Prediction of the drag reduction effect of pulsating pipe flow based on machine learning

Prediction of drag reduction effect caused by pulsating pipe flows is examined using machine learning. First, a large set of flow field data is obtained experimentally by measuring turbulent pipe flows with various pulsation patterns. Consequently, more than 7000 waveforms are applied, obtaining a maximum drag reduction rate and maximum energy saving rate of 38.6% and 31.4%, respectively. The results indicate that the pulsating flow effect can be characterized by the pulsation period and pressure gradient during acceleration and deceleration. Subsequently, two machine learning models are tested to predict the drag reduction rate. The results confirm that the machine learning model developed for predicting the time variation of the flow velocity and differential pressure with respect to the pump voltage can accurately predict the nonlinearity of pressure gradients. Therefore, using this model, the drag reduction effect can be estimated with high accuracy.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.     

基于联合神经网络学习的中文电力计量命名实体识别

随着电力计量业务的不断扩展，迫切需要由业务信息、技术知识、行业标准及其内在联系所组成的电力计量知识图谱，为电网的决策和发展提供更为全面有效的支持。命名实体识别是构建知识图谱的基础。针对电力计量领域需要，结合中文分词技术特点，基于联合学习思想，提出了一种基于联合学习的中文电力计量命名实体识别技术。该技术联合CNN-BLSTM-CRF模型与整合词典知识的分词模型，使其共享实体类别和置信度；同时将2个模型的先后计算顺序改为并行计算，减少了识别误差累积。结果表明，在不需要人工构建特征的情况下，方法的正确率、召回率、F值等均显著优于以往方法。     

游客情感计算的文本大数据挖掘方法比较研究

旅游文本大数据以其方便、快捷和低门槛的特点为游客情感计算提供了极大便利，已经成为旅游大数据的主要来源之一。基于大数据理论和情感理论，以文本大数据为数据源，在全面梳理国内外情感计算相关成果的基础上，利用人工智能中的逻辑/算法编程方法、机器学习方法、深度学习方法对旅游文本大数据进行挖掘，探索最佳的基于文本大数据的游客情感计算方法。研究发现：（1）基于情感词典的游客情感计算模型，其核心是构建情感词典和设计情感计算规则，方法简单，容易实现，适用语料范围广。（2）机器学习，用统计学方法抽取文本中的特征项，具有非线性特征，可靠性较线性特征的情感词典方法高。（3）基于深度学习技术的游客情感计算，效果良好，准确率在85%以上。训练多领域的文本语料易于移植，实用性强，且泛化能力好，较适合大数据时代游客情感计算研究。     

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

     

Quantum algorithm for neighborhood preserving embedding

Neighborhood preserving embedding (NPE) is an important linear dimensionality reduction technique that aims at preserving the local manifold structure. NPE contains three steps, i.e., finding the nearest neighbors of each data point, constructing the weight matrix, and obtaining the transformation matrix. Liang et al. proposed a variational quantum algorithm (VQA) for NPE [Phys. Rev. A 101 032323 (2020)]. The algorithm consists of three quantum sub-algorithms, corresponding to the three steps of NPE, and was expected to have an exponential speedup on the dimensionality n. However, the algorithm has two disadvantages: (i) It is not known how to efficiently obtain the input of the third sub-algorithm from the output of the second one. (ii) Its complexity cannot be rigorously analyzed because the third sub-algorithm in it is a VQA. In this paper, we propose a complete quantum algorithm for NPE, in which we redesign the three sub-algorithms and give a rigorous complexity analysis. It is shown that our algorithm can achieve a polynomial speedup on the number of data points m and an exponential speedup on the dimensionality n under certain conditions over the classical NPE algorithm, and achieve a significant speedup compared to Liang et al.'s algorithm even without considering the complexity of the VQA.     

新型多孔材料用作色谱手性固定相

手性固定相是色谱法分离分析手性化合物的关键。近年来,随着材料科学的迅速发展,越来越多的新型手性材料被作为色谱固定相用于手性分离分析。本文综述了近5年来液相色谱、气相色谱和毛细管电色谱领域的新型手性固定相的研究进展,重点总结了基于手性多孔材料的新型手性固定相研究,最后对手性固定相的研究进行了总结与展望。     

Biosynthesis of zinc oxide nanoparticles using Euphorbia milii leaf constituents: Characterization and improved photocatalytic degradation of methylene blue dye under natural sunlight

A facile biosynthesis route was followed to prepare zinc oxide nanoparticles (ZnO NPs) using Euphorbia milii (E. milii) leaf constituents. The SEM images exhibited presence of spherical ZnO NPs and the corresponding TEM images disclosed monodisperse nature of the ZnO NPs with diameter ranges between 12 and 20 nm. The Brunauer–Emmett–Teller (BET) analysis revealed that the ZnO NPs have specific surface area of 20.46 m²/g with pore diameter of 2 nm–10 nm and pore volume of 0.908 cm³/g. The EDAX spectrum exemplified the existence of Zn and O elements and non-appearance of impurities that confirmed pristine nature of the ZnO NPs. The XRD pattern indicated crystalline peaks corresponding to hexagonal wurtzite structured ZnO with an average crystallite size of 16.11 nm. The FTIR spectrum displayed strong absorption bands at 512 and 534 cm^?1 related to ZnO. The photocatalytic action of ZnO NPs exhibited noteworthy degradation of methylene blue dye under natural sunlight illumination. The maximum degradation efficiency achieved was 98.17% at an illumination period of 50 min. The reusability study proved considerable photostability of the ZnO NPs during photocatalytic experiments. These findings suggest that the E. milii leaf constituents can be utilized as suitable biological source to synthesis ZnO NPs for photocatalytic applications.     

1,3-丁二烯选择性加氢催化剂的设计策略以及构效关系

催化裂化是石油化工的核心单元之一.从催化裂化尾气中分离出来的碳四馏分富含许多的不饱和烯烃,如1-丁烯、顺、反式-2-丁烯以及少量的1,3-丁二烯,这些不饱和烯烃可以通过后续聚合反应,生成合成橡胶和工程塑料的重要原料,具有重要的应用价值.上述工艺过程对原料中1,3-丁二烯的含量(<100~200 ppm)有严苛的要求.采用选择性加氢技术对碳四馏分中的1,3-丁二烯进行选择性加氢,将其转化为更高附加值的单烯烃是一个理想的解决方案.然而,1,3-丁二烯加氢反应得到的单烯烃可能发生深度加氢得到副产物丁烷.因此,开发高效选择性加氢催化剂对碳四资源的利用具有重要的现实意义.另一方面,1,3-丁二烯加氢反应可以作为模型反应,用来考察选择性加氢催化剂的性能.基于此,该反应无论在工业界还是学术界均受到广泛关注.尽管如此,有关1,3-丁二烯加氢催化剂研究进展方面的综述极少.仅有关于1,3-丁二烯加氢作为模型反应的综述报道.本文对过去半个世纪以来1,3-丁二烯加氢反应中不同催化剂的发展历程进行系统综述,特别是包括Pd,Pt和Au等的单一贵金属催化剂.重点介绍以下内容:(1)固体催化剂构效关系,包括活性金属尺寸效应、晶面和形貌效应以及载体效应(晶相、孔道和酸碱性);(2)高性能催化剂的设计新策略,如单原子催化剂、核壳结构催化剂、金属-离子液复合催化体系以及载体的形貌调控;(3)催化剂的反应机理和失活机理.提出了1,3-丁二烯选择性加氢高性能催化剂开发面临的挑战,并对潜在的发展方向进行了展望.本文认为随着纳米技术和金属纳米材料合成方法的快速发展,对贵金属活性组分进行原子层面上的调控(包括形貌、尺寸以及单原子配位环境等)已成为可能.这将有助于研制出一类新型高性能选择性加氢催化材料,从而实现高转化率条件下高附加值单烯烃的定向转化.此外,载体的酸碱性和孔道结构的调控有助于进一步调节催化剂的抗积炭性能,也是未来发展的一个重要方向.     

水热法制备二氧化锡/钨酸铋复合光催化材料及其催化活性研究

本文用水热法制备了正交晶系的纳米球状结构的二氧化锡和正交晶系的由片状聚集成球状结构的钨酸铋,并且对二者进行了复合,制备出了二氧化锡/钨酸铋复合光催化材料。采用X射线衍射(XRD)、扫描电子显微镜(SEM)、比表面积测试仪(BET)、紫外可见分光光度计等技术对复合样品的结构、形貌、比表面积、孔容孔径和光学性质进行了表征。用碘钨灯模拟太阳光,分别以二氧化锡、钨酸铋和二氧化锡/钨酸铋复合材料为催化剂降解罗丹明B(RhB),研究所制备的二氧化锡/钨酸铋复合材料的光催化活性。光催化90 min时二氧化锡、钨酸铋和二氧化锡/钨酸铋对罗丹明B的降解率分别是9%、22%和30%。实验结果表明,在可见光下,二氧化锡/钨酸铋复合材料的光催化活性要高于单一的二氧化锡和钨酸铋。