热冲击作用下层合皮肤组织热传导过程数值模拟

在急剧温度变化等强间断温度冲击作用下的生物层合组织非傅里叶热传导分析中,经典时域连续有限元方法(如Newmark等方法)会在波阵面以后的和层合组织界面附近的区域表现出强烈的数值振荡。这类数值振荡会影响问题求解精度,并带来较大不确定性。针对这类现象,本文发展了改进时域间断Galerkin有限元方法,进一步开展了相关问题的数值模拟。其控制方程的基本未知数(温度)及其时间导数在指定时间间隔内假设存在间断且独立插值。在有限元离散列式中引入比例刚度阵人工阻尼,以成功消除波前位置的虚假数值振荡行为。通过算例对比分析,相比Newmark方法和传统间断Galerkin方法,所提出的改进时域间断Galerkin有限元方法较好消除了波前、波后以及组织界面处的数值振荡,有效捕捉了波阵面的间断行为,提高了计算的精度。     

基于贪婪算法的水上漂移目标救助物资协同调度方法

受复杂水文、气象等条件影响,水上遇险目标在等待外部救援的过程中可能发生漂移,其漂移的速度与方向将直接影响到水上救助物资调度方案的制订。针对水上事故及其救援的特殊性,在对水上遇险目标漂移路径进行预测的基础上,构建了满足运力、时间限制及应急救助物资需求,包括水上遇险目标周边船舶、岸基救助基地及其附近陆地应急物资储备点共同参与的水陆两阶段多层级协同调度模型,并运用贪婪算法对其进行求解,最后通过算例对模型和算法的有效性进行验证。     

Characterisation of the plasma density with two artificial neural network models

This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system.In this model,the input layer is composed of five neurons:the radial position,the axial position,the gas pressure,the microwave power and the magnet coil current.The output layer is the target output neuron:the plasma density.The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe.The effectiveness of two artificial neural network models are demonstrated,the results show good agreements with corresponding experimental data.The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded,and the radial based function is more suitable than the multi layer perceptron in this work.     

多层多孔吸声材料结构参数优化设计

高开孔率的发泡材料(如三聚氰胺、聚氨酯发泡材料)具有优良的吸声、隔热防火、防腐及环保性能,可以作为吸声、阻尼等材料应用于建筑、航空、交通工具等领域.该文基于Biot理论和多层介质声波传播理论(传递矩阵法),建立多层多孔吸声结构背衬刚性壁的理论模型,利用遗传算法优化多层结构厚度和质量.将理论模型计算结果与阻抗管测试结果进...     

Learning finite binary sequences from half‐space data

The problem of inferring a finite binary sequence w *∈{−1, 1}ⁿ is considered. It is supposed that at epochs t=1, 2,…, the learner is provided with random half‐space data in the form of finite binary sequences u ^(t)∈{−1, 1}ⁿ which have positive inner‐product with w *. The goal of the learner is to determine the underlying sequence w * in an efficient, on‐line fashion from the data { u ^(t), t≥1}. In this context, it is shown that the randomized, on‐line directed drift algorithm produces a sequence of hypotheses {w^(t)∈{−1, 1}ⁿ, t≥1} which converges to w * in finite time with probability 1. It is shown that while the algorithm has a minimal space complexity of 2n bits of scratch memory, it has exponential time complexity with an expected mistake bound of order Ω(e^0.139n). Batch incarnations of the algorithm are introduced which allow for massive improvements in running time with a relatively small cost in space (batch size). In particular, using a batch of 𝒪(n log n) examples at each update epoch reduces the expected mistake bound of the (batch) algorithm to 𝒪(n) (in an asynchronous bit update mode) and 𝒪(1) (in a synchronous bit update mode). The problem considered here is related to binary integer programming and to learning in a mathematical model of a neuron. ©1999 John Wiley & Sons, Inc. Random Struct. Alg., 14, 345–381 (1999)     

The capacity of majority rule

Let 𝔹ⁿ={−1, 1}ⁿ denote the vertices of the n-dimensional cube. Let U(m) be a random m-element subset of 𝔹ⁿ and suppose w ∈𝔹ⁿ is a vertex closest to the centroid of U(m). Using a large deviation, multivariate local limit theorem due to Richter, we show that n/π log n is a threshold function for the property that the convex hull of U(m) is contained in the positive half-space determined by w . The decision problem considered here is an instance of binary integer programming, and the algorithm selecting w as the vertex closest to the centroid of U(m) has been previously dubbed majority rule in the context of learning binary weights for a perceptron. © 1998 John Wiley & Sons, Inc. Random Struct. Alg., 12, 83–109, 1998     

Video Action Recognition Using Motion and Multi-View Excitation with Temporal Aggregation

Spatiotemporal and motion feature representations are the key to video action recognition. Typical previous approaches are to utilize 3D CNNs to cope with both spatial and temporal features, but they suffer from huge computations. Other approaches are to utilize (1+2)D CNNs to learn spatial and temporal features in an efficient way, but they neglect the importance of motion representations. To overcome problems with previous approaches, we propose a novel block which makes it possible to alleviate the aforementioned problems, since our block can capture spatial and temporal features more faithfully and efficiently learn motion features. This proposed block includes Motion Excitation (ME), Multi-view Excitation (MvE), and Densely Connected Temporal Aggregation (DCTA). The purpose of ME is to encode feature-level frame differences; MvE is designed to enrich spatiotemporal features with multiple view representations adaptively; and DCTA is to model long-range temporal dependencies. We inject the proposed building block, which we refer to as the META block (or simply “META”), into 2D ResNet-50. Through extensive experiments, we demonstrate that our proposed method architecture outperforms previous CNN-based methods in terms of “Val Top-1 %” measure with Something-Something v1 and Jester datasets, while the META yielded competitive results with the Moment-in-Time Mini dataset.     

强流电子束入射叠靶能量沉积计算

计算了多脉冲相对论强流电子束入射钽-石墨叠靶的能量沉积和轫致辐射谱。能量沉积采用Geant4程序计算,轫致辐射谱根据基本的辐射理论和蒙特卡罗方法计算。结果显示,各层的热区能量沉积呈由大到小的递减分布,截面轫致辐射分布和电子束径向分布主要受钽层的影响。石墨层的低能量沉积率和高热容能改善叠靶的性能。对于单脉冲,钽-石墨层厚比为1∶1时,石墨能全部吸收相邻钽层的热沉积,轫致辐射效率为35.4%;4脉冲情况下,钽-石墨层厚比应为1∶13,总轫致辐射效率降到19.9%。考虑轫致辐射剂量和质量,钽-石墨两者的厚度比为1∶5时,钽层的总厚度应为1.2 mm;当钽-石墨层厚比为1∶10时,钽层的总厚应降到0.7 mm。     

Detection of liner surface defects in solid rocket motors using multilayer perceptron neural networks

Debonding problems along the propellant/liner/insulation interface are a critical point to the integrity and one of the major causes of structural failures of solid rocket motors. Current solutions are typically restricted to methods for assessing the integrity of the rocket motors structure and visually inspecting their components. In this context, this paper presents an improved algorithm to detect liner surface defects that may compromise the bonding between the solid propellant and the insulation. The use of Local Binary Patterns (LBP) provides a structural and statistical approach to texture analysis of liner sample images. Along with color information extraction, these two methods allow the representation of image pixels by feature vectors that are further processed by a Multilayer Perceptron (MLP) neural network classifier. The MLP neural network analyzes liner sample images and classifies each pixel into one of three classes: non-defect, foreign object, and defect. Several tests were executed varying different parameters to find the optimal MLP configuration, and as a result, the best classification accuracy of 99.08%, 90.66%, and 99.48% was achieved for the corresponding classes. Moreover, the defect size estimate showed that the MLP classifier correctly identified defects less than 1 mm long, with a relatively small number of training examples. Positive results indicate that the algorithm can identify liner surface defects with a performance similar to human inspectors and has the potential to assist or even automate the liner inspection process of solid rocket motors.     

Characterisation of the two artificial neural plasma density with network models

This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons： the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron： the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work.