The acoustic emission (AE) method could be used to detect and locate partial discharges (PD) in cast-resin dry-type transformers.However,due to the high sound attenuation in the filled epoxy,the signal is prone to int...The acoustic emission (AE) method could be used to detect and locate partial discharges (PD) in cast-resin dry-type transformers.However,due to the high sound attenuation in the filled epoxy,the signal is prone to interference from external noises and thus,in practice,there is little possibility of detecting PD.In this study,two techniques were developed to alleviate the shortcomings of the AE method.First,a waveguide is installed on the high-voltage (HV) windings,so that the acoustic signals of PD will propagate to the AE sensors that are installed on both terminals of the waveguide.The location of the winding that has PD can then be detected from the difference in arrival time of the acoustic signals.Test results indicate that the waveguide technique is able to enhance the safety of a measurement system and offers the advantages of easy installation and higher flexibility.Second,a specially designed AE sensor pair is used to distinguish whether acoustic signals are generated by PD inside the HV winding or by the corona outside the transformers.Using these two techniques of waveguide and AE sensor pair not only greatly improves sensitivity but also increases the reliability of the measurement system.Practical test results show that the new techniques can be used to locate precisely the PD in HV windings.展开更多
Dry-Type Cast Resin Distribution Transformers(CRT)is the secondgeneration of air-cooled distribution transformers where oil is replaced by resin for electrical insulation.CRT transformers may installed indoor adjacent...Dry-Type Cast Resin Distribution Transformers(CRT)is the secondgeneration of air-cooled distribution transformers where oil is replaced by resin for electrical insulation.CRT transformers may installed indoor adjacent to or near residential areas since they are clean and safe comparing to the conventional transformers.But,as it is obvious,noise discrepancy is intrinsically accompanied with all types of transformers and is inevitable for CRT transformers too.Minimization of noise level caused by such these transformers has biological and ergonomic importance.As it is known the core of transformers is the main source of the noise generation.In this paper,experimental and numerical investigation is implemented for a large number of fabricated CRT transformers in IT Co(Iran Transfo Company)to evaluate the effective geometrical parameters of the core on the overall sound level of transformers.Noise Level of each sample is measured according to criteria of IEC60651 and is reported in units of Decibel(dB).Numerical simulation is done using noncommercial version of ANSYS Workbench software to extract first six natural frequencies and mode shapes of CRT cores which is reported in units of Hz.Three novel non-dimensional variables for geometry of the transformer core are introduced.Both experimental and numerical results show approximately similar response to these variables.Correlation between natural frequencies and noise level is evaluated statistically.Pearson factor shows that there is a robust conjunction between first two natural frequencies and noise level of CRTs.Results show that noise level decreases as the two first natural frequencies increases and vice versa,noise level increases as the two natural frequencies of the core decreases.Finally the noise level decomposed to two parts.展开更多
Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by ut...Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism.This study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion models.We begin by discussing existing surveys of vision transformers and comparing them to this work.Then,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,etc.In the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for Segmentation.We also provide a comprehensive overview of recent transformer models for video tasks and diffusion models.We compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark datasets.Finally,we explore some future research directions to further improve the field.展开更多
本文提出了一种基于双交叉注意力融合的Swin-AK Transformer(Swin Transformer based on alterable kernel convolution)和手工特征相结合的智能手机拍摄图像质量评价方法。首先,提取了影响图像质量的手工特征,这些特征可以捕捉到图像...本文提出了一种基于双交叉注意力融合的Swin-AK Transformer(Swin Transformer based on alterable kernel convolution)和手工特征相结合的智能手机拍摄图像质量评价方法。首先,提取了影响图像质量的手工特征,这些特征可以捕捉到图像中细微的视觉变化;其次,提出了Swin-AK Transformer,增强了模型对局部信息的提取和处理能力。此外,本文设计了双交叉注意力融合模块,结合空间注意力和通道注意力机制,融合了手工特征与深度特征,实现了更加精确的图像质量预测。实验结果表明,在SPAQ和LIVE-C数据集上,皮尔森线性相关系数分别达到0.932和0.885,斯皮尔曼等级排序相关系数分别达到0.929和0.858。上述结果证明了本文提出的方法能够有效地预测智能手机拍摄图像的质量。展开更多
基金supported by the National Science Council,Taiwan (No.NSC 92-2622-E-006-142)the Program of Top 100 Universities Advancement,Ministry of Education,Taiwan
文摘The acoustic emission (AE) method could be used to detect and locate partial discharges (PD) in cast-resin dry-type transformers.However,due to the high sound attenuation in the filled epoxy,the signal is prone to interference from external noises and thus,in practice,there is little possibility of detecting PD.In this study,two techniques were developed to alleviate the shortcomings of the AE method.First,a waveguide is installed on the high-voltage (HV) windings,so that the acoustic signals of PD will propagate to the AE sensors that are installed on both terminals of the waveguide.The location of the winding that has PD can then be detected from the difference in arrival time of the acoustic signals.Test results indicate that the waveguide technique is able to enhance the safety of a measurement system and offers the advantages of easy installation and higher flexibility.Second,a specially designed AE sensor pair is used to distinguish whether acoustic signals are generated by PD inside the HV winding or by the corona outside the transformers.Using these two techniques of waveguide and AE sensor pair not only greatly improves sensitivity but also increases the reliability of the measurement system.Practical test results show that the new techniques can be used to locate precisely the PD in HV windings.
文摘Dry-Type Cast Resin Distribution Transformers(CRT)is the secondgeneration of air-cooled distribution transformers where oil is replaced by resin for electrical insulation.CRT transformers may installed indoor adjacent to or near residential areas since they are clean and safe comparing to the conventional transformers.But,as it is obvious,noise discrepancy is intrinsically accompanied with all types of transformers and is inevitable for CRT transformers too.Minimization of noise level caused by such these transformers has biological and ergonomic importance.As it is known the core of transformers is the main source of the noise generation.In this paper,experimental and numerical investigation is implemented for a large number of fabricated CRT transformers in IT Co(Iran Transfo Company)to evaluate the effective geometrical parameters of the core on the overall sound level of transformers.Noise Level of each sample is measured according to criteria of IEC60651 and is reported in units of Decibel(dB).Numerical simulation is done using noncommercial version of ANSYS Workbench software to extract first six natural frequencies and mode shapes of CRT cores which is reported in units of Hz.Three novel non-dimensional variables for geometry of the transformer core are introduced.Both experimental and numerical results show approximately similar response to these variables.Correlation between natural frequencies and noise level is evaluated statistically.Pearson factor shows that there is a robust conjunction between first two natural frequencies and noise level of CRTs.Results show that noise level decreases as the two first natural frequencies increases and vice versa,noise level increases as the two natural frequencies of the core decreases.Finally the noise level decomposed to two parts.
基金supported in part by the National Natural Science Foundation of China under Grants 61502162,61702175,and 61772184in part by the Fund of the State Key Laboratory of Geo-information Engineering under Grant SKLGIE2016-M-4-2+4 种基金in part by the Hunan Natural Science Foundation of China under Grant 2018JJ2059in part by the Key R&D Project of Hunan Province of China under Grant 2018GK2014in part by the Open Fund of the State Key Laboratory of Integrated Services Networks under Grant ISN17-14Chinese Scholarship Council(CSC)through College of Computer Science and Electronic Engineering,Changsha,410082Hunan University with Grant CSC No.2018GXZ020784.
文摘Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism.This study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion models.We begin by discussing existing surveys of vision transformers and comparing them to this work.Then,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,etc.In the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for Segmentation.We also provide a comprehensive overview of recent transformer models for video tasks and diffusion models.We compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark datasets.Finally,we explore some future research directions to further improve the field.
文摘本文提出了一种基于双交叉注意力融合的Swin-AK Transformer(Swin Transformer based on alterable kernel convolution)和手工特征相结合的智能手机拍摄图像质量评价方法。首先,提取了影响图像质量的手工特征,这些特征可以捕捉到图像中细微的视觉变化;其次,提出了Swin-AK Transformer,增强了模型对局部信息的提取和处理能力。此外,本文设计了双交叉注意力融合模块,结合空间注意力和通道注意力机制,融合了手工特征与深度特征,实现了更加精确的图像质量预测。实验结果表明,在SPAQ和LIVE-C数据集上,皮尔森线性相关系数分别达到0.932和0.885,斯皮尔曼等级排序相关系数分别达到0.929和0.858。上述结果证明了本文提出的方法能够有效地预测智能手机拍摄图像的质量。
