Facial Features of an Air Gun Array Wavelet in the Time-Frequency Domain Based on Marine Vertical Cables

Air gun arrays are often used in marine energy exploration and marine geological surveys.The study of the single bubble dynamics and multibubbles produced by air guns interacting with each other is helpful in understanding pressure signals.We used the van der Waals air gun model to simulate the wavelets of a sleeve gun of various offsets and arrival angles.Several factors were taken into account,such as heat transfer,the thermodynamically open quasi-static system,the vertical rise of the bubble,and air gun post throttling.Marine vertical cables are located on the seafloor,but hydrophones are located in seawater and are far away from the air gun array vertically.This situation conforms to the acquisition conditions of the air gun far-field wavelet and thus avoids the problems of ship noise,ocean surges,and coupling.High-quality 3D wavelet data of air gun arrays were collected during a vertical cable test in the South China Sea in 2017.We proposed an evaluation method of multidimensional facial features,including zeropeak amplitude,peak-peak amplitude,bubble period,primary-to-bubble ratio,frequency spectrum,instantaneous amplitude,instantaneous phase,and instantaneous frequency,to characterize the 3D air gun wave field.The match between the facial features in the field and simulated data provides confidence for the use of the van der Waals air gun model to predict air gun wavelet and facial features to evaluate air gun array.

Digital modulation classification using multi-layer perceptron and time-frequency features

Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

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.

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

结合多维特征与Transformer网络的敌我识别辐射源个体识别

针对目前敌我识别辐射源个体识别(Specific Emitter Identification of Identification Friend or Foe,SEI-IFF)研究不足的问题,提出了一种基于多维特征与Transformer网络的SEI-IFF方法。该方法首先从单个脉冲及信号全局等多维度获取如相位、幅度、时间、功率谱密度等信号特征,结合Transformer网络进一步提取不同IFF辐射源个体特征中如前后关联特性的细微特征并最终实现SEI-IFF。试验结果表明,所提方法针对20个目标搭载的IFF辐射源个体的平均识别正确率达95.3%,可较准确地完成SEI-IFF,有助于提升战场SEI-IFF效率。

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

Radar Signal Intra-Pulse Feature Extraction Based on Improved Wavelet Transform Algorithm

With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.

Jamming Recognition Based on Feature Fusion and Convolutional Neural Network

The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this work,the jamming recognition framework proposed consists of fea-ture fusion and a convolutional neural network(CNN).Firstly,the recognition inputs are obtained by prepossessing procedure,in which the 1-D power spectrum and 2-D time-frequency image are ac-cessed through the Welch algorithm and short-time Fourier transform(STFT),respectively.Then,the 1D-CNN and residual neural network(ResNet)are introduced to extract the deep features of the two prepossessing inputs,respectively.Finally,the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer.Results show the proposed method could reduce the impacts of potential feature loss,therefore improving the generalization ability on dealing with uncertainties.

Underground Pipeline Surveillance with an Algorithm Based on Statistical Time-Frequency Acoustic Features

Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization.Thus,designing a round-the-clock intelligent surveillance system has become crucial and urgent.In this study,we develop an acoustic signal-based excavation device recognition system for underground pipeline protection.The front-end hardware system is equipped with an acoustic sensor array,an Analog-to-Digital Converter(ADC)module(ADS1274),and an industrial processor Advanced RISC Machine(ARM)cortex-A8 for signal collection and algorithm implementation.Then,a novel Statistical Time-Frequency acoustic Feature(STFF)is proposed,and a fast Extreme Learning Machine(ELM)is adopted as the classifier.Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features.In addition,the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.

现代五项运动多维度特征研究进展

通过综述国内外现代五项运动的相关文献,对现代五项运动员与比赛特征进行梳理与总结。现代五项运动员在身体形态指标上高于其他异属组合类项目的平均水平。现代五项运动员损伤部位多集中于头颈部及骨盆和下肢,各子项方面的损伤多集中于跑步,严重损伤主要出现在马术。依据项目特点,现代五项运动的能量供应涵盖了有氧代谢、糖酵解和磷酸原供能系统,游泳项目转项的运动员更易在现代五项的训练中取得突破。运动员比赛中的平均心率与血乳酸水平依据不同子项特征表现不同,其中跑射联项明显高于其他3项。

基于多维特征矩阵和改进稠密连接网络的情感分类

情感脑电信号是一种低信噪比的非平稳时间序列,传统的特征提取与分类方法难以提取不同情感状态时的有效特征并进行分类.针对以上情况,提出一种自动融合脑电信号不同频段和时频域特征的深度学习模型.首先,对预处理后的数据进行分频段处理,提取各频段的微分熵特征.然后,在网络中接入的挤压激励模块,对不同频段特征的微分熵特征赋权值,来获取输入数据的有价值信息,再利用改进的稠密连接网络进行特征融合并分类识别,保证了网络层之间最大程度的信息传输.最后,利用SEED情感脑电信号三分类数据集对算法进行了验证,分类正确率可达96.03%,高于现有的基线学习算法,所提算法可进一步增强网络特征提取能力,具有较快的收敛速度,对提升分类器的性能研究具有重要意义.

汉语学术论文语篇特征变化多维分析

本研究通过自建学术期刊论文语料库,运用Python,对汉语学术论文语篇特征进行多维历时分析,其中包括形式特征分析、语体特征分析、庄雅度特征分析等。研究结果显示,自1980年以来,学术论文语篇特征总体呈现如下变化趋势:论文词汇密度上升,篇幅变长,信息度增强;正式度提高,庄雅度提升,表述主观程度降低。本研究对于揭示汉语学术论文语篇规律,指导学术汉语写作教学具有一定的参考价值。

我国人才研究特征与热点态势--基于国家社会科学基金项目分析

人才是第一资源,人才资源在经济社会发展中有着突出地位和作用。以1983—2023年295项国家社科基金人才研究项目为样本数据进行统计分析。研究表明,立项数量方面,人才研究的年度立项数量整体呈现上升趋势,主要分为平稳阶段、波动增长阶段、平稳增长阶段;学科分布方面,人才研究的交叉学科属性突出,广泛分布于23个基础学科中,主要集中在管理学、教育学、体育学3个学科;研究热点方面,以党和国家重大理论和现实问题为导向,重点关注高层次人才、科技人才、创新人才、后备人才、国际人才,形成了人才培养、人才体制机制、人才流动、人才评价等热点研究版块。存在研究力量区域分布不均衡、高层次创新型人才短缺、人才服务和推动人才发展的体制和机制仍需不断调整和创新等问题。未来,国家社科基金应加大对人才项目的支持,人才研究将朝着与深入实施人才强国战略、不断完善中国特色社会主义人才理论、解决我国人才难题等新的热点相结合的方向拓展。

基于多维特征的通信网络异常数据识别算法

为解决现有方法存在的异常数据识别精度较低的问题,提出一种基于多维特征的通信网络异常数据识别算法。调整粒子群优化算法中粒子的当前速度和位置,获取通信网络多维数据样本;通过数据挖掘中的聚类分析法提取数据特征,确定密度指标,获取数据多维特征;将提取的多维特征引入深度信念网络中进行识别,根据特征频谱幅值变化,实现对通信网络数据异常识别。实验结果表明,该算法能有效识别通信网络异常数据特征,具有较高的识别准确性。

基于动态高分辨图像的粮仓玉米温度变化监测方法

为了监测粮仓内玉米的温度变化情况,为后续的储藏条件提供理论参考,提出了一种基于动态高分辨率图像的玉米粮仓温度变化监测方法。采用热成像传感器进行玉米粮仓基础数据采集,数据由H3C R4900 G3服务器处理和存储。运用绝对定标方法计算干扰率以消除传感器干扰,生成高分辨率图像,并进行几何校正。然后通过影像分割提取差异性特征,影像分割将图像划分为不同区域后提取纹理特征确定监测范围,经多尺度分割和区域整合得到影像像斑,依据像斑差异性参数求解公式确定像斑整合条件。最后在多维特征下进行动态高分辨率监测玉米粮仓温度变化,通过设定卷积滤波中心、卷积层特征表达式等一系列计算得到影像像斑特征,从而实现温度变化监测。实验结果表明:该方法能够有效地监测粮仓玉米温度变化,为后续的粮仓存储提供了有价值的参考。

基于多维参数采样判决机制的LTE-5G网络恶意节点检出算法

为解决以LTE-5G(Long Term Evolution-5G)为代表的新一代移动通信技术存在的网络恶意节点检出效率较低、流量查证识别能力较弱等不足,提出了一种基于多维参数采样判决机制的LTE-5G网络恶意节点检出算法.首先,针对网络恶意节点行为所产生的外溢流量特点,根据节点时域抽样特性及指纹特点,设计了多维参数采样判决机制,该机制通过采样鉴权序列定向匹配节点时域特征并筛除不符合特征的疑似恶意节点,从而达到较高的检测效果.其次,为进一步清除潜伏状态的恶意节点,结合微分机制,构建黑洞数据鉴权阈值,通过该阈值控制网络清洗流量,仅将高于该阈值的疑似节点予以清洗处理,从而降低误判情形,改善因节点离线而导致网络传输性能出现下降的现象.仿真实验表明,与当前常用的基于神经网络的加权投票鉴权机制的数据清洗方案和基于鲸鱼-狮子联合优化机制的数据清洗方案相比,本文算法具有更高的网络传输带宽和恶意节点检出率,以及更低的网络节点离线频次,在实践中具有较高的推广价值.

基于图卷积网络的发明人跨领域合作伙伴识别方法

[研究目的]科学技术与社会的发展促进了不同领域理论、方法和技术的交叉融合,跨领域合作愈发成为合作创新的主流形式,如何帮助发明人定位并准确识别跨领域合作伙伴成为亟待解决的问题。[研究方法]提出一种基于图卷积网络的发明人跨领域合作伙伴识别方法,从多维特征视角下基于发明人专利信息中的合作关系特征、摘要文本特征、领域信息特征使用图卷积网络识别和预测发明人潜在合作伙伴,构建同领域指数和跨领域指数准确识别发明人跨领域合作伙伴。[研究结论]通过对比实验,证明了借助图卷积网络对合作关系特征、摘要文本特征、领域信息特征三维特征联用在进行伙伴识别时能够有效提升模型准确性。借助识别跨领域合作伙伴,有助于促进不同领域之间的交叉合作和知识转移,创造出更具创新性和前瞻性的成果。

组态视角下我国旅游产业发展的类型与路径选择——基于机器学习方法的探索

运用机器学习方法识别旅游产业发展的复杂前因和组态路径,以此赋能我国区域协调发展和共同富裕目标实现。文章基于生产函数理论,以我国298个地级及以上城市为研究对象,采用K均值聚类算法将样本城市划分为发展受阻型、稳中求进型和全面辐射型3种群组类型,运用分类与回归树算法挖掘不同类型城市资源、技术和制度层面多维特征变量与旅游产业发展之间的复杂关系结构。研究发现:1)旅游产业发展的驱动要素具有耦合协调效应,体现为不同类型城市多维特征变量的横向耦合一致性和纵向等级分层性;2)高度相似城市因要素差异化配置获得不同旅游产业发展水平,表明每类城市都有适宜自身发展的组态条件,为推动区域协调发展提供现实基础;3)不同类型城市旅游产业高水平发展的驱动要素具有组合差异性,整体呈现殊途同归的作用效果,发展受阻型城市由“科技筑基-区域开放-文化吸引”驱动,稳中求进型城市由“经济引领-科技创新-数字赋能”驱动,全面辐射型城市由“文化吸引-交通增质”驱动。研究结论为我国城市旅游产业如何依据自身要素禀赋条件获得高水平发展提供了新思路和新参考依据。

基于GGInformer模型的多维时间序列特征提取与预测研究

随着大数据与物联网技术的迅猛发展,多维时间序列数据的应用范围变得更加广泛。面对大量的非线性、高维冗余特征的复杂时间序列,传统的时间序列分析方法已经不能很好地解决多维时间序列的复杂高维特征问题,从而导致预测效果欠佳。针对以上问题,通过对遗传算法和Informer模型进行改进,并融合GRU网络,提出了GGInformer模型。该模型不仅可以有效提取多维时间序列的关键特征,而且较好地解决了长程依赖问题。为了验证模型的预测能力,选取了2种实际数据集与3种公共基准数据集进行实验,相比较Informer基准模型,GGInformer模型在5种数据集上的MSE分别降低了22%,13%,20%,23%和38%。实验结果表明,GGInformer模型可以有效解决多维时间序列数据的复杂特征提取问题,并可以进一步提高时序预测能力。