Analytical model for approximating node throughputs in wireless mesh networks

This paper describes an analytical model and an associated algorithm for assessing the throughputs of each host in wireless mesh networks (WMNs). It provides a framework for studying WMNs, particularly when the performance and parameters in multiple protocol layers have to be jointly evaluated and optimized. From the point of the implementation, a simple recursive formula with N ? 1 iterations is used to obtain the throughput performance of N node WMNs. The produced expressions offer insights into understanding the performance of the individual nodes without referring to a specific medium access control layer or physical layer technology. The model serves as a general tool for capturing the characteristics of the WMNs. Using the model, the complexity of cross‐layer studies is reduced, thus allowing researchers to focus on the modelling of other associated layers. The paper explains the rationale behind the model and provides examples of scenarios for which it is suitable. It is validated using discrete event simulations in the OPNET network simulator. Copyright © 2012 John Wiley & Sons, Ltd.     

Heat treatment influence on the structural properties of supported Ni nanoclusters

Ni nanoclusters supported on Al₂O₃ were prepared using a coprecipitation method. A heat treatment at different temperatures was applied. The obtained samples were analyzed by X-ray diffraction (XRD), X-ray absorption spectroscopy and X-ray photoelectron spectroscopy in order to determine their global, local and electronic structures. The average particle size, the mean squares of the microstrain, the particle size and microstrain distribution functions of the supported Ni nanoclusters were determined by the XRD method using a generalized Fermi function for the approximation of the X-ray line. The recrystallization induced during the heat treatment process was analyzed. The present study indicated strong deformation of the local structure of the active metal in all of the samples investigated due to the metal-support interaction and the effects of the small particle size. Electronic structural investigations showed the presence of Ni in a metallic state as well as Ni²⁺ on the cluster surfaces.     

Intelligent Choice of Machine Learning Methods for Predictive Maintenance of Intelligent Machines

Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are explained. Then factors for the forecast are explained. Requirements for the data are collected and algorithms for machine learning are presented. Based on the relationships found, a process model is presented that shows a fast implementation of the predictive maintenance for machines.     

基于深度学习的事件抽取研究综述

事件抽取是从非结构化的自然语言文本中自动抽取用户感兴趣的事件信息, 并以结构化的形式表示出来. 事件抽取是自然语言处理与理解中的重要方向, 在政府公共事务管理、金融业务、生物医学等不同领域有着很高的应用价值. 根据对人工标注数据的依赖程度, 目前基于深度学习的事件抽取方法主要分为两类: 有监督和远程监督学习方法. 对当前深度学习中事件抽取技术进行了全面的综述. 围绕有监督中CNN、RNN、GAN、GCN与远程监督等方法, 系统地总结了近几年的研究情况, 并对不同的深度学习模型的性能进行了详细对比与分析. 最后, 对事件抽取面临的挑战进行了分析, 针对研究趋势进行了展望.     

Efficient benchmarking of algorithm configurators via model-based surrogates

The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. However, the proper evaluation of new algorithm configuration (AC) procedures (or configurators) is hindered by two key hurdles. First, AC scenarios are hard to set up, including the target algorithm to be optimized and the problem instances to be solved. Second, and even more significantly, they are computationally expensive: a single configurator run involves many costly runs of the target algorithm. Here, we propose a benchmarking approach that uses surrogate scenarios, which are computationally cheap while remaining close to the original AC scenarios. These surrogate scenarios approximate the response surface corresponding to true target algorithm performance using a regression model. In our experiments, we construct and evaluate surrogate scenarios for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems. We generalize previous work by building surrogates for AC scenarios with multiple problem instances, stochastic target algorithms and censored running time observations. We show that our surrogate scenarios capture overall important characteristics of the original AC scenarios from which they were derived, while being much easier to use and orders of magnitude cheaper to evaluate.