On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.     

Intercardinal面上最大副瓣电平的分析方法

对天线方向图计算中涉及的最大副瓣电平的分析方法进行了探讨，提出了用方向图搜索求解最大副瓣电平；同时给出了用该方法求得在同相矩形口径两种分布形式下Ｉｎｔｅｒｃａｒｄｉｎａｌ面上的最大副瓣电子平，并给出了Ｉｎｔｅｒｃａｒｄｉｎａｌ面的定义。     

基于Gabor小波与LBP直方图序列的人脸年龄估计

提出了一种基于Gabor小波和局域二值模式(Local binary pattern,LBP)直方图序列的人脸年龄估计方法。首先对人脸图像提取多方向与多尺度的Gabor幅值域图谱(Gabor magnitude maps,GMMs);然后采用基于局部特征的LBP算子对GMMs编码,并对之分块,由各子块的直方图序列来描述人脸;为进一步降低人脸特征维数,再对人脸直方图序列特征应用主成分分析(PCA);最后使用支持向量机回归(SVR)的LOPO策略对人脸年龄库进行训练和测试。实验结果表明,该方法可以较为快速有效地对人脸图像进行年龄估计。     

基于MVC模式的Web0A系统的设计与实现

为了方便企业的管理和提高协同办公效率,越来越多的企业采用了WebOA系统。针对传统设计模式开发的WebOA系统存在维护成本高、难以扩展等缺陷,文中提出一种基于MVC设计模式的系统开发方案。首先详细介绍了MVC模式的设计思想和ZendFramework的工作流程及原理,然后阐述了系统设计与实现的整个过程。该设计方案充分利用了MVC的结构和特点并已成功应用于某办公自动化系统的开发。实践表明,基于MVC模式开发的OA系统运行稳定,具有较好的灵活性和高效性,并且大大减少了系统的开发周期,达到了预期的效果。     

Ajax技术在Web2.0网站设计中的应用研究

Ajax是Web领域的前沿技术,这种技术提供了新的互联网交互模型,并扩展了Web应用的能力。文中在介绍Ajax技术的工作原理、分析比较Ajax工作模式与传统Web工作模式区别的基础上,运用Ajax技术实现了网页的动态加载,用户的请求能更为迅捷地得到Web应用程序的回应。这样就避免了在网络上频繁地重复发送那些没有更新过的信息。相对于传统的Web应用,Ajax技术在丰富客户端的表现能力、改善用户体验中,表现了强大的交互性能。其原因在于Ajax技术实现了客户端与服务器间的数据通信,并提供了两者间异步通信的能力,因而降低了网络传输的数据量,均衡客户端与服务器间的负载。从而体现了Ajax技术提高系统效率与优化用户界面的优良性能。     

基于模糊模式识别的抽油井示功图判别算法研究

针对有杆抽油井示功图诊断分析问题,研究特定故障情况下示功图特征值算法。把示功图数据通过傅里叶变换由空域转换成频域形式,仅保留较低频率上的系数a0、a1、b1,并将其作为特征值。根据所得到的特征值结合模式识别算法能够实现有杆抽油井示功图快速准确的识别和分类。实际应用表明,利用基准示功图28种类型的特征值的选取,计算出每种示功图的特征矢量,并保存到特征库中。对给定示功图特征值进行模式识别,即可得出该图隶属于哪种模式。     

基于GRACE的京津冀地下水储量变化

基于2003—2019年GRACE陆地水储量变化数据和全球陆地水文模型（global land data assimilation system,GLDAS）数据反演京津冀地下水储量变化,运用时空分析方法对地下水等效水高变化进行时空演变特征分析。在空间变化上,整个京津冀地下水等效水高变化速率约为-51.77 mm/a,其中：北京市变化速率最低,约为-38.15 mm/a;天津市变化速率最高,约为-62.85 mm/a;河北省变化速率与区域平均变化水平相当,约为-52.42 mm/a。基于Sen Slope和Mann-Kendall非参数检验法分析得出西部、西南部和中部地区地下水等效水高下降[JP]趋势最明显,东北部下降趋势最小。在时间变化上,地下水等效水高变化具有一定的季节性规律：夏季变化速率最大,约为-75.99 mm/a;冬季变化速率最小,约为-37.24 mm/a;春秋两季的变化速率大致相同,分别为-52.34 mm/a和-48.21 mm/a。在影响因素分析中,人类活动是引起京津冀地区地下水等效水高变化的主要因素。研究结果可为科学掌握京津冀地下水储量时空变化规律提供数据支撑。     

A two-layer fuzzy pattern matching procedure for the evaluation of conditions involving vague quantifiers

In this note, we are interested in the evaluation of conditions of the form The value of attribute a for Q items of X is in F or more shortly Q items of X are F, where X is a set of items, Q denotes a possibly vague proportion (which may be linguistically expressed, e.g. most), F is a (possibly fuzzy) subset of the attribute domain of a. and where the available knowledge about the value a(x) of the attribute a for any item x may be imprecise or even vague. The evaluation is based on a fuzzy pattern matching procedure repeated two times. Such conditions may be encountered in queries addressed to an incomplete information data base or in the if-part of expert rules.     

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).     

入侵检测主流技术及发展动态

入侵检测技术是网络安全的主要技术和网络研究的热点,入侵检测方法包括基于数据挖掘、粗糙集、模式识别、支持向量机和人工免疫等主要技术,详细分析了各种检测方法在入侵检测应用中的优缺点。通过回顾研究人员近期的研究成果,提出了该技术的主要发展方向,为进一步研究提供参考。