基于滑模的运载器主动段俯仰通道姿控系统设计

与传统比例-积分-微分（PID）控制方法相比，滑模控制（SMC）方法可以比较容易地将不确定性纳入控制器设计中，从而增强系统的鲁棒性。探索了SMC技术在运载器主动段姿态控制中的工程应用，首先通过分析基于趋近律的SMC系统，提出了降低不连续切换项系数的需求，然后研究了基于干扰上界的SMC方法。三通道小偏差仿真结果验证了两种方法的控制效果，表明第2种控制器的鲁棒性更好，稳态误差小，同时发动机喷管摆角需求较小。     

The control of paracetamol particle size and surface morphology through crystallisation in a spray dryer

The parameters governing the crystallisation of paracetamol using various conventional techniques has been extensively studied, however the factors influencing the drug crystallisation using spray drying is not as well understood. The aim of this work was to investigate the crystallisation of an active pharmaceutical ingredient through evaporative crystallisation using a spray dryer to study the physicochemical properties of the drug and to use semi-empirical equations to gain insight into the morphology and particle size of the dried powder. Paracetamol solutions were spray dried at various inlet temperatures ranging from 60 °C to 120 °C and also from a series of inlet feed solvent compositions ranging from 50/50% v/v ethanol/water to 100% ethanol and solid-state characterisation was done. The size and morphology of the dried materials were altered with a change in spray drying parameters, with an increase in inlet temperature leading to an increase in particle Sauter mean diameter (from 3.0 to 4.4 µm) and a decrease in the particle size with an increase in ethanol concentration in the feed (from 4.6 to 4.4 µm) as a result of changes in particle density and atomised droplet size. The morphology of the dried particles consisted of agglomerates of individual crystallites bound together into larger semi-spherical agglomerates with a higher tendency for particles having crystalline ridges to form at higher ethanol concentrations of the feed.     

煤田地震勘探中多井组合爆炸法浅析

地震勘探中炸药作为激发震源的激发条件多样,采用有效的震源激发方式,才能取得高信噪比的地震原始数据。本文依据有关爆破原理,理论结合实践经验,提出在厚黄土区采用多井组合的激发方式,能有效压制噪声,增强性噪比,提高数据质量。     

Empirical surveys of frontier applications: a meta‐review

This contribution is the first attempt to systematically review all empirical surveys that so far have been made available in the broad field of efficiency and productivity analysis using frontier estimation methodologies. We provide a systematic bibliometric review on the many empirical surveys in the field of efficiency and productivity analysis, the most relevant concepts, areas, overlaps, and potentials to explore from its introduction to the most recent surveys. We combine the United Nations’ International Standard Industrial Classification (ISIC) taxonomy for the economic activity with the Journal of Economic Literature (JEL) classification system to classify the empirical surveys and to identify the current gaps in the literature. In addition to the most relevant/generic potential areas for applications (according to the United Nation's ISIC), this methodology provides a cluster analysis with the most relevant concepts that have been considered so far (according to the JEL codes). This overview brings an interesting guide for future work to develop the whole field.     

A benchmark data set for two-phase Coriolis metering

For more than a decade there has been growing interest in the use of Coriolis mass flow metering applied to two-phase (gas/liquid) and multiphase (oil/water/gas) conditions. It is well-established that the mass flow and density measurements generated from multiphase flows are subject to large errors, and a variety of physical models and correction techniques have been proposed to explain and/or to compensate for these errors. One difficulty is the absence of a common basis for comparing correction techniques, because different flowtube designs and configurations, as well as liquid and gas properties, may result in quite different error curves. Furthermore, some researchers with interests in the modelling aspects of the field may not have suitable multiphase laboratory facilities to generate their own data sets. This paper offers a small data set that may be used by researchers as a benchmark i.e. a common data set for comparing correction techniques. The data set was collected at the UK National Flow Laboratory TUV-NEL, using air and a viscous oil, and provides experimental points over a wide flow range (8:1 turndown) and with Gas Volume Fraction (GVF) values up to 60%. As a first investigation using the benchmark data set, we consider how data sparsity (i.e. the flow rate and GVF spacing in the experimental grid) affects the accuracy of a correction model. A range of neural network models are evaluated, based on different subsets of the benchmark data set. The data set and some exemplary code are provided with the paper. Additional data sets are available on a web site created to support this initiative.     

重新找回人工智能的可解释性

针对深度神经网络AI研究的可解释性瓶颈，指出刚性逻辑（数理形式逻辑）和二值神经元等价，二值神经网络可转换成逻辑表达式，有强可解释性。深度神经网络一味增加中间层数来拟合大数据，没有适时通过抽象把最小粒度的数据（原子）变成粒度较大的知识（分子），再把较小粒度的知识变成较大粒度的知识，把原有的强可解释性淹没在中间层次的汪洋大海中。要支持多粒度的知识处理，需把刚性逻辑扩张为柔性命题逻辑（命题级数理辩证逻辑），把二值神经元扩张为柔性神经元，才能保持强可解释性。本文详细介绍了从刚性逻辑到柔性逻辑的扩张过程和成果，最后介绍了它们在AI研究中的应用，这是重新找回AI研究强可解释性的最佳途径。     

基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。     

面向数据流结构的指令映射优化方法

在高性能计算领域,数据流是一类重要的计算结构,也在很多实际场景表现出很好的性能和适用性。在数据流计算模式中,程序是以数据流图来表示的,数据流计算中一个关键的问题是如何将数据流图映射到多个执行单元上。通过分析现有数据流结构的指令映射方法及其不足,提出了基于数据流结构的新型指令映射优化方法。主要是根据多地址共享数据包的特性对指令映射方法进行优化,延迟多地址共享数据路由包的拆分,减少网络拥堵。     

Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data

This paper mainly illustrates the Tree Seeds Algorithm (TSA) to tackle structural damage identification problem. The damage model is simulated by the alterations of both stiffness and mass parameters. The objective function is introduced by minimizing the differences between the measured and calculated acceleration data. To enhance the performance of the standard TSA, two modifications including the bare-bones Gaussian updated mechanism and the withering process are introduced. The modified algorithm is named after the BGTSA. In the numerical simulation part, the BGTSA is firstly used to make comparisons with several state-of-the-art algorithms on the CEC05. Secondly, the BGTSA is utilized to deal with the structural damage identification problem by optimizing the acceleration-based nonlinear objective function. Numerical experiments involving a simply supported beam and a truss are carried out to verify the effectiveness of the proposed algorithm. The final results show that with low amount of acceleration data, the BGTSA can acquire better identification results compared with other evolutionary algorithms. Therefore the proposed algorithm could be viewed as a potential tool to solve the structural damage identification problem.