Nuclear Power Institute of China (NPIC) developed an advanced neutron transport lattice code, namel KYLIN-2. To solve the problem of mass data storage and processing in software, a computing data storage solution based on Hierarchical Data Format v5 (HDF5) was proposed. Firstly, HDF5 file format was researched. Secondly, according to the requirements of KYLIN-2, KYlin-2 Main RESults databank (KYMRES) based on HDF5 was designed. Finally, KYMRES was realized by self-developed HDF5 read/write tool. The performance tests show that KYMRES has a higher I/O efficiency compared with conventional storage solutions, the efficiency of reading and writing is increased to 2.3 and 4.5 times on average that of the old algorithm. KYMRES has significant advantages in mass data storage and processing. and provides a new data storage and management solution for KYLIN-2.

A new improved Kernel Density Estimation (KDE) algorithm used to segment foreground was proposed for the problem of reciprocating pumps and other troubles for segmenting foreground in the field of Coal Bed Methane (CBM) extraction and poor real-time of KDE. Background Subtraction (BS) and three frame difference were applied to divide the image into dynamic and non-dynamic background regions and then KDE was used to segment foreground for the dynamic background region. A new method of determining dynamic threshold was proposed when segmenting foreground region. Mean absolute deviation over the sample and sample variance were combined to compute the bandwidth. And the strategy of combining regular update with real-time update was used to renew the second background model. Random selection strategy instead of First In First Out (FIFO) mode was applied when replacing samples of the second background model. In the simulation experiments, the average time-consuming of per frame image is reduced by 94.18% and 15.38% and moving objects are more complete when comparing the improved KDE with the KDE and Background Subtraction Kernel Density Estimation (BS-KDE) respectively. The experimental results show that the proposed algorithm can detect foreground in the field of CBM extraction accurately and meet the real-time requirement in the standard definition video surveillance system basically.

Traditional n-gram feature extraction tends to produce a high-dimensional feature vector. High-dimensional data not only increases the difficulty of classification, but also increases the classification time. Aiming at this problem, this paper presented a feature extraction method based on Part-of-Speech (POS) tagging sequences. The principle of this method was to use POS sequences as text features to reduce feature dimension, according to the property that POS sequences can represent a kind of text.In the experiment,compared with the n-gram feature extraction, the feature extraction based on POS sequences at least improved the classification accuracy of 9% and reduced the dimension of 4816. The experimental results show that the method is suitable for emotion classification in micro blog.

During the process of service-oriented software testing, Oracle problem that the program result is unpredictable comes into existence frequently because of issues like the unknown communication partners before the service discovery and the potential existence of non-identical implementations of the same service. In this paper, based on the benefits of the SOA environments, Metamorphic Testing (MT) approach was applied in the phase of unit testing and integration testing of service-oriented software. Metamorphic Relation (MR) can be built according to the service properties. A metamorphic testing class can invoke relevant services to execute test cases and use their metamorphic relations to detect failures. Any violation of any relevant metamorphic relation indicates a failure.