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.     

基于支持向量机的多类分类研究

现今流行的分类方法的重要基础是传统的统计学，前提是要有足够的样本，当样本数目有限时容易出现过学习的问题，导致分类效果不理想。引入支持向量机方法，它基于统计学习理论，采用了结构风险最小化原则代替经验风险最小化原则，较好的解决了小样本学习的问题；又由于采用了核函数思想，把非线性空间的问题转换到线性空间，降低了算法的复杂度。对其相关内容包括优化算法及多类分类问题的解决进行了研究，最后用一个实例说明了该方法的可行性和有效性。     

A new family of proximity graphs: Class cover catch digraphs

Motivated by issues in machine learning and statistical pattern classification, we investigate a class cover problem (CCP) with an associated family of directed graphs—class cover catch digraphs (CCCDs). CCCDs are a special case of catch digraphs. Solving the underlying CCP is equivalent to finding a smallest cardinality dominating set for the associated CCCD, which in turn provides regularization for statistical pattern classification. Some relevant properties of CCCDs are studied and a characterization of a family of CCCDs is given.     

幅相一致行波管高频电路CAD研究

 在行波管的设计和装配过程中，各部件的尺寸必须严格控制，高频电路参数的离散对行波管色散特性有极大的影响。使用螺旋导电面模型，模拟计算了高频结构各主要参数离散对色散特性和轴向互作用耦合阻抗的影响。分析计算了夹持杆宽度、翼片高度、螺旋线平均半径、螺距、夹持杆介电常数等离散时对色散特性和轴向互作用耦合阻抗的影响，为新型幅相一致行波管的设计和生产提供了很有价值的参考建议。     

风力发电机组变速恒频控制系统研究

全面介绍了一种最新应用于风力发电中的新型电机—无刷双馈电机，并分析了这种新型电机作变速恒频运行的原理并对这种电机进行了动态特性仿真研究，给出了形式简洁的控制方程和易于实现的控制方案。     

横向射流式单重态氧发生器的性能实验研究

 对氧碘化学激光器的单重态氧发生器（SOG）进行了改进，采用横向射流方式，并对该横向射流式单重态氧发生器的性能进行了检测。实验中过氧化氢碱溶液温度控制在－16℃左右,氯气流量为530mmol/s，He与氯气的流量比为3;采用PS法测量单重态氧分子的产率,吸收法测量氯气的利用率和相对水含量。得出如下结论:在不使用冷阱和分离器的情况下，最高单重态氧分子产率达到58%, 氯气利用率在80%以上,相对水含量小于等于0.5;气体达到最大流量时,发生器仍然能稳定地工作。     

与q形变玻色算符逆算符相关的相干态及其量子统计性质

讨论了ｑ形变玻色算符的广义逆算符作用于ｑ－相干态所得到的两类量子态的数学及量子统计性质。结果表明，ｑ－相干态的光子激发态不存在压缩但呈现反聚束效应，而ｑ－相干态的光子湮灭态却存在压缩但不呈现反聚束效应。     

支持向量机在交通量预测中的应用

交通量预测对于区域交通规划有重要意义。提出一种基于支持向量机理论的交通量预测方法。该方法以统计学习理论为基础，通过和BP神经网络进行比较的实验，证明其在交通量预测中的有效性。     

基于小波包分析及支持向量机的超音速目标识别方法研究

提出了基于小波包分析及支持向量机的超音速目标识别方法 .通过 5 .5 6mm ,7.6 2mm和 12 .7mm三种枪弹试验获取信号 ,用小波包分解激波信号 ,统计每个频带的能量特征 ,用支持向量机方法训练测试样本 ,获得了很好的分类效果 .仿真结果表明基于超音速飞行体产生的激波信号来识别目标是可行的 .     

A new type SVM-projected SVM

Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.