在工业检测场景下,按照是否引入正常样本中不存在的异常,可以将异常检测问题分为结构异常检测和逻辑异常检测两类,逻辑异常检测对网络的全局理解能力提出了更高的要求。针对现有无监督异常检测模型在结构异常上已有较好的检测精度,但无...在工业检测场景下,按照是否引入正常样本中不存在的异常,可以将异常检测问题分为结构异常检测和逻辑异常检测两类,逻辑异常检测对网络的全局理解能力提出了更高的要求。针对现有无监督异常检测模型在结构异常上已有较好的检测精度,但无法适应逻辑异常检测需求的问题,提出一种包含空间聚合模块和通道聚合模块的双自编码器结构,主要由3部分组成。首先设计了并行空空间通道双自编码器架构,从空间和通道两个方向得到包含全局信息的特征向量,提升网络的长程依赖关系;其次设计一个选择性融合模块,融合双自编码器信息,放大包含重要信息的特征,以进一步提高对逻辑异常的表达能力;最后提出在自编码器与学生网络的损失函数中加入余弦损失,避免网络对单个像素差异过于敏感,从而关注于全局差异。在MVTec LOCO AD数据集上进行实验,逻辑异常检测精度达到89.4%,结构异常检测精度达到94.9%,平均检测精度92.1%,超越了基线方法和其他无监督缺陷检测方法,验证了方法的有效性和优越性。展开更多
Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient im...Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient implementation structure,the conventional method based on least mean square(LMS)is widely used,but its performance is not sufficient for LFMCW radar.To achieve a better self-interference cancellation(SIC)result and more optimal radar performance,we present an ADSIC method based on fractional order LMS(FOLMS),which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system.First,we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle.Then,to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals,we construct the performance evaluation model of LFMCW radar,and analyze the relationship between the mean square deviation and the parameters of FOLMS.Finally,the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.展开更多
文摘在工业检测场景下,按照是否引入正常样本中不存在的异常,可以将异常检测问题分为结构异常检测和逻辑异常检测两类,逻辑异常检测对网络的全局理解能力提出了更高的要求。针对现有无监督异常检测模型在结构异常上已有较好的检测精度,但无法适应逻辑异常检测需求的问题,提出一种包含空间聚合模块和通道聚合模块的双自编码器结构,主要由3部分组成。首先设计了并行空空间通道双自编码器架构,从空间和通道两个方向得到包含全局信息的特征向量,提升网络的长程依赖关系;其次设计一个选择性融合模块,融合双自编码器信息,放大包含重要信息的特征,以进一步提高对逻辑异常的表达能力;最后提出在自编码器与学生网络的损失函数中加入余弦损失,避免网络对单个像素差异过于敏感,从而关注于全局差异。在MVTec LOCO AD数据集上进行实验,逻辑异常检测精度达到89.4%,结构异常检测精度达到94.9%,平均检测精度92.1%,超越了基线方法和其他无监督缺陷检测方法,验证了方法的有效性和优越性。
文摘Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient implementation structure,the conventional method based on least mean square(LMS)is widely used,but its performance is not sufficient for LFMCW radar.To achieve a better self-interference cancellation(SIC)result and more optimal radar performance,we present an ADSIC method based on fractional order LMS(FOLMS),which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system.First,we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle.Then,to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals,we construct the performance evaluation model of LFMCW radar,and analyze the relationship between the mean square deviation and the parameters of FOLMS.Finally,the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.
