Design of Prototype‐Based Emotion Recognizer Using Physiological Signals

This study is related to the acquisition of physiological signals of human emotions and the recognition of human emotions using such physiological signals. To acquire physiological signals, seven emotions are evoked through stimuli. Regarding the induced emotions, the results of skin temperature, photoplethysmography, electrodermal activity, and an electrocardiogram are recorded and analyzed as physiological signals. The suitability and effectiveness of the stimuli are evaluated by the subjects themselves. To address the problem of the emotions not being recognized, we introduce a methodology for a recognizer using prototype‐based learning and particle swarm optimization (PSO). The design involves two main phases: i) PSO selects the P% of the patterns to be treated as prototypes of the seven emotions; ii) PSO is instrumental in the formation of the core set of features. The experiments show that a suitable selection of prototypes and a substantial reduction of the feature space can be accomplished, and the recognizer formed in this manner is characterized by high recognition accuracy for the seven emotions using physiological signals.     

A granular description of ECG signals

In this paper, we develop a general framework of a granular representation of ECG signals. The crux of the approach lies in the development and ongoing processing realized in the setting of information granules-fuzzy sets. They serve as basic conceptual and semantically meaningful entities using which we describe signals and build their models (such as various predictive schemes or classifiers). A comprehensive two-phase scheme of the design of the information granules is proposed and described. At the first phase, we discuss the temporal granulation through a series of temporal windows (granular windows) and an aggregation of the values of signal by means of fuzzy sets. To address this issue, offered is a detailed method of building a fuzzy set based on numeric data and a certain optimization criterion that strikes a balance between the highest experimental relevance of the fuzzy set supported by numeric data and its substantial specificity. At the next phase of the granular design, a collection of information granules is further summarized with the use of fuzzy clustering (Fuzzy C-Means). The resulting prototypes (centroids) formed by this grouping process serve as elements of the granular vocabulary. We discuss ways of using these vocabularies in the knowledge-based representation, modeling, and classification of ECG beats.     

基于改进模糊置信规则的意图识别方法

针对传统意图识别方法只能处理某种类型不确定性信息的不足,该文结合模糊集和DS证据理论优势提出一种模糊置信规则库(BRB)信息处理方法。首先在置信规则前提部分改进了前提属性的连接关系,根据数据集统计分布特点设计了模糊集分割,选取Cauchy型分布作为隶属度函数,较好地避免置信规则无法被有效激活进而导致系统无有效输出问题;其次融合处理辨识框架内不同类别的置信分布,建立规则权重和特征权重优化模型,构建了特征空间与类别空间之间的输入输出关系;在此基础上,计算未知意图数据在相应规则模糊域的匹配度和激活度,采用置信度最大原则进行识别决策。通过实验验证、参数敏感性及结果分析、时间复杂度分析,表明该文方法可以获得比其他识别方法更高的正确率,尤其是在小样本条件下更能体现出该方法的有效性和可靠性。     

结合时序注意力机制的多特征融合行人序列图像属性识别方法

目前绝大多数的行人属性识别任务都是基于单张图像的,单张图像所含信息有限,而图像序列中包含丰富的有用信息和时序特征,利用序列信息是提高行人属性识别性能的一个重要途径.本文提出了结合时序注意力机制的多特征融合行人序列图像属性识别网络,该网络除了使用常见的空-时二次平均池化特征聚合和空-时平均最大池化特征聚合提取序列的特征外...     

基于递阶原理的非均匀采样非线性系统的模糊辨识

针对非均匀多采样率非线性系统的建模问题,提出了基于递阶原理的模糊辨识方法.首先,分析了非线性系统在输入信号非均匀周期刷新,输出信号周期采样的情况下,非线性系统可以通过提升技术,利用多个局部线性模型加权组合的模糊模型来描述.在此基础上,利用GK模糊聚类确定模糊模型前件结构,利用基于递阶原理的递推最小二乘辨识算法辨识模糊模型后件参数.同时,通过鞅定理对辨识算法的收敛性进行了研究.最后,通过仿真实例证明了本文方法的有效性.     

Human recognition system for outdoor videos using Hidden Markov model

Human recognition is an essential requirement for human-centric surveillance, activity recognition, gait recognition etc. Inaccurate recognition of humans in such applications may leads to false alarm and unnecessary computation. In the proposed work a robust background modeling algorithm using fuzzy logic is used to detect foreground objects. Three distinct features are extracted from the contours of detected objects. An unique aggregated feature vector is formed using a fuzzy inference system by aggregating three feature vectors. To minimize computation in recognition using Hidden Markov model (HMM), the length of final feature vector is reduced using vector quantization. The proposed method is explained using five basic phases; background modeling and foreground object detection, features extraction, aggregated feature vector calculation, vector quantization, and recognition using Hidden Markov model.     

UKF参数辨识的T-S模糊多模型目标跟踪算法

针对非线性系统中机动目标动态模型不确定性问题，提出了一种新的基于UKF参数辨识的T-S模糊多模型机动目标跟踪算法。在提出的算法中，用多个语义模糊集对目标特征信息进行模糊表示，构建一个通用的T-S模糊语义多模型框架。在T-S模糊语义多模型中，使用模糊C回归聚类算法实现对前件参数的辨识，同时，为了实现系统的非线性特征，引入无迹卡尔曼算法辨识后件参数。仿真结果表明，提出的算法跟踪性能优于传统的交互多模型算法和交互多模型无迹卡尔曼滤波算法，在被跟踪目标突然发生方向改变或目标的动态先验信息不精确等复杂情况时，能够有效地对目标进行精确跟踪。      

模糊C-均值(FCM)聚类法与矢量量化法相结合用于说话人识别

该文提出了一种将模糊C-均值聚类法与矢量量化法相结合进行说话人识别的方法。该算法将从语音信号中提取的 12阶 LPC(线性预测编码)倒谱系数作为待分类样本的 12个指标,先用矢量量化法求出每个说话人表征特征参数的码书,作为模糊聚类算法的聚类中心,最后将待识别的特征矢量以得到的码书为聚类中心,进行聚类识别。该算法所使用的特征参数较少,计算比较简单,但识别率较矢量量化法高。     

基于DBN 的频率与相位编码信号快速调制识别方法

为提升低信噪比条件下雷达/ 通信频率、相位编码信号调制识别性能,降低特征提取复杂度,提出了基于深度信念网络DBN(Deep Belief Network, DBN)以及快速特征提取的调制识别方法。结合快速傅里叶累加算法FAM(FFT Accumulation Method)算法,提出了将循环谱估计图像转化为有效可识别特征向量的提取算法;设计了用于编码信号调制识别的DBN 网络训练与识别框架。仿真结果表明,文中方法较传统方法具有更低的特征提取与预处理复杂度,提取的特征在几种典型编码调制模式信号中具有明显区分,DBN 训练识别框架对雷达/ 通信编码信号调制识别均具有可行性与有效性,在低信噪比条件下对无线电编码信号有更高的识别正确率。     

模糊逻辑与学习方法的车牌颜色识别算法

针对光照环境复杂多变及各地车牌颜色特征不同的情况,提出了一种结合模糊逻辑与学习方法的车牌颜色识别算法.在HSV颜色空间中进行车牌颜色特征提取,并根据反色信息将车牌颜色识别由一个四分类问题转换为两个二分类问题.对HSV颜色空间三个分量的模糊映射进行加权融合,建立基于隶属度的分类函数,相关参数通过学习算法获得.在一个基于DSP的嵌入式车牌识别平台上进行了与其他传统分类方法的对比实验,结果表明本文算法存两个测试集上都取得了更好的识别效果.     

基于三维特征的雷达信号脉内调制识别

针对低信噪比条件下雷达信号识别算法对噪声敏感的问题,提出了一种基于三维特征的雷达信号脉内调制识别算法。该方法通过提取信号的差分近似熵、调和平均分形盒维数和信息维数特征组成三维特征向量,使用遗传算法优化的BP神经网络分类器实现雷达信号的分类识别。仿真结果表明,所提取的三维特征在信噪比为-4~10 dB变化范围内具有较好的类内聚集度和类间分离度,可以实现对不同雷达信号进行识别,证实了该方法的有效性。     

基于LCD和改进PNN的光纤周界振动信号识别

针对马赫-曾德尔光纤周界系统振动信号扰动信 息提取及识别中的问题,提出了一种 基于局部特征尺度分解(LCD)和改进概率神经网络(PNN)的识别方法。首先,采用LCD将振动 信号分解成一系列内禀尺度分量(ISC),再将分解得到的ISC分量每连续3阶一组进行独立成 分分析(ICA),提取扰动信息。其次,提取振动信号的峭度、排列熵、瞬时幅度标准差和瞬 时频率标准差构造具有准确描述能力的特征向量。最后,采用经模糊C均值聚类(FCM)优化后 的PNN对振动信号进行识别分类。利用六种振动信号实验数据进行验证。结果表明,该方法 能够高效准确的识别六种振动信号,平均识别率达到97.17%,识别时 间为0.78 s。该方法在 有效信息提取和振动信号识别方面明显优于传统的LCD算法和PNN算法,具有实际应用价值。     

WB-KNN for emotion recognition from physiological signals

K-nearest neighbor (KNN) has yielded excellent performance in physiological signals based on emotion recognition. But there are still some issues:the majority vote only by the nearest neighbors is too simple to deal with complex (like skewed) class distribution; features with the same contribution to the similarity will degrade the classification accuracy; samples in boundaries between classes are easily misclassified when k is larger. Therefore, we propose an improved KNN algorithm called WB-KNN, which takes into account the weight (both features and classification) and boundaries between classes. Firstly, a novel weighting method based on the distance and farthest neighbors named WDF is proposed to weight the classification, which improves the voting accuracy by making the nearer neighbors contribute more to the classification and using the farthest neighbors to reduce the weight of non-target class. Secondly, feature weight is introduced into the distance formula, so that the significant features contribute more to the similarity than noisy or irrelevant features. Thirdly, a voting classifier is adopted in order to overcome the weakness of KNN in boundaries between classes by combining different classifiers. Results of WB-KNN algorithm are encouraging compared with the traditional KNN and other classification algorithms on the physiological dataset with a skewed class distribution. Classification accuracy for 29 participants achieves 94.219 2% for the recognition of four emotions.     

Analysis of affective ECG signals toward emotion recognition

Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform...     

一种战场电磁信号识别技术

电子技术的突飞猛进让越来越多的电磁设备应用于战场,从而使电磁信号的识别成为摆在广大指战员面前的难题。通过对电磁信号特征进行建模、利用模糊理论构造隶属度函数计算综合模糊隶属度、用证据理论对样本的信息进行合成,从而设计了一种基于模糊集和D-S证据理论的电磁信号识别技术。最后,通过实验仿真证实了该方法的可行性。     

激光/红外复合扫描引信目标识别算法

针对激光、红外单模引信抗环境干扰能力差、目标识别概率不高的问题,提出了一种基于关联的多元线阵激光/红外复合扫描的模糊识别算法。该算法通过建立激光/红外线阵扫描模型,对目标的顶部三维轮廓与顶部红外辐射图像进行特征提取与关联信息融合,对比真实目标特征参数,设计了基于模糊理论的目标识别算法,得出三组识别特征各自的识别概率,在此基础上对三种识别概率继续进行模糊推理融合得出目标的最终识别概率。仿真结果表明,该复合探测目标识别概率比单模识别方法有较大幅度的提高。     

一种基于模糊信息粒化的视频时空显著单元提取方法

提出一种基于模糊信息粒化的视频时空显著单元提取方法,为视频分析及检索等高层应用提供一个有效的内容表示模式.本文首先提出了一种类相关的特征粒化方法,粒化后的模糊粒特征简化了分类关系且在一定程度上解决了感知主观性问题,因而通过简单的分类器可以有效地提取空域中具有高视觉感知显著度的区域(简称为显著区域);其次,通过对显著区域的时域一致性分析提取视频序列中时域连续的显著区域集合,定义为时空显著单元.提取的时空显著单元能作为一种较为通用的语义级内容表示模式.实验结果分别从时域和空域两个方面验证了本文方法的有效性.     

一种面向VLSI实现的手写体数字识别系统

本文介绍一种面向ＶＬＳＩ实现的手写体数字识别系统，其中采用汉明神经网络从模式中提取局部特征，然后对提取出的特征图进行压缩，最后由模糊逻辑识虽器根据压缩的特征图对输入模式进行识别，为了在不增加征集的情况下提高特征提取性能，在系统设计时提出了四种新的技术，如具有多阈值的改进汉明神经网络结构等，在模糊逻辑识别器的设计中，提出了两种新的处理技术，实验表明该系统对手写字符的变形和位移等有较强的处理能力，该系     

基于特征空间分解与融合的语音情感识别

提出了一种语音情感识别中特征空间的优化方法。针对情感类别两两之间的区分度,优化了情感对各自的特征空间,考察了多类分类器分解为两类分类器的方法,采用置信度判决融合的方法进行两类分类器组的重组,实验中比较了单个多类分类器和两类分类器组的识别性能。结果表明,在同等条件下性能提升了8个百分点以上,对多类分类器进行分解,优化每个情感对各自的特征空间,并进行融合的方法适合语音情感识别,对特征空间的优化效果显著。      

基于稀疏自动编码网络的水声通信信号调制识别

研究了基于稀疏自动编码网络的水声通信信号识别方法。首先利用稀疏自动编码网络对接收信号的功率谱识别分类,得到除PSK外信号的调制类型,然后对识别结果为PSK的信号做四次方谱,最后利用稀疏自动编码网络完成对QPSK和8PSK的识别分类。仿真实验表明,稀疏自动编码网络能从接收信号的谱信息中自动提取有效谱特征。与传统基于功率谱特征提取的识别方法相比,本文算法减少了依赖领域知识的特征提取环节,识别性能优于传统算法。