车辆事故预测是避免道路车辆事故发生的重要研究课题.以往的研究使用的事故数据集只包含地理情况、环境情况、交通情况等宏观因素,或者只包含车辆行为和驾驶员行为等微观因素.因为很难收集到同时包含2类因素的事故数据集,很少有研究将这...车辆事故预测是避免道路车辆事故发生的重要研究课题.以往的研究使用的事故数据集只包含地理情况、环境情况、交通情况等宏观因素,或者只包含车辆行为和驾驶员行为等微观因素.因为很难收集到同时包含2类因素的事故数据集,很少有研究将这2类因素结合起来,然而车辆事故往往是两者共同作用的结果.此外,在收集到的数据中没有可以用于预测的事故发生概率标签,所以目前多数的研究关注点只是在于事故是否发生而不能得到准确的概率值.然而在实际应用场景下,驾驶员需要的是不同级别的危险预警信号,而这种信号正是应该由事故概率值决定的.2019年发布的事故宏观因素数据集OSU(Ohio State University)与宏观因素数据集FARS(fatality analysis reporting system)和微观因素数据集SHRP2(strategic highway research program 2)都具有一些相同的特征,为它们的融合提供了机遇.因此,首先得到了一个同时包含宏观和微观因素的数据集,其中事故数据(正样本)融合自OSU、FARS数据集,以及与SHRP2分布相同的数据集Sim-SHRP2(simulated strategic highway research program 2),而安全驾驶数据(负样本)则由自己驾驶汽车获得.然后,针对收集到的数据中没有概率标签的问题,还设计了一个概率级别的无监督深度学习框架来预测准确的概率值,该框架使用迭代的方式为数据集生成准确的概率标签,并使用这些概率标签来进行训练.实验结果表明,该框架可以使用所得到的数据集来灵敏而准确地预测车辆事故.展开更多
针对复杂电子对抗场景中的非理想信道环境,该文提出了一种基于深度学习的异常检测(anomaly detection,AD)方法。首先,分析了利用时频同相/正交(in-phase/quadrature,I/Q)采样数据进行AD的可行性;然后,设计了深度学习网络架构,并提出基...针对复杂电子对抗场景中的非理想信道环境,该文提出了一种基于深度学习的异常检测(anomaly detection,AD)方法。首先,分析了利用时频同相/正交(in-phase/quadrature,I/Q)采样数据进行AD的可行性;然后,设计了深度学习网络架构,并提出基于深度支持向量描述(deep support vector data description,Deep SVDD)和调制识别的AD方法。仿真及实验结果表明:相比于经典的单分类检测算法,该方法检测性能和实时性明显提升,且在非理想信道环境下表现鲁棒。该方法已在某型号项目原理样机上得到验证,具有很高应用价值。展开更多
针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特...针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特征输入SVDD进行通信辐射源个体开集识别实验。结果表明,在不同开放度下,该方法可以将未知通信辐射源个体和已知通信辐射源个体以高准确率区分出来,进而将开集识别转为闭集识别。同时,对已知通信辐射源个体识别有很好的识别准确率和抗噪声能力。展开更多
Defect inspection is critical in semiconductor manufacturing for product quality improvement at reduced production costs.A whole new manufacturing process is often associated with a new set of defects that can cause s...Defect inspection is critical in semiconductor manufacturing for product quality improvement at reduced production costs.A whole new manufacturing process is often associated with a new set of defects that can cause serious damage to the manufacturing system.Therefore,classifying existing defects and new defects provides crucial clues to fix the issue in the newly introduced manufacturing process.We present a multi-task hybrid transformer(MT-former)that distinguishes novel defects from the known defects in electron microscope images of semiconductors.MT-former consists of upstream and downstream training stages.In the upstream stage,an encoder of a hybrid transformer is trained by solving both classification and reconstruction tasks for the existing defects.In the downstream stage,the shared encoder is fine-tuned by simultaneously learning the classification as well as a deep support vector domain description(Deep-SVDD)to detect the new defects among the existing ones.With focal loss,we also design a hybrid-transformer using convolutional and an efficient self-attention module.Our model is evaluated on real-world data from SK Hynix and on publicly available data from magnetic tile defects and HAM10000.For SK Hynix data,MT-former achieved higher AUC as compared with a Deep-SVDD model,by 8.19%for anomaly detection and by 9.59%for classifying the existing classes.Furthermore,the best AUC(magnetic tile defect 67.9%,HAM1000070.73%)on the public dataset achieved with the proposed model implies that MT-former would be a useful model for classifying the new types of defects from the existing ones.展开更多
文摘车辆事故预测是避免道路车辆事故发生的重要研究课题.以往的研究使用的事故数据集只包含地理情况、环境情况、交通情况等宏观因素,或者只包含车辆行为和驾驶员行为等微观因素.因为很难收集到同时包含2类因素的事故数据集,很少有研究将这2类因素结合起来,然而车辆事故往往是两者共同作用的结果.此外,在收集到的数据中没有可以用于预测的事故发生概率标签,所以目前多数的研究关注点只是在于事故是否发生而不能得到准确的概率值.然而在实际应用场景下,驾驶员需要的是不同级别的危险预警信号,而这种信号正是应该由事故概率值决定的.2019年发布的事故宏观因素数据集OSU(Ohio State University)与宏观因素数据集FARS(fatality analysis reporting system)和微观因素数据集SHRP2(strategic highway research program 2)都具有一些相同的特征,为它们的融合提供了机遇.因此,首先得到了一个同时包含宏观和微观因素的数据集,其中事故数据(正样本)融合自OSU、FARS数据集,以及与SHRP2分布相同的数据集Sim-SHRP2(simulated strategic highway research program 2),而安全驾驶数据(负样本)则由自己驾驶汽车获得.然后,针对收集到的数据中没有概率标签的问题,还设计了一个概率级别的无监督深度学习框架来预测准确的概率值,该框架使用迭代的方式为数据集生成准确的概率标签,并使用这些概率标签来进行训练.实验结果表明,该框架可以使用所得到的数据集来灵敏而准确地预测车辆事故.
文摘针对复杂电子对抗场景中的非理想信道环境,该文提出了一种基于深度学习的异常检测(anomaly detection,AD)方法。首先,分析了利用时频同相/正交(in-phase/quadrature,I/Q)采样数据进行AD的可行性;然后,设计了深度学习网络架构,并提出基于深度支持向量描述(deep support vector data description,Deep SVDD)和调制识别的AD方法。仿真及实验结果表明:相比于经典的单分类检测算法,该方法检测性能和实时性明显提升,且在非理想信道环境下表现鲁棒。该方法已在某型号项目原理样机上得到验证,具有很高应用价值。
文摘针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特征输入SVDD进行通信辐射源个体开集识别实验。结果表明,在不同开放度下,该方法可以将未知通信辐射源个体和已知通信辐射源个体以高准确率区分出来,进而将开集识别转为闭集识别。同时,对已知通信辐射源个体识别有很好的识别准确率和抗噪声能力。
基金supported by SK Hynix AICC(P23.03)by the National Research Foundation of Korea(NRF)grant funded by the Ministry of Science and ICT(2023R1A2C3004880)+4 种基金the Ministry of Education(2020R1A6A1A03047902 and 2022R1A6A1A03052954)by Basic Science Research Program through the NRF funded by the Ministry of Education(RS-2024-00415450)by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2019-II191906,Artificial Intelligence Graduate School Program(POSTECH))by the BK21 FOUR projectby Glocal University 30 projects.
文摘Defect inspection is critical in semiconductor manufacturing for product quality improvement at reduced production costs.A whole new manufacturing process is often associated with a new set of defects that can cause serious damage to the manufacturing system.Therefore,classifying existing defects and new defects provides crucial clues to fix the issue in the newly introduced manufacturing process.We present a multi-task hybrid transformer(MT-former)that distinguishes novel defects from the known defects in electron microscope images of semiconductors.MT-former consists of upstream and downstream training stages.In the upstream stage,an encoder of a hybrid transformer is trained by solving both classification and reconstruction tasks for the existing defects.In the downstream stage,the shared encoder is fine-tuned by simultaneously learning the classification as well as a deep support vector domain description(Deep-SVDD)to detect the new defects among the existing ones.With focal loss,we also design a hybrid-transformer using convolutional and an efficient self-attention module.Our model is evaluated on real-world data from SK Hynix and on publicly available data from magnetic tile defects and HAM10000.For SK Hynix data,MT-former achieved higher AUC as compared with a Deep-SVDD model,by 8.19%for anomaly detection and by 9.59%for classifying the existing classes.Furthermore,the best AUC(magnetic tile defect 67.9%,HAM1000070.73%)on the public dataset achieved with the proposed model implies that MT-former would be a useful model for classifying the new types of defects from the existing ones.
