High-resolution seismic inversion method based on joint data-driven in the time-frequency domain

Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance.

Intelligibility evaluation of enhanced whisper in joint time-frequency domain

Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context.

Underdetermined DOA estimation and blind separation of non-disjoint sources in time-frequency domain based on sparse representation method

This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.

Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Surface wave attenuation based polarization attributes in time-frequency domain for multicomponent seismic data

In the paper, we propose a surface wave suppression method in time-frequency domain based on the wavelet transform, considering the characteristic difference of polarization attributes, amplitude energy and apparent velocity between the effective signals and strong surface waves. First, we use the proposed method to obtain time-frequency spectra of seismic signals by using the wavelet transform and calculate the instantaneous polarizability at each point based on instantaneous polarization analysis. Then, we separate the surface wave area from the signal area based on the surface-wave apparent velocity and the average energy of the signal. Finally, we combine the polarizability, energy, and frequency characteristic to identify and suppress the signal noise. Model and field data are used to test the proposed filtering method.

NEW SAR IMAGE INTERPRETATION METHOD OF AIRCRAFT BASED ON JOINT TIME-FREQUENCY ANALYSIS

With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.

Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

Recent developments in deep learning techniques have provided alternative and complementary approaches to the traditional matched-filtering methods for identifying gravitational wave(GW)signals.The rapid and accurate identification of GW signals is crucial to the advancement of GW physics and multi-messenger astronomy,particularly considering the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA.In this study,we used the 2D U-Net algorithm to identify time-frequency domain GW signals from stellar-mass binary black hole(BBH)mergers.We simulated BBH mergers with component masses ranging from 7 to 50 M_(⊙)and accounted for the LIGO detector noise.We found that the GW events in the first and second observation runs could all be clearly and rapidly identified.For the third observing run,approximately 80% of the GW events could be identified.In contrast to traditional convolutional neural networks,the U-Net algorithm can output time-frequency domain signal images corresponding to probabilities,providing a more intuitive analysis.In conclusion,the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers.

Faster-than-Nyquist signaling based on filter bank multicarrier modulation with joint optimization

Multi-carrier faster-than-Nyquist(MFTN)can improve the spectrum efficiency(SE).In this paper,we first analyze the benefit of time frequency packing MFTN(TFP-MFTN).Then,we propose an efficient digital implementation for TFP-MFTN based on filter bank multicarrier modulation.The time frequency packing ratio pair in our proposed implementation scheme is optimized with the SE criterion.Next,the joint optimization for the coded modulation MFTN based on extrinsic information transfer(EXIT)chart is performed.The Monte-Carlo simulations are carried out to verify performance gain of the joint inner and outer code optimization.Simulation results demonstrate that the TFPMFTN has a 0.8 dB and 0.9 dB gain comparing to time packing MFTN(TP-MFTN)and higher order Nyquist at same SE,respectively;the TFP-MFTN with optimized low density parity check(LDPC)code has a 2.9 dB gain comparing to that with digital video broadcasting(DVB)LDPC.Compared with previous work on TFP-MFTN(SE=1.55 bit/s/Hz),the SE of our work is improved by 29%and our work has a 4.1 dB gain at BER=1×10^(-5).

Joint Angle Correction in Rheumatoid Arthritis and Its Reliability Analysis Based on Phase Dispersion Quantification

Background: Rheumatoid arthritis is a form of autoimmune disease characterized by synovitis that can ultimately cause joint deformities and impaired functioning. The cartilage destruction is one of the most important indicators for diagnosis and treatment of rheumatoid arthritis, and it is radiographically manifested as joint space narrowing. Issue: In the literature, the joint space narrowing progression between a baseline and its follow-up finger joint images can be quantified by using image registration algorithm. We found that the inconsistencies of joint angles may lead to characteristic mismatches and thus severely affect the accuracy of joint space narrowing quantifications. Methods: In this work, we introduce a rotation invariant phase only correlation in joint space narrowing quantification for the joint angle correction. Further, we propose a confidence index to quantify the quantification reliability of phase only correlation based on phase dispersion in phase difference spectrum. Conclusion: In our clinical experiments, the proposed quantification method can effectively overcome and manage the mismatch due to the inconsistency of joint angles. Additionally, the confidence index shows a high consistency with the joint space narrowing progression examinations manually done by a trained radiologist and one radiological technologist.

基于权值平滑的自适应调零算法研究与实现

在卫星导航定位系统中,由于导航信号的功率较低,在到达地面时极易受到干扰,导致定位失败。自适应零陷技术可以有效地提高卫星导航接收机的抗干扰能力,然而由于常规的空时联合最小功率响应会时常出现零陷抖动,不能有效的对准干扰来向,从而导致接收机的抗干扰性能下降。对于该情况,本文提出对抗干扰算法中的权值进行平滑滤波处理的方法,并且通过仿真验证、FPGA实现、现场实测等手段验证了该算法的可行性。该方法可有效抑制系统权值以及零陷位置的抖动,从而提高抗干扰算法的稳定性和可靠性,对相关工程领域具有一定的参考价值。

机载SAR受空/地转发干扰识别特征研究

鉴于当前还未有关于合成孔径雷达(SAR)受到空中机动平台产生的间歇采样转发干扰问题的相关研究,针对机载SAR受到来自空中机动/地面固定两类平台的间歇采样转发干扰的识别特征进行研究.首先推导了两类转发干扰的信号模型,从理论上对两类转发干扰的识别特征进行多域联合分析;然后采用基于WVD分布的时频分析方法和基于ω-K的SAR成像算法,从受干扰信号的时域、频域、时频域和图像域等四个维度对两种干扰样式进行了仿真和分析.仿真结果验证了理论分析的正确性,从而为机载SAR遭遇转发干扰时的样式识别提供了可靠的分析思路和识别依据,并进一步给出针对两类转发干扰的反制策略.

美军联合全域指挥与控制的投资

阐述美军联合全域指挥与控制(JADC2)的概念内涵、当前工作内容以及未来发展方向。结合美国国防部2024财年预算请求及Govini公司研究报告,进一步介绍JADC2支出的核算方法,并系统解析美国在JADC2上的资金投入和市场情况、美军联合全域指挥与控制当前面临的挑战。当前,全球安全环境正在发生飞速变化,局部冲突时有发生,搜集、解析和传递信息以保持决策优势的能力将是主导战争胜负的关键。美国国防部提出JADC2战略,旨在构建一个在战场上可以将各部门互联互通的安全网络,以期实现所有战域之间的充分、实时的信息交换。目前美国通过技术研发、合作演习等方式在各个层面积极推动JADC2的建设,并已取得一定阶段性成果。

汽车组合仪表有意电磁干扰空时域联合分离系统设计

有意电磁波与汽车仪器电磁传输波产生的混合干扰,降低了汽车组合仪表的稳定运行能力,造成仪表设备失灵;为避免上述情况的发生,设计汽车组合仪表有意电磁干扰空时域联合分离系统;在步进电机驱动电路中,设置汽车组合仪表控制器、分离电源与PCB电路板、有意电磁干扰数字传感器;分配有意电磁波空时域源地址,通过计算干扰信号平坦度的方式,确定脉冲参数的取值范围,实现汽车组合仪表有意电磁的空时域干扰效应分析;估算有意电磁波干扰信号的数目,按照信号分解的处理原则,求解联合分离修正函数,实现电磁干扰的联合分离;在设置频率范围在100 MHz~1 GHz之间、幅度范围为0.5~5 V、脉冲宽度范围为10~100 ns区间后,通过实验结果表明,设计系统应用后可以在混合信号中,按照空域、时域标准的不同分离有意电磁波,且分离后的信号波长、信号频率依然在1.0μm、300 Hz左右,不会因混合干扰降低汽车组合仪表的稳定运行能力。

基于交叉特征感知融合的多领域虚假新闻检测

各领域虚假新闻的传播对社会造成了严重的影响,不同领域间新闻的领域偏移问题和跨域关联问题也对模型的预测能力造成了极大的挑战.针对上述问题,本文提出了一种基于交叉特征感知融合的多领域虚假新闻检测方法.该方法可以捕捉不同领域间新闻的多种特征差异,并挖掘新闻之间的关联关系,从多个维度控制模型在不同领域的特征融合策略.此外,本文还提出了一种联合训练框架.本方法的模型使用本框架进行训练,在中英文数据集上的预测F1分数分别达到了92.84%和85.49%,相较于最先进的模型,预测效果分别提升了1.16%和1.07%.

基于频域波束降维的车载雷达多参数联合超分辨方法

针对车载雷达多参数联合超分辨计算复杂度高、无法快速实现参数估计的问题,提出了基于频域波束降维的多参数联合超分辨算法。所提算法通过快速傅里叶变换(fast Fourier transform,FFT)将空时多参数域联合数据变换到频域,处理感兴趣区域的多维频域数据,完成空时波束空间降维和基于频域数据的多参数联合超分辨,实现目标信息的快速联合估计。推导了频域子空间正交性及频域波束降维超分辨算法理论。仿真研究了算法的分辨率和估计性能与信噪比的关系。仿真结果表明,所提算法的精度和分辨率远超传统FFT算法,相对于传统多重信号分类(multiple signal classification,MUSIC)算法,所提算法计算量大幅降低。

基于网络信息体系的全域联合指挥与控制技术及未来展望

面向现代战争需求,全域联合作战通过打破传统作战区域限制,能够实现不同军种和作战领域的信息共享、军事力量整合和协同作战,从而提升作战效率、增强指挥灵活性、优化资源配置。全域联合有诸多优势,使基于网络信息体系的全域联合指挥与控制技术成为研究重点。本文系统分析了全域联合指挥与控制的发展历程、技术应用、架构设计和关键技术,同时结合国外的研究现状探讨发展方向,旨在为全域联合指挥与控制技术的发展提供参考。

基于知识图谱和预训练语言模型深度融合的可解释生物医学推理

基于预训练语言模型(LM)和知识图谱(KG)的联合推理在应用于生物医学领域时,因其专业术语表示方式多样、语义歧义以及知识图谱存在大量噪声等问题,联合推理模型并未取得较好的效果。基于此,提出一种面向生物医学领域的可解释推理方法DF-GNN。该方法统一了文本和知识图谱的实体表示方式,利用大型生物医学知识库构造子图并进行去噪,改进文本和子图实体的信息交互方式,增加对应文本和子图节点的直接交互,使得两个模态的信息能够深度融合。同时,利用知识图谱的路径信息对模型推理过程提供了可解释性。在公开数据集MedQA-USMLE和MedMCQA上的测试结果表明,与现有的生物医学领域联合推理模型相比,DF-GNN可以更可靠地利用结构化知识进行推理并提供解释性。

乡村产业振兴中的“联村发展”——对陕南H县“三联”经验的考察

脱贫攻坚结束进入全面推进乡村振兴阶段后,欠发达地区乡村产业发展面临政策环境转型、外在扶持力度减弱等新挑战,如何实现可持续发展成为一个值得关注的问题。为此,H县创新探索出了以“支部联建、资源联享、产业联盟”为核心的联村发展模式。这一模式的核心逻辑是弥合产业发展的市场边界与村庄的行政边界不一致的罅隙,通过跨域机制、整合机制与链接机制打破乡村产业跨边界发展的梗阻,有效整合不同主体的多方资源,推动小农户与大市场衔接,并最终构建乡村产业发展共同体。联村发展不仅有利于巩固拓展脱贫攻坚成果,而且还直接助力乡村产业兴旺与共同富裕政策目标的实现。

面向跨域联合作战的平行指挥控制与管理新范式

瞄准制衡强敌“马赛克战”“决策中心战”等新技术驱动的作战概念及威胁挑战,聚焦未来跨域联合作战指挥控制(command and control,C2)全流程决策需求,遵循平行智能理论框架,提出了基于筹划-准备-执行-评估(planning-readiness-execution-assessment,PREA)环与观察-判断-决策-行动(observe-orient-decide-act,OODA)环的平行指挥控制与管理(command&control and management,C2M)新范式,以期实现智能机器辅助指挥员作战全流程的分层次、个性化决策支持,减轻指挥员认知负担、降低决策复杂度,实现机器对指挥员“人脑”的智能扩展与增强,为塑造全局决策优势提供牵引和支撑。

先进作战管理系统发展研究

本文介绍了先进作战管理系统的发展背景,梳理了系统构成的演变发展;从发展路径、组织管理、采办方式、作战实验4个方面,深入分析了先进作战管理系统的发展特点,提出了有关作战概念创新、作战实验开展、商用技术运用的几点认识。