Improved general correlation for critical heat flux during upflow in uniformly heated vertical tubes

An improved version of the author's earlier correlation for CHF in vertical tubes is presented. It is compared with data that include 23 fluids (water, refrigerants, cryogens, chemicals, and liquid metals), tube diameters 0.315 to 37.5 mm, tube length 1.3 to 940 times diameter, mass flux 4 to 29051 kg/m²s, reduced pressures 0.0014 to 0.96, inlet quality −4 to +0.85, and critical quality −2.6 to +1. These data, from 62 independent sources, are also compared with Katto's general correlation and those of Bowring for water and Subbotin for helium. The present correlation shows much better agreement with data.     

TC4钛合金超高周次轴向振动疲劳试验

应用基于压电超声疲劳试验技术开发的20kHz轴向振动疲劳试验系统,完成了室温下TC4钛合金超高周疲劳试验,获得了TC4合金在107～109周次范围内的轴向振动疲劳寿命曲线(S-N曲线);运用C.Paris推导公式预测了TC4合金材料的寿命,得到各应力水平下破坏率为50％、95％、99％的安全寿命.结果表明:在疲劳循环大于107周次时,试件仍会发生疲劳断裂,疲劳强度随循环次数的增加而下降,并不存在明显的疲劳极限.TC4合金的S-N曲线在107～109周次的范围内呈连续下降型.在轴向振动超高周疲劳试验中,试件的裂纹扩展寿命只占其在50％破坏率下疲劳安全寿命的一小部分,其疲劳寿命主要由试件的裂纹萌生寿命决定.     

基于粗糙集模糊神经网络的爆破振动危害预测

为了探索一种能克服单因素预测的局限性、提高爆破振动危害预测精度的方法,基于粗糙集模糊神经网络理论,建立了综合考虑爆破振动幅值、主频率、主频率持续时间及结构动力特性等10个因素的民房破坏程度预测模型;用铜绿山矿爆破振动和民房破坏情况观测数据,对该模型进行了训练和测试,测试结果与现场观测结果具有良好的一致性。研究表明:粗糙集理论可将现场数据进行属性约简,简化输入变量,缩小神经网络的搜索空间,改善爆破振动的预测性能;基于粗糙集模糊神经网络理论的爆破振动危害预测模型,能更好地考虑各种因素对危害程度的综合影响,避免了单因素预测的局限性。     

Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment

In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method.     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.     

蛋白质空间结构预测的一种优化模型及算法

用理论方法预测蛋白质结构有两个难点,第一是要有一个合理的势函数,第二是要有一个有效的寻优方法找到势函数的全局极小点,本文采用联合残基力场建立了蛋白质空间结构预测模型,然后用我们给出的一种改进模拟退火算法搜索势函数的全局极小点,对脑啡肽的空间结构进行了预测和分析。     

Logistic和Lotka-Volterra模型参数的灰色估计方法研究

根据灰色系统建模原理,对Logistic模型中的参数进行了估计,将此方法应用于Gause(1934)草履虫实验数据的拟合,并与已有拟合结果对比,说明此方法是有效的.然后将其推广至两种群的Lotka—Volterra模型参数的确定,来研究山西省人口—经济系统的发展演化状况,得出了满意的结果.     

天空背景下飞行器结构特征提取的新方法

针对天空背景下低信噪比的飞行器,提出了一种基于SUSAN算法、灰色系统理论和数学形态学相结合的飞行器结构特征提取的新方法。在Visual C++6.0平台下,首先利用SUSAN算法从背景中提取飞行器的结构边缘信息,并与原图像相加实现目标增强;然后用灰色系统理论检测出飞行器的结构特征边缘;最后利用条件膨胀和重构算法,实现云层的抑制,并重构出飞行器目标。实验结果表明:该方法对于实现飞行器的跟踪、结构特征提取以及事后判读有重要的意义,同时验证了该方法的可行性。     

线型优化最大熵线性预测方法自回归模型三种求解方法的比较

采用三种方法：修正协方差法MCOV(Modified Covariance Method)、递推极大似然估计法RMLE(Recursive Maximum Likelihood Estimator)和伯格法(Burg Method)求解线型优化最大熵线性预测方法中的自回归模型系数,并且在不同求解方法情况下,将阶次、信噪比对光谱复原的影响作了详尽的比较.研究结果表明:在线型优化最大熵线性预测方法的自回归模型系数三种求解方法中,修正协方差法要优于递推极大似然估计法和伯格法.     

A Hybrid Method Using HAVOK Analysis and Machine Learning for Predicting Chaotic Time Series

The prediction of chaotic time series systems has remained a challenging problem in recent decades. A hybrid method using Hankel Alternative View Of Koopman (HAVOK) analysis and machine learning (HAVOK-ML) is developed to predict chaotic time series. HAVOK-ML simulates the time series by reconstructing a closed linear model so as to achieve the purpose of prediction. It decomposes chaotic dynamics into intermittently forced linear systems by HAVOK analysis and estimates the external intermittently forcing term using machine learning. The prediction performance evaluations confirm that the proposed method has superior forecasting skills compared with existing prediction methods.