Face recognition based on extreme learning machine

Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.     

SELM: Semi-supervised ELM with application in sparse calibrated location estimation

Indoor location estimation based on Wi-Fi has attracted more and more attention from both research and industry fields. It brings two significant challenges. One is requiring a vast amount of labeled calibration data. The other is real-time training and testing for location estimation task. Traditional machine learning methods cannot get high performance in both aspects. This paper proposed a novel semi-supervised learning method SELM (semi-supervised extreme learning machine) and applied it to sparse calibrated location estimation. There are two advantages of the proposed SELM. First, it employs graph Laplacian regularization to import large number of unlabeled samples which can dramatically reduce labeled calibration samples. Second, it inherits the good property of ELM on extreme training and testing speed. Comparative experiments show that with same number of labeled samples, our method outperforms original ELM and back propagation (BP) network, especially in the case that the calibration data is very sparse.     

A test-driven approach to code search and its application to the reuse of auxiliary functionality

Context

Software developers spend considerable effort implementing auxiliary functionality used by the main features of a system (e.g., compressing/decompressing files, encryption/decription of data, scaling/rotating images). With the increasing amount of open source code available on the Internet, time and effort can be saved by reusing these utilities through informal practices of code search and reuse. However, when this type of reuse is performed in an ad hoc manner, it can be tedious and error-prone: code results have to be manually inspected and integrated into the workspace.

Objective

In this paper we introduce and evaluate the use of test cases as an interface for automating code search and reuse. We call our approach Test-Driven Code Search (TDCS). Test cases serve two purposes: (1) they define the behavior of the desired functionality to be searched; and (2) they test the matching results for suitability in the local context. We also describe CodeGenie, an Eclipse plugin we have developed that performs TDCS using a code search engine called Sourcerer.

Method

Our evaluation consists of two studies: an applicability study with 34 different features that were searched using CodeGenie; and a performance study comparing CodeGenie, Google Code Search, and a manual approach.

Results

Both studies present evidence of the applicability and good performance of TDCS in the reuse of auxiliary functionality.

Conclusion

This paper presents an approach to source code search and its application to the reuse of auxiliary functionality. Our exploratory evaluation shows promising results, which motivates the use and further investigation of TDCS.     

Application of extreme learning machine for series compensated transmission line protection

This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions.     

电阻抗扫描成像计算机辅助诊断方法研究

为了提高电阻抗扫描成像EISI(Electrical Impedance Scanning Imaging)在乳腺疾病检查方面的准确性,将独立分量方法(ICA)应用于EISI数据的特征提取,并结合支持向量机(SVM)进行乳腺疾病计算机辅助诊断(CAD)。实验结果表明:ICA方法所提取的特征对于乳腺疾病分类是有效的。对于良性肿瘤和恶性肿瘤的鉴别,提出的CAD方法灵敏度为74.2%,特异度为82.8%,准确度为80%。结合所提出的CAD方法和传统的EISI检查方法,可以提高EISI在乳腺疾病检查方面的可靠性,降低假阳性率,避免对良性肿瘤患者进行活检。     

基于特征分析的番茄识别及其软件的快速开发

为导引机械手采摘番茄,提出了基于形状与纹理特征分析的番茄识别和软件实现方法。提取番茄和枯叶等干扰物的子图像,选择3个形状特征和5个纹理特征参数进行统计分析,在Matlab下快速验证番茄识别算法,再利用OpenCV函数库在Visual C++下对识别算法进行再现,实现了目标识别软件的快速开发。对674幅番茄图像进行识别试验的结果表明,对生长状态为相互分离的成熟番茄,正确识别率达到90%以上。不同分辨率的番茄图像处理实验结果表明,利用OpenCV函数库编制的程序比Matlab下的代码执行效率显著提高,而且能实时采集并同步处理双目图像,可为采摘机器人目标识别和软件开发提供参考。     

基于极速神经网络的协作过滤方法研究

协作过滤是一种有效的个性化推荐技术,针对该技术随着用户和资源的增多,数据的高维稀疏特性严重导致推荐质量的下降和计算速度减慢的问题,研究并实现了一种基于极速神经网络的协作过滤方法。采用主成分分析解决数据高维稀疏性问题,采用极速神经网络技术解决计算速度慢的问题。实验结果表明,该方法具有良好的泛化性能和学习速度,能很好的满足个性化资源推荐的需求。     

基于移动平均线组合和支持向量机的股市趋势研究

证券投资是一种高风险、高收益的理财方式,而对市场运行趋势的良好把握有助于投资者回避风险、把握机会。提出基于多条移动平均线斜率组合和支持向量机算法,判断当前市场运行趋势,从而采取相应的投资策略。模拟实验证明,该方法取得了良好的效果。