-To cope with the increasing scale of scientific data and computational complexity of daily data, more and more cores have been integrated into GPU(Graphic Processing Units) and its working frequency is continually upgrading, which makes it being widely used in general computing for assisting CPU to accelerate program. While GPU offers powerful computing capability, the problem of the energy consumption becomes particularly prominently and it has become one of the important issues hindering development of GPU. For the purpose of solving this problem, DVFS (Dynamic Voltage Frequency Scaling) becomes an effective solution. Because the previous works only focus on single component and use linear relationship to do DVFS without considering energy saving of other units in system at software runtime, therefore we propose an energy saving model (CDVFS) of considering the characteristics of both GPU and memory at software runtime based on GA-BP (Genetic Algorithm-Back propagation) neural network to make better use of the relationship between components for energy saving. Firstly, the model assumes that functional relation between the software runtime characteristics of GPU and memory and the appropriate frequency which corresponds to the GPU and memory as nonlinear. Secondly, we extract five characteristics and use GA-BP neural network to fit the nonlinear functional relation. At last, experiments demonstrate the effectiveness of the approach and reasonableness of assumption, and also show that CDVFS can get average energy savings of 17.06% compared with previous works within acceptable performance loss.
High fail and dropout rates are the major problems in distance education. Due to a large number of online learners and limited teacher resources, it is essential to accurately identify these potential at-risk students in advance and provide timely aids, which will help to improve the educational outcome. In the online learning environment, students’ online learning behaviors can be recorded easily, with the click data being the most common one. Students’ learning behavior can reflect their learning situation and may differ among different students and periods. This paper proposed a model that uses the short-period activity characteristic and long-term changing pattern to predict the potential at-risk students. The model contains two stages: information extraction and information utilization. The first stage extracts data from the log files and organizes it in a form suitable for the model. In the second stage, according to the different characteristics of students’ short-term and long-term learning behavior, a convolution residual recurrent neural network (CRRNN) model is proposed. The convolutional neural network is used to obtain the representation of the student’s learning behavior in a certain period. Then, the residual recurrent neural network is used to get the behavior changing pattern over the periods. The experimental results indicate that the proposed model has higher performance than the three widely used baseline methods on the OULA dataset and has good practical application value for teaching and management.
The gas suspension phenomenon caused by the yield stress of the drilling fluid affects the accurate calculation of wellbore pressure after gas invasion. At present, most studies on the bubble suspension in the yield stress fluid focus on the single-bubble suspension condition and there are few studies on the gas suspension concentration. This paper carried out the GSC (gas suspension concentration) experiment in the simulated drilling fluid, xanthan solution, with different gas invasion methods. The GSC in the drilling fluid under the conditions of diffuse gas invasion and differential pressure gas invasion was simulated by using two methods of stir-depressurization and continuous ventilation. The results showed that when the size of a single bubble satisfied the single-bubble suspension condition, multiple bubbles can be suspended at the same time. The GSC is affected by the average size of the suspended bubbles, the yield stress of the drilling fluid, and the gas invasion modes. For different gas invasion modes, the empirical models of critical GSC related to the dimensionless number
Bi
are established. Compared with the experimental data, the relative error of the critical GSC in diffuse gas invasion is less than 6% and the relative error of the critical GSC in differential pressure gas invasion is less than 10%. The results of this work can provide guiding significance for accurate calculation of wellbore pressure.
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