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A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images 被引量:2
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作者 S.Velliangiri J.Premalatha 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期625-645,共21页
Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin... Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods. 展开更多
关键词 Adaptive Rood Pattern Search(ARPS) Improved Crow Search Algorithm(ICSA) Enhanced Convolutional Neural network(ecnn) Viola Jones algorithm Speeded Up Robust Feature(SURF)
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基于改进Laplace小波和改进卷积神经网络的压裂车动力端轴承故障识别 被引量:2
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作者 林华钊 王迪 鲁国阳 《机电工程》 CAS 北大核心 2023年第5期691-698,共8页
在强背景噪声工况下,压裂车动力端轴承振动信号故障特征较微弱,导致轴承故障诊断的准确率较低。针对这一问题,提出了一种基于改进Laplace小波(ELW)和改进卷积神经网络(ECNN)的压裂车动力端轴承故障识别方法。首先,采用了一种Laplace小... 在强背景噪声工况下,压裂车动力端轴承振动信号故障特征较微弱,导致轴承故障诊断的准确率较低。针对这一问题,提出了一种基于改进Laplace小波(ELW)和改进卷积神经网络(ECNN)的压裂车动力端轴承故障识别方法。首先,采用了一种Laplace小波振荡频率参数选取策略,使Laplace小波搜寻到了最佳频率参数;然后,采用改进Laplace小波,对采集到的压裂车动力端轴承故障振动信号进行了降噪处理,并在卷积神经网络(CNN)的基础上引入了自注意力机制和编码器、解码器结构,设计出了改进卷积神经网络(ECNN)模型;最后,将压裂车动力端轴承降噪后的信号输入改进卷积神经网络,进行了自动特征提取和故障识别;为了验证该方法的有效性和先进性,将其与其他方法(模型)进行了对比分析。研究结果表明:采用基于改进Laplace小波与和改进卷积神经网络的方法(模型),对压裂车动力端轴承故障进行识别的准确率可高达99.67%,单个样本的测试时间仅为0.14 s;在识别准确率、召回率、F1得分和统计检验等方面,与其他方法(模型)相比,基于改进Laplace小波与改进卷积神经网络的组合模型具有更为优秀的故障识别性能。 展开更多
关键词 压裂车 强背景噪声工况 自动特征提取 故障识别 改进Laplace小波 改进卷积神经网络
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消除非线性随机干扰的一种方法
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作者 侯媛彬 韩崇昭 《西安矿业学院学报》 CAS 1997年第1期55-59,62,共6页
基于BP神经网络的训练规则,针对具有大惯性,大滞后和各种不确定因素影响的非线性系统,设计了一种能消除非线性随机干扰引起的混纯现象的神经网络,并已和执行器、喷油嘴等器件组成系统联机调试,效果理想。
关键词 神经网络 随机干扰 非线性 消除法
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