基于名义应力法的弹性链型接触网疲劳寿命预测

针对高速铁路弹性链型接触网进行接触线疲劳寿命预测分析.利用ANSYS,采用直接积分法对弓网耦合系统进行动态仿真,得到接触线的应力时程;运用雨流计数法得到离散的应力循环,采用应力修正算法得到平均应力为零的疲劳应力谱;通过简化方法估算获得材料S-N曲线,从而计算得到疲劳破坏次数;最终运用线性累积损伤理论预测接触线的疲劳寿命.对比分析接触线不同位置的疲劳寿命值,结果表明:接触线每跨疲劳寿命趋势一致;每跨在吊弦处和定位点处疲劳寿命较低,其中寿命最低值出现在左侧第一根吊弦处,疲劳寿命最低值为20 a左右.结果可为高速铁路弹性链型接触网接触线的实际施工维护和更换周期的确定提供参考.     

ARCADE — An architecture for CAD in electronics

Ever since the introduction of computers in development and engineering departments, design activities have been supported by computer methods in a growing number of design functions, in the interest of reducing development time and cost, and increasing the quality of the products. Computer Aided Design (CAD) — a generic term for these computerized tools — is recognized as a method for drastically reducing the development time of industrial products.CAD methods are currently being used during each of the various phases in the development activity. The introduction of these CAD tools for the various stages in the development cycle, however, was to a large extent effected in isolation, i.e. not as a “subset” of an overall concept. Lack of such a concept, with its inherent disciplines and compatibility aspects, has given rise to sub-optimal efficiency and effectivity. To remedy this situation, a generalized architecture for CAD has been developed. This architecture drastically reduced the large variety if input methods, combines all product data in one well-structured product file, supports all necessary design functions, assures the proper generation of technical documents, and above all, facilitates maximum efficiency by integrating the processes for various stages in the development cycle.The essentials of ARCADE will be described in this article, together with how the system is designed.The “ARCADE” way and method of working in an organization in which development, engineering, manufacturing, logistic and documentation departments are concerned with technical automation issues will, as such, be discussed.Benefits will, of course, also be elucidated.     

Generalized dynamic programming: A variables freezing method

A variables freezing method is designed for widening the possibilities of numerical application of generalized dynamic programming algorithms. Reducing the size of the memory under certain conditions, the method surmounts the “dimensional curse.” This paper is the continuation of [1–3].     

A defect prediction method for software versioning

New methodologies and tools have gradually made the life cycle for software development more human-independent. Much of the research in this field focuses on defect reduction, defect identification and defect prediction. Defect prediction is a relatively new research area that involves using various methods from artificial intelligence to data mining. Identifying and locating defects in software projects is a difficult task. Measuring software in a continuous and disciplined manner provides many advantages such as the accurate estimation of project costs and schedules as well as improving product and process qualities. This study aims to propose a model to predict the number of defects in the new version of a software product with respect to the previous stable version. The new version may contain changes related to a new feature or a modification in the algorithm or bug fixes. Our proposed model aims to predict the new defects introduced into the new version by analyzing the types of changes in an objective and formal manner as well as considering the lines of code (LOC) change. Defect predictors are helpful tools for both project managers and developers. Accurate predictors may help reducing test times and guide developers towards implementing higher quality codes. Our proposed model can aid software engineers in determining the stability of software before it goes on production. Furthermore, such a model may provide useful insight for understanding the effects of a feature, bug fix or change in the process of defect detection.

A CNN-BiLSTM-AM method for stock price prediction

Neural Computing and Applications - In recent years, with the rapid development of the economy, more and more people begin to invest into the stock market. Accurately predicting the change of stock...     

出租车目的地预测的深度学习方法

出租车目的地预测可以掌握出租车的流动方向,便于出租车调度。已有的预测方法多仅利用轨迹序列的原始特征作为预测模型的输入,忽略了原始特征背后的时空数据,造成轨迹时空信息缺失。针对以上问题,提出出租车目的地预测的深度学习方法DLDP。首先采用滑动窗口,基于速度、转角利用统计量计算得到轨迹的高层特征。其次,自动编码器将高层特征转换为固定长度的潜在空间表示,得到轨迹的深度特征。最后,将深度特征和原始特征相结合,一同作为LSTM的输入进行预测。实验表明,DLDP比传统RNN预测模型的准确率提高了9%,平均距离误差减少了1 km。     

A method for comparing numerical variables defined in a region

This paper develops a new method for comparing numerical variables defined in a region. This method covers numerical variables defined over a two-dimensional space, such as temperature and humidity distributions, as well as those defined on a discrete space such as the height of trees and the age of buildings. To evaluate the difference between two variables, this method considers three types of transformations that convert one variable so that it fits the other as well as possible. The result gives a basis for the separate evaluation of spatial and aspatial differences between the variables. The transformations also permit us to describe the spatial difference in more detail. To test the validity of the method, this paper applies it to an analysis of three spatial datasets of different sizes. The result shows that the proposed method is effective for evaluating and visualizing the difference between numerical variables.     

A method for aggregating state variables in large ecosystem models

Simplifying large ecosystem models is essential if we are to understand the underlying causes of observed behaviours. However, such understanding is often employed to achieve simplification. This paper introduces a method for model simplification that can be applied without requiring intimate prior knowledge of the system. Its utility is measured by the resulting values of given model diagnostics relative to those of the original model. The method uses a least-squares criterion to identify sets of state variables that can be aggregated, and then generates a modified model structure and accompanying parameters that enable these variables to be replaced with the aggregates. The method is applied to a model of the nitrogen cycle in Port Phillip Bay, Victoria, Australia. Aside from reducing the model’s order, the method enables the reduced model to retain an ecological interpretation, and reveals insights into the system’s structure.     

A simple method for screening variables before clustering microarray data

A simple and computationally fast procedure is proposed for screening a large number of variables prior to cluster analysis. Each variable is considered in turn, the sample is divided into the two groups that maximise the ratio of between-group to within-group sum of squares for that variable, and the achieved value of this ratio is tested to see if it is significantly greater than what would be expected when partitioning a sample from a single homogeneous population. Those variables that achieve significance are then used in the cluster analysis. It is suggested that significance levels be assessed using a Monte Carlo computational procedure; by assuming within-group normality an analytical approximation is derived, but caution in its use is advocated. Computational details are provided for both the partitioning and the testing. The procedure is applied to several microarray data sets, showing that it can often achieve good results both quickly and simply.     

A hierarchical semantic-based distance for nominal histogram comparison

We propose a new distance called Hierarchical Semantic-Based Distance (HSBD), devoted to the comparison of nominal histograms equipped with a dissimilarity matrix providing the semantic correlations between the bins. The computation of this distance is based on a hierarchical strategy, progressively merging the considered instances (and their bins) according to their semantic proximity. For each level of this hierarchy, a standard bin-to-bin distance is computed between the corresponding pair of histograms. In order to obtain the proposed distance, these bin-to-bin distances are then fused by taking into account the semantic coherency of their associated level. From this modus operandi, the proposed distance can handle histograms which are generally compared thanks to cross-bin distances. It preserves the advantages of such cross-bin distances (namely robustness to histogram translation and histogram bin size issues), while inheriting the low computational cost of bin-to-bin distances. Validations in the context of geographical data classification emphasize the relevance and usefulness of the proposed distance.     

A measure of variance for hierarchical nominal attributes

The need for measuring the dispersion of nominal categorical attributes appears in several applications, like clustering or data anonymization. For a nominal attribute whose categories can be hierarchically classified, a measure of the variance of a sample drawn from that attribute is proposed which takes the attribute’s hierarchy into account. The new measure is the reciprocal of “consanguinity”: the less related the nominal categories in the sample, the higher the measured variance. For non-hierarchical nominal attributes, the proposed measure yields results consistent with previous diversity indicators. Applications of the new nominal variance measure to economic diversity measurement and data anonymization are also discussed.     

Coverage probability of prediction intervals for discrete random variables

Prediction interval is a widely used tool in industrial applications to predict the distribution of future observations. The exact minimum coverage probability and the average coverage probability of the conventional prediction interval for a discrete random variable have not been accurately derived in the literature. In this paper, procedures to compute the exact minimum confidence levels and the average confidence levels of the prediction intervals for a discrete random variable are proposed. These procedures are illustrated with examples and real data applications. Based on these procedures, modified prediction intervals with the minimum coverage probability or the average coverage probability close to the nominal level can be constructed.     

Coverage probability of prediction intervals for discrete random variables

Prediction interval is a widely used tool in industrial applications to predict the distribution of future observations. The exact minimum coverage probability and the average coverage probability of the conventional prediction interval for a discrete random variable have not been accurately derived in the literature. In this paper, procedures to compute the exact minimum confidence levels and the average confidence levels of the prediction intervals for a discrete random variable are proposed. These procedures are illustrated with examples and real data applications. Based on these procedures, modified prediction intervals with the minimum coverage probability or the average coverage probability close to the nominal level can be constructed.     

A hybrid time-delay prediction method for networked control system

This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation component and detail components of time-delay sequences are fgured out.Next,one step prediction of time-delay is obtained through echo state network(ESN)model and auto-regressive integrated moving average model(ARIMA)according to the diferent characteristics of approximate component and detail components.Then,the fnal predictive value of time-delay is obtained by summation.Meanwhile,the parameters of echo state network is optimized by genetic algorithm.The simulation results indicate that higher accuracy can be achieved through this prediction method.     

A hybrid prediction method for low-subsonic turbulent flow noise

A hybrid method is proposed for prediction of low-subsonic, turbulent flow noise. In this method, the noise sources in the near wall turbulences or in the wake are computed by the incompressible large eddy simulation (LES), while the generation and propagation of the acoustic waves are solved by the linearized perturbed compressible equations (LPCE), with acoustic sources represented by a material derivative of the hydrodynamic pressure, DP/Dt. The accuracy of the present method is critically assessed for two experiments conducted at the Ecole Centrale de Lyon and the University Erlangen, where aeroacoustic measurements were taken for (i) the flat plate self-noise at zero angle of attack and (ii) the forward-facing step noise , respectively. The noise sources are identified and analyzed further to determine their spectral-dependent, spanwise coherence functions, γ_ij of the wall pressure fluctuations, in order to quantify the sizes of the noise sources. The far-field sound pressure level (SPL) spectra predicted by the present method are found in excellent agreement with the experimental measurements.     

A novel soft computing method for engine RUL prediction

Prognostics is an engineering discipline focused on predicting the Remaining Useful Life (RUL) of a system or a component using raw multimedia (sensor) data. This paper presents a novel machine learning model for this task, which includes a smart ensemble of gradient boosted trees (GBT) and feed-forward neural networks. It incorporates discussions on the poor performance of MLPs and the need of ensemble models. Initial stages of data exploration and pre-processing are also comprehensively documented. Experiments are performed on the four run-to-failure C-MAPSS datasets defined by the 2008 PHM Data Challenge Competition. It concludes by presenting evaluations of multiple prediction models like MLP, SVR, CNN & gradient boosted trees (GBT). Gradient Boosted Trees are efficient in the sense that they produce an encouraging scoring model with minimum effort and also return feature importance information. The proposed method uses stacking ensemble of feed-forward neural networks and gradient boosted trees, as first level learner, and, a single-hidden layer- fully-connected neural network as the meta learner. This ensemble provides better results than any of the models alone or weighted average of their predictions. The proposed method outperforms MLP, SVR, CNN and GBT.

     

A numerical method for prediction of curved rail wear

Important published papers on rail wear in the past were reviewed. A numerical method was put forward to predict curved rail wear during a railway vehicle curving. The numerical method was discussed in detail. It considered a combination of Kalker’s non-Hertzian rolling contact theory, rail material wear model, the coupling dynamics of the vehicle and track, and the three-dimensional contact geometry analysis of wheel-rail. In its numerical implementation, the dynamical parameters of all the parts of the vehicle and track, such as normal loads and creepages of the wheels and rails, were firstly obtained through the curving dynamics analysis. The wheel-rail contact geometry calculation gave the wheel-rail contact geometry parameters, which were used in the wheel-rail rolling contact calculation with Kalker’s non-Hertzian rolling contact theory modified. The friction work densities on the contact areas of the wheels and rails were obtained in the rolling contact calculation, and were used to predict the rail running surface wears caused by the multiple wheels of the vehicle simultaneously with the rail material wear model. In the rail material wear model, it was assumed that the mass loss of each unit area was proportional to the frictional work density in the contact area. A numerical example was present to verify the present method. The numerical results of the example are reasonable, and indicate that the high rail wear of the curved track caused by the leading wheelset is much more serious than those caused by the other three wheels of the same bogie.     

矿井瓦斯涌出量预测研究新方法

矿井瓦斯涌出预测是建立一个复杂的非线性变化系统的过程,传统的瓦斯涌出量预测方法存在一定的局限性。瓦斯涌出量的预测即是建立函数关系模型,预测模型建立的准确与否取决于各个影响因素之间的相互作用、相互耦合的特性。文中将灰色理论与遗传算法有机地结合起来,以灰色理论为基础,利用遗传算法优化隐含层神经元个数和网络中的连接权值,建立瓦斯涌出量预测模型。预测实验结果表明,该方法对瓦斯涌出量预测适应性强、方法快捷、预测结果精度高。