铜导线断裂痕迹形态的图像特征提取与分析

纹理是断口的重要特征,不同的断裂原因造成断口的纹理特征是不一样的,它提示了图像中亮度值空间变化的重要信息。图像宏观上表现出来的是二维特性,可以将其灰度变化看成是图像的第三维特征来进行研究,这个"第三维"的表现可以用图像的能量或者所含信息量来表示。常用的图像纹理特征提取的方法有:统计法、利用空间自相关函数作纹理测度、频谱法、联合概率矩阵法、纹理的句法结构分析法。本文主要从频谱法和灰度共生矩阵法两个方面对4种不同原因造成的铜导线断口图像做纹理特征的提取与分析。

模糊逆Fisher鉴别分析及其在人脸识别中的应用

在逆Fisher鉴别分析的基础上,引入了模糊数学的思想,提出了模糊逆Fisher鉴别分析并成功应用于人脸识别。模糊逆Fisher鉴别分析通过隶属度函数将样本归入所有的类别之中,根据隶属度重新定义了类间散布矩阵和类内散布矩阵,进而将样本的原始分布信息通过相应的隶属度函数完全融入到了最后提取到的特征中。在ORL和FERET人脸库上的实验结果证明了基于模糊逆Fisher鉴别准则特征提取方法的优越性。

基于特征学习与支持向量机的仪表识别算法

为了解决当前工业仪表示数在采图环境恶劣和样本数据量大的情况下所导致的算法识别不准确的问题,分别从特征学习与机器学习识别的角度出发,提出了基于特征学习与支持向量机的工业仪表状态识别算法。首先,提取仪表图像区域字符的几何特征和颜色特征,对这些提取出的特征进行归一化处理,设计出特征提取分析算子,达到精准提取有用特征数据的目的。然后,基于支持向量机,计算出分类器的最优平面和约束条件,从而建立仪表识别算子,进一步精确识别仪表示数。最后,基于软件开发环境QT实现算法,并系统集成。实验测试结果显示:与当前仪表识别技术相比,此算法拥有更高的准确性与稳定性,能够准确地根据仪表数字识别出电压,从而确定仪表工作状态是否正常。

基于震例研究的地震预测预报分析

地震是一种自然灾害,对人类社会造成了巨大的破坏和损失。地震预测预报是减轻地震灾害影响的重要手段之一。本论文旨在通过对历史地震事件进行研究和分析,探讨基于震例的地震预测预报方法。通过研究历史地震事件,我们可以发现地震的发生规律和特点,并从中寻找潜在的预测指标。基于震例的地震预测预报方法主要依靠数学模型和统计分析来推断未来地震的可能性和趋势。通过收集、整理和分析大量的地震数据,我们可以构建预测模型,以便更好地理解地震活动的规律和趋势。

Feature extraction and damage alarming using time series analysis

Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.

基于计算机视觉的作物营养诊断系统的关键技术研究现状

近年来,随着高分辨率数码照相机和摄像机的问世,基于计算机视觉的方法成为无损诊断作物营养的解决方案之一。详细分析了基于计算机视觉的作物营养诊断关键技术的研究现状,主要包括冠层图像分割技术、冠层图像特征提取与分析技术、诊断模型建立技术。同时指出了现有研究方法所存在的问题以及未来的研究方向。

Principal Component Feature for ANN-Based Speech Recognition

Using function approximation technology and principal component analysis method, this paper presents a principal component feature to solve the time alignment problem and to simplify the structure of neural network. Its extraction simulates the processing of speech information in human auditory system. The experimental results show that the principal component feature based recognition system outperforms the standard CDHMM and GMDS method in many aspects.

基于小波包分解的声目标识别

小波变换是处理非平稳信号的一个有力工具,研究了基于小波包分析的声信号特征提取方法,并应用该方法对直升机等4种目标的声信号进行了特征提取,降低了特征向量的维数。采用设计改进的BP神经网络分类器对声目标进行分类,分类结果准确率高,获得满意的实验效果。

Study on Chironomid Larvae Recognition Based on DWT and Improved KNN

A chironomid larvae images recognition method based on wavelet energy feature and improved KNN is developed. Wavelet decomposition and color information entropy are selected to construct vectors for KNN that is used to classify of the images. The distance function is modified according to the weight determined by the correlation degree between feature and class, which effectively improves classification accuracy. The result shows the mean accuracy of classification rate is up to 95.41% for freshwater plankton images, such as chironomid larvae, cyclops and harpacticoida.

DISCRIMINANT INDEPENDENT COMPONENT ANALYSIS AS A SUBSPACE REPRESENTATION

Subspace modeling plays an important role in face recognition. Independent Component Analysis （ICA）, a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Com- ponent Analysis （PCA） technique, which addresses high order statistics as well as second order statistics. In this paper, a new scheme of subspace-based representation called Discriminant Independent Component Analysis （DICA） is proposed, which combines the strength＂ of unsupervised learning of ICA and supcrvised learning of Linear Discriminant Analysis （LDA）, and efficiently enhances the generalization ability of ICA-based representation method. Based on DICA subspace analysis, a set of optimal vectors called ＂discriminant independent faces＂ are learned from face samples. The effectiveness of our method is demonstrated by performance comparisons with some popular methods such as ICA, PCA, and PCA＋LDA. On the large scale database of IIS, significant improvements are observed when there are fewer training samples per person available.

Horror Video Recognition Based on Fuzzy Comprehensive Evolution

Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognition algorithm based on fuzzy comprehensive evolution model is proposed. Three low-level video features are extracted as typical features, and they are video key-light, video colour energy and video rhythm. Analytic Hierarchy Process （AHP） is adopted to estimate the weights of extracted features in fuzzy evolution model. Horror evaluation （membership function） is on shot scale and it is constructed based on the knowledge that videos which share the same affective have similar low-level features. K-Means algorithm is implemented to help finding the most representative feature vectors. The experimental results demonstrate that the proposed approach has good performance in recognition precision, recall rate and F1 measure.

Extracting invariable fault features of rotating machines with multi-ICA networks

This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together.Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.

Risk based security assessment of power system using generalized regression neural network with feature extraction

A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.

Analysis of characteristic functions for equivalent circuit model in monolithic transformer

A model of monolithic transformers is presented, which is analyzed with characteristic functions. A closed- form analytical approach to extract all the model parameters for the equivalent circuit of Si-based on-chip transformers is proposed. A novel de-coupling technique is first developed to reduce the complexity in the Y parameters for the transformer, and the model parameters can then be extracted analytically by a set of characteristic functions. Simulation based on the extracted parameters has been carried out for transformers with different structures, and good accuracy is obtained compared to a 3-demensional full-wave numerical electro- magnetic field solver. The presented approach will be very useful to provide a scalable and wide-band compact circuit model for Si-based RF transformers.

Application of Wavelet Packet Energy Spectrum to Extract the Feature of the Pulse Signal

The wavelet packet is presented as a new kind of multiscale analysis technique followed by Wavelet analysis. The fundamental and realization arithmetic of the wavelet packet analysis method are described in this paper. A new application approach of the wavelet packed method to extract the feature of the pulse signal from energy distributing angle is expatiated. It is convenient for the microchip to process and judge by using the wavelet packet analysis method to make the pulse signals quantized and analyzed. Kinds of experiments are simulated in the lab, and the experiments prove that it is a convenient and accurate method to extract the feature of the pulse signal based on wavelet packed-energy spectrumanalysis.

Machine-learning-aided precise prediction of deletions with next-generation sequencing

When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is low.To address the problem,an integrated strategy is proposed.It organically combines the fundamental theories of the three mainstream methods(read-pair approaches,split-read technologies and read-depth analysis) with modern machine learning algorithms,using the recipe of feature extraction as a bridge.Compared with the state-of-art split-read methods for deletion detection in both low and high sequence coverage,the machine-learning-aided strategy shows great ability in intelligently balancing sensitivity and false discovery rate and getting a both more sensitive and more precise call set at single-base-pair resolution.Thus,users do not need to rely on former experience to make an unnecessary trade-off beforehand and adjust parameters over and over again any more.It should be noted that modern machine learning models can play an important role in the field of structural variation prediction.

基于CNN卷积神经网络的病虫害图像识别应用技术综述

农作物病虫害种类繁多,对农作物的产品和品质造成重大影响,而我国一线农业生产人员专业技术水平良莠不齐,导致现场缺乏有效和及时的客观判断。基于此,分析了传统和现代病虫害快速检测的方式和优缺点,并针对基于CNN卷积神经网络的农作物病虫害AI图像识别技术的要点和应用步骤进行详细阐述。该技术具有较高的实用价值和可操作性,可促进AI图像识别技术与传统农业生产的深度融合和赋能。

Nonlinearly correlated failure analysis and autonomic prediction for distributed systems

In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.

基于流形学习的SAR目标识别

主成分分析法(PCA)及其常用的推广的线性特征提取方法在SAR识别中通过变量的少数几个线性组合来解释随机向量的协方差结构并提取特征值,然而在线性变化及特征选取中容易丢失大量信息,对样本的描述性不够。针对该问题,本文提出了一种基于形学习算法,根据每一类MSTAR目标图像存在小幅姿态、方向微弱变化,从而判断处于高维数据空间的某个低维流形上这一特征,利用混合因子分析模型来对流形建模,根据不同目标所在的流形的特征参数,构建全局字典,实验证明,所提出的方法在识别率及速度上优于常规的线性特征提取方法。

Fault Feature Extraction of Rotating Machinery Based on Wavelet Transformation and Multi-resolution Analysis

This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.