Jin Hyun Park

Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis

Abstract Conventional process monitoring based on principal component analysis (PCA) has been applied to many industrial chemical processes. However, such PCA-based approaches assume that the process is operating in a steady state and consequently that the process data are normally distributed and contain no time correlations. These assumptions significantly limit the applicability of PCA-based approaches to the monitoring of real processes. In this paper, we propose a more exact and realistic process monitoring method that does not require that the process data be normally distributed. Specifically, the concept of conventional PCA is expanded such that a Gaussian mixture model (GMM) is used to approximate the data pattern in the model subspace obtained by PCA. The use of a mixture of local Gaussian models means that the proposed approach can be applied to arbitrary datasets, not just those showing a normal distribution. To use the GMM for monitoring, the overall T2 and Q statistics were used as the monitoring guidelines for fault detection. The proposed approach significantly relaxes the restrictions inherent in conventional PCA-based approaches in regard to the raw data pattern, and can be expanded to dynamic process monitoring without developing a complicated dynamic model. In addition, a GMM via discriminant analysis is proposed to isolate faults. The proposed monitoring method was successfully applied to three case studies: (1) simple two-dimensional toy problems, (2) a simulated 4×4 dynamic process, and (3) a simulated non-isothermal continuous stirred tank reactor (CSTR) process. These application studies demonstrated that, in comparison to conventional PCA-based monitoring, the proposed fault detection and isolation (FDI) scheme is more accurate and efficient.

An enhanced PID control strategy for unstable processes

An enhanced PID control strategy is proposed for unstable processes. Ultimate data sets and the process gain estimated from an improved relay feedback method are used to identify unstable processes as unstable first-order plus time-delay models. We also suggest a simple control structure including an inner feedback loop and the corresponding tuning relations to manipulate unstable processes more efficiently and systematically. The proposed control method not only guarantees the simplicity and the easiness, but also shows much better control performance than previous control methods for unstable processes.

New gene selection method for classification of cancer subtypes considering within-class variation

In this work we propose a new method for finding gene subsets of microarray data that effectively discriminates subtypes of disease. We developed a new criterion for measuring the relevance of individual genes by using mean and standard deviation of distances from each sample to the class centroid in order to treat the well‐known problem of gene selection, large within‐class variation. Also this approach has the advantage that it is applicable not only to binary classification but also to multiple classification problems. We demonstrated the performance of the method by applying it to the publicly available microarray datasets, leukemia (two classes) and small round blue cell tumors (four classes). The proposed method provides a very small number of genes compared with the previous methods without loss of discriminating power and thus it can effectively facilitate further biological and clinical researches.

Improved relay auto-tuning with static load disturbance

In the presence of static load disturbance, a relay auto-tuning experiment shows significant errors in estimating the ultimate gain and period. This paper shows how to overcome the problem without any information on the static gain of the process and how to obtain process gain from one relay experiment.

Gene selection and classification from microarray data using kernel machine

The discrimination of cancer patients (including subtypes) based on gene expression data is a critical problem with clinical ramifications. Central to solving this problem is the issue of how to extract the most relevant genes from the several thousand genes on a typical microarray. Here, we propose a methodology that can effectively select an informative subset of genes and classify the subtypes (or patients) of disease using the selected genes. We employ a kernel machine, kernel Fisher discriminant analysis (KFDA), for discrimination and use the derivatives of the kernel function to perform gene selection. Using a modified form of KFDA in the minimum squared error (MSE) sense and the gradients of the kernel functions, we construct an effective gene selection criterion. We assess the performance of the proposed methodology by applying it to three gene expression datasets: leukemia dataset, breast cancer dataset and colon cancer dataset. Using a few informative genes, the proposed method accurately and reliably classified cancer subtypes (or patients). Also, through a comparison study, we verify the reliability of the gene selection and discrimination results.

Nonlinear regression using RBFN with linear submodels

Radial basis function networks (RBFNs) have been widely used for function approximation and pattern classification as an alternative to conventional artificial neural networks. In this paper, RBFN with local linear functions is developed and applied to mapping nonlinear functions and modeling air pollutant emission. This extended version of the traditional RBFN has a linear function of inputs as a connecting weight, which is functionally equivalent to the first-order Sugeno fuzzy model. There are three kinds of parameters determined through proper training algorithms: the centers and spreads of each radial basis function, and the connection weights. The extended RBFN (ERBFN) is trained by a hybrid learning algorithm, which uses an iterative nonlinear optimization technique to obtain the center and spread of each radial basis function and the least squares method to obtain the connection weights. To avoid capturing a local optimum, the nonlinear parameters are initialized using a modified K-means clustering method, which has cluster-merging characteristic so as to automatically determine the number of basis functions. The proposed ERBFN method was applied to the approximation of three different functional forms and to the modeling of a real process. The results confirm that the proposed methodology gives considerably better performance and shows faster learning in comparison to previous methods.

Optimal approach for classification of acute leukemia subtypes based on gene expression data.

The classification of cancer subtypes, which is critical for successful treatment, has been studied extensively with the use of gene expression profiles from oligonucleotide chips or cDNA microarrays. Various pattern recognition methods have been successfully applied to gene expression data. However, these methods are not optimal, rather they are high‐performance classifiers that emphasize only classification accuracy. In this paper, we propose an approach for the construction of the optimal linear classifier using gene expression data. Two linear classification methods, linear discriminant analysis (LDA) and discriminant partial least‐squares (DPLS), are applied to distinguish acute leukemia subtypes. These methods are shown to give satisfactory accuracy. Moreover, we determined optimally the number of genes participating in the classification (a remarkably small number compared to previous results) on the basis of the statistical significance test. Thus, the proposed method constructs the optimal classifier that is composed of a small size predictor and provides high accuracy.

Closed-loop on-line process identification using a proportional controller

Abstract A new identification method using a proportional controller is proposed for autotic tuning of PID controller. A second-order plus time-delay (SOPTD) model is obtained by a least-squares method in the frequency domain. The suggested method can remove the restriction in applicability of previous methods so that it can be applied to any stable closed-loop response, regardless of the type of the transient such as overdamped, underdamped and inverse response. It is simple so that it does not need any complex techniques such as root finding or optimization method. With the obtained model parameters, the three parameters of PID controller can be calculated from the integral of the time-weighted absolute value of the error (ITAE) tuning rule on-line. Simulation study shows the robustness with respect to measurement noise and disturbance at the identification stage.

Discovery of differentially expressed genes related to histological subtype of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common human malignancies in the world. To identify the histological subtype‐specific genes of HCC, we analyzed the gene expression profile of 10 HCC patients by means of cDNA microarray. We proposed a systematic approach for determining the discriminatory genes and revealing the biological phenomena of HCC with cDNA microarray data. First, normalization of cDNA microarray data was performed to reduce or minimize systematic variations. On the basis of the suitably normalized data, we identified specific genes involved in histological subtype of HCC. Two classification methods, Fisherapos;s discriminant analysis (FDA) and support vector machine (SVM), were used to evaluate the reliability of the selected genes and discriminate the histological subtypes of HCC. This study may provide a clue for the needs of different chemotherapy and the reason for heterogeneity of the clinical responses according to histological subtypes.

An Enhanced Process Identification Method for Automatic Tuning of PID Controller

Abstract An enhanced process identification method with biased-relay feedback is proposed for the design of PID controller This paper suggested a new theorem to get the transfer function of a process from the definition of Laplace transformation without any approximation. Based on this theorem, the process is identified as the second-order plus time delay (SOPTD) model with a least-squares method in a viewpoint of amplitude ratio and phase angle between the process and the SOPTD model. Since the proposed identification method does not approximate the relay and process outputs as sinusoidal signals, then the obtained SOPTD model can represent the process dynamics more accurately. A simulation study demonstrates its effectiveness and robustness to the measurement noise.