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Detection of Abnormal Cardiac Rhythms Using Feature Fusion Technique with Heart Sound Spectrograms 认领 引用 被引量：1
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作者 Saif Ur Rehman Khan Zia Khan 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期2030-2049,共20页
A heart attack disrupts the normal flow of blood to the heart muscle,potentially causing severe damage or death if not treated promptly.It can lead to long-term health complications,reduce quality of life,and signific... A heart attack disrupts the normal flow of blood to the heart muscle,potentially causing severe damage or death if not treated promptly.It can lead to long-term health complications,reduce quality of life,and significantly impact daily activities and overall well-being.Despite the growing popularity of deep learning,several drawbacks persist,such as complexity and the limitation of single-model learning.In this paper,we introduce a residual learning-based feature fusion technique to achieve high accuracy in differentiating abnormal cardiac rhythms heart sound.Combining MobileNet with DenseNet201 for feature fusion leverages MobileNet lightweight,efficient architecture with DenseNet201,dense connections,resulting in enhanced feature extraction and improved model performance with reduced computational cost.To further enhance the fusion,we employed residual learning to optimize the hierarchical features of heart abnormal sounds during training.The experimental results demonstrate that the proposed fusion method achieved an accuracy of 95.67%on the benchmark PhysioNet-2016 Spectrogram dataset.To further validate the performance,we applied it to the BreakHis dataset with a magnification level of 100X.The results indicate that the model maintains robust performance on the second dataset,achieving an accuracy of 96.55%.it highlights its consistent performance,making it a suitable for various applications. 展开更多
关键词 Cardiac rhythms Feature fusion Residual learning BreakHis Spectrogram sound
Continuous frequency and phase spectrograms: a study of their 2D and 3D capabilities and application to musical signal analysis 认领 引用 被引量：1
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作者 Laurent NAVARRO Guy COURBEBAISSE Jean-Charles PINOLI 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2008年第2期199-206,共8页
A new lighting and enlargement on phase spectrogram （PS） and frequency spectrogram （FS） is presented in this paper. These representations result from the coupling of power spectrogram and short time Fourier transf... A new lighting and enlargement on phase spectrogram （PS） and frequency spectrogram （FS） is presented in this paper. These representations result from the coupling of power spectrogram and short time Fourier transform （STFT）. The main contribution is the construction of the 3D phase spectrogram （3DPS） and the 3D frequency spectrogram （3DFS）. These new tools allow such specific test signals as small slope linear chirp, phase jump case of musical signal analysis is reported. The main objective is to and small frequency jump to be analyzed. An application detect small frequency and phase variations in order to characterize each type of sound attack without losing the amplitude information given by power spectrogram 展开更多
关键词 Frequency spectrogram （FS） Phase spectrogram （PS） Time-frequency representations Musical signals
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Research on data diagnosis method of acoustic array sensor device based on spectrogram 认领 引用 被引量：4
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作者 Xing Lei Hang Ji +3 位作者 Qiang Xu Ting Ye Shengfu Zhang Chengjun Huang 《Global Energy Interconnection》 EI CSCD 2022年第4期418-433,共16页
Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods su... Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods such as ultrasonic method and pulse current method.However,due to the sensitivity of the acoustic array sensor and the influence of the equipment operation site interference,the acoustic array sensor device for partial discharge type diagnosis by phase resolved partial discharge(PRPD)map might occasionally presents incorrect results,thus affecting the power equipment operation and maintenance strategy.The acoustic array sensor detection device for power equipment developed in this paper applies the array design model of equal-area multi-arm spiral with machine learning fast fourier transform clean(FFT-CLEAN)sound source localization identification algorithm to avoid the interference factors in the noise acquisition system using a single microphone and conventional beam forming algorithm,improves the spatial resolution of the acoustic array sensor device,and proposes an acoustic array sensor device based on the acoustic spectrogram.The analysis and diagnosis method of discharge type of acoustic array sensor device can effectively reduce the system misjudgment caused by factors such as the resolution of the acoustic imaging device and the time domain pulse of the digital signal,and reduce the false alarm rate of the acoustic array sensor device.The proposed method is tested by selecting power cables as the object,and its effectiveness is proved by laboratory verification and field verification. 展开更多
关键词 Acoustic array sensor device Acoustic spectrogram Partial discharge Power equipment False alarm rate
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User Recognition System Based on Spectrogram Image Conversion Using EMG Signals 认领 引用 被引量：2
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作者 Jae Myung Kim Gyu Ho Choi +1 位作者 Min-Gu Kim Sung Bum Pan 《Computers, Materials & Continua》 SCIE EI 2022年第7期1213-1227,共15页
Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technol... Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technology(5G)based mobile devices.The EMG signals generated inside the body with unique individual characteristics are being studied as a part of nextgeneration user recognition methods.However,there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time.Hence,it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over time.In this paper,we propose a user recognition system that applies EMG signals to the short-time fourier transform(STFT),and converts the signals into EMG spectrogram images while adjusting the time-frequency resolution to extract multidimensional features.The proposed system is composed of a data pre-processing and normalization process,spectrogram image conversion process,and final classification process.The experimental results revealed that the proposed EMG spectrogram image-based user recognition system has a 95.4%accuracy performance,which is 13%higher than the EMGsignal-based system.Such a user recognition accuracy improvement was achieved by using multidimensional features,in the time-frequency domain. 展开更多
关键词 EMG user recognition spectrogram CNN
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Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms 认领 引用 被引量：2
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作者 Sonali S.Patil Sujit S.Pardeshi Abhishek D.Patange 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第7期177-199,共23页
In-process damage to a cutting tool degrades the surfacenish of the job shaped by machining and causes a signicantnancial loss.This stimulates the need for Tool Condition Monitoring(TCM)to assist detection of failure ... In-process damage to a cutting tool degrades the surfacenish of the job shaped by machining and causes a signicantnancial loss.This stimulates the need for Tool Condition Monitoring(TCM)to assist detection of failure before it extends to the worse phase.Machine Learning(ML)based TCM has been extensively explored in the last decade.However,most of the research is now directed toward Deep Learning(DL).The“Deep”formulation,hierarchical compositionality,distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform eciently in a high-noise environment of cross-domain machining.With this motivation,the design of dierent CNN(Convolutional Neural Network)architectures such as AlexNet,ResNet-50,LeNet-5,and VGG-16 is presented in this paper.Real-time spindle vibrations corresponding to healthy and various faulty congurations of milling cutter were acquired.This data was transformed into the time-frequency domain and further processed by proposed architectures in graphical form,i.e.,spectrogram.The model is trained,tested,and validated considering dierent datasets and showcased promising results. 展开更多
关键词 Milling tool inserts health monitoring vibration spectrograms deep learning convolutional neural network
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An Improved Forest Fire Detection Model Using Audio Classification and Machine Learning 认领 引用
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作者 Kemahyanto Exaudi Deris Stiawan +4 位作者 Bhakti Yudho Suprapto Hanif Fakhrurroja MohdYazid Idris Tami AAlghamdi Rahmat Budiarto 《Computers, Materials & Continua》 SCIE EI 2026年第1期2062-2085,共24页
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc... Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments. 展开更多
关键词 Audio classification convolutional neural network(CNN) environmental science forest fire detection machine learning spectrogram analysis IoT
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基于多通道声发射信号融合的水电机组空化故障诊断 认领 引用 被引量：1
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作者 肖龙 肖湘曲 +3 位作者 何志宏 师博威 徐恺 李超顺 《水利学报》 EI CSCD 北大核心 2026年第2期293-305,共13页
针对水电机组空化故障因信号单一及噪声干扰而难以识别的问题,本文提出一种基于多通道声发射信号融合的水电机组空化故障诊断方法。首先,在水电机组空化模拟试验台采集空化试验的多通道声发射信号,将多通道声发射信号经数据压缩处理形... 针对水电机组空化故障因信号单一及噪声干扰而难以识别的问题,本文提出一种基于多通道声发射信号融合的水电机组空化故障诊断方法。首先,在水电机组空化模拟试验台采集空化试验的多通道声发射信号,将多通道声发射信号经数据压缩处理形成水电机组空化故障数据集;再将声发射信号变换成梅尔时频图,对频率进行加权处理,以去除高频信号中的噪声和突出低频信号中的特征;最后,结合卷积块注意力模块(CBAM)和D-S证据理论构建出基于决策级融合的多通道深度卷积神经网络模型,进行水电机组空化故障样本的训练和测试,得到故障诊断结果。结果表明,该方法能有效区分不同工况下的空化故障,与其他模型方法对比,具有较高的诊断精度和良好的抗噪能力,对实际中的水电机组空化故障诊断应用有较大参考作用。 展开更多
关键词 多通道信号融合 声发射信号 水电机组空化故障诊断 梅尔时频图 深度卷积神经网络
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Joint spectrogram segmentation and ridge-extraction method for separating multimodal guided waves in long bones 认领 引用 被引量：10
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作者 ZHANG ZhengGang XU KaiLiang +1 位作者 TA DeAn WANG WeiQi 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS 2013年第7期1317-1323,共7页
Ultrasonic guided waves (GWs) can be used to evaluate long bones effectively because of the ability to provide the information of the whole bone. In this study, a joint spectrogram segmentation and ridge-extraction (J... Ultrasonic guided waves (GWs) can be used to evaluate long bones effectively because of the ability to provide the information of the whole bone. In this study, a joint spectrogram segmentation and ridge-extraction (JSSRE) method was proposed to separate multiple modes in long bones. First, the Gabor time-frequency transform was applied to obtain the spectrogram of multimodal signals. Then, a multi-class image segmentation algorithm was used to find the corresponding region of each mode in the spectrogram, including an improved watershed transform and a region growing procedure. Finally, the ridges were extracted and the time domain signals representing individual modes were reconstructed from these ridges in each region. The validations of this method were discussed by simulated multimodal signals with different signal-to-noise ratios (SNR). The correlation coefficients between the original signals without noise and the reconstructed signals were calculated to analyze the results quantitatively. The results showed that the extracted ridges were in good agreement with generated theoretical dispersion curves, and the reconstructed signals were highly related to the original signals, even under the SNR=3 dB situation. 展开更多
关键词 multimodal guided waves long bone spectrogram segmentation
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Wheeze detecting method based on spectrogram entropy analysis 认领 引用 被引量：5
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作者 LI Jiarui HONG Ying 《Chinese Journal of Acoustics》 CSCD 2016年第4期508-515,共8页
In order to eliminate the subjectivity of wheeze diagnosis and improve the accuracy of objective detecting methods,this paper introduces a wheeze detecting method based on spectrogram entropy analysis.This algorithm m... In order to eliminate the subjectivity of wheeze diagnosis and improve the accuracy of objective detecting methods,this paper introduces a wheeze detecting method based on spectrogram entropy analysis.This algorithm mainly comprises three steps which are preprocessing,features extracting and wheeze detecting based on support vector machine(SVM).Herein,the preprocessing consists of the short-time Fourier transform(STFT) decomposition and detrending.The features are extracted from the entropy of spectrograms.The step of detrending makes the difference of the features between wheeze and normal lung sounds more obvious.Moreover,compared with the method whose decision is based on the empirical threshold,there is no uncertain detecting result any more.Results of two testing experiments show that the detecting accuracy(AC) are 97.1%and 95.7%,respectively,which proves that the proposed method could be an efficient way to detect wheeze. 展开更多
关键词 NLS Wheeze detecting method based on spectrogram entropy analysis STFT SVM
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基于声信号多特征时频谱图的供水管网漏损检测 认领 引用
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作者 孙庆帅 张英杰 +1 位作者 刘华亮 李述杰 《控制与决策》 EI CSCD 北大核心 2026年第5期1381-1391,共11页
基于声信号的漏损检测方法在人工巡检中具有重要应用价值,正逐步发展为一种新兴的远程监测手段.传统基于深度学习的漏损声信号识别方法往往受到信号预处理流程繁琐以及环境噪声干扰的限制,难以在其他供水管网中实现良好的泛化性能,漏损... 基于声信号的漏损检测方法在人工巡检中具有重要应用价值,正逐步发展为一种新兴的远程监测手段.传统基于深度学习的漏损声信号识别方法往往受到信号预处理流程繁琐以及环境噪声干扰的限制,难以在其他供水管网中实现良好的泛化性能,漏损检测的准确率亦有待进一步提升.鉴于此,首先,针对供水管网声信号构建高时间分辨率和高频率分辨率下的线性谱图和对数梅尔谱图,兼顾声信号的高频与低频特征,突出短时动态变化以及微弱频率特征,并以并行方式输入至卷积神经网络;然后,引入并行机制的时-频注意力卷积块进行特征提取,增强对时间和频率维度的细粒度特征捕捉能力;最后,利用真实供水管网声信号数据和物理仿真数据对所提出方法进行漏损检测性能实验验证,实验结果表明,所提出方法显著提高了对漏损事件的识别率,具有良好的鲁棒性和泛化能力. 展开更多
关键词 供水管网 漏损检测 声信号分析 时频谱图 线性谱图 对数梅尔谱图 注意力机制
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基于改进MobileViT的气固两相流检测方法 认领 引用
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作者 仝卫国 王用沛 +2 位作者 林礼榕 陈波帆 钟海增 《动力工程学报》 CAS CSCD 北大核心 2026年第5期154-164,共11页
针对气固两相流流动特性复杂导致流动参数难以准确检测的问题,结合神经网络技术对其流动过程中产生的音频信号进行分析,提出一种基于改进MobileViT的气固两相流检测方法。以轻量化网络MobileViT作为基础模型,首先通过谱减法对采集到的... 针对气固两相流流动特性复杂导致流动参数难以准确检测的问题,结合神经网络技术对其流动过程中产生的音频信号进行分析,提出一种基于改进MobileViT的气固两相流检测方法。以轻量化网络MobileViT作为基础模型,首先通过谱减法对采集到的音频信号进行去噪,并提取其梅尔频谱图作为模型的输入;其次,在模型中引入双向空洞空间金字塔池化模块,提取特征图在水平和垂直维度上的多尺度特征信息;然后,在模型中引入全局局部空间注意力机制,增强模型对特征图关键区域特征信息的捕捉和表达能力;最后,使用细节增强卷积替换MobileViT block中的3×3标准卷积,以提取出更为丰富的特征图局部细节特征信息。结果表明:改进后的模型对6种流量条件下的气固两相流具有较好的识别效果,准确率为98.833%,较原模型提高3.166%。 展开更多
关键词 气固两相流 神经网络 音频信号 MobileViT 梅尔频谱图
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面向语音识别增强的量子衍生最优分数阶声谱图 认领 引用
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作者 孙磊 章先恒 +4 位作者 廖一鹏 白森杰 李浩 高跃明 廖赐麟 《华南理工大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第7期50-65,共16页
针对传统声谱图在语音信号时频表示中分辨率不足、特征判别性弱的问题,该文提出了一种基于量子衍生牛顿-拉夫逊算法的最优分数阶声谱图生成方法,以提升语音识别与分类任务的性能。首先,采用量子编码对牛顿-拉夫逊算法进行种群初始化,通... 针对传统声谱图在语音信号时频表示中分辨率不足、特征判别性弱的问题,该文提出了一种基于量子衍生牛顿-拉夫逊算法的最优分数阶声谱图生成方法,以提升语音识别与分类任务的性能。首先,采用量子编码对牛顿-拉夫逊算法进行种群初始化,通过量子旋转门引导个体向最优解方向收敛,引入量子变异和灾变机制以维持种群多样性、避免早熟收敛,同时结合模拟退火和Lévy飞行策略,增强算法的全局最优搜索能力;然后,对音频信号进行加窗、分帧预处理,通过分数阶傅里叶变换生成分数阶声谱图,再通过梅尔滤波器进行尺度压缩,得到分数阶梅尔声谱图;最后,引入可调节的分数阶参数α,扩展信号在时频域的表示自由度,并以信息熵最小化为目标函数,利用量子衍生牛顿-拉夫逊算法对α、帧长和帧移等超参数进行自适应优化,得到最优分数阶声谱图。在CEC 2022标准测试函数、公用的情感识别数据集RAVDESS、声音分类数据集UrbanSound8K及自建母音歌唱发音数据集上的仿真实验结果表明:量子衍生牛顿-拉夫逊算法相比现有优化算法全局寻优能力更强、求解高维复杂问题的稳定性更高;所生成的最优分数阶声谱图能有效聚焦信号能量、增强特征可分性,在语音识别任务的准确率、召回率和F1分数上均显著优于传统语音特征提取方法。该文方法为复杂语音信号的高精度特征提取提供了新思路,可有效提升语音识别效果,具有良好的鲁棒性与应用前景。 展开更多
关键词 语音识别增强 分数阶傅里叶变换 分数阶声谱图 量子衍生优化 牛顿-拉夫逊算法
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Speech endpoint detection in low-SNRs environment based on perception spectrogram structure boundary parameter 认领 引用 被引量：9
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作者 WU Di ZHAO Heming +4 位作者 HUANG Chengwei XIAO Zhongzhe ZHANG Xiaojun XU Yishen TAO Zhi 《Chinese Journal of Acoustics》 2014年第4期428-440,共13页
The Perception Spectrogram Structure Boundary（PSSB）parameter is proposed for speech endpoint detection as a preprocess of speech or speaker recognition.At first a hearing perception speech enhancement is carried out... The Perception Spectrogram Structure Boundary（PSSB）parameter is proposed for speech endpoint detection as a preprocess of speech or speaker recognition.At first a hearing perception speech enhancement is carried out.Then the two-dimensional enhancement is performed upon the sound spectrogram according to the difference between the determinacy distribution characteristic of speech and the random distribution characteristic of noise.Finally a decision for endpoint was made by the PSSB parameter.Experimental results show that,in a low SNR environment from-10 dB to 10 dB,the algorithm proposed in this paper may achieve higher accuracy than the extant endpoint detection algorithms.The detection accuracy of 75.2%can be reached even in the extremely low SNR at-10 dB.Therefore it is suitable for speech endpoint detection in low-SNRs environment. 展开更多
关键词 Speech endpoint detection in low-SNRs environment based on perception spectrogram structure boundary parameter
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基于改进EfficientNetV2的铝液泄漏声音识别与预警机制 认领 引用
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作者 梁艳辉 温承杰 +2 位作者 闫军威 周璇 张洪涛 《华南理工大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第2期38-51,共14页
铝液泄漏是导致铝加工深井铸造爆炸事故的直接原因。为解决实际工程中铝液泄漏判断方法滞后性强、准确率低和监测范围受限等问题,该文提出了基于改进EfficientNetV2的铝液泄漏声音识别方法。该方法通过声音特征判断铝液泄漏,以扩大监测... 铝液泄漏是导致铝加工深井铸造爆炸事故的直接原因。为解决实际工程中铝液泄漏判断方法滞后性强、准确率低和监测范围受限等问题,该文提出了基于改进EfficientNetV2的铝液泄漏声音识别方法。该方法通过声音特征判断铝液泄漏,以扩大监测范围;同时通过优化堆叠因子、引入高效通道注意力机制改进EfficientNetV2结构,以进一步提升识别速率与准确率。首先,利用拾音器采集不同场景下的声音数据,构建包含7类声音场景的声音数据库;然后,从声音信号中提取对数梅尔语谱图作为特征集,输入到改进的EfficientNetV2模型进行训练与验证,最终得到铝液泄漏声音识别模型。实验结果表明:改进的EfficientNetV2识别准确率达95.48%;与原始EfficientNetV2、ResNet、 RegNet及DenseNet相比,改进模型的浮点运算次数分别为上述模型的12.34%、8.64%、11.14%和10.80%,参数量分别为上述模型的11.37%、9.55%、15.95%和17.24%,CPU环境下每秒处理图像帧数分别为上述模型的6.53倍、6.14倍、4.41倍和8.00倍,说明改进的EfficientNetV2具有快速准确的识别性能。此外,基于该文提出的铝液泄漏声音识别方法,构建了铝液泄漏风险预警机制,并将该机制应用于铸造单元的实时风险监测。实践结果验证了所提识别方法与预警机制的有效性,可为铝加工深井铸造爆炸事故的预防提供技术参考。 展开更多
关键词 铝加工深井铸造 铝液泄漏 声音识别 风险预警 改进的EfficientNetV2 对数梅尔语谱图
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基于Mel时频谱和3DCNN-SVM的柔性薄壁轴承故障诊断 认领 引用
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作者 郭明军 吴海江 +1 位作者 赵学智 李书平 《兵器装备工程学报》 CAS CSCD 北大核心 2026年第4期236-246,共11页
针对柔性薄壁轴承存在的故障特征提取困难、智能诊断模型泛化性能有限和小样本场景分类精度不足等问题,提出了一种基于Mel时频谱,融合三维卷积神经网络和支持向量机的故障诊断方法(3D convolutional neural network-support vector mach... 针对柔性薄壁轴承存在的故障特征提取困难、智能诊断模型泛化性能有限和小样本场景分类精度不足等问题,提出了一种基于Mel时频谱,融合三维卷积神经网络和支持向量机的故障诊断方法(3D convolutional neural network-support vector machine,3DCNN-SVM)。首先将经短时傅里叶变换后的振动信号通过Mel滤波器组处理,并融合多尺度窗口长度生成三维Mel时频谱特征张量,以增强故障特征的辨识度并压缩数据量,提升中低频弱时变特征在强噪声下的可分辨性;然后利用3DCNN深度挖掘三维特征张量中的时空演化规律;最后采用一对多的SVM替代传统Softmax分类器,优化小样本场景下的决策边界。实验结果表明,在不同训练集和测试集划分比例下,模型的平均识别率达99.90%,最高识别率为100%,显著优于单一3DCNN模型。由此说明,融合多尺度的Mel时频谱和3DCNN-SVM可有效提取信号的三维时频信息,且能提升模型的准确率和泛化性能。 展开更多
关键词 柔性薄壁轴承 故障诊断 Mel时频谱 3D卷积神经网络 支持向量机
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全国碳市场与地方试点碳市场价格间的动态因果关系研究 认领 引用
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作者 田洪志 张熙 +1 位作者 姚峰 胡仪元 《生态经济》 北大核心 2026年第7期6-14,共9页
论文考察了“双轨制”碳定价体系下,全国碳市场与湖北、广东、深圳三个地方试点碳市场之间的价格因果关系。研究发现:全国碳市场价格与深圳、广东碳试点市场价格存在双向因果关系,其中全国碳价对深圳碳价的因果影响强度大于反向影响,而... 论文考察了“双轨制”碳定价体系下,全国碳市场与湖北、广东、深圳三个地方试点碳市场之间的价格因果关系。研究发现:全国碳市场价格与深圳、广东碳试点市场价格存在双向因果关系,其中全国碳价对深圳碳价的因果影响强度大于反向影响,而广东碳价对全国碳价的因果影响强度大于反向影响;在地方碳市场间,仅广东碳价单向影响深圳碳价。此外,中国全国碳市场价格与欧盟碳市场价格之间不存在显著因果关系。通过频谱测度图分析,全国碳价在短、中期内对深圳碳价影响较小,深圳碳价对全国碳价主要表现为短期和长期影响;全国碳价与广东碳价间的双向因果关系在长期内才显著。因此,应加快地方碳试点市场并入全国碳统一大市场的节奏,借鉴广东远期交易模式探索碳期货业务,积极开展国际碳排放权交易,提升中国碳价格的国际影响力。 展开更多
关键词 全国碳市场价格 地方试点碳市场价格 单方向因果测度 频谱图
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Manifestation of attosecond XUV fields temporal structures in attosecond streaking spectrogram 认领 引用
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作者 陈光龙 曹云玖 Dong Eon Kim 《Chinese Optics Letters》 SCIE EI CAS CSCD 2011年第6期100-103,共4页
The features of an attosecond extreme ultraviolet （XUV） field are encoded in the attosecond XUV spectrogram. We investigate the effect of the temporal structures of attosecond XUV fields on the attosecond streaking ... The features of an attosecond extreme ultraviolet （XUV） field are encoded in the attosecond XUV spectrogram. We investigate the effect of the temporal structures of attosecond XUV fields on the attosecond streaking spectrogram. Factors such as the number of attosecond XUV pulses and the temporal chirp of attosecond XUV pulses are considered. Results indicate that unlike the attosecond streaking spectrogram for an attosecond XUV field with two pulses of a half-cycle separation of streaking field, the spectrogram for the attosecond XUV field with three pulses demonstrates fine spectral fringes in separated traces. 展开更多
关键词 Manifestation of attosecond XUV fields temporal structures in attosecond streaking spectrogram NIR
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数据中心供配电系统电源可靠性设计研究 认领 引用
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作者 李敏 刘清东 《通信电源技术》 2026年第10期118-120,共3页
针对数据中心高密度负荷波动、末端扰动传递和储能短时衔接中的连续供电判据分散问题,提出负荷纹谱引导重构、末端微域隔离解耦与双储接续协同控稳技术,沿进线、母线、列头柜及电源分配单元(Power Distribution Unit,PDU)等完成轨迹采... 针对数据中心高密度负荷波动、末端扰动传递和储能短时衔接中的连续供电判据分散问题,提出负荷纹谱引导重构、末端微域隔离解耦与双储接续协同控稳技术,沿进线、母线、列头柜及电源分配单元(Power Distribution Unit,PDU)等完成轨迹采集、纹谱分簇、边界划界与分段接续设计,并在通信云枢纽数据中心开展算例校核。测试结果表明,该设计方案可缩短关键负荷失电持续时长,提升供电连续性,满足数据中心场景下电源可靠运行需求。 展开更多
关键词 数据中心 供配电系统 电源可靠性 负荷纹谱 双储协同
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基于DenseNet和迁移学习的声纹识别方法 认领 引用 被引量：1
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作者 陈润强 王卫辰 +1 位作者 徐亚博 李烈 《现代电子技术》 北大核心 2026年第2期171-177,共7页
传统的声纹识别方法受环境噪声和个体变化等因素的影响,准确率难以进一步提升。为此,提出一种基于DenseNet和迁移学习的语谱图声纹识别方法,以进一步提高声纹识别系统的性能。使用DenseNet的声纹识别模型对源域语音进行训练;采用迁移学... 传统的声纹识别方法受环境噪声和个体变化等因素的影响,准确率难以进一步提升。为此,提出一种基于DenseNet和迁移学习的语谱图声纹识别方法,以进一步提高声纹识别系统的性能。使用DenseNet的声纹识别模型对源域语音进行训练;采用迁移学习将源域训练的DenseNet模型迁移到目标域训练数据;在目标域测试数据上验证迁移后模型的性能,并对比分析迁移前后DenseNet模型和ResNet模型的声纹识别性能。实验结果表明,与原始ResNet模型、DenseNet模型和经迁移学习的ResNet模型相比,经迁移学习的DenseNet模型的识别准确率分别提高了3.89%、6.67%和3.34%,且具有较快的收敛速度。 展开更多
关键词 声纹识别 DenseNet 迁移学习 语谱图 ResNet 语音信号处理
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基于多粒度声谱图的托辊异常状态检测方法 认领 引用
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作者 党颖滢 曹现刚 +6 位作者 张鑫媛 李翔宇 毛怡文 樊红卫 董明 万翔 段雍 《工矿自动化》 CSCD 北大核心 2026年第2期59-68,共10页
在井下复杂工况下,胶带摩擦与煤流冲击产生的机械噪声、风流扰动噪声及多设备耦合噪声相互叠加,导致托辊故障特征声纹极易被环境噪声掩盖;同时,托辊异常样本获取困难、标注成本高,使得基于传统监督学习的托辊异常状态检测方法难以有效... 在井下复杂工况下,胶带摩擦与煤流冲击产生的机械噪声、风流扰动噪声及多设备耦合噪声相互叠加,导致托辊故障特征声纹极易被环境噪声掩盖;同时,托辊异常样本获取困难、标注成本高,使得基于传统监督学习的托辊异常状态检测方法难以有效推广。针对上述问题,提出一种基于多粒度声谱图与注意力自编码器(MG-AAE)的无监督托辊异常状态检测方法,该方法仅利用正常工况托辊声音训练模型,无需故障标签。构建由Mel声谱图与Mel频率倒谱系数(MFCCs)组成的多粒度复合声谱特征,兼顾能量轮廓与细粒度声纹;在编码器中引入高斯差分金字塔(GDP)与多头注意力机制(MHA),通过多尺度建模与自适应加权融合,抑制稳态背景噪声并突出关键故障频带;以多维重构均方误差作为异常判据,实现托辊异常状态的自动识别。实验结果表明,在仅使用正常样本训练的前提下,MG-AAE模型在跨设备与真实工况评估中均展现出优异性能。基于MIMII数据集4类典型设备的评估显示,在0 dB强噪声工况下,MG-AAE模型的平均特征曲线下的面积(AUC)与局部AUC(pAUC)分别达到84.2%和70.4%,较自编码器模型提升7.3%和5.6%。在真实托辊数据上,AUC达95.47%,异常样本重构误差约为正常样本的1.40倍。说明该方法具有良好的跨设备泛化与低误报率特性,可为煤矿带式输送机托辊状态异常检测提供有效技术支撑。 展开更多
关键词 托辊 无监督异常检测 多粒度声谱图 Mel声谱图 Mel频率倒谱系数 自编码器 复合声学特征
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