Search

• The logarithmic normalized functional singular values are employed as the features for performing the classification. • Different classifiers are employed for performing the classification. • The performance is evaluated under the white noise environment. Electroencephalograms (EEGs) play a significant role in both the detection and the prediction of the epileptic seizures. This paper proposes to employ the logarithmic normalized functional singular values as the features for performing the classification of both the two class problem (normal and seizure set) and the three class problem (normal, seizure free and seizure set). Here, the EEGs are taken from two well known datasets. First, each EEG is decomposed via the singular spectrum analysis (SSA) to obtain the singular values. Then, the logarithmic normalized functional values of these singular values are calculated to form the feature vectors for performing the classification of the epileptic seizure. Next, different classifiers including the support vector machine (SVM), the k nearest neighbor classifier, the extreme learning machine (ELM) and the artificial neural network (ANN) are employed for performing the classification. Finally, a ten fold cross validation procedure is employed to ensure the reliability and the stability of the classifiers. The computer numerical simulation results show that the k nearest neighbor classifier achieves the best performance compared to other classifiers for performing both the two class epileptic classification and the three class epileptic classification. Our proposed method also achieves the higher classification accuracies compared to the case without performing the logarithmic normalized operation. Moreover, the performance of our proposed method is evaluated on the original EEGs under the white noise environment at different signal to noise ratios. Similar results are obtained. [ABSTRACT FROM AUTHOR]

Carbonate petroliferous basins usually develop fault-reservoir composite migration pathway systems. Accurate estimates of transport efficiency are important for locating oil and gas prospects, which is of considerable interest to oil explorers. The Cambrian Longwangmiao Formation in the Moxi–Gaoshiti area in the Sichuan Basin, the largest single monoblock gas accumulation in China with complicated migration pathway systems, was selected as an example to demonstrate the method. Using a combination of geologic, statistical, experimental data analyses, and quantitative characterization, a quantitative method for calculating the fault-reservoir composite transport efficiency is established for the first time. The results show that fault-reservoir composite migration pathway systems control the hydrocarbon accumulation. The analysis of data from 17 drilling wells using this method indicated a strong correlation between well productivity and composite transport efficiency, and the matching degree of actual and predicted results was as high as 83%. The best locations for oil and gas exploration are near areas with a high composite transport efficiency. Project planners can quickly find oil and gas prospects by matching calculated composite transport efficiencies to observed petroleum occurrences in a basin to avoid costly drilling mistakes. The method will have wide application in other carbonate basins around the world. • The fault-reservoir composite migration pathway controls hydrocarbon accumulation. • Quantitatively method for the composite transport efficiency is first established. • Composite transport efficiency is useful for location of oil and gas prospects. • Prospects for future drilling in the Moxi-Gaoshiti area of China are recommended. [ABSTRACT FROM AUTHOR]