Le Chen

Decoupling control based on support machines ath-order inversion for the boiler-turbine coordinate systems

A boiler-turbine unit is a typical multi-input multi-output (MIMO) industrial control system in power plant, which has nonlinear and couple characteristics. As there exist strong couplings between main steam pressure and power output, we present a new coordinated control strategy: ath-order inverse system method based on Support Vector Machine (SVM). Cascading SVM inversion with the original system can form a pseudo-linear system. Then we can solve the problem of nonlinear system by the traditional linear system methods. Simulations show that this strategy has good control performance and can be implanted for engineering.

An iterative filtering algorithm based on signaling game idea

An iterative digital image filtering algorithm based on signaling game idea is presented. Aim at shortages of the standard medium filtering algorithm, this paper provides an improving approach through appointing signaling game idea. The method is that choose filtering window, sort every pixel value in the window and find the median first and then compare the median and the window central pixel value, and obtain the probability that the window central pixel is signal point or noise point according to the similarity between the medium and window central pixel value, finally, construct membership function to fix the value of window central pixel and bring the value amended to the following pixel filtering operation. As the method above, we repeat the operation until all the value of pixels in the image have been amended. The results of computer simulation prove that the improved algorithm not only can suppress the noise but also keep details of the image well.

SVM-Based Cost-sensitive Classification Algorithm with Error Cost and Class-dependent Reject Cost

In such real data mining applications as medical diagnosis, fraud detection and fault classification, and so on, the two problems that the error cost is expensive and the reject cost is class-dependent are often encountered. In order to overcome those problems, firstly, the general mathematical description of the Binary Classification Problem with Error Cost and Class-dependent Reject Cost (BCP-EC2RC) is proposed. Secondly, as one of implementation methods of BCP-EC2RC, the new algorithm, named as Cost-sensitive Support Vector Machines with the Error Cost and the Class-dependent Reject Cost (CSVM-EC2RC), is presented. The CSVM-EC2RC algorithm involves two stages: estimating the classification reliability based on trained SVM classifier, and determining the optimal reject rate of positive class and negative class by minimizing the average cost based on the given error cost and class-dependent reject cost. The experiment studies based on a benchmark data set illustrate that the proposed algorithm is effective.

Cost-sensitive SVM with Error Cost and Class-dependent Reject Cost

In such real data mining applications as medical diagnosis, fraud detection and fault classification, and so on, the two problems that the error cost is expensive and the reject cost is class-dependent are often encountered. In order to overcome those problems, firstly, the general mathematical description of the Binary Classification Problem with Error Cost and Class-dependent Reject Cost (BCP-EC2RC) is proposed. Secondly, as one of implementation methods of BCP-EC2RC, the new algorithm, named as Cost-sensitive Support Vector Machines with the Error Cost and the Class-dependent Reject Cost (CSVM-EC2RC), is presented. The CSVM-EC2RC algorithm involves two stages: estimating the classification reliability based on trained SVM classifier, and determining the optimal reject rate of positive class and negative class by minimizing the average cost based on the given error cost and class-dependent reject cost. The experiment studies based on a benchmark data set illustrate that the proposed algorithm is effective.

SVM-Based Multiclass Cost-sensitive Classification with Reject Option for Fault Diagnosis of Steam Turbine Generator

The steam turbine generator faults not only damage the generator itself, but also cause outages and loss of profits, for this reason, many researchers work on the fault diagnosis. But misdiagnosing may also lead to serious losses. In order to improve the diagnosis reliability and reduce the loss caused by misdiagnosis, in this paper, cost integrated multiclass SVM with reject option (CIMC-SVM) is proposed. Experimental results show that CIMC-SVM is able to improve the diagnosis reliability and minimize the average cost.

Cost-sensitive Multi-class SVM with Reject Option: A Method for Steam Turbine Generator Fault Diagnosis

—The steam turbine generator faults not only damage the generator itself, but also cause outages and loss of profits. The traditional fault diagnosis systems care only about high diagnosis accuracy. But different misdiagnoses may lead to quite different losses and it is unreliable if misdiagnoses were accepted. In order to reduce the total loss caused by misdiagnoses and improve the diagnosis reliability, in this paper, cost integrated multiclass SVM with reject option (CIMCR-SVM) is proposed. Firstly, we present a very simple and effective method to make the multi-class classifiers cost-sensitive. Secondly, diagnosis reliabilities were evaluated by a reliability evaluator, and reject option is introduced for rejecting classified samples with lower diagnosis reliabilities. Experimental results demonstrate that CIMCR-SVM is able to minimize the average cost and improve the diagnosis reliability.