包埋二氧化硅-亚甲基蓝纳米复合物的电流型癌胚抗原免疫传感器的研究

合成了一种新型的SiO2-亚甲基蓝(SiO2-MB)纳米复合物.SiO2-MB纳米复合物具有不同于一般的纳米SiO2的性质,它能将MB的电子转移到电极表面,而且更重要的是减小了MB的渗漏.SiO-MB纳米复合物作为免疫传感器的媒介体使用,并采用壳聚糖(CS)包埋此复合物形成CS-SiO2-MB复合膜滴涂于洁净的玻碳电极(GCE)表面,然后在复合膜的表面固定纳米金(nano-Au)并吸附癌胚抗体(anti-CEA),制备出了性能良好的电流型免疫传感器.通过循环伏安考察了电极的电化学特性,并采用透射电子显微镜(TEM)对SiO2-MB纳米复合物的微观结构进行了表征.在最优条件下,该传感器在癌胚抗原(CEA)浓度为1～80 ng/mL范围内有良好的线性关系,检测下限为0.3 ng/mL(3倍信噪比).而且,该传感器制作简单,检测快速,稳定性较好.     

Robust neural network tracking controller using simultaneous perturbation stochastic approximation

This paper considers the design of robust neural network tracking controllers for nonlinear systems. The neural network is used in the closed-loop system to estimate the nonlinear system function. We introduce the conic sector theory to establish a robust neural control system, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the neural network. The neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed neural control system guarantees closed-loop stability of the estimation system, and a good tracking performance. The performance improvement of the proposed system over existing systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against system disturbance.     

Labview和Matlab混合编程方法的研究与实现

LabVIEW是一种高性能的图形化虚拟仪器编程软件,它在测试与测量、过程控制与处理、科学研究和分析等方面有广泛的应用,但是,LabVIEW在数值分析和数据运算处理等方面显得有些力不从心;通过其强大的外部接口,提出了基于DDE,DLL,ActiveX自动化,MATLAB Script节点技术的LabVIEW和MATLAB混合编程,并结合小波降噪、虚拟模糊热点温度分析仪实例,对这些方法进行了比较分析;利用混合编程既良好地发挥了仪器语言的优势,又扩展了算法工具箱,探索了智能虚拟仪器新的开发途径。     

A novel generic hebbian ordering-based fuzzy rule base reduction approach to mamdani neuro-fuzzy system

There are two important issues in neuro-fuzzy modeling: (1) interpretability--the ability to describe the behavior of the system in an interpretable way--and (2) accuracy--the ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.     

Discrete J-spectral factorization

This paper deals with the J-spectral factorization for general discrete rational matrices. A simple approach based on the Kalman filtering in Krein space is proposed. The main idea is to construct a stochastic state space filtering model in Krein space such that the spectral matrix of the output is equal to the rational matrix to be factorized. The spectral factor is then easily derived by using the generalized Kalman filtering in Krein space, which is similar to the H₂ spectral factorization. Our approach unifies the treatment of the H₂ spectral factorization and the J-spectral factorization. The applications of the derived results in H_∞ and risk-sensitive estimation for both nonsingular and singular systems are demonstrated.