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In this paper the linear block code decoder is constructed by neural network. The neural network will be adapted for a single-bit error. Each layer of a neural network will simulate a linear block code decoder stage. The syndrome generator, the error detection, and the error correction stages of the linear block code decoder will be simulated by the proposed neural network.
Wireless sensor networks (WSNs) have attracted significant interests of many researchers because they have great potential as a means of obtaining information of various environments remotely. WSNs have their wide range of applications, such as natural environmental monitoring in forest regions and environmental control in office buildings. In WSNs, hundreds or thousands of micro-sensor nodes with...
We propose an automatic factorization method for time series signals that follow Boltzmann distribution. Generally time series signals are fitted by using a model function for each sample. To analyze many samples automatically, we have to apply a factorization method. When the energy dynamics are measured in thermal equilibrium, the energy distribution can be modeled by Boltzmann distribution law...
Rotating machinery anomaly detection is of paramount significance for industries to prevent catastrophic breakdown and improve productivity and personnel safety. The kernel classifier support vector machine (SVM) has shown excellent performance towards this purpose, but it is difficult to optimize relevant hyper-parameters. In this paper, we propose a new anomaly detection approach by merging Gaussian...
We solve Nash equilibrium of stochastic games using heuristic Q-learning method based on ldquoheuristic learningrdquo + ldquo Q-learningrdquo under the framework of noncooperative general-sum games. Determining whether a strategy Nash equilibrium exists in a stochastic game is NP-hard even if the game is finite. Therefore normal Q-learning method based on iterative learning canpsilat solve stochastic...
An integrated approach based on innovation diffusion theory and lifestyle theory for customer segmentation of mobile commerce on the train using multivariate statistical analysis is proposed for Taiwan Railway Administration. Firstly, the contents of mobile commerce on the train are identified as segmentation variables and key factor facets for mobile commerce are redefined by using factor analysis...
To implement visual target classification, this paper proposes a collaborative statistical learning algorithm for online support vector machine(SVM) classifier learning in wireless multimedia sensor network (WMSN). For achieving robust target classification, classifier learning should be carried out iteratively for updating classifiers according to various situations. Because only unlabeled samples...
With the rapid increase in the use of databases, missing data make up an important and unavoidable problem in data management and analysis. Because the mining of association rules can effectively establish the relationship among items in databases, therefore, discovered rules can be applied to predict the missing data. In this paper, we present a new method that uses association rules based on weighted...
Adaboost is an ensemble learning algorithm that combines many base-classifiers to improve their performance. Starting with Viola and Jonespsila researches, Adaboost has often been used to local feature selection for object detection. Adaboost by Viola-Jones consists of following two optimization schemes: (1) training of the local features to make base-classifiers, and (2) selection of the best local...
The apparent increase in number and magnitude of jellyfish blooms in the worlds oceans has lead to concerns over potential disruption and harm to global fishery stocks. Because of the potential harm that jellyfish populations can cause and to avoid impact it would be helpful to model jellyfish populations so that species presence or absence can be predicted. Data on the presence or absence of jellyfish...
Determining the optimum number of clusters is an ill posed problem for which there is no simple way of knowing that number without a priori knowledge. The purpose of this paper is to provide a simultaneous two-level clustering algorithm based on self organizing map, called DS2L-SOM, which learn at the same time the structure of the data and its segmentation. The algorithm is based both on distance...
This paper proposes a novel method for data editing. The goal of data editing in instance-based learning is to remove instances from a training set in order to increase the accuracy of a classifier. To the best of our knowledge, although many diverse data editing methods have been proposed, this is the first work which uses semi-supervised learning for data editing. Wilson editing is a popular data...
In this paper, the properties of hyperbolic function are analysed at first; then a key exchange algorithm is proposed which is based on improved hyperbolic function in combination with module computation. Moreover in comparison with the correspondent methods such as RSA and EIGamal etc, our algorithm is proven more secure and practical.
This paper first reviews extreme learning machine (ELM) in light of coverpsilas theorem and interpolation for a comparative study with radial-basis function (RBF) networks. To improve generalization performance, a novel method of combining a set of single ELM networks using stacked generalization is proposed. Comparisons and experiment results show that the proposed stacking ELM outperforms a single...
Heat shock protein 90 (HSP90) regulates the correct folding of nascent protein in tumor cells. Through the ATPase domain of HSP90, inhibition of its activity is a manipulation for anticancer treatment. Two series of anticancer compounds, flavonoids and YC-1 derivatives, were employed in this study. The reference ligand in the docking simulation showed the significant RMSD of 0.87 with respect to the...
The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision...
Conventional cost functions of adaptive filtering are usually related to the errorpsilas dispersion, such as errorpsilas moments or errorpsilas entropy, but neglect the shape aspects (peaks, kurtosis, tails, etc.) of the error distribution. In this work, we propose a new notion of filtering (or estimation) in which the errorpsilas probability density function (PDF) is shaped into a desired one. As...
In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through...
Remote control and monitoring are very necessary in decentralized manufacturing environments. This is evidenced by todaypsilas distributed shop floors where agility and responsiveness are required to maintain high productivity and flexibility. However, there exists a lack of an effective system architecture that integrates remote condition monitoring and control of automated equipment; that give much...
The paper puts forward a concept of cognitive sensor networks and investigates a feasibility of artificial neural networks application for its realization. It describes a design of novel hierarchical configurations imitating the structural topology of brain-like architectures. They are composed from artificial neural networks distributed over network platforms with limited resources. The paper examines...
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