空间场景中传感器探测能力的可视化方法

为了有效地在空间场景中表现各种类型传感器的侦察探测能力,提出了一种通用的可视化方法.根据传感器类型定义其探测范围表面;由于描述传感器探测范围的曲面具有公共顶点,因此将其离散为可以利用射线进行描述的三角形;解决了这种可视化方式涉及到的关键技术,包括离散射线与地球球面交点和切点的计算、不同显示类型下绘制策略的设计、基于一维纹理的脉冲效果实现等.按组件对象模型规范设计实现了传感器组件,并成功应用到空间场景可视化控件中.     

远场涡流法套损测试仪中信号检测系统的研制

分析了远场涡流套损检测中检测信号特点,给出了一种以数字相敏检波(DPSD)算法为核心的应用于远场涡流套损测试仪中的信号检测系统。通过分析整个测试系统的误差模型,给出了满足仪器测量精度要求的硬件电路设计及算法设计。实验结果表明,该系统很好地完成了信号幅度和相位差提取,为后期的缺陷识别及软件成像做好了充分的准备。     

基于主动式静电探测技术的目标定位方法

针对被动式静电探测技术无法对不带电目标进行探测的问题,提出了一种基于主动式静电探测技术的目标定位方法。在对带电和不带电目标分析的基础上,得到了相应的通过检测电流实现目标定位的探测方程,并在Maxwell 3D中进行了仿真。仿真结果表明:基于该方法的探测距离与目标的带电量、主动静电场的强弱以及电流检测电路的灵敏度有关。     

基于FPGA的多传感器管道内漏磁检测系统

针对传统管道漏磁检测器检测精度的不足,提出了新式的基于FPGA的高精度管道漏磁检测系统设计,以适应813 mm管径的管道检测任务。主要介绍了系统逻辑设计,实现了多达400路传感器漏磁检测信号的采集与存储。该设计融合了多种总线协议,可有效解决管道漏磁检测中的采集速率、功耗和精度的问题。经实验验证,方案切实可行,为设计高精度管道漏磁检测系统提供了新的解决方案。     

基于AMR传感器的车辆检测算法

深入研究了各向异性磁阻(AMR)传感器的数据采集原理和特征波形向量提取方法,提出了一种基于AMR传感器的车辆检测算法:自适应状态机的车辆检测算法。该算法可以自适应地更新阈值和基线,利用状态机,达到对车辆的准确和高效的检测。可以用于检测道路交通车流量,也可以应用在大型停车场的车辆诱导系统。     

Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing

Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study.     

Recurrent type-2 fuzzy neural network using Haar wavelet energy and entropy features for speech detection in noisy environments

This paper proposes a new method to detect the boundary of speech in noisy environments. This detection method uses Haar wavelet energy and entropy (HWEE) as detection features. The Haar wavelet energy (HWE) is derived by using the robust band that shows the most significant difference between speech and nonspeech segments at different noise levels. Similarly, the wavelet energy entropy (WEE) is computed by selecting the two wavelet energy bands whose entropy shows the most significant speech/nonspeech difference. The HWEE features are fed as inputs to a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for classification. The RSEIT2FNN is used because it uses type-2 fuzzy sets, which are more robust to noise than type-1 fuzzy sets. The recurrent structure in the RSEIT2FNN helps to remember the context information of a test frame. The RSEIT2FNN outputs are compared with a parameter threshold to determine whether it is a speech or nonspeech period. The HWEE-based RSEIT2FNN detection was applied to speech detection in different noisy environments with different noise levels. Comparisons with different detection methods verified the advantage of the proposed method of using HWEE.     

Induction motors bearing fault detection using pattern recognition techniques

This paper proposes a systematic procedure based on a pattern recognition technique for fault diagnosis of induction motors bearings through the artificial neural networks (ANNs). In this method, the use of time domain features as a proper alternative to frequency features is proposed to improve diagnosis ability. The features are obtained from direct processing of the signal segments using very simple calculation. Three different cases including, healthy, inner race defect and outer race defect are investigated using the proposed algorithm. The ANNs are trained with a subset of the experimental data for known machine conditions. Once the network is trained, efficiency of the proposed method is evaluated using the remaining set of data. The obtained results indicate that using time domain features can be effective in accurate diagnosis of various motor bearing faults with high precision and low computational burden.     

人工免疫系统中人工抗原提呈细胞的设计

反向选择模型是人工免疫系统中的经典模型,但存在训练代价高、覆盖率低的问题.危险理论模型是人工免疫系统中新引进的模型,在降低训练代价,提高覆盖率上具有优势.建立人工免疫危险理论模型必须解决的关键问题之一是:在保持人工免疫系统自适应特征的前提下,设计人工抗原提呈细胞.本文从抗原提呈细胞的生物学原理出发,设计了人工免疫系统中的人工抗原提呈细胞,给出单个人工抗原提呈细胞的结构、细胞上Toll样受体的结构,以及人工抗原提呈细胞群体的结构;设计了人工抗原提呈细胞群体的生命周期.初步的实验结果证明所设计的人工抗原提呈细胞具有自适应发现恶意软件的能力.     

一种高效的小时延故障模拟方法

随着集成电路工艺尺寸不断缩小,电路规模不断增大,要得到很高的小时延故障覆盖率所需的测试向量越来越多,致使小时延故障模拟成本越来越高.为了降低模拟成本,提出一个高效的小时延故障模拟器.模拟方法中引入新的波形表达方式,按电路结构的拓扑顺序进行分级模拟,最后可得到每个故障的检测区间,并且应用时延故障概率分布来计算故障覆盖率.实验结果表明,此方法能大幅降低模拟时间和内存消耗.