In an information explosion era, the large scale and structure complexity of datasets become problems in approximation calculation. Dynamic computing is an efficient approach to solve these problems. With the development of existing updating method applied to the dynamic approximation in multi-granular rough sets, a vector matrix based method for computing and updating approximations in Variable Precision Multi-Granulation Rough Sets (VPMGRS) was proposed. Firstly, a static algorithm for computing approximations based on vector matrix for VPMGRS was presented. Secondly, the searching area for updating approximations in VPMGRS was reconsidered, and the area was shrunk according to the properties of VPMGRS, effectively improving the time efficiency of the approximation updating algorithm. Thirdly, according to the new searching area, a vector matrix based algorithm for updating approximations in VPMGRS was proposed based on the static algorithm for computing approximations. Finally, the effectiveness of the designed algorithm was verified by experiments.

In terms of the problem that a safety-critical system will be paused, detected and resumed when security tools alert, and the delay between the occurrence and discovery of the false alarms (false positive or false negative) results in an effect on the availability of the guest Operating System (OS), a scheme based on virtualization was proposed. When a false alarm occurred, the operations of the suspicious application were quarantined correctly to avoid substantial system-wide damages. Then the operations of the suspicious application were logged and application inter-dependency information was generated according to its interactions with other applications. When the false alarm was determined, measures such as resuming the application's operations and killing the relevant applications according to the operation logs and inter-dependency information were taken so that the guest OS could reach the correct operating status quickly. The experimental results show that the scheme can reduce the overhead caused by rollback and recovery when a false alarm occurs. Compared to the situation without the proposed scheme, the overhead of handling the false alarm is reduced by 20%-50%. The proposed scheme can effectively reduce the effect of false alarm on the availability of clients, and can be applied in the cloud platform which provides services to safety-critical clients.

Since the segmentation performance of existing binary segmentation algorithm for video is excessively low, a binary probability segmentation algorithm in real-time based on Graphics Processing Unit (GPU) was proposed. The algorithm implemented a probabilistic segmentation based on the Quadratic Markov Measure Field (QMMF) model by regularizing the likelihood of each pixel of frame belonging to forground class or background class. In this algorithm, first two kinds of likelihood models, Static Background Likelihood Model (SBLM) and Unstable Background Likelihood Model (UBLM) were proposed. Secondly, the probability of each pixel belonging to background was computed by tonal transforming, cast shadow detecting and camouflage detecting algorithm. Finally, the probability of background which makes the energy function have a minimum value was computed by Gauss-Seidel model iteration and the binary value of each pixel was calculated. Moreover, illumination change, cast shadow and camouflage were included to improve the accuracy of segmentation algorithm. In order to fulfill the real-time requirement, a parallel version of our algorithm was implemented in a NVIDIA GPU. The accuracy and GPU execution time of the segmentation algorithm were analyzed. The experimental results show that the average missing rate and false detection rate of ViBe+ and GMM+ are 3 and 6 times those of QMMF, the average execution time of GPU of ViBe+ and GMM+ is about 1.3 times that of QMMF. Moreover, the average speedup of algorithm was computed and it is about 76.8.

At present, in the field of target recognition, the highest accuracy algorithm is the Deformable Part Model (DPM) for human detection. Aiming at the disadvantage of large amount of calculation, a parallel solution method based on Graphics Processing Unit (GPU) was proposed. In this paper, with the GPU programming model of OpenCL, the details of the whole DPM algorithm were implemented by the parallel methods,and optimization of the memory model and threads allocation was made. Through the comparison of the OpenCV library and the GPU implementation, under the premise of ensuring the detection effect, the execution efficiency of the algorithm was increased by nearly 8 times.

In order to get the maximum distribution attribute reduction rapidly in inconsistent decision information systems, a new decision maximum distribution binary relation was defined after analyzing the existing methods. And the judgment theorems for judging maximum distribution consistent sets were obtained, from which we can provide a new maximum distribution attribute reduction algorithm in inconsistent decision information systems. Moreover, the characterization of core attributes, relative necessary attributes, unnecessary attributes were discussed based on decision maximum distribution binary relation. Finally, a case study illustrates the validity of the method.