Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately ...Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.展开更多
Ge complementary tunneling field-effect transistors(TFETs) are fabricated with the NiGe metal source/drain(S/D) structure. The dopant segregation method is employed to form the NiGe/Ge tunneling junctions of suffi...Ge complementary tunneling field-effect transistors(TFETs) are fabricated with the NiGe metal source/drain(S/D) structure. The dopant segregation method is employed to form the NiGe/Ge tunneling junctions of sufficiently high Schottky barrier heights. As a result, the Ge p-and n-TFETs exhibit decent electrical properties of large ON-state current and steep sub-threshold slope(S factor). Especially, I_d of 0.2 μA/μm is revealed at V_g-V_(th) = V_d = ±0.5 V for Ge pTFETs,with the S factor of 28 mV/dec at 7 K.展开更多
The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission (AE) sour...The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission (AE) sources of inner cracks in rail, AE sources with different types, depths and propagation distances are examined for crack in rail. The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model. Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources. The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail. There are uniform AE mode features existing in the AE signals from AE sources in rail web, however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes. Different AE source types have the different propagation features in rail. It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.展开更多
The Qingxi Depression, over an area of merely 490 km2, is a petroliferous depositional center within the Jiuxi Basin. Lower Cretaceous source rocks in this depression are a suite of mudstones, dolomitic mudstones and ...The Qingxi Depression, over an area of merely 490 km2, is a petroliferous depositional center within the Jiuxi Basin. Lower Cretaceous source rocks in this depression are a suite of mudstones, dolomitic mudstones and argillaceous dolostones formed in a deep lacustrine environment. Although their distribution area is small, their thickness is sizable. High abundance and favorable types of organic matter provide an important material basis for petroleum generation. The majority of the source rocks in the Qingxi Depression are of maturation conditions for generating significant volumes of petroleum, and with only one peak generation period that commenced in the Neogene. The Himalayan movement results in a northerly overthrusting of the Qilian Mountains nappe to form a series of compressional faults, shear faults and rock fractures, all of which serve as main conduits for petroleum migration from west to east, and, in addition, as the reservoir space of the Qingxi Oilfield. Based on these factors, it is suggested that the future exploration be on the Qingxi low bulge and favorable fracturing zone within this depression.展开更多
基金This work was supported in part by the National Key R&D Program of China 2021YFE0110500in part by the National Natural Science Foundation of China under Grant 62062021in part by the Guiyang Scientific Plan Project[2023]48-11.
文摘Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.
基金Supported by the National Natural Science Foundation of China under Grant No 61504120the Zhejiang Provincial Natural Science Foundation of China under Grant No LR18F040001the Fundamental Research Funds for the Central Universities
文摘Ge complementary tunneling field-effect transistors(TFETs) are fabricated with the NiGe metal source/drain(S/D) structure. The dopant segregation method is employed to form the NiGe/Ge tunneling junctions of sufficiently high Schottky barrier heights. As a result, the Ge p-and n-TFETs exhibit decent electrical properties of large ON-state current and steep sub-threshold slope(S factor). Especially, I_d of 0.2 μA/μm is revealed at V_g-V_(th) = V_d = ±0.5 V for Ge pTFETs,with the S factor of 28 mV/dec at 7 K.
基金supported by the China Scholarship Council,the National Natural Science Foundation of China(61171197,61201307,61371045)the Innovation Funds of Harbin Institute of Technology(Grant IDGA18102011)the Promotive Research Fund for Excellent Young and Middle-Aged Scientisits of Shandong Province(BS2010DX001)
文摘The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission (AE) sources of inner cracks in rail, AE sources with different types, depths and propagation distances are examined for crack in rail. The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model. Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources. The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail. There are uniform AE mode features existing in the AE signals from AE sources in rail web, however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes. Different AE source types have the different propagation features in rail. It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.
文摘The Qingxi Depression, over an area of merely 490 km2, is a petroliferous depositional center within the Jiuxi Basin. Lower Cretaceous source rocks in this depression are a suite of mudstones, dolomitic mudstones and argillaceous dolostones formed in a deep lacustrine environment. Although their distribution area is small, their thickness is sizable. High abundance and favorable types of organic matter provide an important material basis for petroleum generation. The majority of the source rocks in the Qingxi Depression are of maturation conditions for generating significant volumes of petroleum, and with only one peak generation period that commenced in the Neogene. The Himalayan movement results in a northerly overthrusting of the Qilian Mountains nappe to form a series of compressional faults, shear faults and rock fractures, all of which serve as main conduits for petroleum migration from west to east, and, in addition, as the reservoir space of the Qingxi Oilfield. Based on these factors, it is suggested that the future exploration be on the Qingxi low bulge and favorable fracturing zone within this depression.