Yang Yinghua

A new fault detection and diagnosis method based on principal component analysis in multivariate continuous processes

The fault detection and diagnosis methods based on principal component analysis (PCA) have been developed widely because they need no detailed information about the process mechanism model and really can detect faults promptly. However the existing diagnosis algorithms such as expert systems or contribution plots, etc. still have some trouble when they are applied in real industrial processes, which leads to more extensive research on this topic. In this paper, the proposed diagnosis method utilizes the on-line loading plot and cluster analysis to give accurate cause for abnormal process conditions, which is grounded on the fact that faults normally change the correlation of process variables which may indicate more direct information about the failure cause. Thus, the principal components score plot and square predicted error (SPE) plot are used to detect the abnormal process operation condition, the loading plot and cluster analysis are used to diagnose the faults. The result shows that accurate conclusion could be obtained easily by this method.

A new method for coplanar camera calibration based on neural network

As an essential step of 3D reconstruction, research on the camera calibration methods has great important significance of theoretical study and practical value. In this paper, a new simply, flexible and more accurate coplanar camera calibration method is proposed based on neural network. This method only requires a coplanar target and without camera motion. The neural network is used to learn the relationships between the image information and the 3D information to emend aberrance of camera, and it neither requires the inner and outer parameters of the camera and any prior knowledge of the parameters. The experimental results of image simulation show that the proposed method is correct and effective.

Wavelet edge detection using two-dimensional otsu model and local enhancement

In the paper, we use wavelet technique to detect edges in small scale along the direction of gradient maximum. Edges that we extracted are accurate and single-pixel wide. But the photo also contains a lot of noise, so we set threshold to extract the ideal edge points. Currently, the threshold is set mostly by peoples experience that needing a lot of trial or set the average gray value of the image directly, but the overall effect is not satisfactory. In response to the problem, we propose a method of using two-dimensional otsu model to obtain the threshold, the two-dimensional otsu method not only considers the gray value of pixels but also takes the pixels outside their fields of space-related information into account and it takes a good performance in the presence of noise of image. And we do not need to set any parameter to get the threshold. After that, we propose the corresponding solution to the problem that some edge points can not be detected: local enhancement method. In the method, we first operate the fuzzy edges of the original image, and then use the method we have proposed to detect the edges again. Finally, the simulation shows the correctness and effectiveness of the algorithm and it can also detect the fuzzy edges with an advantage of positioning accurate and having low noise.

Kernel entropy component analysis based process monitoring method with process subsystem division

Aiming at the features that modem industrial processes always have some characteristics of complexity and nonlinearity and the process data usually contain both Gaussion and non-Gaussion information at the same time, a new process performance monitoring and fault diagnosis method based on subsystem division and kernel entropy component analysis (Sub-KECA) is proposed in this paper. KECA as a new method for data transformation and dimensionality reduction, which chooses the best principal component vector according to the maximal Renyi entropy rather than judging by the top eigenvalue and eigenvector of the kernel matrix simply. Besides, it can be optimized and anti-disturb due to the application of subsystem division. The proposed method is applied to process monitoring of the Tennessee Eastman(TE) process. The positive simulation results indicate that this method is more feasible and efficient when comparing with KPCA method and original KECA method.

Neural network pole placement controller for nonlinear systems through linearisation

A new adaptive pole placement controller for unknown nonlinear plants is developed using a modified neural network. The modified neural network is composed of two parts: one is a linear neural network (LNN), which is the linearised model at the operating point; and the other is a multilayered feedforward neural network (MFNN), which approximates the nonlinear dynamics of the plant that can not be modelled by the LNN. Then a fast learning algorithm is presented for training the proposed neural network. The controller design is based on the LNN, and the output of the MFNN is considered as a measurable disturbance and is eliminated through feedforward control. Simulation results reveal that the proposed training algorithm is much faster than the standard algorithm and the new adaptive pole placement controller can effectively control a class of nonlinear plants.

An effective DOA estimation method of coherent signals based on reconstruct weighted noise subspace

For the problem that traditional DOA (Direction Of Arrival) estimation algorithms often fail to deal with coherent signals, a new high accuracy DOA estimation method based on weighted noise subspace is proposed. Considering the received data matrix obtained by uniform liner array, the proposed method makes full use of the cross-covariance information of it to construct an augmented matrix and performs singular value decomposition on it. In order to obtain more accurate signal vectors, a new weighted noise subspace is reconstructed, and the weighted factor matrix is designed to maintain the consistency of eigenvectors. Finally, combined the new noise subspace and weighted factor matrix completes the DOA estimation of coherent signals. Simulation results are presented to demonstrate the effectiveness of the proposed method, especially in low signal-to-noise ratio (SNR) and small number of snapshots. And without losing the aperture of array, the new method can estimate the DOA of coherent signals successfully.

A novel process monitoring method based on improved DTW-MKECA

Aiming at the features that modem industrial processes always have some characteristics of complexity and non-linearity, an integration of improved dynamic time warping (DTW) and multi-way kernel entropy component analysis (MKECA) is proposed in this paper. DTW-MKECA applies DTW method at first to process unequal-length data, which ensures that the original data distribution can be preserved. Then it employs MKECA method to make data from the input space map to high dimensional feature space and analysis these data. MKECA ensures that little information of the original data is lost during the dimensionality reduction, meanwhile, a monitoring model can be established. The proposed process monitoring method combines the advantages of DTW and MKECA. The simulation results of monitoring between modeling data and online data of reheating furnace demonstrate the availability of proposed method in this paper.

A method for measuring the position of discharging billet in heating furnace based on image recognition

According to the characteristics of the noise of discharging billet, method based on scale product using wavelet transform for denoising is introduced in this thesis. Then evaluation criteria based on edge detection which evaluates the quality of image denoising is proposed, according to the requirements of captured pictures in the thesis. With the position of slab changing in image, gray of edge is changing. So it is unsuitable to detect edge by the fixed threshold value. Therefore, in order to improve image contrast between slab edge and background, image enhancement based on wavelet transform is adopted. Then, the slab edge is detected by Canny operator. After that using Hough transform extract complete and smooth edges of slab. In image, the size of slab is known. Taking advantage of reconstructed image, the relationship of pixel and actual size is assured. At last we can calculate the real position of the slab in reheating furnace.

Multi-user detection based on improved-KICA

In this paper Improved kernel independent component analysis (KICA) algorithm is proposed for detection of direct sequence code division multiple access (DS-CDMA) signals and compared with KICA and FastICA algorithms. ICA based technique is based on independence of source signals and these conditions are satisfied in multi-user CDMA environment. The aim is to recover a set of unknown mutually independent source signals from their observed mixtures without knowledge of the mixing coefficients. KICA which is advanced recently is a non-linear method for blind source separation (BSS). Combining a KICA element to conventional signal detection reduces multiple access interference (MAI) and enables a robust, computationally efficient structure. For traditional KICA algorithm is influenced by kernel function and also ignores noise, in this paper Improved-KICA algorithm using optimal kernel function and considering the noise is proposed for multi-user DS-CDMA signal. In this paper bit error rate simulations of these algorithms has been given for different number of users, SNR and compared. The results show that the proposed Improved-KICA is more effective compared with traditional algorithms and performs better at separating the source signals from the mixed CDMA signals with noise.

A new classic camera calibration method based on coplanar Points

As a necessary step of 3D reconstruction, and a premise and base for computer vision obtaining the spatial information of 3D objects, research on the camera calibration methods has great important significance of theoretical study and practical value. This paper introduces a new simply, flexible and more accurate classic camera calibration method based on coplanar points. This method only requires a coplanar target and without camera motion. We use step-by-step calibration thinking, and first calibrate the principal point, and then neglect the lens distortion and linearly solve all the parameters, when high accuracy is required, we can include Wengs lens distortion model and solve the distortion coefficient by nonlinear algorithm. The experiment of images proves the proposed method is accurate and effective.