目前的脑电(EEG)情感识别模型忽略了不同时段情感状态的差异性,未能强化关键的情感信息。针对上述问题,提出一种多上下文向量优化的卷积递归神经网络(CR-MCV)。首先构造脑电信号的特征矩阵序列,通过卷积神经网络(CNN)学习多通道脑电的...目前的脑电(EEG)情感识别模型忽略了不同时段情感状态的差异性,未能强化关键的情感信息。针对上述问题,提出一种多上下文向量优化的卷积递归神经网络(CR-MCV)。首先构造脑电信号的特征矩阵序列,通过卷积神经网络(CNN)学习多通道脑电的空间特征;然后利用基于多头注意力的递归神经网络生成多上下文向量进行高层抽象特征提取;最后利用全连接层进行情感分类。在DEAP(Database for Emotion Analysis using Physiological signals)数据集上进行实验,CR-MCV在唤醒和效价维度上分类准确率分别为88.09%和89.30%。实验结果表明,CR-MCV在利用电极空间位置信息和不同时段情感状态显著性特征基础上,能够自适应地分配特征的注意力并强化情感状态显著性信息。展开更多
如何在遥感图像数据库中快速、准确地找出目标图像,是检索系统的核心所在。基于对上下文语境敏感的贝叶斯网络(content-sensitive Bayesian network,CSBN),建立了含有方向关系的检索模型,并根据城市区域的特点,提出了适合城市区域检索...如何在遥感图像数据库中快速、准确地找出目标图像,是检索系统的核心所在。基于对上下文语境敏感的贝叶斯网络(content-sensitive Bayesian network,CSBN),建立了含有方向关系的检索模型,并根据城市区域的特点,提出了适合城市区域检索的方法。首先,通过贝叶斯网络对图像进行检索;然后,依据图像的平均高频信号强度(average high frequency signal strength,AHFSS)对候选图像进行筛选;最后,得到含有城市区域这一高级语义特征的最终检索结果。为了确定图像内部的方向关系,采用东北、西北、东南和西南4个区域的方向描述图像的8种方向关系,有效降低了算法的时间复杂度。实验结果表明,该方法可有效地描述图像的场景语义,并具有较高的查准率和检索效率,可满足用户的需求。展开更多
To cope with the problem of emitter identification caused by the radar words＇ uncertainty of measured multi-function radar emitters, this paper proposes a new identification method based on stochastic syntax-directed...To cope with the problem of emitter identification caused by the radar words＇ uncertainty of measured multi-function radar emitters, this paper proposes a new identification method based on stochastic syntax-directed translation schema（SSDTS）. This method, which is deduced from the syntactic modeling of multi-function radars, considers the probabilities of radar phrases appearance in different radar modes as well as the probabilities of radar word errors occurrence in different radar phrases. It concludes that the proposed method can not only correct the defective radar words by using the stochastic translation schema, but also identify the real radar phrases and working modes of measured emitters concurrently. Furthermore, a number of simulations are presented to demonstrate the identification capability and adaptability of the SSDTS algorithm.The results show that even under the condition of the defective radar words distorted by noise,the proposed algorithm can infer the phrases, work modes and types of measured emitters correctly.展开更多
文摘目前的脑电(EEG)情感识别模型忽略了不同时段情感状态的差异性,未能强化关键的情感信息。针对上述问题,提出一种多上下文向量优化的卷积递归神经网络(CR-MCV)。首先构造脑电信号的特征矩阵序列,通过卷积神经网络(CNN)学习多通道脑电的空间特征;然后利用基于多头注意力的递归神经网络生成多上下文向量进行高层抽象特征提取;最后利用全连接层进行情感分类。在DEAP(Database for Emotion Analysis using Physiological signals)数据集上进行实验,CR-MCV在唤醒和效价维度上分类准确率分别为88.09%和89.30%。实验结果表明,CR-MCV在利用电极空间位置信息和不同时段情感状态显著性特征基础上,能够自适应地分配特征的注意力并强化情感状态显著性信息。
文摘如何在遥感图像数据库中快速、准确地找出目标图像,是检索系统的核心所在。基于对上下文语境敏感的贝叶斯网络(content-sensitive Bayesian network,CSBN),建立了含有方向关系的检索模型,并根据城市区域的特点,提出了适合城市区域检索的方法。首先,通过贝叶斯网络对图像进行检索;然后,依据图像的平均高频信号强度(average high frequency signal strength,AHFSS)对候选图像进行筛选;最后,得到含有城市区域这一高级语义特征的最终检索结果。为了确定图像内部的方向关系,采用东北、西北、东南和西南4个区域的方向描述图像的8种方向关系,有效降低了算法的时间复杂度。实验结果表明,该方法可有效地描述图像的场景语义,并具有较高的查准率和检索效率,可满足用户的需求。
基金supported by the National Natural Science Foundation of China (No. 61002026)
文摘To cope with the problem of emitter identification caused by the radar words＇ uncertainty of measured multi-function radar emitters, this paper proposes a new identification method based on stochastic syntax-directed translation schema（SSDTS）. This method, which is deduced from the syntactic modeling of multi-function radars, considers the probabilities of radar phrases appearance in different radar modes as well as the probabilities of radar word errors occurrence in different radar phrases. It concludes that the proposed method can not only correct the defective radar words by using the stochastic translation schema, but also identify the real radar phrases and working modes of measured emitters concurrently. Furthermore, a number of simulations are presented to demonstrate the identification capability and adaptability of the SSDTS algorithm.The results show that even under the condition of the defective radar words distorted by noise,the proposed algorithm can infer the phrases, work modes and types of measured emitters correctly.
