Donald. C. Wunsch

Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design.

Modern graphics processing units (GPU) and game consoles are used for much more than simply 3D graphics applications and video games. From machine vision to finite element analysis, GPU's are being used in diverse applications, collectively called General Purpose computation onf graphics processor units (GPGPU). Additionally, game consoles are entering the market of high performance computing as inexpensive nodes in computing clusters. This paper explores the capabilities and limitations of modern GPU's and game consoles, surveying the ADP and neural network technologies that can be applied to these devices.

Early detection of a tumor's site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. One of the major problems in cancer type recognition-oriented gene expression data analysis is the overwhelming number of measures of gene expression levels versus the small number of samples, which causes the curse of dimension issue. Here, we use diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions, for dimensionality reduction. The derived data are then clustered with Fuzzy ART to form the division of the cancer samples. Experimental results on the small round blue-cell tumor data set demonstrate the effectiveness of our proposed method in addressing multidimensional gene expression data and identifying different types of tumors.

This paper presents a suite of neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control inputs are calculated using local signals, the transient and overall system stability can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system dynamics and the inter-connection terms, thus the requirements for exact system parameters are relaxed. Simulation studies with a three-machine power system demonstrate the effectiveness of the proposed controller designs.

Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. From machine vision to finite element analysis, CPU's are being used in diverse applications, collectively called general purpose graphics processor utilization. This paper explores the capabilities and limitations of modern GPU's and surveys the neural computation technologies that have been applied to these devices.