Improved Pattern Tree for Incremental Frequent-Pattern Mining

By analyzing the existing prefix-tree data structure, an improved pattern tree was introduced for processing new transactions. It firstly stored transactions in a lexicographic order tree and then restructured the tree by sorting each path in a frequency-descending order. While updating the improved pattern tree, there was no need to rescan the entire new database or reconstruct a new tree for incremental updating. A test was performed on synthetic dataset T1014D100K with 100 000 transactions and 870 items. Experimental results show that the smaller the minimum sup- port threshold, the faster the improved pattern tree achieves over CanTree for all datasets. As the minimum support threshold increased from 2% to 3.5%, the runtime decreased from 452.71 s to 186.26 s. Meanwhile, the runtime re- quired by CanTree decreased from 1 367.03 s to 432.19 s. When the database was updated, the execution time of im- proved pattern tree consisted of construction of original improved pattern trees and reconstruction of initial tree. The experiment results showed that the runtime was saved by about 15% compared with that of CanTree. As the number of transactions increased, the runtime of improved pattern tree was about 25% shorter than that of FP-tree. The improved pattern tree also required less memory than CanTree.

An improved bearing fault detection strategy based on artificial bee colony algorithm

The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.

Feature selection algorithm for text classification based on improved mutual information

In order to solve the poor performance in text classification when using traditional formula of mutual information （MI） , a feature selection algorithm were proposed based on improved mutual information. The improved mutual information algorithm, which is on the basis of traditional improved mutual information methods that enbance the MI value of negative characteristics and feature＇ s frequency, supports the concept of concentration degree and dispersion degree. In accordance with the concept of concentration degree and dispersion degree, formulas which embody concentration degree and dispersion degree were constructed and the improved mutual information was implemented based on these. In this paper, the feature selection algorithm was applied based on improved mutual information to a text classifier based on Biomimetic Pattern Recognition and it was compared with several other feature selection methods. The experimental results showed that the improved mutu- al information feature selection method greatly enhances the performance compared with traditional mutual information feature selection methods and the performance is better than that of information gain. Through the introduction of the concept of concentration degree and dispersion degree, the improved mutual information feature selection method greatly improves the performance of text classification system.

Technology and Practice of Stabiliing Oil Production and Controlling Water Cut in Kalamkas Oilfield in Central Asia

In this paper, by in-depth geological research of Kalamkas Oilfield in Central Asia, the geological body has been re-ascertained; combined with fine study of reservoir engineering, based on the understanding of the distribution of remaining oil horizontal wells have been given full play to stabilizing oil production and controlling water cut, reducing the producing pressure drop, improving well productivity and other advantages, and the development and deployment has been optimized; horizontal wells have been applied to solve problems such as old well casing damages, shutting down wells, low-productivity and low- efficiency wells, and high water cut wells to improve the utilization rate of old wells; through separate layer system improved injection production pattern, adjustment wells have been optimized and deployed, and part measures wells have been preferably selected to tap the residual oil improve the degree of reserves control realize the stabilization of oil production and control of water cut in an old oilfield, and further improve the development effects.

Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI

Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.

A Theoretical Outline for National Security Studies for the New Era

China is tasked with attaining the great rejuvenation of the Chinese nation and facing global changes unseen in a century.To this end,a pressing task for China’s national security studies in the new era,under the guidance of a holistic concept of national security,is to provide an analytical framework and scholarly insights.Seven theoretical propositions can be derived when we clarify the relationships between security level,security capability,and security threats.They are:absolute security is out of reach;growing security investment that eyes absolute security will create a security dilemma;under closed conditions,the country should put the brakes on building relative security when it achieves equilibrium security;under open conditions,hegemonic powers may generate security capabilities that exceed equilibrium security and tend to“protect”or prey on countries whose development has relatively high output efficiency rather than those whose security capabilities have relatively high output efficiency;and following separate technical routes for dealing with intentional and unintentional threats may achieve a higher security level than managing in an undifferentiated way.The“Great Yu Improvement”pattern is pivotal to“Building a Community with a Shared Future for Mankind”as it can transform other actors’capacity to address intentional threats into a capacity for shielding oneself against unintentional threats;and appropriate allocation of excessive security capability and stronger national system security capability are major solutions to the uncertain nature of security threats.