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.     

密度泛函理论方法研究填料表面对聚合物摩擦化学的影响

采用超声分散法制备出氧化铝、高岭土、氧化硅/聚四氟乙烯复合材料, 使用线性往复摩擦磨损试验机对比三种复合材料的摩擦学性能. 结果表明 质量分数10%的氧化铝、高岭土能将聚四氟乙烯的磨损率降低约4个数量级, 而氧化硅仅能降低约3个数量级. 对金属对偶表面形成的转移膜的形貌和化学成分进行分析发现 氧化铝、高岭土/聚四氟乙烯在金属对偶面上形成了高度羧酸盐化的转移膜. 用密度泛函理论对三种填料表面上碳氟分子吸附过程进行模拟, 结果显示氧化铝、高岭土表面的路易斯酸性位点促进了碳氟分子的脱氟过程, 产生了更多的羧酸螯合物的中间产物; 氧化硅缺少路易斯酸性位点, 因此不能促进高度羧酸盐化的转移膜形成.     

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.     

High-efficient surface tailoring via reverse atom transfer radical polymerization and reversible addition-fragmentation chain-transfer polymerization in an aqueous system initiated by a monocenter redox pair

The commonly used multi-center initiation methods always lead to the formation of quantities of homopolymer in the surface tailoring based on reverse atom transfer radical polymerization (ATRP) and reversible addition-fragmentation chain-transfer (RAFT) polymerization. In this study, a monocenter redox pair constructed of silica bearing tert-butyl hydroperoxide groups and ascorbic acid (SiO₂-TBHP/AsAc) was applied to substitute the commonly used initiation method of R-supported RAFT grafting polymerization. All the propagating radicals were restricted on the surface of solid particles during the whole procedure theoretically, resulting in a higher grafting efficiency of 95.1% combined with the “controllable” feature at 10 h. This redox pair was also used to initiate the reverse ATRP in miniemulsion successfully with a grafting efficiency of 86.3% at 10 h. The grafting efficiency obtained under this monocenter initiation method was significantly higher than that of the frequently reported surface modification by reverse ATRP and RAFT polymerization. In addition, the high-efficient surface tailoring was traced and confirmed by nuclear magnetic resonance, Fourier transform infrared, X-ray photoelectron spectroscopy, thermogravimetric analysis, transmission electron microscopy, and other analysis tests. The advantage of this monocenter redox pair will open a new avenue for the potential “high-efficient” surface tailoring of various materials.     

Visible light-driven acridone catalysis for atom transfer radical polymerization

Acridone as a new kind of visible light photocatalyst has been developed to catalyze metal free atom transfer radical polymerization (ATRP). The photocatalyst possess low excited state potential as can undergo an oxidative quenching pathway to initiate ATRP of vinyl monomers. Kinetic study and light on/off reaction demonstrate the “living”/controlled nature of the polymerization by light. Block copolymers can be achieved by using PMMA as macroinitiator to reinitiate polymerization of other vinyl monomers, which shows highly preserved Br chain-end functionality in the synthesized polymers. Moreover, the polymerization can be conducted under air atmosphere as most photocatalysts need anaerobic condition, which may give inspiration of further application of this kind of photocatalyst.