Optimal design of elastic beams with segmentwise constant height in the case of dynamic loading

A shape optimization method for elastic beams with segmentwise constant height is proposed. The ends of the beam are simply supported, clamped or free. Two problems are discussed; (i) a beam is loaded by lateral pressure, (ii) a beam is struck by an attached mass. External loads and volume of the beam are prescribed and the deflection in a given cross-section is to be minimized. For solving this problem, the deflections are expanded into a Fourier series by eigenfunctions. Numerical results are given for two-, three- and four-stepped beams.     

工业项目的海绵城市建设标准与技术要点

工业用地是城市建设用地的主要类型之一,是海绵城市建设的重要区域。通过梳理工业项目硬化面积高、径流污染程度高等特征,针对性提出其海绵城市建设的实施路径、建设标准和技术应用等实施建议。工业项目的海绵城市设计应遵循因地制宜的原则,应合理布局绿地并建设海绵设施,通过精细化的标高设计进行径流组织设计;对于降雨径流污染严重的三类工业项目,其海绵措施应以雨水调蓄池(应急事故池)为主,严禁设置具有渗透功能的源头控制设施。对于三类工业项目,海绵城市所提出的年径流总量控制率的目标与事故池的环保要求基本一致,建议相关部门在制定(修订)的应急事故池设计标准时,其中的径流污染控制体积可与对应区域年径流总量控制率标准协同一致。     

基于信道状态特征的手势动作识别技术研究

手势识别作为人和机器之间重要的交互手段,在日常生活中具有广泛的应用场景。基于无线信号特别是WiFi的手势识别由于其无接触、成本低等优点成为当前热门的方式。为解决传统基于无线信号手势识别算法没有充分利用信号相位特征的缺点,本文提出利用WiFi信道状态信息幅值和相位结合的方式进行手势识别。通过子载波降维和动作曲线提取对接收的WiFi信号进行处理,并将信号的幅值和相位结合,利用机器学习算法对数据进行训练和分类,实现了单手向前、单手向后、单手向左、单手向右、单手向上和单手向下六种手势的识别。实验结果显示,本文算法在近距离和远距离下的精确度分别为96%和92%。     

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.     

Matching-Pursuits Dissimilarity Measure for Shape-Based Comparison and Classification of High-Dimensional Data

In this paper, a new matching pursuits dissimilarity measure (MPDM) is presented that compares two signals using the information provided by their matching pursuits (MP) approximations, without requiring any prior domain knowledge. MPDM is a flexible and differentiable measure that can be used to perform shape-based comparisons and fuzzy clustering of very high-dimensional, possibly compressed, data. A novel prototype based classification algorithm, which is termed the computer aided minimization procedure (CAMP), is also proposed. The CAMP algorithm uses the MPDM with the competitive agglomeration (CA) fuzzy clustering algorithm to build reliable shape based prototypes for classification. MP is a well known sparse signal approximation technique, which is commonly used for video and image coding. The dictionary and coefficient information produced by MP has previously been used to define features to build discrimination and prototype based classifiers. However, existing MP based classification applications are quite problem domain specific, thus making their generalization to other problems quite difficult. The proposed CAMP algorithm is the first MP based classification system that requires no assumptions about the problem domain and builds a bridge between the MP and fuzzy clustering algorithms. Experimental results also show that the CAMP algorithm is more resilient to outliers in test data than the multilayer perceptron (MLP) and support-vector-machine (SVM) classifiers, as well as prototype-based classifiers using the Euclidean distance as their dissimilarity measure.     

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.

     

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.