The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. Due to the special security requirements, researching in the high reliability of the power and communication network in drone monitoring system become special important. The reliability of the communication network and power in the drone monitoring system has been studied. In order to assess the reliability of the system power supply in the drone emergency monitoring system, the accelerated life tests under constant stress were presented based on the exponential distribution. Through a comparative analysis of lots of factors, the temperature was chosen as the constant accelerated stress parameter. With regard to the data statistical analysis, the type-I censoring sample method was put forward. The mathematical model of the drone monitoring power supply was established and the average life expectancy curve was obtained under different temperatures through the analysis of experimental data. The results demonstrated that the mathematical model and the average life expectancy curve were fit for the actual very well. With overall consideration of the communication network topology structure and network capacity the improved EED-SDP method was put forward in drone monitoring. It is concluded that reliability analysis of power and communication network in drone monitoring system is remarkably important to improve the reliability of drone monitoring system.

An asymmetric classifier based on kernel partial least squares is proposed for software defect prediction. This method improves the prediction performance on imbalanced data sets. The experimental results validate its effectiveness.

Software Defined Networking (SDN), a new network architecture, allows for centralized network management by separating the control plane from the forwarding plane. Because forwarding and control is separated, distributed denial of service (DDoS) assaults provide a greater threat to SDN networks. To address the problem, this paper uses a joint high-precision attack detection combining self-attentive mechanism and support vector machine: a trigger mechanism deployed at both control and data layers is proposed to trigger the initial detection of DDoS attacks; the data in the network under attack is screened in detail using a combination of self-attentive mechanism and support vector machine; the control plane is proposed to initiate attack defense using the OpenFlow protocol features to issue flow tables for accurate classification results. The experimental results show that the trigger mechanism can react to the attack in time with less than 20% load, and the accurate detection mechanism is better than the existing inspection and testing methods, with a precision rate of 98.95% and a false alarm rate of only 1.04%. At the same time, the defense strategy can achieve timely recovery of network characteristics.

A kernel based asymmetric learning method is developed for software defect prediction. This method improves the performance of the predictor on class imbalanced data, since it is based on kernel principal component analysis. An experiment validates its effectiveness.

A Balanced Neighborhood Classifier (BNEC) is proposed for class imbalanced data. This method is not only well positioned to capture the class distribution information, but also has the good merits of high-fitting-performance and simplicity. Experiments on both synthetic and real data sets show its effectiveness.

An active learning method, called Two-stage Active learning algorithm (TAL), is developed for software defect prediction. Combining the clustering and support vector machine techniques, this method improves the performance of the predictor with less labeling effort. Experiments validate its effectiveness.

Using a self-developed ASTM test system of contact material electrical properties under low voltage (LV), small-capacity, the current-frequency variable and a photoelectric analytical balance, the electric performance comparison experiments and material weighing of silver-based electrical contact materials, such as silver/tungsten and silver/cadmium oxide contact materials, are completed under LV, pure resistive load and small current at 400 Hz/50 Hz. The surface profiles and constituents of silver/tungsten contact material were observed and analyzed by SEM and EDAX. Researches indicate that the form of the contact material arc burnout at 400 Hz is stasis, not an eddy flow style at 50 Hz; meanwhile, the area of the contact burnout at 400 Hz is less than that of 50 Hz, and the local ablation on the surface layer at 400 Hz is more serious. Comparing the capacities of the silver-based contact materials with different second element such as CAgW50, CAgNi10, CAgC4 and CAgCdO15 at 400 Hz, no matter what the performances of arc erosion resistance or welding resistance, it can be found that the capacities of the silver/tungsten material is the best.

An arc model has been applied in this paper to study the fundamental interruption environment of a 550 kV SF6 single-break tank circuit breaker. The full differential model takes into account of all important physical mechanisms and is implemented into a commercial Computational Fluid Dynamics (CFD) package, PHOENICS. The model takes a magneto-hydro-dynamics (MHD) approach and the governing equations are solved using the Finite Volume Method (FVM). Through the simulation, the flow velocity vector and mach number for capacitive current switching and short-circuit current breaking are analyzed, and flow dynamic characteristics are obtained. The simulation can provide helpful reference for the design of 550 kV SF6 single-break tank circuit breaker.

An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.