Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base, how to use triple information to generate natural questions is a key technology in question answering systems. The traditional way of generating questions require a lot of manual intervention and produce lots of noise. To solve these problems, we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions. The semi-automated model can generate question templates and real questions combining the knowledge base and center graph. The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network. Meanwhile, the attention mechanism is utilized in the decoding layer, which makes the triples and generated questions more relevant. Finally, the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.     

基于熵权TOPSIS的工程咨询机构BIM技术应用能力评价

构建工程咨询机构BIM技术应用能力评价的指标体系,提出改进的熵权TOPSIS评价模型,以4个典型的工程咨询单位为例,进行BIM技术运用能力评价。研究表明,BIM行业的整体水平已经有了明显的提升,但是区域间的发展还存在一定差距。     

破损水文观测孔涌水封堵技术在高河矿的应用

针对高河矿奥灰水文观测孔空水管锈蚀断裂漏水严重的问题,分析了存在的问题,制定了钻孔涌水封堵技术方案及相应的注浆施工工艺,并进行了为期7天的井下试验。封堵注浆压力达到6 MPa,使用注浆水泥50 t,水泥浆液凝固良好,注浆阀门打开后无出水现象。     

总变差模型在书画打印宣纸印刷质量预测中的应用

为了建立适用于书画打印宣纸印刷质量的预测模型,本研究测量了14种书画打印宣纸的粗糙度、白度、不透明度、定量、光泽度和针对宣纸特别设定的帘纹深浅以及帘纹疏密度等表面物理参量,并在相同条件下,使用喷墨打印设备输出并测量印品色度值,利用总变差模型构建去除帘纹色差的测定方法,得到与人眼视觉特征相符的色差。运用GRNN广义回归神经网络结合书画打印宣纸表面物理参量与宣纸去帘纹后的色差值,建立预测模型。结果表明,该模型能够在仅测量书画打印宣纸表面物理参量的情况下,便能较为准确地预测书画打印宣纸印刷质量,为书画打印宣纸印刷前的选纸工作提供指导依据。     

油藏中后期减氧空气驱提高采收率机理研究

尕斯库勒油田E¹₃油藏已经进入高含水时期，储采失衡加剧，综合含水率达到了80%以上，原油产量下降，经济效益变差。为了提高油藏原油采出程度、改善油藏开发效果，调研了国内外减氧空气驱的机理研究以及现场试验的资料，建立了以油藏真实孔渗饱数据为基础的一维条形地层机理模型，并选取该油藏Y12-27井组进行了减氧空气驱可行性验证。研究表明:纵向顶部减氧空气驱驱油效果优于水驱和氮气驱；驱替压力对原油采出程度影响不大；注水转注气可以提高原油采出程度；对于减氧空气驱，由于低温氧化反应的作用，氧气浓度对原油采出程度有一定影响，但比较微弱，其中，氧气浓度为10%时，驱替结束采出程度最高；尕斯库勒油田E¹₃油藏属于注水开发“双高”油藏，适用于减氧空气驱；对于该油藏Y12-27井组，顶部减氧空气驱驱油效果好于水驱和氮气驱，建议氧气浓度超过10%时采取关井等措施。     

定距螺旋桨对船用主机外特性的要求分析

为解决定距螺旋桨与船用主机在船舶运行工况的机桨匹配问题,结合船舶系泊和航行试验的相关标准,分析和归纳船舶运行数据,将实船测试与船机桨匹配理论计算相结合,得出运输船和拖轮在常用工况及特殊工况下定距螺旋桨对主机外特性的定量要求,为配套不同细分市场时船用主机的优化和开发提供参考依据。     

Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

A comparative study on hygroscopic and physiochemical properties of chicken powders obtained by different drying methods

Abstract

Different drying methods (spray drying (SD), vacuum drying (VD), microwave vacuum drying (MVD), and infrared vacuum drying (IFVD)) were applied in order to compare the hygroscopicity behavior of chicken powders. The hygroscopicity curves and glass transition temperature were used to evaluate the influence of ambient humidity and temperature on moisture absorption of powders. The results showed that the chicken powder dried by MVD had the lowest moisture absorption, followed by IFVD, VD, and SD. The hygroscopicity of SD chicken powders was different from other three kinds of chicken powders due to the physical properties of particles and the changes of protein secondary structure as detected by the Fourier transform-infrared spectrometer. For the three vacuum drying methods, the difference of protein secondary structure was the main reason of differences in hygroscopicity. Although MVD chicken powders were slightly inferior to SD chicken powders in taste, MVD chicken powders were the best in terms of smell and color as suggested by instrumental sensory parameter evaluations. It was found that MVD had a positive effect on reducing moisture absorption and maintaining sensory quality of chicken powders.     

转载溜槽衬板磨损预测的DEM仿真方法

针对EDEM自动划分网格较稀疏问题,采用Hypermesh划分仿真模型网格,利用Herz-Mindlin接触理论的Archard磨损模型对转载溜槽磨损问题进行仿真,在网格单元上提取出漏斗和溜管衬板的接触能量和磨损量,分析了磨损量与接触能量的关系。假设转载溜槽衬板磨损深度为物料与溜槽衬板的使用时间为线性函数,根据现场衬板磨损量的测量值,将衬板的磨损系数关系的看作隐式的单变量方程,应用EDEM软件的仿真结果求解出磨损系数的标定值。分析了漏斗和溜管的磨损机理。该方法可用于转载溜槽的磨损预测。