36140985
Hybrid Neuro-Predictive-Fuzzy Algorithm for a Model Helicopter's Yaw Angle Control
A method formats disc surfaces in a disc drive for storage of data in a group of tracks on the disc surfaces. The disc drive performs seek operations to seek among cylinders on the disc surfaces as a function of time according to an actual seek profile. An approximation of the actual seek profile is determined such that the actual seek profile falls within the approximation. The disc surfaces are formatted according to the approximation of the actual seek profile.
32539682
A combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater
Abstract This paper deals with modeling a power plant component with mild nonlinear characteristics using a modified neural network structure. The hidden layer of the proposed neural network has a combination of neurons with linear and nonlinear activation functions. This approach is particularly suitable for nonlinear system with a low grade of nonlinearity, which can not be modeled satisfactorily by neural networks with purely nonlinear hidden layers or by the method of least square of errors (the ideal modeling method of linear systems). In this approach, two channels are installed in a hidden layer of the neural network to cover both linear and nonlinear behavior of systems. If the nonlinear characteristics of the system (i.e. de-superheater) are not negligible, then the nonlinear channel of the neural network is activated; that is, after training, the connections in nonlinear channel get considerable weights. The approach was applied to a de-superheater of a 325 MW power generating plant. The actual plant response, obtained from field experiments, is compared with the response of the proposed model and the responses of linear and neuro-fuzzy models as well as a neural network with purely nonlinear hidden layer. A better accuracy is observed using the proposed approach.
52873143
Sliding-window top-k queries on uncertain streams
Query processing on uncertain data streams has attracted a lot of attentions lately, due to the imprecise nature in the data generated from a variety of streaming applications, such as readings from a sensor network. However, all of the existing works on uncertain data streams study unbounded streams. This paper takes the first step towards the important and challenging problem of answering sliding-window queries on uncertain data streams, with a focus on arguably one of the most important types of queries---top-k queries. 
 
The challenge of answering sliding-window top-k queries on uncertain data streams stems from the strict space and time requirements of processing both arriving and expiring tuples in high-speed streams, combined with the difficulty of coping with the exponential blowup in the number of possible worlds induced by the uncertain data model. In this paper, we design a unified framework for processing sliding-window top-k queries on uncertain streams. We show that all the existing top-k definitions in the literature can be plugged into our framework, resulting in several succinct synopses that use space much smaller than the window size, while are also highly efficient in terms of processing time. In addition to the theoretical space and time bounds that we prove for these synopses, we also present a thorough experimental report to verify their practical efficiency on both synthetic and real data.
728817
Study of Coupling Based on Genetic Neural Network Multivariable Nonlinear Complex System
In this paper a method of decoupling has been put forward. Combine genetic neural network with multivariable nonlinear system adaptive decoupling control algorithm. Establish a control model of multivariable parameter in order to cancel the couplings of various variables. This method improves the stability and precision. According to the METLAB emulator proving, genetic neural network multivariable nonlinear decoupling model can cancel multivariable coupling problem preferably, make the system optimized.
2909075
Design of Multiple Classifier Systems for Time Series Data
In previous work, we showed that the use of Multiple Input Representation(MIR) for the classification of time series data provides complementary information that leads to better accuracy. [4]. In this paper, we introduce the Static Minimization-Maximization approach to build Multiple Classifier Systems(MCSs) using MIR. SMM consists of two steps. In the minimization step, a greedy algorithm is employed to iteratively select the classifiers from the knowledge space to minimize the training error of MCSs. In the maximization step, a modified version of Behavior Knowledge Space(BKS), Balanced Behavior Knowledge Space(BBKS), is used to maximize the expected accuracy of the whole system given that the training error is minimized. Several popular techniques including AdaBoost, Bagging and Random Subspace are used as the benchmark to evaluate the proposed approach on four time series data sets. The results obtained from our experiments show that the performance of the proposed approach is effective as well as robust for the classification of time series data. In addition, this approach could be further extended to other applications in our future research.
22828362
Construction of low-density parity-check codes by superposition
This paper presents a superposition method for constructing low-density parity-check (LDPC) codes. Several classes of structured LDPC codes are constructed. Codes in these classes perform well with iterative decoding, and their Tanner graphs have girth at least six.
5724720
Heterogeneous CAD Hybrid Data Exchange Based on Feature Semantics and Geometry Representation
Product data exchange (DE) is a key technology to implement collaborative design of heterogeneous CAD. Based on analyzing layered expression of product CAD model and current methods of DE, heterogeneous CAD hybrid data exchange (HDE) based on feature semantics and geometry representation is presented. The method integrates advantages of feature semantics exchange (FSE) and geometric data exchange (GDE). It features high flexibility and reliability. A case illustrates the validity of the method