We present a dynamic modelfor access control mechanism used in computer communication network applied to MPEG video transmission over Internet. This modelis different fromthosedeveloped inthe previous works related to this topic. In our model, token buckets supported by data buffersare used to shape incoming traffic and one multiplexor, serving all the token pools, multiplexes all theconforming traffic. The model is governed by a system of discrete nonlinear difference equations. Weuse neural network as the feedback controller which receives at its input (measurable) available information and provides at its output the optimal control. The simulated annealing algorithm isusedto optimize the system performance by adjusting the weights. For illustration, we presentnumerical results which show that the system performance of MPEG video server can be improved by using neural network and simulated annealing approach.
Abstract:We describe a promoter recognition method named PCA-HPR to locate eukaryotic promoter regions and predict transcription start sites (TSSs). We computed codon (3-mer) and pentamer (5-mer) frequencies and created codon and pentamer frequency feature matrices to extract informative and discriminative features for effective classification. Principal component analysis (PCA) is applied to the feature matrices and a subset of principal components (PCs) are selected for classification. Our system uses three neural network classifiers to distinguish promoters versus exons, promoters versus introns, and promoters versus 3' un-translated region (3'UTR). We compared PCA-HPR with three well-known existing promoter prediction systems such as DragonGSF, Eponine and FirstEF. Validation shows that PCA-HPR achieves the best performance with three test sets for all the four predictive systems.
To improve transformer longitudinal differential protections reliability, this paper deeply analyzes generation mechanism and characteristic of transformer inrush current, and uses PSCAD/EMTDC software to simulate 188 kinds of transformer operation states. They are including internal fault current, inrush current and no-load closing with internal fault. On the background of those simulations, it proposes a simple and accurate method to identify inrush current based on SVM. SVM selects Gaussion Kernel, and takes three-phase differential current, fundamental, secondary harmonic and third harmonic as characteristic quantities. Many cross-validation results verify that the training SVM has high accuracy. This method can identify inrush current and internal fault current (including no-load closing with internal fault current) rapidly and accurately. It takes less time, and is easy to perform.
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