Aero-engine damage is an important factor affecting flight safety. There are two main problems in the current computer vision-based damage detection of engine borescope image:one is that the complex background of borescope image makes the model detect the damage with low accuracy; the other one is that the data source of borescope image is limited, which leads to fewer detectable classes for the model. In order to solve these two problems, a Mask R-CNN (Mask Region-based Convolutional Neural Network) based progressive detection network from binary classification to multiple classification was proposed for aero-engine damage images. By adding a binary classification detection branch to the Mask R-CNN, firstly, the damage in the image was detected in binary way and regression optimization was performed to the localization coordinates. Secondly, the original detection branch was used to progressively perform multiple classification detection, so as to further optimize the damage detection results by regression and determine the damage class. Finally, instance segmentation was performed to the damage through the Mask branch according to the results of multiple classification detection. In order to increase the detection classes of the model and verify the effectiveness of the method, a dataset of 1 315 borescope images with 8 damage classes was constructed. The training and testing results on this set show that the Average Precision (AP) and AP75 (Average Precision under IoU (Intersection over Union) of 75%) of multiple classification detection are improved by 3.34% and 9.71% respectively, compared with those of Mask R-CNN. It can be seen that the proposed method can effectively improve the multiple classification detection accuracy for damages in borescope images.

In view of the lack of effective resource demand forecasting and optimal allocation in Platform-as-a-Service (PaaS) platform, a resource demand forecasting model and an allocation method were proposed. Firstly, according to the periodicity of the application demand for resources in PaaS platform, the resource sequence was segmented. And on the basis of short-term prediction, combined with the multi-periodicity characteristics of the application, a comprehensive prediction model was established by using the multiple regression algorithm. Then, based on MapReduce architecture, a PaaS platform resource allocation system based on Master-Slave mode was designed and implemented. Finally, the resources were allocated based on current task request and resource demand prediction results. The experimental results show that, compared with autoregressive model and exponential smoothing algorithm, the proposed resource demand forecasting model and allocation method has the mean absolute percentage error drop of 8.71 percentage points and 2.07 percentage points respectively, root mean square error drop of 2.01 percentage points and 0.46 percentage points respectively. It can be seen that the prediction result of the prediction model has little error and its fitting degree with real value is high, while high accuracy costs little time. Besides, the average waiting time of PaaS platform with the proposed prediction model for resource requests decreases significantly.

Concerning the problems that Two-Dimensional Multiple-Input Multiple-Output (2D MIMO) systems only consider the effects of horizontal radiation pattern, ignoring the effects of vertical radiation pattern, and the closed-form on the sum rate of 2D MIMO system over K (Rayleigh/Gamma) fading channels involves special functions, two closed-form upper bounds on achievable sum rate of Three Dimensional Distributed Multiple-Input Multiple-Output (3D D-MIMO) systems with Zero-Forcing (ZF) receivers over K composite fading channels were proposed. The upper bounds considered Rayleigh multipath fading, Gamma shadow fading, geometric path-loss, 3D antenna radiation loss, and user distribution. The experimental results show that the obtained expressions accurately match with the Monte Carlo simulation conclusions.

Support Vector Data Description (SVDD), which is one of the widely applied kernel methods, has not taken the information of data distribution into full consideration. Concerning this issue, the optimization of SVDD was first reformulated equivalently, and then the distance in the optimization was redefined. Finally, a new algorithm called Minimum Variance Support Vector Data Description (MVSVDD) was presented, which exploited the information of data distribution. The experimental results denote that, in contrast to SVDD, MVSVDD obtains clear enhancement in generalization performance, and has better ability of describing data.

Some problems of the current measures for association rules were analyzed. A new measure named consult was defined and added to the mining algorithm for association rules. According to the value of consult, association rules were classified into positive, negative and invalid association rules. The new algorithm could find out the negative-item-contained rules. Finally, the algorithm was evaluated and analyzed through experiments and practices.

Temporal representation and reasoning is a main research topic in artificial intelligence. Most common models can only represent certain temporal information, but many events happen with uncertain temporal information in real life. A new model for representing uncertain and certain temporal information was proposed to describe events and facts with time indeterminacy. This model firstly defined some temporal objects (such as time point and time period), then defined several relations among temporal objects and discussed the transitivity between the relations. Finally, two examples were analyzed, using this model to solve the uncertain temporal reasoning problem.