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Process operating performance assessment judges the optimal degree of the production online. The assessment result guides the production to achieve the maximal benefit. Hence, the study on operating performance assessment is of great significance. To extract the most correlations between the process variables and the assessment indices, an operating performance assessment method based on canonical...
Prediction of fouling in power plant heat exchanger is greatly influenced by the periodic fouling process and change in dynamics of the operational parameters. In order to address this, a prediction model using Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network is proposed to monitor cleanliness factor. The experimental results demonstrate that the proposed model can predict the...
This paper proposes a hybrid negative correlation learning in which each individual neural network in an neural network ensemble would either learn a data point by negative correlation learning or learn to be different to the neural network ensemble. The implementation is through randomly splitting the training set into two subsets for each individual neural network in learning. On one subset of the...
This paper proposes a novel relative wavelet bispectrum (RWB) approach for EEG signal feature extraction method to differentiate the signal between the alcoholic over the non-alcoholic subjects. Firstly, the EEG signal is calculated for its autocorrelation frequencies as the basic step in the bispectrum calculation. Then, the discrete wavelet transform (DWT) is applied substituting the FFT which usually...
This paper discusses a methodology to construct a synthetic dataset using realistic geophysical data and the L-MEB model to compute synthetic brightness temperatures (Tb's) and to train a Neural Network (NN) for global retrievals of soil moisture (SM). The trained NNs are applied to real Tb's measured by the Soil Moisture and Ocean Salinity (SMOS) satellite (L-MEB NN). The objective is twofold. First,...
Identification of predictors of Boolean networks from observed data is an earlier-stage work of determining Boolean networks via experiments. This paper aims to identify predictors that are consistent with the observed data from experiments. Based on the algebraic expression of Boolean works which is established by aid of a new modelling tool, called semi-tensor product of matrices which is proposed...
We apply soft control method on an opinion dynamic model, the weighted DeGroot model, to change the convergent opinion value x. The interaction network plays an important role in the dynamics of system, and the soft control performance (Δx, the difference between the new convergent opinion value x and the original convergent opinion value x). In this paper, we define a new network feature Ω, called...
DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data. They ignore the semantically similar and dissimilar constraints between different modalities, and cannot take advantage of unlabeled data. This paper...
Post-concussion syndrome (PCS) is associated with incomplete recovery following a mild traumatic brain injury (mTBI). Currently, there is no biomarker to diagnose post-concussion syndrome. Although microstructural damage to white matter tracts is postulated and reported in some studies, there is no diagnostic biomarker available. In this paper, we present preliminary results of a novel and simple-toadminister...
A novel identification algorithm is presented in this paper for neuro-fuzzy based single-input-single-output (SISO) Wiener model with colored noises. The independent identical distribution (iid) Gaussian random signals are adopted to identify the Wiener system, leading to the separation of linear part from nonlinear counterpart in the identification problem. Therefore, correlation analysis method...
In this paper, the relevance of deep neural network (DNN) is studied in big data scenarios, specifically for prognostics applications. It is observed that fault predictions can be performed more efficiently when DNN is used with a pre-processing step. A novel hierarchical dimension reduction (HDR) approach is therefore proposed as a pre-processing step to DNN. This two-step approach is shown to be...
The effort required for the development of a software system is predicted through the cost of software estimation. Completion of project within time and budget limits is required for accurate cost estimation. Effort and cost estimation can be done through various modes. A new hybrid algorithm which is a combination of concepts of Artificial Bee Colony (ABC) and Local search procedures is used here...
The cascade correlation neural network structure is proposed in this paper, which is used to predicting the closing price of the stocks related to state bank of India at the end of the particular day. The underlying fact of any neural network architecture is to minimize the error between the original outcome and expected result of the problem, by adjusting its weights in the architecture to the possible...
Biometrics refers to metrics or parameters related to human characteristics. Several Biometric security systems and authentication proceduresare already in use ranging from fingerprint scanners to facial recognition software. Even with the rapid advancement of these security systems, they are still susceptible to frauds. The proposed ECG based authentication (ECGA) could prove to be highly secure...
Myoelectric pattern recognition (MPR) can be used for intuitive control of virtual and robotic effectors in clinical applications such as prosthetic limbs and the treatment of phantom limb pain. The conventional approach is to feed classifiers with descriptive electromyographic (EMG) features that represent the aimed movements. The complexity and consequently classification accuracy of MPR is highly...
A better understanding of the composition of rumen microbial communities and the association between host genetic and microbial activities has important applications and implication in bioscience. Being capable of revealing the full extent of microbial gene diversity, metagenomics-based approaches hold great promises in this endeavor. This study investigates the rumen microbial community in cattle...
Emotional processing of ex-combatants is affected by chronic exposure to violent events. For a successful reintegration into society, it is necessary to discriminate their brain responses from civilian people, as a first stage to develop treatment strategies. This paper presents a comparative analysis between a Multilayer Perceptron Neural Network and a Fuzzy C-Means classifier to differentiate ex-combatant...
This paper introduces the reasons for big data analytics in distribution network studies and potential benefits it could give. Summary of the most common data mining methods used in power system studies is also given, followed by a comparative analysis. A use case is shown at the end in order to present some examples of extraction of useful information from raw data stored in a real distribution utility's...
Nowadays flood water level predictions have become one of the most popular subject matter among researcher because this natural disaster damages people's life and property. In addition, flood is also one of the natural disasters that occur frequently around the world. However, since the dynamic of the flood itself is highly nonlinear, it is a very difficult task to predict the flood water level ahead...
Terrorist attacks change dynamically in social and geographic spaces. In this paper, terrorist attacks in the Middle East are analyzed using methods of network science, statistical methods, geographic information science, and artificial neural networks designed from a socio-spatial perspective. Based on the Global Terrorism Database (GTD), firstly the distribution and trends of terrorist attacks are...
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