Design of Lu2O3-reinforced Cf/SiC-ZrB2-ZrC ultra-high temperature ceramic matrix composites: Wetting and interfacial reactivity by ZrSi2 based alloys

The wettability and infiltration of molten ZrSi₂ and ZrSi₂-Lu₂O₃ alloys into C_f/SiC and B₄C-infiltrated C_f/SiC composites were investigated to understand the interfacial interactions that occur during the development of C_f/SiC-ZrC and C_f/SiC-ZrB₂-ZrC-Lu₂O₃ materials. A significant evaporation of Si from the liquid affected the wetting behaviour of the alloy when tested in a vacuum at 1670 °C. The better wetting and spreading of the alloy over the surface was observed for the composites with lower overall porosity (12 %). On the other hand, the formation of an outer dense layer, followed up by the uniform infiltrated region up to ～ 1 mm was observed for the C_f/SiC with higher porosity (21 %). The infiltrated alloy reacted with SiC matrix to form ZrC or with B₄C-infiltrated SiC matrix to form ZrB₂-ZrC-SiC. The Lu₂O₃ particles were not wetted by the melt, and were pushed away of the reaction zone by the solidification front.     

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced‐dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal‐to‐noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal‐to‐noise ratio screening method. The application results of the signal‐to‐noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non‐salient input features.     

适于深层次CNN的配电网过电压数据集建立方法

随着人工智能算法的快速发展,在配网过电压识别中层数较少的卷积神经网络已得到应用。深层次网络有较高的识别率,但需要大量数据样本,目前已有数据集数据量不足,不能满足深层级网络训练所需。为此提出了一种满足深层次网络训练所需的配电网过电压数据集建立方法。首先利用电磁暂态仿真软件EMTPworks仿真10 kV配网5种典型的过电压并且编辑相应的JavaScript脚本,通过改变故障初相角、过渡电阻、线路长度等参数产生16 272个数据。然后将三相过电压一维信号进行连续小波变换,得到相应二维时频图,并根据原始信号的特征自动标记二维时频图,从而建立了完整的配电网过电压数据集。最后利用卷积神经网络（CNN）对5类过电压信号数据的有效性进行验证。结果表明,构造的数据集数据规模大,有效性高,能够满足深层次网络需要。     

Evolutionary design of neural network architectures: a review of three decades of research

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.

     

Reversible Anion‐Driven Switching of an Organic 2D Crystal at a Solid–Liquid Interface

Ionic self‐assembly of charged molecular building blocks relies on the interplay between long‐range electrostatic forces and short‐range, often cooperative, supramolecular interactions, yet has been seldom studied in two dimensions at the solid–liquid interface. Here, we demonstrate anion‐driven switching of two‐dimensional (2D) crystal structure at the Au(111)/octanoic acid interface. Using scanning tunneling microscopy (STM), three organic salts with identical polyaromatic cation (PQPC₆⁺) but different anions (perchlorate, anthraquinonedisulfonate, benzenesulfonate) are shown to form distinct, highly ordered self‐assembled structures. Reversible switching of the supramolecular arrangement is demonstrated by in situ exchange of the anion on the pre‐formed adlayer, by changing the concentration ratio between the incoming and outgoing anion. Density functional theory (DFT) calculations reveal that perchlorate is highly mobile in the adlayer, and corroborate why this anion is only resolved transiently in STM. Surprisingly, the templating effect of the anion persists even where it does not become part of the adlayer 2D fabric, which we ascribe to differences in stabilization of cation conformations by the anion. Our results provide important insight into the structuring of mixed anion–cation adlayers. This is essential in the design of tectons for ionic self‐assembled superstructures and biomimetic adaptive materials and valuable also to understand adsorbate–adsorbate interactions in heterogeneous catalysis.     

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.     

Interactive High‐Quality Visualization of Higher‐Order Finite Elements

Higher‐order finite element methods have emerged as an important discretization scheme for simulation. They are increasingly used in contemporary numerical solvers, generating a new class of data that must be analyzed by scientists and engineers. Currently available visualization tools for this type of data are either batch oriented or limited to certain cell types and polynomial degrees. Other approaches approximate higher‐order data by resampling resulting in trade‐offs in interactivity and quality. To overcome these limitations, we have developed a distributed visualization system which allows for interactive exploration of non‐conforming unstructured grids, resulting from space‐time discontinuous Galerkin simulations, in which each cell has its own higher‐order polynomial solution. Our system employs GPU‐based raycasting for direct volume rendering of complex grids which feature non‐convex, curvilinear cells with varying polynomial degree. Frequency‐based adaptive sampling accounts for the high variations along rays. For distribution across a GPU cluster, the initial object‐space partitioning is determined by cell characteristics like the polynomial degree and is adapted at runtime by a load balancing mechanism. The performance and utility of our system is evaluated for different aeroacoustic simulations involving the propagation of shock fronts.