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Multicomponent Seismic Data Reconstruction via Anti-Aliasing Vector POCS Method

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Journal of Earth Science 2024年第4期35卷 1385-1392页

作者： Fan Li Jianjun Gao Yun Wang Chunming Wang Sian Hou school of Geophysics and information Technology China University of GeosciencesBeijing 100083China Research Institute of Petroleum exploration and Development Petro ChinaBeijing 100083China

0 INTRODUCTION Due to the ability to comprehensively utilize P and S wave information,multi-component seismic exploration has already played an important role in fracture detection,lithology prediction,fluid identification,etc.,and it has also become an important technology for complex reservoir exploration(Yuan et al.,2021;Stewart et al.,2003).

关键词： reservoir Seismic prediction

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Study of inter-well interference in shale gas reservoirs by a robust production data analysis method based on deconvolution

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Petroleum Science 2024年第4期21卷 2502-2519页

作者： Wen-Chao Liu Cheng-Cheng Qiao Ping Wang Wen-Song Huang Xiang-Wen Kong Yu-Ping Sun He-Dong Sun Yue-Peng Jia school of Civil and Resource Engineering University of Science and Technology Beijing PetroChina Research Institute of Petroleum exploration and Development

In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering, this study proposes a robust production data analysis method based on deconvolution, which is used for multi-well inter-well interference research. In this study, a multi-well conceptual trilinear seepage model for multi-stage fractured horizontal wells was established, and its Laplace solutions under two different outer boundary conditions were obtained. Then, an improved pressure deconvolution algorithm was used to normalize the scattered production data. Furthermore, the typical curve fitting was carried out using the production data and the seepage model solution. Finally,some reservoir parameters and fracturing parameters were interpreted, and the intensity of inter-well interference was compared. The effectiveness of the method was verified by analyzing the production dynamic data of six shale gas wells in Duvernay area. The results showed that the fitting effect of typical curves was greatly improved due to the mutual restriction between deconvolution calculation parameter debugging and seepage model parameter debugging. Besides, by using the morphological characteristics of the log-log typical curves and the time corresponding to the intersection point of the log-log typical curves of two models under different outer boundary conditions, the strength of the interference between wells on the same well platform was well judged. This work can provide a reference for the optimization of well spacing and hydraulic fracturing measures for shale gas wells.

关键词： Shale gas Inter-well interference Deconvolution Production data analysis Typical curves Multi-stage fractured horizontal well

Full-domain collaborative deployment method of multiple interference sources and evaluation of its deployment effect

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Defence Technology（防务技术） 2024年第2期32卷 572-595页

作者： Yue Wang Fuping Sun Xian Wang Jinming Hao Kai Xiao school of Geospatial information The PLA Information Engineering UniversityZhengzhou 450001HenanChina

This paper realizes the full-domain collaborative deployment of multiple interference sources of the global satellite navigation system(GNSS)and evaluates the deployment effect to enhance the ability to disturb the attacker and the capability to defend the GNSS during navigation *** evaluation indicators for the jamming effect of GNSS suppressive and deceptive jamming sources are first created,their evaluation models are built,and their detection procedures are sorted out,as the basis for determining the deployment *** principles for collaboratively deploying multi-jamming sources are developed to obtain the deployment structures(including the required number,structures in demand,and corresponding positions)of three single interference sources required by collaboratively ***,simulation and hardware-in-loop testing results are presented to determine a rational configuration of the collaborative deployment of multi-jamming sources in the set situation and further realize the full-domain deployment of an interference network from ground,air to *** evaluation indices for the deployment effect are finally developed to evaluate the deployment effect of the proposed configuration and further verify its reliability and rationality.

关键词： Jamming effect Multiple interference sources Collaborative deployment Effect evaluation Defense capability

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On the size generalizibility of graph neural networks for learning resource allocation

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Science China(information Sciences) 2024年第4期67卷 239-254页

作者： Jiajun WU Chengjian SUN Chenyang YANG Beihang University school of Electronics and information Engineering

Size generalization is important for learning resource allocation policies in wireless systems with time-varying scales. If a neural network for learning a wireless policy is not generalizable to the size of its input, it has to be re-trained whenever the system scale changes, which hinders its practical use due to the unaffordable training costs. Graph neural networks(GNNs) have been shown with size generalization ability empirically when optimizing resource allocation. Yet, are GNNs naturally size generalizable? In this paper,we argue that GNNs are not always size generalizable for resource allocation. We find that the aggregation and activation functions of the GNNs for learning a class of wireless policies play a key role in their size generalization ability. We take the GNN with the mean aggregator, called mean-GNN, as an example to reveal a size generalization condition. To demonstrate how to satisfy the condition, we learn power and bandwidth allocation policies for ultra-reliable low-latency communications and show that selecting or pretraining the activation function in the output layer of mean-GNN can make the GNN size *** results validate our analysis and evaluate the performance of the learned policies.

关键词： size generalization graph neural networks resource allocation permutation equivariance

Protein acetylation sites with complex-valued polynomial model

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Frontiers of Computer Science 2024年第3期18卷 217-226页

作者： Wenzheng BAO Bin YANG school of information Engineering Xuzhou University of TechnologyXuzhou 221018China school of information Science and Engineering Zaozhuang UniversityZaozhuang 277160China

Protein acetylation refers to a process of adding acetyl groups(CH3CO-)to lysine residues on protein *** one of the most commonly used protein post-translational modifications,lysine acetylation plays an important role in different *** our study,we developed a humanspecific method which uses a cascade classifier of complexvalued polynomial model(CVPM),combined with sequence and structural feature descriptors to solve the problem of imbalance between positive and negative *** gene expression programming and differential evolution are utilized to search the optimal CVPM *** also made a systematic and comprehensive analysis of the acetylation data and the prediction *** performances of our proposed method are 79.15%in Sp,78.17%in Sn,78.66%in ACC 78.76%in F1,and 0.5733 in MCC,which performs better than other state-of-the-art methods.

关键词： protein acetylation complex-valued polynomial model machine learning

Intelligent secure near-field communication

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Science China(information Sciences) 2024年第9期67卷 340-341页

作者： Nan ZHANG Jifa ZHANG Chengwen XING Na DENG Nan ZHAO school of information and Communication Engineering Dalian University of Technology school of information and Electronics Beijing Institute of Technology

The research and development efforts for the sixth generation(6G) wireless communication are underway to fulfill the growing demands for wireless communications. 6G is expected to become the key enabler for diverse new applications and services [1]. To achieve the excellent performance, 6G will implement extremely large-scale antenna arrays(ELAAs), terahertz [2], and new types of antennas with metamaterials, leading to a paradigm shift for the electromagnetic characteristics.

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TransTM:A device-free method based on time-streaming multiscale transformer for human activity recognition

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Defence Technology（防务技术） 2024年第2期32卷 619-628页

作者： Yi Liu Weiqing Huang Shang Jiang Bobai Zhao Shuai Wang Siye Wang Yanfang Zhang Institute of information Engineering Chinese Academy of Sciences School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina school of information Management Beijing Information Science and Technology UniversityBeijingChina

RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy *** RFID-based HAR methods use modeling,CNN,or LSTM to extract features ***,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF *** solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called *** model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without ***,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human *** with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and ***,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification *** results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.

关键词： Human activity recognition RFID Transformer

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Study on Coded Permutation Entropy of Finite Length Gaussian White Noise Time Series

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Chinese Journal of Electronics 2024年第1期33卷 185-194页

作者： Huihui SUN Xiaofeng ZHANG school of Physics and information Technology Shaanxi Normal University

As an extension of permutation entropy(PE), coded permutation entropy(CPE) improves the performance of PE by making a secondary division for ordinal patterns defined in PE. In this study, we provide an exploration of the statistical properties of CPE using a finite length Gaussian white noise time series theoretically. By means of the Taylor series expansion, the approximate expressions of the expected value and variance of CPE are deduced and the Cramér-Rao low bound(CRLB) is obtained to evaluate the performance of the CPE estimator. The results indicate that CPE is a biased estimator, but the bias only depends on relevant parameters of CPE and it can be easily corrected for an arbitrary time series. The variance of CPE is related to the encoding patterns distribution,and the value converges to the CRLB of the CPE estimator when the time series length is large enough. For a finitelength Gaussian white noise time series model, the predicted values can match well with the actual values, which further validates the statistic theory of CPE. Using the theoretical expressions of CPE, it is possible to better understand the behavior of CPE for most of the time series.

关键词： Time series analysis White noise Predictive models Taylor series Entropy Encoding Time measurement

Visible Light Indoor Positioning System Based on Pisarenko Harmonic Decomposition and Neural Network

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Chinese Journal of Electronics 2024年第1期33卷 195-203页

作者： Li ZHAO Yi REN Qi WANG Lange DENG Feng ZHANG school of Electronic information Engineering Xi’an Technological University

Visible-light indoor positioning is a new generation of positioning technology that can be integrated into smart lighting and optical communications. The current received signal strength(RSS)-based visible-light positioning systems struggle to overcome the interferences of background and indoor-reflected noise. Meanwhile, when ensuring the lighting, it is impossible to use the superposition of each light source to accurately distinguish light source information; furthermore, it is difficult to achieve accurate positioning in complex indoor environments. This study proposes an indoor positioning method based on a combination of power spectral density detection and a neural network. The system integrates the mechanism for visible-light radiation detection with RSS theory, to build a back propagation neural network model fitting for multiple reflection channels. Different frequency signals are loaded to different light sources at the beacon end, and the characteristic frequency and power vectors are obtained at the location end using the Pisarenko harmonic decomposition method. Then, a complete fingerprint database is established to train the neural network model and conduct location tests. Finally, the location effectiveness of the proposed algorithm is verified via actual positioning experiments. The simulation results show that, when four groups of sinusoidal waves with different frequencies are superimposed with white noise, the maximum frequency error is 0.104 Hz and the maximum power error is 0.0362 W. For the measured positioning stage, a 0.8 m × 0.8 m × 0.8 m solid wood stereoscopic positioning model is constructed, and the average error is 4.28 cm. This study provides an effective method for separating multi-source signal energies, overcoming background noise, and improving indoor visible-light positioning accuracies.

关键词： Neural networks Measurement uncertainty Position measurement Harmonic analysis Light emitting diodes Frequency estimation Indoor positioning systems