某型激光测距机故障诊断仪的设计

为解决部队条件下某型激光测距机故障维修难度大的难题,依据故障字典法的基本思想,通过分析该型测距机的电路特点和总结典型故障原因,结合故障诊断和维修经验,提出该型激光测距机故障诊断仪的设计方法。首先根据电路功能划分故障子集,其次由单片机产出激励信号并测试关键节点的信号,最后根据故障现象和测试结果编制故障字典。试用结果表明,该诊断仪能快速实现该型激光测距机整机功能和电路故障的诊断,提高了装备维修的效率。     

Sparse signal representation and its applications in ultrasonic NDE

Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration.     

改进的正交匹配追踪的语音增强算法*

为了提高传统正交匹配追踪（Orthogonal Matching Pursuit,OMP ）算法的语音增强性能和运算速度,本研究基于稀疏编码理论,提出了一种改进的OMP算法的语音增强算法。其一,将K-奇异值分解（K-singular value decomposition,K-SVD）算法与OMP算法相结合,通过设置能量阈值的方法,提高OMP算法的语音增强性能;其二,通过改进传统OMP算法中信号稀疏逼近的计算方法,提高算法的运算速度。改进的OMP算法的语音增强算法与传统K-SVD语音增强算法相比,采用PESQ评价增强语音的质量,NCM评价语音的可懂度。在NCM的值基本保持不变的情况下,PESQ的值平均提高约12.47%,取得了更好的增强效果。取得了更好的增强效果。改进的OMP算法的运算速度与传统OMP算法相比提高近一倍。     

一种改进的字典学习方法在医疗疾病分析中的应用

针对传统字典学习算法忽略样本与字典原子之间关联信息及有限的训练集容易出现过拟合的现象,文中提出了通过加权机制及二范数约束的方法,建立样本与字典原子之间的权重关系,并对编码系数用二范数进行约束。采用留一交叉验证法在抑郁症数据集上比较了算法的精确度、灵敏度和错误率等指标。实验结果表明,改进的算法在医疗疾病分类中有良好的效果,分类精确度平均提高了21.87%,灵敏度和错误率等方面也有良好的表现。     

基于结构字典学习的图像复原方法

本文提出一种新的结构字典学习方法,并利用它进行图像复原。首先给出结构字典学习的基本内容和方法,然后将傅里叶正则化方法和结构字典学习方法有效整合到图像复原算法中。结构字典学习方法是先将原图像进行结构分解,再分别学习出每个结构图像中的字典,最后利用这些字典对原图像进行稀疏的表示。结合傅里叶正则化,提出了一种有效的迭代图像复原算法:第一步在傅里叶域利用正则化反卷积方法得到图像的初步估计;第二步采用结构字典学习的方法对遗留的噪声进行去噪处理。实验结果表明,提出的方法在改进信噪比和视觉质量上都要优于6种先进的图像复原方法,改进的信噪比平均提升0.5 d B以上。     

基于稀疏表示的低比特率可伸缩图像编码算法研究

根据图像的几何结构特性,从人类视觉系统特性出发,建立了Gabor感知多成份字典,进而模拟人类视觉通路的层次处理机制,构建了稀疏编码网络,能够有效去除图像中的高阶冗余,形成更为稀疏的表示。对稀疏表示系数重组后进行比特平面量化,实现了低比特率的可伸缩编码。实验结果表明,在低比特率下,本文算法压缩后重构图像的感知质量要明显优于JPEG2000,峰值信噪比也与其相当,并且对于图像中的边缘和纹理等细节保持效果更佳。     

一种基于匹配追踪的语音盲源分离算法

研究基于Gabor的过完备字典的匹配追踪（Matching Pursuit,MP）稀疏分解算法,首先对混合语音信号进行稀疏分解。针对传统MP算法运行时间长,占用存储范围大以及语音信号稀疏分解特性的特点,利用快速傅里叶变换（Fast Fourier Transformation,FFT）的MP稀疏分解缩小了最佳原子的搜索范围,提高运行速度。然后基于峭度的自适应盲源分离算法,通过自适应地学习算法中的激活函数最终实现语音信号的盲源分离。此算法经过仿真实验,证明分离效果比传统算法有了一定的改进,实验结果证实算法的有效性。     

A Pattern Dictionary Method for Anomaly Detection

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel–Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.     

兆瓦级NBI加热系统弧流电源故障诊断方法探究

针对中国环流器2号A(HL-2A)装置弧流电源的特点,以及弧流电源故障诊断的条件与现状,提出了结合中性束注入(NBI)控制系统的相关功能模块与基于Matlab/Simulink仿真技术构建的故障字典,以实现弧流电源运行状态的在线监测及其故障模式的诊断。NBI控制系统在监测到弧流电源故障状态后,调用存储的弧流电源输出信号进行分析,提取信号的特征值,然后通过查找弧流电源故障字典以诊断出可能的故障模式。结合实验数据论证了该故障诊断法对弧流电源简单故障定位准确,对复杂故障能缩小定位范围,具有原理简单、可操作性强等优点。     

融合内外特征的图像超分辨率算法

针对单一先验知识不足以约束病态严重的图像超分辨率问题,本文提出了融合内外特征的图像超分辨率算法.针对图像的自相似性,通过采用基于内部特征的深度卷积网络学习来增强输入图像的细节纹理,去除超分辨率图像伪影;同时,使用基于外部图像的稀疏约束方法来学习图像结构信息,并结合高频残差字典来解决超分辨率重建中的高频信息缺失问题;最后通过卷积稀疏方法分别从基础层和细节层来融合内外特征的重建图像,以获得细节清晰、去伪影的超分辨率图像,进一步提高图像质量.与传统算法相比,本文算法在重建图像的纹理特征和质量上都得到了增强,且视觉效果与峰值信噪比较传统算法有所改善.