Accepted Papers
Recent catalogue
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Vol.69 No.24
2020-12-20
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Vol.69 No.23
2020-12-05
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Vol.69 No.22
2020-11-20
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Vol.69 No.21
2020-11-05
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GENERAL
2020, 69 (24): 240201.
doi: 10.7498/aps.69.20201106
Abstract +
After the COVID-19 epidemic leveled off in China, many provinces have started to resume schooling. Long-term contact between students and teachers in such a closed environment in schooling can increase the possibility of the outbreak. Although the school closure can effectively alleviate the epidemic, large-scale students’ isolation not only causes social panic but also brings huge social and economic burden, so before the emergence of school epidemics, one should select and adopt more scientific prevention and control measures. In this study, according to the virus excretion of COVID-19 patients in the disease period, the infectious capacity of patients is redefined. After introducing it into the traditional suspected-exposed-infected-removed (SEIR) model, a continuous infection model that is more consistent with the actual transmission of COVID-19 patients is proposed. Secondly, the effective distance between students is calculated through real contact data. Based on the analysis of the effective distance, three types of isolation area prevention and control measures are proposed and compared with the recently proposed digital contact tracking prevention and control measures. Simulating the spread of COVID-19 in schools through real student contact data and continuous infection models, in order to compare the preventions and control effects of various prevention and control measures in the school epidemic situation, and evaluating the social influence of measures by accumulating the number of quarantines when prevention and control measures are adopted, we find that the COVID-19 can lead the cases to happen on a larger scale in the continuous infection model than in the traditional SEIR model, and the prevention and control measures verified in the continuous infection model are more convincing. Using digital contact tracking prevention and control measures in schools can achieve similar results to those in closed schools with the smallest number of quarantines. The research in this paper can help schools choose appropriate prevention and control measures, and the proposed continuous infection model can help researchers more accurately simulate the spread of COVID-19.
GENERAL
2020, 69 (24): 240501.
doi: 10.7498/aps.69.20200725
Abstract +
A large number of animal experiments show that there is irregular chaos in the biological nervous systems. An artificial chaotic neural network is a highly nonlinear dynamic system, which can realize a series of complex dynamic behaviors, optimize global search and neural computation, and generate pseudo-random sequences for information encryption. According to the superposition theory of sinusoidal signals with different frequencies of brain waves, a non-monotone activation function based on the multifrequency-frequency conversion sinusoidal function and a piecewise function is proposed to make a neural network more consistent with the biological characteristics. The analysis shows that by adjusting the parameters, the activation function can exhibit the EEG signals in its different states, which can simulate the rich and varying brain activities when the brain waves of different frequencies and types work at the same time. According to the activation function we design a new chaotic cellular neural network. The complexity of the chaotic neural network is analyzed by the structural complexity based SE algorithm and C0 algorithm. By means of Lyapunov exponential spectrum, bifurcation diagram and basin of attraction, the effects of the activation function’s parameters on its dynamic characteristics are analyzed in detail, and it is found that a series of complex phenomena appears in the chaotic neural network, such as many different types of chaotic attractors, coexistent chaotic attractors and coexistence limit cycles, which improves the performance of the chaotic neural network, and proves that the multi-frequency sinusoidal chaotic neural network has rich dynamic characteristics, so it has a good prospect in information processing, information encryption and other aspects.
GENERAL
2020, 69 (24): 240502.
doi: 10.7498/aps.69.20201019
Abstract +
Many image compression and encryption algorithms based on traditional compressed sensing and chaotic systems are time-consuming, have low reconstruction quality, and are suitable only for grayscale images. In this paper, we propose a general image compression encryption algorithm based on a deep learning compressed sensing and compound chaotic system, which is suitable for grayscale images and RGB format color images. Color images can be directly compressed and encrypted, but grayscale images need copying from 1 channel to 3 channels. First, the original image is divided into multiple 3 × 33 × 33 non-overlapping image blocks and the bilinear interpolation Bilinear and convolutional neural network are used to compress the image, so that the compression network has no restriction on the sampling rate and can obtain high-quality compression of image. Then a composite chaotic system composed of a two-dimensional cloud model and Logistic is used to encrypt and decrypt the compressed image (sliding scrambling and vector decomposition), and finally the decrypted image is reconstructed. In the reconstruction network, the convolutional neural network and bilinear interpolation Bilinear are mainly responsible for reconstructing the contour structure information, and the fully connected layer is mainly responsible for reconstructing and combining the color information to reconstruct a high-quality image. For grayscale images, we also need to calculate the average value of the corresponding positions of the 3 channels of the reconstructed image, and change the 3 channels into 1 channel. The experimental results show that the general image encryption algorithm based on deep learning compressed sensing and compound chaos system has great advantages in data processing quality and computational complexity. Although in the network the color images are used for training, the quality of grayscale image reconstruction is still better than that of other algorithms. The image encryption algorithm has a large enough key space and associates the plaintext hash value with the key, which realizes the encryption effect of one image corresponding to one key, thus being able to effectively resist brute force attacks and selective plaintext attacks. Compared with it in the comparison literature, the correlation coefficient is close to an ideal value, and the information entropy and the clear text sensitivity index are also within a critical range, which enhances the confidentiality of the image.
GENERAL
2020, 69 (24): 240701.
doi: 10.7498/aps.69.20201036
Abstract +
NUCLEAR PHYSICS
2020, 69 (24): 242101.
doi: 10.7498/aps.69.20200925
Abstract +
Astronomical statistics shows that the mass of neutron star is of the order of the solar mass, but the radius is only about ten kilometers. Therefore, the neutron star is highly condensed and there may be a variety of competing material phases inside the compact star. Hadron-quark deconfinement phase transition that is poorly understood at high density can be studied by the matter properties of hybrid star. The hybrid star contains many kinds of material phases, which cannot be described uniformly by one theory. So, different material phases are described by different theories. The hadronic phase is described by the relativistic mean-field theory with parameter set FSUGold including ω2ρ2 interaction term, and the quark phase is described by an effective mass bag model in which the quark mass is density-dependent. The hadron-quark mixed phase is constructed by the Gibbs phase transition, and the properties of hybrid star in β equilibrium is studied in this model. It is found that the bag constant B has a great influence on the starting point and ending point of the hadron-quark deconfinement phase transition and the particle composition in the hybrid star. Comparing with the starting point of phase transition, the influence of B on the ending point of phase transition is very obvious. For the hybrid star, the equation of state of matter becomes stiffer at low density and softer at high density as B increases. The overall effect is that the slope of the mass-radius curve increases with B increasing. The calculated results show that the maximum mass of hybrid star is between 1.3 solar mass and 1.4 solar mass (M☉), and the radius is between 9 km and 12 km. In addition, the influence of attractive and repulsive Σ potential on the properties of hybrid stars are studied. The results show that the Σ potential has a great influence on the particle composition in the hybrid star. We also find that the repulsive Σ potential makes the hybrid star have a greater maximum mass then an attractive Σ potential. For the attractive Σ potential, the maximum mass of hybrid star is 1.38M☉, while for the repulsive Σ potential, the maximum mass of hybrid stars is 1.41M☉.
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
2020, 69 (24): 244101.
doi: 10.7498/aps.69.20200978
Abstract +
An aperiodic metasurface antenna array with low radar cross section (RCS) is designed. The upper patches of the two antenna elements have the same shape and are placed at an orthogonal position, which can effectively reduce the workload of simulating the reflection characteristics of the patch. As antenna elements, they have identical operational band and polarization mode, and as metasurfaces, they can form an effective phase difference of 180° ± 37°. The RCS of the array is reduced mainly by phase cancellation under the x polarization and by absorption under the y polarization. According to the coding metamaterial theory, the two elements can be coded aperiodically by using the programming software. Regarding element A and element B as “0” and “1”, respectively, the coding matrix can be solved by a genetic algorithm. Element A and element B are arranged according to positions “0” and “1” to obtain a proposed array. The scattering field of proposed array is diffusive, and the peak RCS is effectively reduced. In order to highlight the characteristics of the proposed array, the chessboard-type array is designed for comparison. The simulation results show that the radiation performance of proposed array is good. Comparing with the metal board of the same size, the 6 dB reduction bandwidth of the monostatic RCS is 4.8－7.4 GHz (relative bandwidth is 42.6%) under the x polarization and 4.6－7.8 GHz (relative bandwidth is 51.6%) under the y polarization. Comparing with the chessboard type array, the scattering energy distribution of the designed antenna array is very uniform and the peak RCS in space reduces obviously. When a 4.8 GHz electromagnetic wave is incident with different incident angles and polarization modes, the scattering field is diffusive. Compared with other similar arrays, the proposed array has advantages of simple design process and even scattering field. The experimental results are in good agreement with the simulation results. This work makes full use of the scattering characteristics of the antenna element itself to solve the problem that the array antenna possesses both good radiation characteristics and low scattering characteristics at the same time, and improves the design process of the antenna patch. This design method has certain universality and reference significance for designing the low RCS antenna array.
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
2020, 69 (24): 244201.
doi: 10.7498/aps.69.20200948
Abstract +
As a basic optical element, optical lens is widely used for realizing the focusing, imaging and optical communication systems. Light of different wavelengths will propagate at different speeds. A beam of polychromatic light will produce chromatic dispersion after passing through a single optical device, which prevents the ordinary lenses from focusing the light of different wavelengths into a point. This means that the light of different wavelengths cannot be focused ideally. Traditional focusing systems can solve this problem by superimposing multiple lenses, but this is at the expense of increasing the complexity, weight, and cost of the system, and is not suitable for highly integrated nano-optical systems. At present, a better solution is to use the plane metalens, that is, using the metasurface to control the amplitude, phase and polarization at each point in space. However, the plane metalens is difficult to directly integrate on the chip. An intelligent algorithm developed by combining finite element method with genetic algorithm is used to optimize the design of multi-channel on-chip wavelength router devices and polarization router devices. In this paper, combining with years’ research results of the theory of multiple scattering coherent superposition of disordered media, the use of intelligent algorithm to design an on-chip integrated nano-lens that can achieve efficient focusing from the visible to the near infrared band. In the lens structure SiO2 serves as a substrate, and the arrangement structure of SiC rectangular column is designed. The substrate size is only 2 μm × 2 μm. The lens achieves low-dispersion focusing in the band from 470 nm to 1734 nm, with a focusing efficiency of over 55% at the highest level and 30% at the lowest level, and an average focusing efficiency of 42.1%. A 200-nm waveguide is added behind the focusing region. After refocusing through the waveguide, the laser beam with a size of 2 μm can be focused by the coupling of the lens and the waveguide into a beam below 200 nm in size. The focusing efficiency goes up to 80%. At the same time, the intelligent algorithm can be applied to different types of structures. The focusing lens structures composed of triangle, diamond, or circular nano columns are designed, which can achieve an approximate focusing effect and efficient coupling propagation efficiency. This work provides important ideas for developing broadband and efficient focusing nano-lens, as well as a new way to achieve the high-density integrated nanophotonic devices.
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
2020, 69 (24): 244202.
doi: 10.7498/aps.69.20200920
Abstract +
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
2020, 69 (24): 244203.
doi: 10.7498/aps.69.20200933
Abstract +
The traditional window of high-speed aircraft is hemispherical, and the aberration produced by such a window is constant. However, the hemispherical window is difficult to meet the requirements of a high speed flight of aircraft. Aspheric windows are usually used to replace hemispherical windows to increase the aerodynamic performance. However, the aspheric window will introduce dynamic aberrations that fluctuate with the change of scanning field-of-view (FOV), which becomes the key issue of the development of optoelectronic imaging systems for high-speed aircraft. For the ellipsoidal window optical system with scanning FOV of ±60°, an aberration correction method in large FOV combined with the static correction and non-wavefront-sensor adaptive optical correction is studied. In the initial optical structure design, the types of system aberration are reduced and the fifth-order Zernike aberration is eliminated during initial aberration correction, thus, the number of the subsequent adaptive optimization control variables is reduced. According to the characteristics of the deformable mirror, the driving voltage of the driver is generally taken as a variable of the genetic algorithm. However, when the deformable mirror used has many units, too many variables will directly lead the optimization speed of the algorithm to greatly decrease. So, according to the aberration characteristics of the ellipsoidal optical window, using the conversion matrix between the Zernike polynomial coefficients and the voltages of the deformable mirror driver, the optimization variable is reduced from 140 driver voltages to 2−9 Zernike stripe polynomial coefficients in number. Finally, the genetic algorithm based on Zernike model is used to control the shape of the deformable mirror and correct the residual aberration. Taking 2−9 Zernike mode coefficients, 2−16 Zernike mode coefficients and 140 driver voltages as the variables of genetic algorithm respectively, the optimization generations of genetic algorithm under different variables are obtained. The simulation results show that the optimization speed of each typical scanning field of view is increased more than 95% by changing the variable from 140 driver voltages to 2−9 Zernike mode coefficients, and the imaging quality is close to the diffraction limit. This optimization method can not only correct the aberrations caused by the special-shaped optical window, but also compensate for the error caused by processing and aligning the optical system.
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
2020, 69 (24): 244204.
doi: 10.7498/aps.69.20200805
Abstract +
There is serious noise interference in the decryption process of the joint transform correlator (JTC) optical encryption system, so the quality of the decrypted image cannot meet the accuracy requirements in most cases. The quality of decrypted image can be improved to a certain extent when the phase key is designed by the Gerchberg-Saxton algorithm and the iterative algorithm fuzzy control algorithm, but the complexity of the design process is inevitable and the quality of the decrypted image still needs improving. Recently, the in depth learning technology has attracted the attention of scholars in the fields of computer vision, natural language processing and optical information processing. In order to deal with the noise interference in the JTC optical encryption system, combining the current deep learning method, in this paper we propose a new denoising method for JTC optical image encryption system based on in depth learning, the dense modules are added into the generated network to enhance the reuse of feature information and improve the performance of the network. The latest self-attention mechanism area is added into the network to distinguish the weights of different channels and learn the relationship between channel and channel, so that the network can selectively strengthen the useful feature information but suppress useless feature information. The density module and the channel attention module are integrated into a DCAB synthesis module, which can effectively extract the image feature information and improve the performance of the network. The receptive field of the convolution kernel is enlarged by two down-sampling and the feature map is restored to its original size by two up-sampling. The VGG-19 is used to extract high-frequency details and texture features of images, meanwhile, the non-adversarial loss and mean-square error (MSE) loss are added into the loss function to reduce the difference among the image samples. The quality of noise-reduced images in this method are obviously better than that of the existing denoising algorithms by evaluating intuitive visual observation or SSIM (structural similarity), PSNR (peak signal to noise ratio) and MSE. The results of numerical calculation and simulation experiments show that this method can greatly eliminate the influence of noise on the JTC optical image encryption system, and effectively improve the effectiveness and feasibility of JTC optical image encryption system for high-quality image encryption.
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