Aiming at the problem that the machine learning classification algorithms have insufficient ability to identify minority samples in the imbalanced data classification problems， an imbalanced data classification method L-CCSmote （Least absolute shrinkage and selection operator Constructive Covering Synthetic minority oversampling technique） was proposed by taking the telecom customer churn scenario as an example. Firstly， the churn costumer related features were extracted through Lasso （Least absolute shrinkage and selection operator） to optimize the model input. Then， a neural network was built through Constructive Covering Algorithm （CCA） to generate coverages that conformed to the overall distribution of samples. Finally， a single-sample coverage strategy， a sample diversity strategy and a sample density peak strategy were further proposed to perform a hybrid sampling to balance the data. Total of 13 imbalanced datasets and 2 desensitized telecom customer datasets were selected from KEEL data base， and the proposed method was verified on Logistic Regression （LR） and Support Vector Machine （SVM） classification algorithms respectively. On LR classification algorithm， compared with the Synthetic Minority Oversampling TEchnique Edited nearest neighbor （SMOTE-Enn）， the proposed method had the average Geometric MEAN （G-MEAN） increased by 2.32%. On SVM classification algorithm， compared with the Borderline-SMOTE （Borderline Synthetic Minority Oversampling Technique）， the proposed method had the average G-MEAN increased by 2.44%. Experimental results show that the proposed method can solve the influence of class skew distribution on classification， and its recognition ability for rare classes is better than that of the classical balanced data classification methods.

In the case of Channel State Information (CSI) distortion and channel fast changing, the existing clustering scheme needs to get CSI of all the base stations and generates cluster structure slowly. Concerning the problem, a fast clustering scheme based on Affinity Propagation (AP) algorithm was proposed in this paper. The scheme just needs CSI of neighboring base stations. Firstly, sparse similarity matrix was formed by the average Signal to Interfere Ratio (SIR) of cooperation between neighboring base stations. Then, among neighboring base stations, the interaction and update of collaborative information was done to quickly generate multiple clusters. Finally, the average SIR of cooperation between clusters was normal when the smaller clusters were combined to achieve the purpose of uniform cluster size. The simulation results show that the performance of the proposed scheme is better than the existing scheme in terms of convergence and cluster size uniformity.

The characteristics of image affective features were analyzed and a method was provided to divide affective features into 3 levels. Typical affective features for colorful natural scenes were chosen and an investigation was carried out to collect users' impressions on images. Then, based on color features and users' evaluations, the mapping between color and subjective impressions was established by multiple linear regressions, which could be used to extract affective features automatically. Finally, the validity of 3 levels was verified. Besides, the mapping is also testified effective to forecast and index the affective features correctly.

A remote monitoring system of rectifier based on an improved heartbeat mechanism was developed according to quickness and stability of remote TCP/IP communication. Different heartbeat mechanism was designed at server and client, and server and client can judge the states of network connections through transmitting heartbeat packets between server and client. The mechanism can request to reconnect with remote clients or server when network is at the jam and inform users when network is at the disconnection. The mechanism was realized by using Socket with the model of single server and multi clients. The heartbeat model had been applied to a remote monitoring system of rectifier based on Client/Server model. The testing result of system and model shows that the heartbeat model has better performance and improves the availability and dependability of the system.