In order to solve the problem of low accuracy of some distortion types judgment by image single distortion type judgment algorithm, an image single distortion type judgment method based on two-channel Convolutional Neural Network (CNN) was proposed. Firstly, the fixed size image block was obtained by cropping the image, and the high-frequency information map was obtained by Haar wavelet transform of the image block. Then, the image block and the corresponding high-frequency information map were respectively input into the convolutional layers of different channels to extract the deep feature map, and the deep features were fused and input into the fully connected layer. Finally, the values of the last layer of the fully connected layer were input into the Softmax function classifier to obtain the probability distribution of the single distortion type of the image. Experimental results on LIVE database show that, the proposed method has the image single distortion type judgement accuracy up to 95.21%, and compared with five other image single distortion type judgment methods for comparison, the proposed method has the accuracies for judging JPEG2000 and fast fading distortions improved by at least 6.69 percentage points and 2.46 percentage points respectively. The proposed method can accurately identify the single distortion type in the image.

Focusing on the detection problem of false data that conform to a certain pattern or rule, a false trend time series detection method based on randomness analysis was proposed. Based on the analysis of time series composition, firstly the simple forgery method and complex forgery method of false trend time series were explored, and decomposed into two parts:false trendness and false randomness. Then the false trend of time series was extracted by the approximation of base function, the false random of time series was analyzed with the randomness theory. Finally, monobit frequency and frequency within a block were adopted to test whether the false random part has randomness, which established a detection method of false time series with a certain trend feature. The simulation results show that proposed method can decompose the false time series and extract the false trend part effectively, meanwhile realize the detection of simple and complex forged data. It also supports the authenticity analysis for the numerical data obtained by means of observation or monitoring equipment, which improves the discrimination range of false data with average detection accuracy of 74.7%.

Aiming at the problem that the existing crowd-sourcing system can not effectively control the active enthusiasm of the workers and the quality of task completion in the process of crowd-sourcing interaction, a worker reputation model based on active degree was proposed to realize the quality control of the crowd-sourcing platforms. The model improved the average reputation model, and the concepts of active factor and historical factor were put forward from the point of view of workers' active degree and historical reputation value. First, the active factor of the worker was calculated according to his participating days in the crowd in the last 30 days, and then the historical reputation value of the crowd-sourcing worker was calculated according to the historical factor. Finally, the reputation value of the crowd-sourcing worker based on active degree was calculated based on the calculated active factor and historical reputation value, which was used to measure the ability of the crowdsourcing worker. The theoretical analysis and test results showed that compared with the average reputation model, the task completion quality of crowdsourcing workers selected by the worker reputation model based on active degree was increased by 4.95% and the completion time was decreased by 25.33%; compared with the trust model based on evidence theory, the task completion quality was increased by 6.63% and the completion time was decreased by 25.11%. The experimental results show that the worker reputation model based on active degree can effectively improve the quality of crowdsourcing tasks and reduce the completion time.

Android system is becoming the most popular mobile operating system for its opening property. However, the opening also brings some problems, the resource leak is one of the common ones. For the problems that resource leak is existed in Android system and no benchmarks has been provided for this specific issue, a benchmark named ResLeakBench for resource leak problem was proposed. First, official Android reference and a lot of real apps were studied, then the operation of resources and their common application scenarios were generalized. Second, 35 self-designed test apps were put into the benchmark according to the collected information; besides, to ensure the practicality of the benchmarks, 35 real apps related to resources were added into the benchmark. Finally, to evaluate the ResLeakBench, the resource leak analysis tool Relda2 and resource leak fixing tool RelFix were tested on the benchmark, and some shortages of Relda2 and RelFix were found. The experimental results show that ResLeakBench is a practical benchmark.

The traditional lightning data forecasting methods often use single optimal machine learning algorithm to forecast, not considering the spatial and temporal variations of meteorological data. For this phenomenon, an ensemble learning based multi-machine learning model was put forward. Firstly, attribute reduction was conducted for meteorological data to reduce dimension; secondly, multiple heterogeneous machine learning classifiers were trained on data set and optimal base classifier was screened based on predictive quality; finally, the final classifier was generated after weighted training for optimal base classifier by using ensemble strategy. The experimental results show that, compared with the traditional single optimal algorithm, the prediction accuracy of the proposed model is increased by 9.5% on average.

The unexpected power loss or system crash can result in data inconsistency upon updating a persistent data structure. Most existing file systems use some consistency techniques such as write-ahead logging, copy-on-write to avoid this situation. These mechanisms, however, introduce a significant overhead, and fail to adapt to the diversity of directory and heterogeneity of data reliability demands. Existing file-adaptive journaling technique is required to modify legacy applications. Therefore, a directory-adaptive journaling mode selective mechanism for Android systems was proposed to choose different journaling modes with strong or weak consistency guarantees in terms of different directories reliability demands. This mechanism is transparent to developers, and also matches the feature of Android systems, hence, it greatly reduces the consistency guarantee overhead without sacrifice of reliability. The experimental results show that modified file system can identify directories in which a file resides, meanwhile, choose reasonable pre-defined journaling mode.

In the distributed data fusion architecture, the general batches should be organized to make local tracks be correspondent with systemic tracks, after local tracks are associated in fusion center. Thus, following tracks' fusion can be dealt conveniently. In the practical engineering systems, the association mistakes can make the following automatic algorithm for organizing batches invalid, and cause systemic tracks intermittent and fail to work. An automatic algorithm for organizing batches of associated local tracks was proposed. In this algorithm, the local tracks would be distributed with general batches according to the desired degree between association of tracks and fusion of tracks. It could make the association tracks be correspondent with the systemic tracks. Besides reflecting real associated relationship, the proposed algorithm also guaranteed the stability of fused tracks batches and made the batch represent the same object in different time. The proposed algorithm has been applied into the practical engineering system, and it has good stability.