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The research of computer technology in the field of education makes Intelligent Tutoring System (ITS) birth and develop rapidly. The main advantage of an ITS is it can provide appropriate teaching content according to students’ knowledge state, level and ability. Reasonable evaluation of students’ learning performance is the key and difficult point in ITS design. Using fuzzy logic to evaluate students’ learning performance is not a very new method. However, most approaches rely extremely on student’s answer time for each question, which are seldom collected in common exams. Given this insight, we present a new fuzzy inference mechanism in this paper to calculate students’ ranking order without dependence of the answer times. According to the importance, complexity and difficulty of the problem, new reasoning rules are set up to accurately evaluate the comprehensive level of problem. The results of the case study show that each student’s grades are distinguished, even for those with the same grades. The proposed method in this paper enriches the present evaluation approach of students’ performance, and is utilizable to analyze the direct relationship of students’ scores with the attributes of each question.

In the process of tomographic reconstruction, the attitude and center point of a specimen, from which the projection data are collected, suffer from misalignment due to mechanical imperfection and calibration error. Such misalignment leads to poor reconstruction quality. And effective automatic alignment approaches have been proposed. The alignment approaches are of good use for kinds of application scenarios such as X-CT and electron tomography. These scenarios demand not only high performance, but also that the component of automatic alignment can be integrated and upgraded in the whole solution. Thus, we propose an FPGA accelerator for state-of-the-art tomographic alignment algorithm. We first introduce a multi-ray access approach that modifies the order of data access for easier on-chip data management. Making use of BRAMs on FPGAs and effective local data management strategy, data reuse is reinforced, and data transfer latency with DRAM is covered by computation. Also, we introduce an FPGA-customized processing engine at a low cost to improve data throughput. Moreover, a streaming structure with multiple paralleled PEs further improves the performance of our algorithm. Experiments demonstrate that our accelerator achieves a 44. 5x speed-up for the state-of-the-art alignment on Xilinx ZCU102 over a 16-thread multicore CPU implementation, and a 1. 60x speed-up with 7. 8x energy reduction over an OpenCL implementation on Nvidia Titan V.

This thesis is based on the theory of planned behavior, and the influence of entrepreneurship education on College Students' network entrepreneurial intention. Study found that entrepreneurship education of undergraduate network does pioneering work will have significant positive effects, and support environment of financial support plays a moderating effect between entrepreneurship education and entrepreneurial intention, and non-venture financial support in between entrepreneurship education and entrepreneurial network will not up regulation effect. Thus, to improve the system of entrepreneurship education for college students to improve the entrepreneurial environment, and enhance the students' entrepreneurial intentions.

The text feature matrix of domain-ontology has the following three characteristics: high-dimension, sparse and independence of dimensions. Independence means that text implications of dimensions are different from each other. Many clustering algorithms take into account the characteristics of high-dimension and sparse, but ignore the impact of independence. And the artificial interference in parameters can often affect our clustering results. In this paper, we propose a new clustering algorithm by enriching connotation of similarity and minimizing the influence of subjective parameters. The experimental results verify the validity of our algorithm.

Staged combustion technology is adopted in the trapped vortex combustor, a pilot combustor is used for flame stability and a main combustor is used for power. Low emissions combustion can be achieved by controlling mixing of oil and gas in pilot combustor and main combustor. Experimental research has been conducted to investigate the emissions of pilot stage of double vortex combustor. The combustor uses evaporating tubes as fuel injection device, and the aviation kerosene is chosen as the fuel of the combustor. The experimental results indicate that the emissions index of CO of pilot stage are between 8 and 137 g/Kg fuel, and the emissions index of NO x are between 1 and 2.4 g/Kg fuel. The influence of inlet temperature and inlet velocity on emissions has been studied in detail. The experimental results also indicate that the area increase of inlet hole in the cavity will help to reduce the emissions of CO and NO x .