Condition Identification Based on Vibration Measurements for Free Spanning Submarine Pipelines

Free spanning pipelines are suspended between two points on an uneven seaffoor. The variations of structural conditions, such as the changes in soil property, flow velocity, axial force and span length etc., directly affect working performance of the whole submarine pipeline system. But until now few researches have focused on condition identification for free span （CIFS）. A method to identify the operational conditions of free spanning submarine pipelines based on vibration measurements is proposed in this paper. Firstly, the ill-posedness of CIFS is analyzed in detail. Secondly, the framework for CIFS based on the nonlinear kernel discriminant analysis （KDA） is established. Thirdly, the internal structural characteristics of natural frequencies, normalized frequencies and frequency change ratios are studied. And then the condition feature vector for CIFS is extracted by use of the vibration measurements. Finally, the validity of the proposed approach is evaluated by a case study. The results demonstrate that the proposed approach can effectively identify each condition of free span when condition variation occurs even if under measurement noise. It is concluded that the proposed method is a promising tool for CIFS in real applications.

KFDA and clustering based multiclass SVM for intrusion detection

To improve the classification accuracy and reduce the training time, an intrusion detection technology is proposed, which combines feature extraction technology and multiclass support vector machine （SVM） classification algorithm. The intrusion detection model setup has two phases. The first phase is to project the original training data into kernel fisher discriminant analysis （KFDA） space. The second phase is to use fuzzy clustering technology to cluster the projected data and construct the decision tree, based on the clustering results. The overall detection model is set up based on the decision tree. Results of the experiment using knowledge discovery and data mining （KDD） from 99 datasets demonstrate that the proposed technology can be an an effective way for intrusion detection.

KDLPCCA-Based Projection for Feature Extraction in SSVEP-Based Brain-Computer Interfaces

An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.