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Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning

Artificial Intelligence in Geosciences 2022年第1期3卷 192-202页

作者： Yihuai Lou Lukun Wu Lin Liu Kai Yu Naihao Liu Zhiguo Wang Wei Wang Center for Hypergravity Experimental and Interdisciplinary Research Zhejiang UniversityHangzhouZhejiang 310058China MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhouZhejiang 310058China School of Software Engineering Xi’an Jiaotong UniversityXi’anShaanxi 710049China Petrochina CBM Company Limited Beijing 100028China Capiticalonline Data Service Co. LTDBeijing 100012China School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’anShaanxi 710049China School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anShaanxi 710049China PowerChina Huadong Engineering Corporation Limited HangzhouZhejiang 310014China

Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent ***,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic *** based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters ***,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale *** overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data *** firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic ***,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial ***,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and *** results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.

关键词： Irregularly sampled seismic data reconstruction Deep learning U-Net Discrete wavelet transform Convolutional block attention module

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Localized collocation schemes and their applications

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Acta Mechanica Sinica 2022年第7期38卷 1-28,I0002页

作者： Zhuojia Fu Zhuochao Tang Qiang Xi Qingguo Liu Yan Gu Fajie Wang Key Laboratory of Ministry of Education for Coastal Disaster and Protection Hohai UniversityNanjing210098China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and MaterialsHohai UniversityNanjing211100China Department of Fluid Dynamics and Thermodynamics Faculty of Mechanical EngineeringUniversity of LjubljanaLjubljana1000Slovenia Laboratory for Simulation of Materials and Processes Institute of Metals and TechnologyLjubljana1000Slovenia School of Mathematics and Statistics Qingdao UniversityQingdaoShandong266071China College of Mechanical and Electrical Engineering National Engineering Research Center for Intelligent Electrical Vehicle Power SystemQingdao UniversityQingdao266071China

This paper presents a summary of various localized collocation schemes and their engineering *** basic concepts of localized collocation methods(LCMs)are first introduced,such as approximation theory,semianalytical collocation methods and localization *** on these basic concepts,five different formulations of localized collocation methods are introduced,including the localized radial basis function collocation method(LRBFCM)and the generalized finite difference method(GFDM),the localized method of fundamental solutions(LMFS),the localized radial Trefftz collocation method(LRTCM),and the localized collocation Trefftz method(LCTM).Then,several additional schemes,such as the generalized reciprocity method,Laplace and Fourier transformations,and Krylov deferred correction,are introduced to enable the application of the LCM to large-scale engineering and scientific computing for solving inhomogeneous,nonisotropic and time-dependent partial differential *** typical benchmark examples are presented to show the recent developments and applications on the LCM solution of some selected boundary value problems,such as numerical wave flume,potential-based inverse electrocardiography,wave propagation analysis and 2D phononic crystals,elasticity and in-plane crack problems,heat conduction problems in heterogeneous material and nonlinear time-dependent Burgers’***,some conclusions and outlooks of the LCMs are summarized.

关键词： Localization Collocation method Semianalytical Computational mechanics

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Two-mode terms in Wigner transport equation elucidate anomalous thermal transport in amorphous silicon

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Physical Review B 2025年第9期111卷 094206-094206页

作者： Jin Yang Xueyan Zhu Alan J. H. McGaughey Yee Sin Ang Wee-Liat Ong ZJU-UIUC Institute College of Energy Engineering Zhejiang University Jiaxing Haining Zhejiang 314400 China China Academy of Engineering Physics Software Center for High Performance Numerical Simulation Beijing 100088 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China Department of Mechanical Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Science Mathematics and Technology Singapore University of Technology and Design 487372 Singapore State Key Laboratory of Clean Energy Utilization Zhejiang University Hangzhou Zhejiang 310027 China

Over the past decades, our understanding of thermal transport in amorphous materials has predominantly relied on the inherently harmonic Allen-Feldman theory, which has been found to be insufficient. In this study, the Wigner transport formalism is adopted to explicitly account for anharmonicity. In studying the thermal transport in amorphous silicon, the results highlight that amorphous materials are not generally computationally equivalent to crystals with disordered primitive cells. A method that leverages the properties of the two-mode terms in the Wigner transport formalism is proposed to predict the bulk thermal conductivity of amorphous materials using finite-size models. In doing so, the need for mode classification schemes required in the Allen-Feldman theory is eliminated, and similarities are discovered between the two-mode terms and the carriers commonly used to describe thermal transport in amorphous materials, i.e., propagons, diffusons, and locons. Two competing trends are identified that shed light on the recently discovered anomalous decrease in the high-temperature thermal conductivity in some amorphous materials.

关键词： Heat transfer

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Secrecy Performance Analysis of Artificial Noise Assisted Secure Transmission for MISO systems with Multiple Eavesdroppers

Secrecy Performance Analysis of Artificial Noise Assisted Se...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Panpan Xu Weijie Tan Kun Niu Zhen Li Changgen Peng School of Mathematics and Statistics Guizhou University Guiyang China Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou China Guizhou Big Data Academy Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China College of Big Data and Information Engineering Guizhou University Guiyang China

In the paper, we investigate the secure communication of multiple-input single-output (MISO) systems with multiple eavesdroppers. We jointly design the beamforming (BF) and the artificial noise (AN) in MISO systems with multiple eavesdroppers to enhance the secrecy rate. Furthermore, under the secrecy interrupt constraint, we obtain the calculation formulas of the secrecy interrupt probability and the optimal secrecy rate. The simulation results demonstrate that AN with a higher dimension can effectively obstruct eavesdroppers and enhance the system's optimal secrecy rate. In addition, it is proved that the 1/4 full-dimension AN's secrecy rate close to that of the full-dimension AN in the system with multiple eavesdroppers, allowing the trade-off between system secrecy performance and artificial noise design complexity to be realized.

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A Color Image Encryption Using One Quaternion-Valued Neural Network

SSRN

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SSRN 2022年

作者： Lin, Dongyuan Zhang, Qiangqiang Chen, Xiaofeng Li, Zhongshan Wang, Shiyuan College of Electronic and Information Engineering Southwest University Chongqing 400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing 400715 China College of Mathematics and Statistics Chongqing Jiaotong University Chongqing 400074 China College of Mathematics and Statistics Georgia State University AtlantaGA30302 United States

Color image encryption via real-valued chaos system generally requires multiple real-value systems. To this end, this article presents a encryption algorithm for color image that requires only a quaternion-valued neural network system. First, an inertial memristor-based quaternion-valued neural network (IMQVNN) is proposed and the exponential synchronization criteria for the addressed systems are given. Then, an example and its simulation results are given to verify the rationality of the synchronization criteria. Based on the drive-response systems, a encryption algorithm for color image is therefore put forward. Finally, the effectiveness of the proposed cryptosystem is verified by the simulation results and security performance analysis. © 2022, The Authors. All rights reserved.

关键词： Memristors

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Automatic depth optimization for a quantum approximate optimization algorithm

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Physical Review A 2022年第3期105卷 032433-032433页

作者： Yu Pan Yifan Tong Yi Yang State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China Department of Mathematics and Statistics McGill University Montreal H3A 0B9 QC Canada

Quantum approximate optimization algorithm (QAOA) is a hybrid algorithm whose control parameters are classically optimized. In addition to the variational parameters, the right choice of hyperparameter is crucial for improving the performance of any optimization model. Control depth, or the number of variational parameters, is considered as the most important hyperparameter for QAOA. In this paper we investigate the control depth selection with an automatic algorithm based on proximal gradient descent. The performances of the automatic algorithm are demonstrated on seven-node and ten-node max-cut problems, which show that the control depth can be significantly reduced during the iteration whereas achieving an sufficient level of optimization accuracy. With theoretical convergence guarantee, the proposed algorithm can be used as an efficient tool for choosing the appropriate control depth as a replacement of random search or empirical rules. Moreover, the reduction of control depth will induce a significant reduction in the number of quantum gates in circuit, which improves the applicability of QAOA on noisy intermediate-scale quantum devices.

关键词： Machine learning Quantum algorithms Quantum control

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Testing the unified bounds of the quantum speed limit

arXiv

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arXiv 2024年

作者： Wu, Yaozu Yuan, Jiale Zhang, Chuanyu Zhu, Zitian Deng, Jinfeng Zhang, Xu Zhang, Pengfei Guo, Qiujiang Wang, Zhen Huang, Jiehui Song, Chao Li, Hekang Wang, Da-Wei Wang, H. Agarwal, Girish S. Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control School of Physics State Key Laboratory for Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China Hangzhou Global Scientific and Technological Innovation Center Hangzhou311215 China School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai201620 China College of Optical Science and Engineering Zhejiang University Hangzhou310027 China Institute for Quantum Science and Engineering Department of Biological and Agricultural Engineering Physics and Astronomy Texas A&M University College StationTX77843 United States

Quantum speed limits (QSLs) impose fundamental constraints on the evolution speed of quantum systems. Traditionally, the Mandelstam-Tamm (MT) and Margolus-Levitin (ML) bounds have been widely employed, relying on the standard deviation and mean of energy distribution to define the QSLs. However, these universal bounds only offer loose restrictions on the quantum evolution. Here we introduce the generalized ML bounds, which prove to be more stringent in constraining dynamic evolution, by utilizing moments of energy spectra of arbitrary orders, even noninteger orders. To validate our findings, we conduct experiments in a superconducting circuit, where we have the capability to prepare a wide range of quantum photonic states and rigorously test these bounds by measuring the evolution of the system and its photon statistics using quantum state tomography. While, in general, the MT bound is effective for short-time evolution, we identify specific parameter regimes where either the MT or the generalized ML bounds suffice to constrain the entire evolution. Our findings not only establish new criteria for estimating QSLs but also substantially enhance our comprehension of the dynamic evolution of quantum systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum optics

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A Novel Adaptive Dynamic Ga Combined with Am to Optimize Ann for Multi-Output Prediction: Small Samples Enhanced in Industrial Processing

SSRN

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SSRN 2023年

作者： Zheng, Juanjuan Liu, Chao Huang, Shaofu He, Yan Anhui University of Science and Technology College of Mechanical Engineering Huainan232000 China Anhui University of Science and Technology Institute of Environment-friendly Materials and Occupational Health Wuhu241003 China Chongqing University State Key Laboratory of Mechanical Transmission Chongqing400030 China Chongqing University College of Mathematics and Statistics Chongqing400030 China

Multi-output prediction based on small samples produced in industrial processing plays an important role in improving processing efficiency and reduce production costs. However, insufficient sample capacity will prompt the inaccuracy and instability of the multi-output regression prediction. To solve this problem, this paper proposes a method of jointly optimizing artificial neural network by utilizing adaptive dynamic genetic algorithm and adaptive moment estimation (ADGA-AM-ANN) based on enhanced small samples. First, the data is enhanced by adding noise to the output data to prevent overfitting due to insufficient sample capacity. Then, the adaptive dynamic genetic algorithm is employed to grope for the optimal parameters of the artificial neural network in the global range. This will avoid artificial neural network falling into a local minimum in the training process and instability in the model prediction process. Finally, the adaptive moment estimation is employed to optimize the gradient of optimal parameters to accelerate the convergence speed of the model and further deepen the stability of the model prediction. The case of the cutting process is employed to verify the effectiveness of the proposed method in both accuracy and stability. © 2023, The Authors. All rights reserved.

关键词： Neural networks

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Data-Driven Prediction of Complex Crystal Structures of Dense Lithium

arXiv

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arXiv 2023年

作者： Wang, Xiaoyang Wang, Zhenyu Gao, Pengyue Zhang, Chengqian Lv, Jian Wang, Han Liu, Haifeng Wang, Yanchao Ma, Yanming State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China DP Technology Beijing100080 China College of Engineering Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China

Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for new yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal phases containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the newly observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine-learning potentials. Copyright © 2023, The Authors. All rights reserved.

关键词： Lithium

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Underwater image enhancement via histogram similarity-oriented color compensation complemented by multiple attribute adjustment

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Intelligent Marine Technology and Systems 2023年第1期1卷 1-15页

作者： Wang, Hao Frery, Alejandro C. Li, Mingjie Ren, Peng College of Control Science and Engineering China University of Petroleum (East China) Qingdao China College of Mathematics and Statistics Victoria University of Wellington Wellington New Zealand Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi’an China South China Sea Institute of Planning and Environmental Research State Oceanic Administration Guangzhou China College of Oceanography and Space Informatics China University of Petroleum (East China) Qingdao China

Underwater images are often influenced by color casts, low contrast, and blurred details. We observe that images taken in natural settings typically have similar histograms across color channels, while underwater images do not. To improve the natural appearance of an underwater image, it is critical to improve the histogram similarity across its color channels. To address this problem, we develop a histogram similarity-oriented color compensation method that corrects color casts by improving the histogram similarity across color channels in the underwater image. In addition, we apply the multiple attribute adjustment method, including max-min intensity stretching, luminance map-guided weighting, and high-frequency edge mask fusion, to enhance contrast, saturation, and sharpness, effectively addressing problems of low contrast and blurred details and eventually enhancing the overall appearance of underwater images. Particularly, the method proposed in this work is not based on deep learning, but it effectively enhances a single underwater image. Comprehensive empirical assessments demonstrated that this method exceeds state-of-the-art underwater image enhancement techniques. To facilitate public assessment, we made our reproducible code available at https://***/wanghaoupc/UIE_HS2CM2A.

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