MERLIN: a device diagnosis system based on analytic models

An approach to computer-aided interpretation of parametric test data for integrated circuit process-problem diagnosis is presented. In contrast to a conventional expert system, which reasons with a knowledge base consisting of rules acquired from human experts, the system presented centers its knowledge around analytic device equations. By using equations as the basis of the system knowledge, a more universal, well-organized, and concise level of knowledge is encoded. With the use of objects to present this knowledge, a great deal of useful information outside that described by only the symbolic expressions can be represented, and extension to qualitative or numeric models should be straightforward. The result is an expressive, well-organized, easily built, and easily maintained knowledge base. The authors describe the system's interactive graphical displays and the automatic data interpretation algorithms. Examples evaluating n-channel metal oxide semiconductor (NMOS) parameter variations, interconnect parameter variations, and interconnect yields are used for illustration     

一种微机控制的多测头凸轮轴测量系统

本文介绍一种由机械装置，高精度圆光栅编码器，光栅位移传感器，微型计算机，接口控制电路等组成的多测头凸轮轴测量系统，该系统安装调整方便，定位准确，测量精度高，操作简单，工作可靠，能快速实现对凸轮轴的自动测量。     

MCS接枝共聚物的结构和形态

将甲基丙烯酸甲酯（ＭＭＡ）、苯乙烯（Ｓ）在氯化聚乙烯（ＣＰＥ）存在下进行悬浮接枝共聚，获得ＭＣＳ树脂。本文着重考察了ＭＣＳ树脂的化学结构及胶粒形态。结果表明，确实发生了接枝共聚。ＣＰＥ用量、ＲＳＨ含量、引发剂浓度、反应温度、溶胀时间及聚合转化率对ＭＣＳ树脂的接枝率、接枝效率均有影响。ＭＣＳ树脂是接枝共聚物、ＭＭＡ／Ｓ共聚物与ＭＭＡ均聚物的混合物，橡胶相ＣＰＥ在ＭＣＳ树脂中的分散情况为“海岛结构”，     

V型天线的行波加载公式

本文研究了ｖ型天线上电流分布与阻抗加载的关系，导出了支持天线上行波电流的阻抗加载公式。讨论了天线张角、振子半径以及振子长度与加载量的关系和规律，说明了加载Ｖ型天线的宽带特性。文中还计算了在高斯脉冲激励下，不同加载量的天线上瞬态电流的分布，由此可以表明所导加载公式的正确性与重要性。     

用于计算机教育的Android实验软件(英文)

In this paper,we address the need for better hands-on learning materials in computing education and the need for integrating mobile computing education into computing curricular.We propose an innovative learning approach that uses state-of-the-art mobile computing technologies and devices to design a highly accessible and real-world relevant hands-on labware for computing education.The labware employs a modular design such that the learning materials can both be combined together into a single course of mobile program development and be integrated into the related computing courses as building blocks.The labware will help students understand the concepts,improve their problem solving skills,and prepare them for the mobile computing industrial workforce.The pilot modules of the labware have been presented to undergraduate students and have received positive feedbacks.     

基于SSL技术实现网络通信双向加密

加密技术对于保障信息安全具有举足轻重的作用。由于无线网络的广泛应用,我们无法控制攻击者获取信号,但是,我们可以使用强加密让攻击者无法解析原文。SSL技术就是一种很好的加密机制。为此,文章分析了SSL3.0协议的作用,并以Web通信和FTP通信的双向加密为例,介绍了SSL技术实现方式。     

舰载雷达探测误差传递与灵敏度分析

在舰艇及编队指控系统中,来自不同平台的传感器探测系统偏差失配是影响态势一致性的关键因素。本文采用理论建模和数字仿真相结合的方法,对舰载雷达探测和处理涉及各环节的偏差进行分析,建立了雷达测量、坐标转换、平台定姿定位和基线转换等各环节偏差影响模型和舰载雷达探测的等效偏差模型。理论分析和计算机仿真结果表明,雷达方位测量、平台艏向和甲板艏向形变引起的偏差均可以直接迭加到方位上,合并为方位偏差;同时,在目标俯仰角小于10°的情况下,基本上可以忽略甲板形变、平台姿态偏差对目标方位测量的影响。     

An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.

     

The parameterized level set method for structural topology optimization with shape sensitivity constraint factor

In recent years, the parameterized level set method (PLSM) has attracted widespread attention for its good stability, high efficiency and the smooth result of topology optimization compared with the conventional level set method. In the PLSM, the radial basis functions (RBFs) are often used to perform interpolation fitting for the conventional level set equation, thereby transforming the iteratively updating partial differential equation (PDE) into ordinary differential equations (ODEs). Hence, the RBFs play a key role in improving efficiency, accuracy and stability of the numerical computation in the PLSM for structural topology optimization, which can describe the structural topology and its change in the optimization process. In particular, the compactly supported radial basis function (CS-RBF) has been widely used in the PLSM for structural topology optimization because it enjoys considerable advantages. In this work, based on the CS-RBF, we propose a PLSM for structural topology optimization by adding the shape sensitivity constraint factor to control the step length in the iterations while updating the design variables with the method of moving asymptote (MMA). With the shape sensitivity constraint factor, the updating step length is changeable and controllable in the iterative process of MMA algorithm so as to increase the optimization speed. Therefore, the efficiency and stability of structural topology optimization can be improved by this method. The feasibility and effectiveness of this method are demonstrated by several typical numerical examples involving topology optimization of single-material and multi-material structures.

     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.