A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images’ features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods

A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images’ features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods

Acoustic Three-dimensional Chern Insulators with Arbitrary Chern Vectors

The Chern vector is a vectorial generalization of the scalar Chern number, being able to characterize the topological phase of three-dimensional (3D) Chern insulators. Such a vectorial generalization extends the applicability of Chern-type bulk-boundary correspondence from one-dimensional (1D) edge states to two-dimensional (2D) surface states, whose unique features, such as forming nontrivial torus knots or links in the surface Brillouin zone, have been demonstrated recently in 3D photonic crystals. However, since it is still unclear how to achieve an arbitrary Chern vector, so far the surface-state torus knots or links can emerge, not on the surface of a single crystal as in other 3D topological phases, but only along an internal domain wall between two crystals with perpendicular Chern vectors. Here, we extend the 3D Chern insulator phase to acoustic crystals for sound waves, and propose a scheme to construct an arbitrary Chern vector that allows the emergence of surface-state torus knots or links on the surface of a single crystal. These results provide a complete picture of bulk-boundary correspondence for Chern vectors, and may find use in novel applications in topological acoustics

Observation of Dirac hierarchy in three-dimensional acoustic topological insulators

Dirac cones (DCs) play a pivotal role in various unique phenomena ranging from massless electrons in graphene to robust surface states in topological insulators (TIs). Recent studies have theoretically revealed a full Dirac hierarchy comprising an eightfold bulk DC, a fourfold surface DC, and a twofold hinge DC, associated with a hierarchy of topological phases including first-order to third-order three-dimensional (3D) topological insulators, using the same 3D base lattice. Here, we report the first experimental observation of the Dirac hierarchy in 3D acoustic TIs. Using acoustic measurements, we unambiguously reveal that lifting of multifold DCs in each hierarchy can induce two-dimensional (2D) topological surface states with a fourfold DC in a first-order 3D TI, one-dimensional (1D) topological hinge states with a twofold DC in a second-order 3D TI, and zero-dimensional (0D) topological corner states in a third-order 3D TI. Our work not only expands the fundamental research scope of Dirac physics, but also opens up a new route for multidimensional robust wave manipulation

Effect of uniaxial stress on magnetic property of laminated amorphous sheets up to kilohertz range

Amorphous alloys are widely used as the core material for motors and transformers, which are subjected to stresses during manufacturing and operation processes. For instance, the compressive stress can influence the magnetic properties of amorphous alloys which leads to local overheating in the device and reduce the reliability. A high-frequency magnetizer with a stress application unit is designed and constructed to investigate the magnetic properties of amorphous alloys up to kilohertz range under stress conditions. The amorphous alloy sheets will fracture under stress because of their poor ductility and brittleness, so this paper uses laminated amorphous alloy sheets as the sample. The hysteresis loops and loss characteristics of amorphous alloys under stress ranging from 20 MPa tensile stress to 20 MPa compressive stress at a frequency of 50 Hz to 4 kHz are investigated. This research can provide data support for the design of cores in electromagnetic devices

A Repetitive Low Impedance High Power Microwave Driver

A low impedance high power microwave (HPM) driver is designed, which can be used in studying multi-gigawatt HPM devices such as the magnetically insulated transmission line oscillator (MILO), based on a helical pulse forming line (PFL) and the Tesla pulse transformer technology. The co-axial PFL is insulated by ethanol–water mixture, whose dielectric constant can be adjusted; and the helical line increases the output pulse width as well as the impedance to make a better match with the load. By the optimal combination of PFL charging voltage and output switch working voltage, the reliability of the PFL can be improved. The Tesla transformer has partial magnetic cores to increase the coupling coefficient and is connected like an autotransformer to increase the voltage step-up ratio. The primary capacitor of the transformer is charged by a high voltage constant current power supply and discharged by a triggered switch. A transmission line is installed between the PFL and the HPM load, to further increase the load voltage. A ceramic disk vacuum interface is used for improving the vacuum of the HPM tube. The experiments show that the driver can operate at 30 GW peak power, 75 ns pulse width and 5 Hz repetition rate

Effects of simultaneous loading of temperature and biaxial stress on the 1&2D magnetic properties of non-oriented electrical steel sheets

As the effect of factors like temperature and stress on the magnetic properties of electrical steel materials are gradually being emphasized, many research teams are carrying out related tests. Currently, most studies focus only on the effect of a single factor on the magnetic properties, while in reality, multiple factors exist simultaneously in electrical equipment. Therefore, accurate data of alternating (1D) and rotational (2D) magnetic properties under simultaneous loading of temperature and stress are very important. In this paper, a system based on a vertical rotational single sheet tester (VRSST) with temperature and stress loading parts is designed and constructed. Then, the 1D and 2D magnetic properties under many combinations of different temperatures, uniaxial and biaxial stress are measured and analyzed