Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time

The rainflow counting algorithm for material fatigue is both simple to implement and extraordinarily successful for predicting material failure times. However, it neglects memory effects and time-ordering dependence, and therefore runs into difficulties dealing with highly intermittent or transient stochastic loads with heavy tailed distributions. Such loads appear frequently in a wide range of applications in ocean and mechanical engineering, such as wind turbines and offshore structures. In this work we employ the Serebrinsky–Ortiz cohesive envelope model for material fatigue to characterize the effects of load intermittency on the fatigue-crack nucleation time. We first formulate efficient numerical integration schemes, which allow for the direct characterization of the fatigue life in terms of any given load time-series. Subsequently, we consider the case of stochastic intermittent loads with given statistical characteristics. To overcome the need for expensive Monte-Carlo simulations, we formulate the fatigue life as an up-crossing problem of the coherent envelope. Assuming statistical independence for the large intermittent spikes and using probabilistic arguments we derive closed expressions for the up-crossing properties of the coherent envelope and obtain analytical approximations for the probability mass function of the failure time. The analytical expressions are derived directly in terms of the probability density function of the load, as well as the coherent envelope. We examine the accuracy of the analytical approximations and compare the predicted failure time with the standard rainflow algorithm for various loads. Finally, we use the analytical expressions to examine the robustness of the derived probability distribution for the failure time with respect to the coherent envelope geometrical properties.     

Thermal and rheological effects in a classical Graetz problem using a nonlinear Robertson-Stiff fluid model

The present article elaborates the Graetz problem for the Robertson-Stiff fluid model with imposed iso-thermal conditions. The closed-form expression of Robertson-Stiff fluid velocity is obtained. Employing the classical separation of variables approach, the energy equation of the said problem is reduced into an eigenvalue problem. The solution of the eigenvalue problem is developed numerically via the MATLAB built-in algorithm BVP4C. The constants appearing in series solutions are computed by Simpson's rule. The special case of this analysis with appropriate scaling is also applicable for the Bingham, power-law, and Newtonian fluid models. The impact of the dissipation function on Nusselt numbers and mean temperature is also considered. The pictorial representation of average temp7erature and Nusselt number are discussed in the presence of the plug radius, power-law index, and Brinkman number. It is observed that the presence of the plug radius and power-law index delay the prevalence of fully developed conditions for the Nusselt number. Moreover, the local Nusselt number for channel confinement attains higher values as compared with tube confinement. The present investigation has numerous applications in the field of engineering, nanotechnology, biomedical sciences, and development of several thermal types of equipment or microfluidic devices.     

Multi-task learning for video anomaly detection

We propose a multi-task learning framework for video anomaly detection based on a novel pipeline. Our model contains two crossing streams, one stream employs the backbone of Attention-R2U-net for future frame prediction, while the other is designed based on an encoder–decoder network to reconstruct the input optical flow maps. In addition, the latent layers of the two streams are merged together and assigned with a Deep SVDD-based loss at each location individually. Through the combination of these three tasks, the two-stream-crossing pipeline can be trained end-to-end to provide a comprehensive evaluation for the anomaly targets. Experimental results on several popular benchmark datasets show that our model outperforms the state-of-the-art competing models, which can be applied to different types of anomalous targets and meanwhile achieves remarkable precision.     

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.     

46700m~2楼房一次爆破拆除

东北汽配城为框架结构,总建筑面积46700m2,周边环境非常复杂。该建筑物共有260根柱子,断面尺寸不尽相同。按建筑伸缩缝将楼房分为5个部分,分别采用了原地坍塌、定向倾倒、折叠倾倒一次控制爆破技术,并将土石方爆破的斜线起爆技术用于拆除爆破。由于爆破设计、施工及安全措施得当,爆破获得圆满成功。     

The provably good parallel seeding algorithms for the k-means problem with penalties

As a classic NP-hard problem in machine learning and computational geometry, the k-means problem aims to partition the given points into k sets to minimize the within-cluster sum of squares. The k-means problem with penalties (k-MPWP), as a generalizing problem of the k-means problem, allows a point that can be either clustered or penalized with some positive cost. In this paper, we mainly apply the parallel seeding algorithm to the k-MPWP, and show sufficient analysis to bound the expected solution quality in the case where both the number of iterations and the number of points sampled in each iteration can be given arbitrarily. The approximate guarantee can be obtained as , where is a polynomial function involving the maximal ratio M between the penalties. On one hand, this result can be viewed as a further improvement on the parallel algorithm for k-MPWP given by Li et al., where the number of iterations depends on other factors. On the other hand, our result also generalizes the one solving the k-means problem presented by Bachem et al., because k-MPWP is a variant of the k-means problem. Moreover, we present a numerical experiment to show the effectiveness of the parallel algorithm for k-means with penalties.     

PHC管桩静压施工技术及主要技术问题的处理

介绍PHC管桩静压施工技术,施工过程中主要技术问题的处理方法,并举六个在施工过程中遇到问题经处理所取得效果的典型实例给予佐证,为类似条件下的静压管桩施工所遇技术问题提供了一些有效的解决方法。     

普通带式输送机特殊应用综述

普通带式输送机在井下特殊环境下,正常安装方式和使用方法,无法保证煤炭正常生产。根据现场实际情况,因地制宜,充分利用现有设备,对其进行合理技术革新、改造,采用非常规安装方式、方法,成功地解决生产中各种技术疑难问题。     

高等院校中教学软件存在的问题及其发展

教学软件是计算机辅助教学的重更内容之一。它综合了计算机科学与技术、教育学、心理学等学科。尤其在高等院校中教学软件的开发与使用对教学改革和教学管理具有十分重要的意义。但由于种种原因,教学软件存在着一定的问题,制约了教学软件的发展。本文主要阐述了存在的主要问题及促进教学软件发展的方法和建议。     

缠绕式双滚筒提升机优化起吊检修方案

提升机滚筒主轴检修过程中,由于组装"龙门架"困难,起吊滚筒时又容易造成主轴位移,引起偏摆,导致联轴器螺栓剪切变形。针对此问题,结合提升机滚筒结构,重新设计托架结构,提升检修安全系数。