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The threat of false data injection (FDI) attacks have raised wide interest in the research and development of smart grid security. This paper presents a comparative study on the utilization of supervised learning classifiers to detect direct and stealth FDI attacks in the smart grid. A detailed formulation of the problem for detection with classifiers is first described with proper assumptions and...
To efficiently identifying the bearing fault in gear-rotor-bearing system of wind turbines, this paper presented an algorithmic solution to carry out the analysis of vibration signals of bearings by the use of ensemble empirical mode decomposition (EEMD) and support vector machine (SVM). Through appropriately decomposing the obtained original vibration signals into a collection of intrinsic mode functions...
Sampling rate required in the Nth Power Nonlinear Transformation (NPT) method is typically much greater than Nyquist rate, which causes heavy burden for the Analog to Digital Converter (ADC). Taking advantage of the sparse property of PSK signals' spectrum under NPT, we develop the NPT method for PSK signals with Sub-Nyquist rate samples. In this paper, combined the NPT method with Compressive Sensing...
As the most frequent drug target, G protein-coupled receptors (GPCRs) are a large family of seven trans-membrane receptors that sense molecules outside the cell and activate inside signal transduction pathways. The activity and lifetime of activated receptors are regulated by receptor phosphorylation. Therefore, investigating the exact positions of phosphorylation sites in GPCRs sequence could provide...
For least squares support vector machine (LS-SVM) classifier to the loss of sparseness and generalization, a pruning modeling method is proposed based on Quadratic Renyi entropy. The kernel principal component is adopted for data pre-processing, and the training set is divided randomly. Then the concept of quadratic Renyi entropy is introduced as the basis of training and pruning in LS-SVM classifier...
Recently, learning to rank technique has attracted much attention. However, the lack of labeled training data seriously limits its application in real-world tasks. In this paper, we propose to break this bottleneck by considering the cross-domain ldquotransfer of rank learningrdquo problem. Simultaneously, we propose a novel algorithm called TransRank, which can effectively utilize the labeled data...
Learning to rank technique is attacking much attention in search engine optimization. However, it cost a lot to collect labeled data for rank learning. In addition, the features of learning better ranking functions, such as click-through features and social annotation features, proposed from different viewpoints. In this paper, we propose to consider the learning to rank problem in the co-training...
Feature selection on multi-label documents for automatic text categorization is an under-explored research area. This paper presents a systematic document transformation framework, whereby the multi-label documents are transformed into single-label documents before applying standard feature selection algorithms, to solve the multi-label feature selection problem. Under this framework, we undertake...
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