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Nondestructive discrimination of moldy pear core based on the recurrenceplots of vibration acoustic signals and deep convolutional neural networks
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作者 Yue Yang Kang Zhao +2 位作者 Jin Zhao Ye Song Tao Shen 《International Journal of Agricultural and Biological Engineering》 2025年第6期230-240,共11页
Moldy core is a serious internal defect in pears.Since there is no significant difference in appearance between the healthy pears and those with mild moldy core,it is still a great challenge for the early detection of... Moldy core is a serious internal defect in pears.Since there is no significant difference in appearance between the healthy pears and those with mild moldy core,it is still a great challenge for the early detection of moldy pear core.This study transformed the vibration acoustic signals(VA signal)of pears into recurrence plots and Markov transition field to enable image-based classification of moldy cores.In addition to traditional machine-learning baselines(Random Forest and k-Nearest Neighbors)trained on LBP-extracted texture features from RP/MTF,the deep models were constructed and compared,which include ResNet-101,DenseNet-121,SqueezeNet,Vision Transformer(ViT),and an improved SqueezeNet(ISqueezeNet).Hyperparameters were tuned via Bayesian optimization over optimizer type,learning rate,batch size,and L2 weight decay,yielding model-specific optimal settings.Under these configurations,the ISqueezeNet achieved the highest test accuracy of 93.05%,with class-wise accuracies of 89.28%(healthy),96.15%(slight),and 94.44%(moderate and severe).Comparisons with lightweight networks(MobileNetV1 and ShuffleNetV2)further showed that ISqueezeNet attains superior accuracy with favorable parameter efficiency and inference speed.Grad-CAM visualizations confirmed that the model focuses on lesionrelevant regions,supporting interpretability and practical reliability.These results indicate that the proposed approach is promising for early,nondestructive detection of moldy pear cores. 展开更多
关键词 moldy pear core deep convolutional neural network vibration acoustic signals recurrence plots Markov transition field nondestructive detection
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Comparison study of typical algorithms for reconstructing time series from the recurrence plot of dynamical systems 被引量:1
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作者 刘杰 石书婷 赵军产 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第1期131-137,共7页
The three most widely used methods for reconstructing the underlying time series via the recurrence plots (RPs) of a dynamical system are compared with each other in this paper. We aim to reconstruct a toy series, a... The three most widely used methods for reconstructing the underlying time series via the recurrence plots (RPs) of a dynamical system are compared with each other in this paper. We aim to reconstruct a toy series, a periodical series, a random series, and a chaotic series to compare the effectiveness of the most widely used typical methods in terms of signal correlation analysis. The application of the most effective algorithm to the typical chaotic Lorenz system verifies the correctness of such an effective algorithm. It is verified that, based on the unthresholded RPs, one can reconstruct the original attractor by choosing different RP thresholds based on the Hirata algorithm. It is shown that, in real applications, it is possible to reconstruct the underlying dynamics by using quite little information from observations of real dynamical systems. Moreover, rules of the threshold chosen in the algorithm are also suggested. 展开更多
关键词 recurrence plot chaotic system time series analysis correlation analysis
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Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots 被引量:10
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作者 Tuan D.Pham Karin Wardell +1 位作者 Anders Eklund Goran Salerud 《IEEE/CAA Journal of Automatica Sinica》 EI CSCD 2019年第6期1306-1317,共12页
There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for... There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects. 展开更多
关键词 Deep learning early Parkinson’s disease(PD) fuzzy recurrence plots long short-term memory(LSTM) neural networks pattern classification short time series
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Recognition of dynamically varying PRI modulation via deep learning and recurrence plot 被引量:1
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作者 WANG Pengcheng LIU Weisong LIU Zheng 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第4期815-826,共12页
Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classificati... Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences.The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification,while the actual situation is that radar pulses reach the receiver continuously,and there is no completely reliable method to achieve this division in the case of non-cooperative reception.Based on the above actual needs,this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model,which does not need to divide the PRI sequence in advance.Compared with the sliding window method,it can more effectively realize the recognition of the dynamically varying PRI mo dulation. 展开更多
关键词 you look only once(YOLO) pulse repetition interval(PRI)modulation recurrence plot
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Middle ear reconstruction estimated by recurrence plot technique
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作者 Rafal Rusinek Jerzy Warminski +1 位作者 Marek Zadrozniak Marcin Szymanski 《Theoretical & Applied Mechanics Letters》 CAS 2012年第4期64-68,共5页
Middle ear surgery techniques have enabled to improve hearing destroyed by a disease. Despite huge improvement in instrumentation and techniques the results of hearing improvement surgery are still difficult to predic... Middle ear surgery techniques have enabled to improve hearing destroyed by a disease. Despite huge improvement in instrumentation and techniques the results of hearing improvement surgery are still difficult to predict. This paper presents the results of vibrations measurements in a human middle ear obtained at the Medical University of Lublin. Vibrations of the stapes in the case of the intact ossicular chain, after cement incus rebuilding and incus interpositions are compared each other. In this aim a new approach of ossicles vibrations observation is introduced in order to complete information obtained from classical approach which bases on the transfer function. Measurements of ossicular chain vibrations are performed on fresh human temporal bone specimen using the laser doppler vibrometer. Next, after classical research, the extended analysis with the recurrence plots technique is performed. 展开更多
关键词 middle ear vibrations recurrence plot laser doppler vibrometer
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Analysis of Key Features of Non-Linear Behavior Using Recurrence Plots. Case Study: Urban Pollution at Mexico City
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作者 Marco A. Aceves-Fernandez Jesus Carlos Pedraza-Ortega +3 位作者 Artemio Sotomayor-Olmedo Juan M. Ramos-Arreguín Jose Emilio Vargas-Soto Saul Tovar-Arriaga 《Journal of Environmental Protection》 2012年第9期1147-1160,共14页
The use of Recurrence plots have been extensively used in various fields. In this work, Recurrence Plots (RPs) investigates the changes in the non-linear behaviour of urban air pollution using large datasets of raw da... The use of Recurrence plots have been extensively used in various fields. In this work, Recurrence Plots (RPs) investigates the changes in the non-linear behaviour of urban air pollution using large datasets of raw data (hourly). This analysis has not been used before to extract information from large datasets for this type non-linear problem. Two different approaches have been used to tackle this problem. The first approach is to show results according to monitoring network. The second approach is to show the results by particle type. This analysis shows the feasibility of using Recurrence Analysis for pollution monitoring and control. 展开更多
关键词 recurrence plot AIR Quality AIR POLLUTION Modelling ATMOSPHERIC POLLUTION recurrence Quantification ANALYSIS
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Nonlinear dynamics in Divisia monetary aggregates:an application of recurrence quantification analysis
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作者 Ioannis Andreadis Athanasios D.Fragkou +1 位作者 Theodoros E.Karakasidis Apostolos Serletis 《Financial Innovation》 2023年第1期467-483,共17页
We construct recurrence plots(RPs)and conduct recurrence quantification analysis(RQA)to investigate the dynamic properties of the new Center for Financial Stability(CFS)Divisia monetary aggregates for the United State... We construct recurrence plots(RPs)and conduct recurrence quantification analysis(RQA)to investigate the dynamic properties of the new Center for Financial Stability(CFS)Divisia monetary aggregates for the United States.In this study,we use the lat-est vintage of Divisia aggregates,maintained within CFS.We use monthly data,from January 1967 to December 2020,which is a sample period that includes the extreme economic events of the 2007–2009 global financial crisis.We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin.The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008.Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates,we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times. 展开更多
关键词 Divisia monetary aggregates recurrence plots Moving windows Deterministic dynamics Stochastic structures
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Recurrence Quantification Analysis of Rough Surfaces Applied to Optical and Speckle Profiles
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作者 Oscar Sarmiento Martinez Darwin Mayorga Cruz +1 位作者 Jorge Uruchurtu Chavarín Estela Sarmiento Bustos 《Journal of Applied Mathematics and Physics》 2016年第4期720-732,共13页
In this paper, Recurrence Quantification Analysis (RQA) is set as a practical nonlinear data tool to establish and compare surface roughness (Ra) through percentage parameters of a dynamical system: Recurrence (%REC),... In this paper, Recurrence Quantification Analysis (RQA) is set as a practical nonlinear data tool to establish and compare surface roughness (Ra) through percentage parameters of a dynamical system: Recurrence (%REC), Determinism (%DET) and Laminarity (%LAM). Variations in surface roughness of different machining procedures from a typical metallic casting comparator are obtained from scattering intensity of a laser beam and expressed as changes in the statistics of speckle patterns and profiles optical properties. The application of the analysis (RQA) by Recurrence Plots (RPs), allowed to distinguish between machining procedures, highlighting features that other methods are unable to detect. 展开更多
关键词 RQA recurrence plots ROUGHNESS Optical Data Processing Speckle Patterns
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基于递归图和卷积神经网络-门控循环单元的水轮机空化状态识别方法
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作者 刘忠 乔帅程 +2 位作者 邹淑云 郑佳稳 吴怡恬 《哈尔滨工程大学学报》 北大核心 2026年第2期248-254,共7页
针对复杂噪声干扰环境下难以有效提取水轮机空化诱导的声发射信号特征,进而影响空化状态识别准确度的问题,本文提出一种基于递归图和卷积神经网络-门控循环单元组合网络的水轮机空化状态识别方法。对水轮机空化声发射信号进行相空间重构... 针对复杂噪声干扰环境下难以有效提取水轮机空化诱导的声发射信号特征,进而影响空化状态识别准确度的问题,本文提出一种基于递归图和卷积神经网络-门控循环单元组合网络的水轮机空化状态识别方法。对水轮机空化声发射信号进行相空间重构,通过递归分析获得不同空化状态下的递归图,将其作为空化特征图像输入到卷积神经网络中。通过卷积神经网络提取隐藏在递归图中的空化特征,在门控循环单元中提取隐藏特征中的时序信息并完成空化状态识别。研究表明:以递归图数据集为输入的卷积神经网络-门控循环单元模型的空化识别准确率为96.8%,高于时频图和马尔可夫变迁场等其他图像数据集;本文方法对多工况下水轮机空化状态识别的平均F1分数为0.94,对非线性信号的特征提取和分类具有更高的识别准确率和泛化性能。 展开更多
关键词 水轮机 空化 声发射信号 特征提取 递归图 卷积神经网络 门控循环单元 深度学习
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UC信号多层递归定量指标对胎儿异常诊断的价值
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作者 白雪利 张曙萱 《医学理论与实践》 2026年第4期550-554,共5页
目的:研究宫缩(UC)信号多层递归定量指标与胎儿异常的相关性。方法:选取捷克技术大学—布尔诺大学附属医院建立的开源数据库中脐动脉血pH值<7.15的39例UC信号为研究组,选取数据库中脐动脉血pH值>7.15的513例UC信号为对照组,采用... 目的:研究宫缩(UC)信号多层递归定量指标与胎儿异常的相关性。方法:选取捷克技术大学—布尔诺大学附属医院建立的开源数据库中脐动脉血pH值<7.15的39例UC信号为研究组,选取数据库中脐动脉血pH值>7.15的513例UC信号为对照组,采用自适应噪声完备集合经验模态分解(CEEMDAN)方法将研究组和对照组UC信号进行多层分解重构;对重构结果进行递归定量分析(RQA),比较不同组别递归定量指标的水平;利用多因素logistic回归分析发生胎儿状态异常的影响因素;采用受试者工作特征(ROC)曲线分析递归定量指标对胎儿状态异常的预测价值。结果:研究组和对照组的多层递归图具有不同的纹理结构,与对照组相比,研究组递归定量指标的递归率(RR)和捕捉时间(TT)水平升高,差异均具有统计学意义(P<0.05),RR和TT水平升高是发生胎儿状态异常的危险因素(P<0.05)。RR和TT独立预测胎儿状态异常的ROC曲线下面积(AUC)分别为0.823和0.787,而二者联合预测胎儿状态异常的AUC为0.912,显著高于二者单独预测的AUC(Z=2.109,P<0.05;Z=2.128,P<0.05)。结论:UC信号多层递归定量分析指标RR和TT对胎儿状态异常有一定预测价值。 展开更多
关键词 宫缩 多层 递归图 递归定量分析 诊断价值
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基于小波基—改进卷积自编码器的故障诊断方法
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作者 王娜 刘佳林 王子从 《兵器装备工程学报》 北大核心 2025年第8期351-360,共10页
针对故障诊断中,传统小波包分解(wave packet decomposition,WPD)提取故障特征主观性强、且模型诊断效率低的问题,提出了一种基于小波基—改进卷积自编码器的故障诊断方法。基于能量—泰尔指数准则来准确选择小波基函数,以改善传统小波... 针对故障诊断中,传统小波包分解(wave packet decomposition,WPD)提取故障特征主观性强、且模型诊断效率低的问题,提出了一种基于小波基—改进卷积自编码器的故障诊断方法。基于能量—泰尔指数准则来准确选择小波基函数,以改善传统小波包基函数确定主观性较强的缺点,获得初始故障特征;引入无阈值递归图(thresholdless recurrence plot,TRP),并与小波包分解相结合,从空间角度进一步增强故障特征的显著性,并减少冗余特征;将softmax分类器嵌入到卷积自编码器(convolutional autoencoder,CAE)中,并构建新的损失函数,来确保参数训练的准确性,并通过一次迭代学习即可实现参数更新,进一步提高了模型的诊断效率。通过在CWRU数据集和JNU数据集上的仿真,结果表明,所提方法的诊断准确率分别为99.67%和98.33%,相比于其他方法具有更好的诊断精度及效率。 展开更多
关键词 故障诊断 小波包分解 泰尔指数 无阈值递归图 卷积自编码器 损失函数
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基于WP-TRP的滚动轴承故障诊断方法
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作者 王娜 崔月磊 +1 位作者 罗亮 王子从 《东北大学学报(自然科学版)》 北大核心 2025年第3期20-27,共8页
针对故障诊断中传统时频域法提取特征时易受主观因素影响而导致冗余,且深度学习算法受训练数据影响导致计算复杂性较高的缺点,将时域和频域结合,提出一种基于小波包-无阈值递归图(WPTRP)的滚动轴承故障诊断方法.首先,提出递减信息熵准则... 针对故障诊断中传统时频域法提取特征时易受主观因素影响而导致冗余,且深度学习算法受训练数据影响导致计算复杂性较高的缺点,将时域和频域结合,提出一种基于小波包-无阈值递归图(WPTRP)的滚动轴承故障诊断方法.首先,提出递减信息熵准则,以克服小波包分解的主观性,获取更准确的时频域特征;在此基础上,引入无阈值递归图思想,充分提取数据初始时域特征,并利用奇异值分解进一步降低冗余特征,提高计算效率.然后,引入海洋捕食者算法来获得支持向量机最优参数,实现故障诊断的准确分类.最后,通过标准滚动轴承数据集仿真验证了所提方法的有效性. 展开更多
关键词 故障诊断 小波包分解 信息熵 无阈值递归图 奇异值分解 海洋捕食者算法
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基于FDTRP-ALDCNN的小样本轴承故障诊断方法
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作者 王娜 刘佳林 王子从 《铁道科学与工程学报》 北大核心 2025年第9期4271-4283,共13页
针对滚动轴承在小样本条件下诊断精度低的问题,提出一种基于频域无阈值递归图与自适应线性可变卷积神经网络(frequency domain thresholdless recurrence plot-adaptive linear deformable convolutional neural network,FDTRP-ALDCNN)... 针对滚动轴承在小样本条件下诊断精度低的问题,提出一种基于频域无阈值递归图与自适应线性可变卷积神经网络(frequency domain thresholdless recurrence plot-adaptive linear deformable convolutional neural network,FDTRP-ALDCNN)的滚动轴承故障诊断方法。首先,使用快速傅里叶变换(fast fourier transform,FFT)将一维时域信号转为频域信号,并与无阈值递归图(thresholdless recurrence plot,TRP)相结合,以有效构建初始特征,提高模型输入质量;其次,采用线性可变卷积核(linear deformable convolutional kernel,LDConv)替换卷积神经网络中方形卷积核,从而能够根据采样数据的分布来调整卷积核形状,准确获取空间信息中的关键特征,提高小样本数据的利用率;再次,设计自适应交叉熵(adaptive cross entropy,ACE)损失函数,根据样本分类损失自适应调整分类器对难分与易分样本的拟合程度,增强难分样本损失在整体分类损失中的显著性,进一步提高小样本下的模型诊断精度;最后,采用CWRU滚动轴承数据集对所提方法进行3组仿真验证。对比仿真的结果表明,所提模型在不同小样本数量下均有较高的诊断准确率,最高可达到99.82%。而对2组不平衡数据集的泛化性分析可知,本模型的诊断准确率分别达到98.56%与99.3%,泛化能力优于其他模型,且具有良好的稳定性。并通过消融实验验证了FFT、LDConv与ACE损失函数对提高故障诊断精度的有效性。综上所述,所提方法能够有效诊断出小样本轴承故障,具有较高的实际应用价值。 展开更多
关键词 故障诊断 小样本 无阈值递归图 线性可变卷积核 卷积神经网络 交叉熵损失函数
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信息融合的NRP-AlexNet-SENet风电齿轮箱故障诊断 被引量:1
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作者 龙霞飞 何志成 +3 位作者 曾进辉 周凌 梁凯 伍席文 《太阳能学报》 北大核心 2025年第9期143-151,共9页
针对风电机组齿轮箱故障振动信号的非线性、特征信息混叠和诊断精度低等问题,结合时域特征分析与多传感器信息融合技术,提出一种无阈值递归图(NRP)与深度学习相结合的早期故障识别方法。首先,将时域指标作为特征参数并采用特征级与数据... 针对风电机组齿轮箱故障振动信号的非线性、特征信息混叠和诊断精度低等问题,结合时域特征分析与多传感器信息融合技术,提出一种无阈值递归图(NRP)与深度学习相结合的早期故障识别方法。首先,将时域指标作为特征参数并采用特征级与数据级融合技术构建数据信息;其次,采用NRP将一维数据信息转换为二维彩色可视特征图;然后,构建一种AlexNet-SENet网络结构,利用嵌入SENet注意力机制的改进AlexNet使其自适应选择并重点搜索结构与节点的关键特征信息。最后,以华中科技大学行星齿轮箱动力学试验平台采集的振动数据集为实例,结果表明该方法的诊断准确率为99%,能更有效提取故障特征信息,具有更高的分类诊断精度。 展开更多
关键词 风电机组齿轮箱 故障诊断 深度学习 信息融合 无阈值递归图 注意力机制
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On the boundedness of running-in attractors based on recurrence plot and recurrence qualification analysis 被引量:4
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作者 Guodong SUN Hua ZHU +2 位作者 Cong DING Yu JIANG Chunling WEI 《Friction》 SCIE CSCD 2019年第5期432-443,共12页
A feature parameter was proposed to quantitatively explore the boundedness of running-in attractors;its variation throughout the friction process was also investigated. The enclosing radius R was built with recurrence... A feature parameter was proposed to quantitatively explore the boundedness of running-in attractors;its variation throughout the friction process was also investigated. The enclosing radius R was built with recurrence plots(RPs) and recurrence qualification analysis(RQA) by using the time delay embedding and phase space reconstruction. Additionally, the typology of RPs and the recurrence rate(RR) were investigated to verify the applicability of R in characterizing the friction process. Results showed that R is larger at the beginning, but exhibits a downward trend in the running-in friction process;R becomes smooth and trends to small steady values during the steady-state friction period, and finally shows an upward trend until failure occurs. The evolution of R, which corresponded with the typology of RPs and RR during friction process, can be used to quantitatively analyze the variation of the running-in attractors and friction state identifacation. Hence, R is a valid parameter, and the boundedness of running-in attractors can offer a new way for monitoring the friction state of tribological pairs. 展开更多
关键词 RUNNING-IN ATTRACTOR BOUNDEDNESS enclosing radius recurrence plot dynamic evolvement
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基于无阈值递归图和CNN-LSTM的人体活动识别算法 被引量:4
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作者 史立宇 孙杨帆 +2 位作者 谢溢翀 黄旭萍 周彪 《传感器与微系统》 北大核心 2025年第3期130-133,共4页
人体活动识别(HAR)可为智慧生活、医疗监护、虚拟现实等上下文感知系统提供重要的基础信息,是模式识别领域的热门研究方向。针对现有基于惯性传感器的活动识别深度学习算法对于多维时间序列特征的提取效果欠佳的问题,提出了一种基于无... 人体活动识别(HAR)可为智慧生活、医疗监护、虚拟现实等上下文感知系统提供重要的基础信息,是模式识别领域的热门研究方向。针对现有基于惯性传感器的活动识别深度学习算法对于多维时间序列特征的提取效果欠佳的问题,提出了一种基于无阈值递归图(URP)和卷积神经网络—长短期记忆(CNN-LSTM)的活动识别算法。首先,使用SMOTE-ENN算法对惯性数据集进行增强,平衡各个类别样本数量比例;然后,使用URP方法将多维惯性传感时序波形构造为对应多个二维递归矩阵;最后,构建CNN-LSTM组合的分类模型。通过在UCI-HAR、WISDM公开数据集上的实验结果表明:所提算法在测试集上4种分类指标均得到提高,其中准确率分别达到98.32%和98.97%,性能优于现存的其他深度学习算法。 展开更多
关键词 人体活动识别 数据增强 深度学习 无阈值递归图 卷积神经网络 长短期记忆神经网络
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基于信号递归图和卷积宽度学习的小样本辐射源个体识别方法
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作者 陈宇鹏 黄科举 +2 位作者 刘辉 邝龙坤 杨俊安 《系统工程与电子技术》 北大核心 2025年第9期3086-3092,共7页
针对当前辐射源个体识别方法在小样本条件下容易过学习、识别准确率低的问题,提出一种基于信号递归图和卷积宽度学习的小样本辐射源个体识别方法。该方法将辐射源信号转换为递归图作为宽度学习网络的输入,将辐射源数据时序特征转化为图... 针对当前辐射源个体识别方法在小样本条件下容易过学习、识别准确率低的问题,提出一种基于信号递归图和卷积宽度学习的小样本辐射源个体识别方法。该方法将辐射源信号转换为递归图作为宽度学习网络的输入,将辐射源数据时序特征转化为图像空间特征;此外,提出一种卷积宽度学习网络,将宽度学习中特征节点的计算方式由矩阵乘法替换为卷积运算,通过稀疏连接和权值共享减少模型参数数量,从而减轻模型过拟合风险。通过对公开数据集实验,验证了所提算法在少量训练样本数量条件下相较于其他算法有更好的识别性能。 展开更多
关键词 递归图 卷积宽度学习 小样本 辐射源个体识别
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结合递归图与LeNet网络的足底压力身份识别方法
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作者 袁田 辛义忠 《仪器仪表学报》 北大核心 2025年第6期338-347,共10页
针对身份识别领域足底压力采集设备在传感器数量和位置配置方面存在的差异性,以及足底压力特征通常依赖于对步态周期数据进行完整分割所带来的时间成本增加问题,提出一种基于足底压力信号的无阈值递归图和LeNet网络的身份识别方法。首... 针对身份识别领域足底压力采集设备在传感器数量和位置配置方面存在的差异性,以及足底压力特征通常依赖于对步态周期数据进行完整分割所带来的时间成本增加问题,提出一种基于足底压力信号的无阈值递归图和LeNet网络的身份识别方法。首先使用自制足底压力采集设备,在常规混凝土地面采集28名无足部及下肢疾病的健康成年参与者无负重等干扰状态自然行走过程中的足底压力数据;再经数据重构算法对足底压力数据进行预处理,将其转化为无阈值递归图;最后将生成的图像作为LeNet网络的输入,完成特征提取与身份识别,并对单一区域及多区域组合方案的结果进行分析比较。实验结果表明,足跟内侧区域、足跟外侧区域、第二跖骨区域和大脚趾区域的组合身份识别性能以最少的传感器数量和高识别精度优于其他方案,其中准确率、精确率、召回率和F 1分数分别达到99.25%、99.22%、99.39%、99.26%。不同区域的身份识别性能受行走过程中不同阶段和受力大小的影响,但随着区域数量的增加,该影响逐渐减弱。此外,实验结果还显示,使用足底压力信号的无阈值递归图进行身份识别的方法无需依赖严格的步态分割,依然能够保持较高的识别精度。为身份识别技术在生物特征识别领域的应用提供了新的思路与技术支持,在公共安全等领域具有潜在的应用价值。 展开更多
关键词 足底压力特征 无阈值递归图 LeNet网络 身份识别
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气爆破岩振动信号优化分解与相关特征分析
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作者 付晓强 戴良玉 +1 位作者 俞缙 邵艺强 《中国安全科学学报》 北大核心 2025年第5期64-72,共9页
为解决变分模态分解过程中模态数和二次惩罚因子难以确定的问题,提出灰狼优化-变分模态分解(GWO-VMD)算法。以龙龙隧道气爆法施工为依托,采用集成化采集模块采集气爆破岩过程中振动信号,利用相空间重构递归图(RP)相似度模型准确判别信号... 为解决变分模态分解过程中模态数和二次惩罚因子难以确定的问题,提出灰狼优化-变分模态分解(GWO-VMD)算法。以龙龙隧道气爆法施工为依托,采用集成化采集模块采集气爆破岩过程中振动信号,利用相空间重构递归图(RP)相似度模型准确判别信号GWO-VMD主分量;重构得到去除干扰项的真实信号,揭示气爆信号能量在时频域的分布特征,并量化数码电子雷管精度误差。结果表明:与传统的变分模态算法相比,GWO-VMD算法在气爆破岩信号信噪比提升和自适应相关特征提取方面具有显著优势,具有很强的时变频率追踪性能,能够准确识别数码雷管起爆精度,有效识别隧道爆破雷管灾害源特征。 展开更多
关键词 气爆破岩 振动信号 优化分解 相关特征 递归图 灰狼优化-变分模态分解(GWO-VMD)
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自动驾驶事故碰撞时刻的相空间重构研究
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作者 李浩 陈子豪 +4 位作者 于海涛 韩杰 张雷 侯庆坤 卜德军 《中国汽车(中英文对照)》 2025年第6期323-335,共13页
自动驾驶汽车事故责任主体认定难,碰撞时刻作为交通事故定责分析的核心要素,对事故责任划分至关重要。以真实事故案例为研究材料,选取事故过程记录的汽车电子数据时序信号,基于相空间重构理论,构建出反映原始系统动力学特性的高维相空间... 自动驾驶汽车事故责任主体认定难,碰撞时刻作为交通事故定责分析的核心要素,对事故责任划分至关重要。以真实事故案例为研究材料,选取事故过程记录的汽车电子数据时序信号,基于相空间重构理论,构建出反映原始系统动力学特性的高维相空间,并结合递归图分析相空间数据突变点,确定交通事故的碰撞时刻。研究结果表明,相空间重构理论和递归图分析方法,可以从系统动力学特性变化角度分析自动驾驶汽车事故碰撞时刻,具有科学、准确、直观的特点,为自动驾驶汽车交通事故鉴定分析提供了新思路。 展开更多
关键词 自动驾驶 交通事故 碰撞时刻 汽车电子数据 相空间重构 递归图
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