With the in-depth research and extensive application of outlier detection technology, more and more excellent algorithms have been proposed. However, the existing outlier detection algorithms still use the evaluation metrics of traditional classification, which leads to the problems of singleness and poor adaptability of evaluation metrics. To solve these problems, the first type of High True positive rate-Area Under Curve (HT_AUC) and the second type of Low False positive rate-Area Under Curve (LF_AUC) were proposed. First, the commonly used outlier detection evaluation metrics were analyzed to illustrate their advantages and disadvantages as well as applicable scenarios. Then, based on the existing Area Under Curve (AUC) method, the HT_AUC and the LF_AUC were proposed aiming at the high True Positive Rate (TPR) demand and low False Positive Rate (FPR) demand respectively, so as to provide more suitable metrics for performance evaluation as well as quantization and integration of outlier detection algorithms. Experimental results on real-world datasets show that the proposed method is able to better satisfy the demands of the first type of high true rate and the second type of low false positive rate than the traditional evaluation metrics.

To improve the perception ability of indoor mobile robots, the classification method for the commonly structured corridor-scenes, Spiking Neural Network (SNN) and NeuCube, which is a novel computing model based on SNN, were studied. SNN can convey spatio-temporal information by spikes. Besides, SNN is more suitable for analyzing dynamic and time-series data, and for recognizing data of various patterns than traditional Neural Network (NN). It is easy to be implemented by hardware. The principle, learning methods and calculation steps of NeuCube were discussed. Then seven common corridor scenes were recognized by the classification method based on multi-sonar-sensor information and NeuCube. The experimental results show that the proposed method is effective. Additionally, it is helpful for improving autonomy and intelligence of mobile robots.

Dynamic consolidation of Virtual Machine (VM) is a promising solution to address the energy inefficiency of data centers. This paper focused on VM placement and its optimization. First, in order to improve the energy efficiency, a CPU utilization-based best fit decreasing algorithm was presented to complete the VM placement. However, due to the variability of workloads experienced by applications, the VM placement should be optimized continuously in an online manner. Therefore, a threshold-based active VM migration mechanism was proposed to solve the dynamic optimization. Extensive simulation results show the proposed algorithms can significantly reduce the energy consumption and the number of VM migrations, while keeping the metrics of Performance Degradation due to Migration (PDM) and Overload Time per Active Server (OTAS) in low level.