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In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed Content Tree (IX-tree) or Multi-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient(More)
Outlier detection is an important task in data mining that enjoys a wide range of applications such as detections of credit card fraud, criminal activity and exceptional patterns in databases. In recent years, there have been numerous research work in outlier detection and the new notions such as distance-based outliers and density-based local outliers have(More)
On-road charging systems for electric vehicles (EVs) have shown revolutionary potential in extending driving range and reducing battery capacities. The optimal equivalent load resistances to maximize receiving power of each EV according to different EV amounts are investigated. This paper introduces a typical on-road charging system with a single(More)
An opportunity wireless charging system for electric vehicles when they stop and wait at traffic lights is proposed in this paper. In order to solve the serious power fluctuation caused by random access loads, this study presents a power stabilization strategy based on counting the number of electric vehicles in a designated area, including counting method,(More)
This paper investigated the interregional correlation changed by sport training through electroencephalography (EEG) signals using the techniques of classification and feature selection. The EEG data are obtained from students with long-time professional sport training and normal students without sport training as baseline. Every channel of the 19-channel(More)
Overhead high voltage power line (HVPL) online monitoring equipment is playing an increasingly important role in smart grids, but the power supply is an obstacle to such systems' stable and safe operation, so in this work a hybrid wireless power supply system, integrated with inductive energy harvesting and wireless power transmitting, is proposed. The(More)
Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band(More)
The high order pattern discovery algorithm is applied to classify schizophrenia and health's EEG signals. Samples of 780 schizophrenia and health EEG pieces are classified. The result shows that the classification accuracy can achieve 90% in 6-order. The 6-orders are associated with frontal polar, temporal and occipital regions.
Brain shift contributes mostly to the error of prediction in Image-Guided Neurosurgery (IGNS). In order to solve this problem, we build a statistical learning model between the quantity of the brain shift and we factors that impinge on the brain shift, and we predict the brain shift by using this model. The prediction of the brain shift can be regarded as(More)