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Low-carbon, the only way to the sustainable development of all countries around the world, has become a hot topic. Carbon flux (FC) is closely related to many factors in ecological environment as an index of global carbon emissions. Therefore, it is very important to find effective methods to study the relationship between FC and environmental factors. A predicted model based on wavelet networks is...
Artificial Neural networks are utilized to predict flow properties of a confined, isothermal, and swirling flowfield in an axisymmetric sudden expansion combustor using a two-component laser Doppler velocimetry capable of measuring the mean velocity components and their statistics. Generalized feedforward, radial basis function, and coactive neuro-fuzzy inference system neural networks are tested...
Associative memories are devices that are able to learn messages and to recall them in presence of errors or erasures. Their mechanics is similar to that of error correcting decoders. However, the role of correlation is opposed in the two devices, used as the essence of the retrieval process in the first one and avoided in the latter. In this paper, original codes are introduced to allow the effective...
This paper presents the methodology how to utilize sensor networks in order to predict human's thermal comfort and sensation. The neural network was dynamically organized on the basis of correlations with the thermal sensation of the occupants and many other values in the sensor network, and the structure of the neural network was updated cyclically. In this paper, the air-conditioning system in an...
Nowadays, worms and other outside threats in the network recognized to be a serious and unexpected behavior. The main issue was addressed based on the behavioral patterns of worms that reflect application communications typical of worms. This representation of worm's behavior differs from those used in contemporary enterprise postures, which reliance on a particular type of signature-based intrusion...
The paper set up regional logistics prediction model based on the chaotic nerve network according to regional logistics characteristic, judge regional logistics chaotic characteristic Utilize phase space reconstruction technology at first, Positive Lyapunov exponent and correlation dimension prove the regional logistics has Chaotic characteristics. Then set up neural network prediction models on the...
In this paper the feasibility of artificial neural network technology for air fine particles pollution prediction of main traffic route was discussed. The concentration data of PM2.5, PM5 and PM10 were measured in Zhongshan road, the main traffic route of Chongqing, China. Parameter Φ of emission capacity of motor vehicles was used as the independent variable of prediction model. RBF and BP neural...
This paper presents a novel approach for estimating the rotor position of a Switched Reluctance Motor (SRM) drive system using the Cascade Correlation Artificial Neural Network Algorithm (CCNNA). This technique estimates rotor position by measuring the three-phase voltages and currents and using magnetic characteristics of the SRM, with the aid of an ANN. The rotor position estimating technique is...
Consider two uniform samplers operating simultaneously on a signal, with sample spacings MT and NT where M and N are coprime integers, and T has time or space dimension. It can be shown that the difference coarray of this pair of sampling arrays has elements at all integer multiples of T, regardless of how large M and N are. This implies that any application which depends only on second order statistics,...
Coverage model is the main technique to evaluate the thoroughness of dynamic verification of a Design-under-Verification (DUV). However, rather than achieving a high coverage, the essential purpose of verification is to expose as many bugs as possible. In this paper, we propose a novel verification methodology that leverages the early bug prediction of a DUV to guide and assess related verification...
In this paper two different methods for non-technical losses (NTL) detection are analyzed and new approach is proposed, based on the noticed drawbacks. It is shown that NTL can be successfully detected by a neural network trained by “artificial”, i.e., generated samples. This approach eliminates the need for many hard-to-obtain real life samples and the network can easily be trained to detect some...
This paper examines a correlation between Croatian equity index CROBEX's value change and Croatian open investment funds share price change by applying data mining techniques. Apart from the type of the fund, in consideration is taken the fact how much funds invest in Croatian equities that is the equities which rate on Zagreb Stock Exchange. While working, the problem was observed in two aspects,...
This paper investigates the effect of diversity caused by Negative Correlation Learning (NCL) in the combination of neural classifiers and presents an efficient way to improve combining performance. Decision Templates and Averaging, as two non-trainable combining methods and Stacked Generalization as a trainable combiner are investigated in our experiments. Utilizing NCL for diversifying the base...
Neural network ensemble is a learning paradigm where many neural networks are used together to solve a particular problem. This paper presents a new method to construct a neural network ensemble (NNE) based on Correlation, Interaction Validation and Entropy (CIENNE). The method consists of two parts: a sub-algorithm to construct best component neural networks with Correlation and Interaction Validation,...
We study the effect of outdated channel estimation on the outage performance of amplify-and-forward (AF) relay selection, where only one out of the set of available relays is activated. In particular, we derive closed-form expressions for the outage probability of two variations of AF relay selection, namely best relay selection and partial relay selection, when the selection is based upon outdated...
The linear minimum mean-squared error (MSE) channel estimator for systems employing per-subcarrier transmit antenna selection is developed. It is shown that the frequency domain correlations after the selection process is approximated well using a simple explicit function. Performance of resultant estimators, which assume a certain correlation model, deteriorates quickly at high signal-to-noise ratios...
Image feature and similarity measure are important topics in content-based image retrieval. In this paper, we present energy signal sequences of Energy entropy, Entropy, Averagy residual, Standard deviation from Pulse-Coupled Neural Networks (PCNN) as image feature respectively, and Correlation Coefficient (CC) as the similarity metrics in image retrieval system. The pulse image sequence generated...
The absence of standard in black tea assessment was one main obstacle in its quality assurance. This research were contains process of black tea assessment software development, software problem solving concept, and the software evaluation made. This paper was a proof of simple concept that an expert system should automatically find and chose relevant parameters from relationship between raw image...
High-performance concrete (HPC) is a very complex material and hence very hard to predict its compressive strength. This paper deals with building a regression model for predicting concrete's compressive strength. First of all, eight process variables are identified as determinants of Concrete Compressive Strength (CCS). These variables are Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer,...
Based on the artificial neural networks and grey correlation analyze, this paper presents a model forecasting the infection rate of computer viruses according to the number of vulnerabilities, the percentage of viruses infecting via web browsing and downloading and the percentage of viruses infecting via portable storage media. The prediction is realized precisely by MATLAB. The three factors are...
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