Energy conversion performances during biomass air gasification process under microwave irradiation

Biomass gasification technology under microwave irradiation is a new and novel method, and the energy conversion performances during the process play a guiding role in improving the energy conversion efficiencies and developing the gasification simulation models. In order to improve the energy utilization efficiency of microwave biomass gasification system, this study investigated and presented the energy conversion performances during biomass gasification process under microwave irradiation, and these were materialized through detailing (a) the energy conversion performance in the microwave heating stage, and (b) the energy conversion performance in the microwave assisted biomass gasification stage. Different forms of energies in the biomass microwave gasification process were calculated by the method given in this study based on the experimental data. The results showed that the useful energy (energy in silicon carbide (SiC), 18.73 kJ) accounted for 31.22% of the total energy input (electrical energy, 60.00 kJ) in the heating stage, and the useful energy (energy in the products, 758.55 kJ) accounted for 63.41% of the total energy input (electrical and biomass energy, 1196.28 kJ) in the gasification stage. During the whole biomass gasification process under microwave irradiation, the useful energy output (energy in the products, 758.55 kJ) accounted for 60.38% of the total energy input (electrical and biomass energy, 1256.28 kJ), and the energy in the gas (523.40 kJ) product played a dominate role in product energy (758.55 kJ). The energy loss mainly included the heat loss in the gas flow (89.20 kJ), magnetron loss (191.80 kJ) and microwave dissipation loss (198.00 kJ), which accounted for 7.10%, 15.27% and 15.76% of the total energy, respectively. The contents detailed in this study not only presented the energy conversion performances during microwave assisted gasification process but also supplied important data for developing gasification simulation models.     

New exact solutions of the Tzitzéica-type equations in non-linear optics using the expa function method

One specific class of non-linear evolution equations, known as the Tzitzéica-type equations, has received great attention from a group of researchers involved in non-linear science. In this article, new exact solutions of the Tzitzéica-type equations arising in non-linear optics, including the Tzitzéica, Dodd–Bullough–Mikhailov and Tzitzéica–Dodd–Bullough equations, are obtained using the exp_a function method. The integration technique actually suggests a useful and reliable method to extract new exact solutions of a wide range of non-linear evolution equations.     

喷油方式对斜齿轮齿面温度的影响

为探讨喷油方式对齿面温度的影响，以斜齿轮为研究对象，通过CFD仿真和试验研究不同喷油方式对斜齿轮齿面温度的影响。针对斜齿轮喷油润滑过程的特点，分析其齿面摩擦生热量，简化轮齿喷油润滑计算模型，并基于CFX进行齿面对流换热仿真研究，得到了不同喷油方式下的齿面温度；基于功率开放式斜齿轮试验平台，对不同喷油方式下的齿面温度进行了试验测量，与仿真计算结果进行对比分析。结果表明：在高速重载斜齿轮传动系统中，啮出侧喷油润滑的方式比啮入侧喷油润滑方式的冷却降温效果要好。     

大规模多状态计算系统的容错性能分析

针对异构计算节点组成的大规模多状态计算系统的容错性能分析问题，提出了一种计算系统容错性能的评估方法。该方法采用自定义的两级容错性能形式化描述框架进行系统描述，通过构造多值决策图（Multi-value Decision Diagram，MDD）模型对系统进行容错性能建模，并基于构造的模型高效地计算出部件故障的条件下计算系统在特定性能水平上运行的概率，减少了计算的冗余性。实验结果表明，该方法在模型的大小和构建时间上均优于传统方法。该方法的提出将对系统操作员或程序设计者具有重要意义，使其确保系统适合预期应用。     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Quantifying co-creative writing experiences

Collaboration with artificial intelligence (AI) is a growing trend even in the field of creativity. This paper examines which quantitative metrics can be used to comparatively analyse human-computer co-creativity with children. To study this question, 24 schoolchildren of age 10–11 wrote a poem with three co-creative poetry writing processes: a human-computer, a human-human, and a human-human-computer process. The computational participant in the processes was an AI-based application called the Poetry Machine. The children were asked to evaluate their user experience with a 5-point Likert-type questionnaire after each writing process and a comparative questionnaire after finishing all processes. The metrics used in the evaluation were immediate fun, long-term enjoyment, creativity, self-expression, outcome satisfaction, ease of starting and finishing writing, quality of ideas and support from others, and ownership.

Significant differences were found in fun, long-term enjoyment, quality of ideas, support, and ownership. The high number of statistically relevant results was enabled by exposing all participants to all writing processes, and the comparative questionnaire. The human-human-computer process was evaluated the best in long-term enjoyment and the human-computer process the weakest in support and idea quality. Creativity and ease of finishing writing turned out to be outlining metrics for the co-creative processes.     

用于厚屏蔽小探测器的蒙特卡罗模拟减方差方法研究

反应堆屏蔽计算中经常出现厚屏蔽、小探测器问题，常规蒙特卡罗方法难以有效解决。基于自动重要抽样（AIS）方法，本文提出了小探测器自动重要抽样(SDAIS)方法，并针对小探测器问题，优化了AIS方法的虚粒子赌分裂算法。该方法在自主开发的蒙特卡罗屏蔽程序MCShield上进行了实现。使用NUREG/CR-6115 PWR基准题验证该方法的正确性和计算效率。结果表明，SDAIS方法可有效地解决厚屏蔽小探测器问题，相比AIS方法及传统的重要性方法，计算效率提升1~2个量级。