Time Fourier(시간 푸리에)란 무엇입니까?
Time Fourier 시간 푸리에 - The main purpose of this paper is to represent a methodenabling vibration components to be extracted from a high-resolution Short-Time Fourier-Transformation (STFT) based spectrogram assessed as an image to support transient analysis on rotating machines. [1] A smeared spectrogram is a result of the smoothing kernel in the short-time Fourier-transform (STFT). [2] In this paper, we propose a novel seismic time-frequency analysis method via the time-reassigned synchrosqueezing transform (TSST), in which the time-frequency coefficients are reassigned in the time direction rather than in the frequency direction as the short-time Fourier-based synchrosqueezing transform (FSST) does. [3] In this study, a specific laser reflection method with coverslips and real-time Fourier-transform infrared (RT-FTIR) spectroscopy were used to determine the effects of the double-bond concentration, monomer ratio, photoinitiator, and incident light intensity on the real-time volume shrinkage and conversion rate. [4] Namely, we represent functions $f$ via the so-called short-time Fourier, alias Fourier-Wigner or Bargmann transform $V_g f$ ($g$ being a fixed window function), and we measure their regularity and decay by means of mixed Lebesgue norms in phase space of $V_g f$, that is in terms of membership to modulation spaces $M^{p,q}$, $0< p,q\leq \infty$. [5] In the past, we have seen excellent works on time-frequency analysis of a signal such as short-time Fourier, wavelet, Hilbert and Hilbert-Huang transforms among others. [6] We extracted a total of three features for the shrunk DS-CNN model: the short-time Fourier-transformed (STFT) feature, the Mel-frequency cepstrum coefficient (MFCC) feature, and the fused features of these two. [7] Vibration analysis considered the following: use of the Fast Fourier Transform (FFT) method, application of the Short-Time Fourier-Transformation (STFT) method, and observation of the acceleration of vibrations recorded during processing. [8] Bubble activities at the focus were measured by passive cavitation detection (PCD) to quantify the scattering and inertial cavitation levels using short‐time Fourier‐transform (STFT). [9]convolutional neural network 컨볼루션 신경망
In this letter, based on the short-time Fourier transform (STFT) and convolutional neural network (CNN), we firstly develop a STFT-CNN method for spectrum sensing. [1] This method fuses three short-time Fourier transform time–frequency graphs disturbed by three consecutive pulse periods into a new graph as the input of the convolutional neural network (CNN). [2] In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). [3] Spectrograms of vibration data are generated by means of Short-time Fourier Transform and then provided as input to a convolutional neural network. [4] Here, a new convolutional neural networks (CNN) method based on short-time Fourier transform (STFT) to diagnose lung tissues via Raman spectra readily is proposed. [5] Recently, this observation motivated the development of Short-Time Fourier Neural Networks (STFNets) that learn directly in the frequency domain, and were shown to offer large performance gains compared to Convolutional Neural Networks (CNNs) when designing supervised learning models for IoT tasks. [6] To compare both architectures, they are set up as convolutional neural networks and the sensors are transformed to the time-frequency domain through the short-time Fourier transform (STFT). [7] For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. [8] S1 and S2 in the training dataset were transformed to spectra by short-time Fourier transform and be feed to the two-stream convolutional neural network. [9] This study proposes to combine a convolutional neural network with a short-time Fourier transform to identify different brain patterns between methamphetamine abstainers and controls. [10] We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). [11] We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). [12] To classify these hand gestures, basically simple CNN (convolutional neural network) models with raw data, short-time Fourier transform (STFT), wavelet transform (WT), and scale average wavelet transform (SAWT) are applied, and their performances are compared. [13] In a recent work on direction-of-arrival (DOA) estimation of multiple speakers with convolutional neural networks (CNNs), the phase component of short-time Fourier transform (STFT) coefficients of the microphone signal is given as input and small filters are used to learn the phase relations between neighboring microphones. [14]이 편지에서는 STFT(단시간 푸리에 변환) 및 CNN(컨볼루션 신경망)을 기반으로 먼저 스펙트럼 감지를 위한 STFT-CNN 방법을 개발합니다. [1] 이 방법은 3개의 연속적인 펄스 주기에 의해 교란된 3개의 단시간 푸리에 변환 시간-주파수 그래프를 합성곱 신경망(CNN)의 입력으로 새 그래프로 융합합니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14]
continuous wavelet transform 연속 웨이블릿 변환
The proposed model generates scalogram and spectrogram representations by transforming preprocessed 30 s ECG segments from time domain to the frequency domain using continuous wavelet transform and short time Fourier transform, respectively. [1] The average accuracy value obtained using the proposed approach is higher than the short-time Fourier transform (STFT), discrete-time continuous wavelet transform (DT-CWT), and Stockwell transform (ST) based time-frequency analysis methods with deep convolutional BLSTM models to detect AF. [2] Two spectral analysis methods: Short Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT), were then applied to get the spectrogram of the gunshot audio signal. [3] It is viewed as a hybrid of continuous wavelet transform and short-time Fourier transform. [4] The EEG signals are subjected to continuous wavelet transform, short-time Fourier transform, and smoothed pseudo-Wigner–Ville distribution (SPWVD) techniques to obtain scalogram, spectrogram, and SPWVD-based time–frequency representation (TFR) plots, respectively. [5] Linear TFRs such as Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Stockwell Transform (S-Transform) and Quadratic TFRs like Wigner Ville Distribution (WVD), Pseudo Wigner Ville Distribution (PWVD), Choi William Distribution (CWD), and Rihaczek Distribution (RD) are designed in Verilog and performed over FPGA. [6] Four commonly used time-frequency analysis methods are discussed and compared, such as the short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert-Huang transform (HHT), and Wigner–Ville distribution (WVD). [7] The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. [8] For this purpose, the proposed model generates scalogram and spectrogram representations by transforming preprocessed 30-sec ECG segments from time domain to the frequency domain using Continuous Wavelet Transform (CWT) and Short Time Fourier transform (STFT), respectively. [9] In this paper, firstly, experimental analysis is performed to validate the laboratory prototype setup using FFT (fast Fourier transform), STFT (short-time Fourier transform) and CWT (continuous wavelet transform). [10] The combustion fluctuation-resolving abilities of different TFA methods, including the short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert–Huang transform (HHT), were investigated using a reconstruction signal. [11] Furthermore, to gain an insight into the combustion stability and CCV, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) methodologies are applied to estimate the combustion stability and CCV of the TF combustion process. [12] Classical estimators, like the short-time Fourier transform or the continuous-wavelet transform optimize either temporal or frequency resolution, or find a suboptimal tradeoff. [13] The milling force signals were measured by cutting force sensor and then analyzed by using time-frequency analysis approaches such as short-time Fourier transform (STFT) and continuous wavelet transform (CWT) in order to identify chatter. [14]제안된 모델은 연속 웨이블릿 변환과 단시간 푸리에 변환을 각각 사용하여 시간 영역에서 주파수 영역으로 전처리된 30초 ECG 세그먼트를 변환하여 스칼로그램 및 스펙트로그램 표현을 생성합니다. [1] 제안된 접근 방식을 사용하여 얻은 평균 정확도 값은 deep convolutional BLSTM을 사용하는 STFT(단시간 푸리에 변환), DT-CWT(이산 시간 연속 웨이블릿 변환) 및 ST(Stockwell 변환) 기반 시간-주파수 분석 방법보다 높습니다. AF를 감지하는 모델. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14]
time frequency domain 시간 주파수 영역
Then, the echo sequence in time domain after zero interpolation is transformed to the time-frequency domain by short-time Fourier transform (STFT). [1] The algorithm transforms the degraded sound in an ’image’ in the time-frequency domain via a short-time Fourier transform. [2] In the time-frequency domain, the spectrogram obtained via short-time Fourier transform provides spectral information and ${{\rm A}_t}$ of both direct and reflected signals simultaneously. [3] The date sets are then transformed into time-frequency domain by short-time Fourier transform (STFT), resulting in time-frequency graph (TFG). [4] First, the WBI-corrupted SAR echo is characterized in the time-frequency domain by short-time Fourier transform (STFT) with adaptive window width, determined by the proposed window width optimization method. [5] After motion compensation, the target signal in a range unit is transformed into high-resolution time-frequency domain by means of the short-time iterative adaptive approach (STIAA), which can avoid the troubled cross-term interference and effectively break through the time-frequency resolution restriction in comparison with the conventional short-time Fourier transform (STFT). [6] Therefore, in this study, the welding current signal measured in real-time in the GMAW process was pre-processed by a short time Fourier transform (STFT) to obtain a time-frequency domain feature image (spectrogram). [7] In this paper, we propose a novel feature fusion-based iEEG classification method, a deep learning model termed Time-Frequency Hybrid Network (TF-HybridNet), in which short-time Fourier transform (STFT) and 1d convolution layers are performed on the input iEEG in parallel to extract features of the time-frequency domain and feature maps. [8] The first was the Short-time Fourier Transform (STFT) where the time-frequency domain image is extracted as the feature used for status identification. [9] First, a WBI-contaminated SAR echo is represented in the time-frequency domain by using the short-time Fourier transform (STFT). [10]그런 다음 0 보간 후 시간 영역의 에코 시퀀스는 STFT(단시간 푸리에 변환)에 의해 시간-주파수 영역으로 변환됩니다. [1] 알고리즘은 단시간 푸리에 변환을 통해 시간-주파수 영역에서 '이미지'의 저하된 사운드를 변환합니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10]
time frequency representation 시간 주파수 표현
The short-time Fourier transform-based SST (FSST for short) reassigns the frequency variable to sharpen the time-frequency representation and to separate the components of a multicomponent non-stationary signal. [1] In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. [2] While it is acknowledged that the synchrosqueezing transform (SST) improves the readability of the time-frequency representation (TFR) of the modes of MCSs, and that SST-based demodulation (DSST) is more efficient than SST itself for mode retrieval (MR), it is unclear whether DSST outperforms downsampled short-time Fourier transform (STFT) in that matter. [3] Three-dimensional inputs of short-time Fourier transform (STFT) magnitudes and an additional time-frequency representation based on phase information are investigated as well as two-dimensional STFT or constant-Q transform (CQT) magnitudes. [4] As input to our proposed model, we evaluate common time-frequency representations of the acoustic signal, such as short-time Fourier, continuous wavelet transform and Mel spectrograms. [5] , the Short-Time Fourier Transform (STFT) to produce a time-frequency representation that is subsequently transformed to bending angle (BA) and impact height (IH) coordinates by non-linear mapping. [6]단시간 푸리에 변환 기반 SST(줄여서 FSST)는 주파수 변수를 재할당하여 시간-주파수 표현을 선명하게 하고 다성분 비정상 신호의 구성요소를 분리합니다. [1] 본 논문에서는 다양한 정상 또는 불량 조건에서 원시 데이터로부터 시간-주파수 표현 진동 이미지를 획득하기 위한 전처리 단계로 단시간 푸리에 변환(STFT)을 제안합니다. [2] nan [3] nan [4] nan [5] nan [6]
time frequency image 시간 주파수 이미지
We proposed an algorithm that can automatically extract the dispersion coefficients of lightning whistler: (1) using two seconds time window on the SCM VLF data from the ZH-1 satellite to obtain segmented data; (2) generating time-frequency profile (TFP) of the segmented waveform by performing a band-pass filter and the short-time Fourier transform with a 94% overlap; (3) annotating the ground truth of the whistler with the rectangular boxes on the each time-frequency image to construct the training dataset; (4) building the YOLOV3 deep neural network and setting the training parameters; (5) inputting the training dataset to the YOLOV3 to train the whistler recognition model; (6) detecting the whistler from the unknown time-frequency image to extract the whistler area with the rectangle box as a sub-image; (7) conducting the BM3D algorithm to denoise the sub-image; (8) employing an adaptive threshold segmentation algorithm on the denoised sub-image to obtain the binary image which represents the whistler trace with the black pixel and other area with white pixel. [1] A short time Fourier transform is applied to each electrode of the MI-EEG signal to generate a time-frequency image, and the parts corresponding to the</p><p>우리는 번개 휘파람의 분산 계수를 자동으로 추출할 수 있는 알고리즘을 제안했습니다. (2) 대역 통과 필터 및 94% 중첩으로 단시간 푸리에 변환을 수행하여 분할된 파형의 시간-주파수 프로파일(TFP)을 생성하는 단계; (3) 훈련 데이터 세트를 구성하기 위해 각 시간-주파수 이미지에 직사각형 상자로 휘슬러의 실측을 주석 처리하는 단계; (4) YOLOV3 심층 신경망을 구축하고 훈련 매개변수를 설정합니다. (5) 휘슬러 인식 모델을 훈련시키기 위해 훈련 데이터 세트를 YOLOV3에 입력하는 단계; (6) 미지의 시간-주파수 이미지로부터 휘슬러를 검출하여 직사각형 상자를 서브 이미지로 포함하는 휘슬러 영역을 추출하는 단계; (7) BM3D 알고리즘을 수행하여 하위 이미지를 잡음 제거하는 단계; (8) 잡음이 제거된 하위 이미지에 적응형 임계값 분할 알고리즘을 사용하여 검은색 픽셀로 휘슬러 자취를 나타내고 흰색 픽셀로 다른 영역을 나타내는 이진 이미지를 얻습니다. [1] MI-EEG 신호의 각 전극에 단시간 푸리에 변환을 적용하여 시간-주파수 영상을 생성하고, <inline-formula> <tex-math notation="LaTeX">$\alpha $ < /tex-math></inline-formula> 및 <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> 밴드는 가로채고, 융합되고, 가장 가까운 이웃 보간 방법에 의해 EEG 전극 맵에 추가로 배열되어 두 개의 키 밴드 이미지 시퀀스를 형성합니다. [2] nan [3] nan [4] nan [5]
empirical mode decomposition 경험적 모드 분해
Besides raw data, the algorithms for processing input signals include short-time Fourier transform (STFT), Cepstrum, wavelet packet transform (WPT), and empirical mode decomposition (EMD). [1] In order to select the correct method in engineering application, five kinds of joint time-frequency analysis methods, such as short-time Fourier transform, Wigner-Ville distribution, S transform, wavelet transform and empirical mode decomposition are compared, and the advantages and disadvantages of these methods for dealing with the vibration signal of transformer core are analyzed in this paper. [2] We developed weak-signal processing using a band-pass filter, empirical mode decomposition, fast Fourier transform, and short-time Fourier transform to process and analyze the seismic data and accurately extract the signals for the debris flow, landslide, flood, and noise attenuation in the hazard chain. [3] After that, the continuous wavelet packet analysis and short-time Fourier transform method are used to analyze the acceleration signal, compared with local mean decomposition and empirical mode decomposition, LMD is used to decompose the acceleration signals, by calculating the correlation coefficient between the fault signal components and the original faultless signal components, the failure severity for the key components of suspension system is obtained. [4]원시 데이터 외에도 입력 신호 처리 알고리즘에는 STFT(단시간 푸리에 변환), Cepstrum, WPT(웨이블릿 패킷 변환) 및 EMD(경험적 모드 분해)가 포함됩니다. [1] 엔지니어링 응용에서 올바른 방법을 선택하기 위해 단시간 푸리에 변환, 위그너-빌 분포, S 변환, 웨이블릿 변환 및 경험적 모드 분해와 같은 5가지 결합 시간-주파수 분석 방법을 비교하고 장점과 이 논문에서는 변압기 코어의 진동 신호를 처리하는 이러한 방법의 단점을 분석합니다. [2] nan [3] nan [4]
non stationary signal 비정상 신호
The non-stationary signal considered for this study are respiratory signals, and four time-frequency analysis methods, namely short-time fourier transform (STFT), continuous wavelet transform (CWT), Wigner–Ville distribution (WVD), and constant Q-Gabor transform (CQT) were considered to analyze the lung sounds. [1] In fact, we propose to use the Short-Time Fourier Transform (STFT) as a non-stationary signal processing technique to extract useful information from the EEG signals. [2] The harmonics and attenuation characteristics of these non-stationary signals cannot be obtained effectively by the ordinary short-time Fourier transform algorithm. [3] Short-time Fourier transform (STFT) is a fundamental concept that enables the observation of dynamic spectral evolution for non-stationary signals. [4]본 연구에서 고려한 비정상 신호는 호흡 신호와 4가지 시간-주파수 분석 방법, 즉 단시간 푸리에 변환(STFT), 연속 웨이블릿 변환(CWT), 위그너-빌 분포(WVD), 상수 Q- 가보 변환(CQT)은 폐음을 분석하기 위해 고려되었습니다. [1] 실제로, 우리는 EEG 신호에서 유용한 정보를 추출하기 위해 비정상 신호 처리 기술로 STFT(Short-Time Fourier Transform)를 사용할 것을 제안합니다. [2] nan [3] nan [4]
transform continuous wavelet 연속 웨이블릿 변환
Some commonly used time-frequency (TF) analyzing methods, such as the short-time Fourier transform, continuous wavelet transform, and the Stockwell transform, can capture a signal’s TF features and reconstruct signal without loss. [1] short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM-ML pipeline, we obtain a powerful machinery (QM-SP-ML) that can be used for representation, visualization and forward design of molecules. [2] Signal processing tools such as fast Fourier transform, short-time Fourier transform (STFT), discrete wavelet transform, continuous wavelet transform (CWT), and time-series data mining are used to diagnose the faults, with a central focus on additive noise impacts on processed data. [3] In this paper, we applied short-time Fourier transform, S-transform, continuous wavelet transform, fast discrete wavelet transform, synchrosqueezing transform, synchroextracting transform, continuous wavelet synchrosqueezing, filter bank synchrosqueezing, empirical mode decomposition, and Fourier decomposition methods on the near-source strong motion signals from the 7 May 2020 Mosha-Iran earthquake to study and compare the frequency content of this event estimated by these methods. [4]단시간 푸리에 변환, 연속 웨이블릿 변환 및 스톡웰 변환과 같이 일반적으로 사용되는 시간-주파수(TF) 분석 방법은 신호의 TF 기능을 캡처하고 손실 없이 신호를 재구성할 수 있습니다. [1] QM-ML 파이프라인에서 단시간 푸리에 변환, 연속 웨이블릿 분석 및 위그너-빌 분포)를 통해 분자의 표현, 시각화 및 정방향 설계에 사용할 수 있는 강력한 기계(QM-SP-ML)를 얻습니다. [2] nan [3] nan [4]
transform infrared spectroscopy 적외선 분광법 변환
The distinct photopolymerization profiles of acrylateswere studied byreal time Fourier transform infrared spectroscopy, which indicated that the ketone-2/amine/Iod system could induce the highest final conversion of acrylatesin thick films condition, while ketone-5/amine/Iod system could induce the highest final conversion of acrylatesin thin films condition. [1] This study aimed to elucidate the mechanism of touch cure by measuring the degree of conversion (DC) of composite cement applied with or without an accelerator-containing tooth primer (TP) versus an accelerator-free primer using real-time Fourier-transform infrared spectroscopy (RT-FTIR) and attenuated total reflection (ATR)–FTIR. [2] The photochemical properties of the TiQ/Iod photoinitiating system have been probed by electron paramagnetic resonance, laser flash photolysis, and real-time Fourier transform infrared spectroscopy, which provide an insight into the possible radical/cationic pathways. [3]아크릴레이트의 독특한 광중합 프로파일은 실시간 푸리에 변환 적외선 분광법에 의해 연구되었으며, 이는 케톤-2/아민/아이오드 시스템이 후막 조건에서 아크릴레이트의 가장 높은 최종 전환을 유도할 수 있는 반면 케톤-5/아민/아이오드 시스템은 아크릴레이트신 박막 조건의 가장 높은 최종 전환율. [1] 본 연구는 실시간 푸리에 변환 적외선 분광법을 이용하여 촉진제 함유 치아프라이머(TP)와 무촉진제 프라이머를 도포한 복합시멘트의 전환도(DC)를 측정하여 접촉중합의 기전을 밝히는 것을 목적으로 하였다. (RT-FTIR) 및 감쇠 전반사(ATR)-FTIR. [2] nan [3]
time frequency distribution 시간 주파수 분포
This paper first constructs two-dimensional time-frequency distribution matrices by using short-time Fourier transformation (STFT). [1] Power quality signals were analyzed using linear time-frequency distribution (TFD) namely short-time Fourier transform (STFT) and proposed Gabor transform (GT), and the best technique for power quality detection was determined based on the performance analysis of varied window length. [2] This paper presents analysis on different characteristics of harmonic signal using frequency distribution technique, namely Fourier transform (FT), and proposal of time-frequency distribution (TFD) technique, which is a short time Fourier transform (STFT). [3]본 논문에서는 먼저 STFT(단시간 푸리에 변환)를 사용하여 2차원 시간-주파수 분포 행렬을 구성합니다. [1] 전력 품질 신호는 STFT(short-time Fourier Transform)와 GT(Gabor Transform)라는 선형 TFD(Time-Frequency Distribution)를 이용하여 분석하였으며, 다양한 윈도우 길이에 대한 성능 분석을 통해 전력 품질 검출을 위한 최적의 기법을 결정하였다. . [2] nan [3]
time frequency resolution 시간 주파수 분해능
Compared with short-time Fourier transform, synchroextracting transform with the advantage of high time-frequency resolution can detect the natural frequency shift of the cracked blade earlier. [1] However, they present the problem of time-frequency resolution trade-offs of Short-Time Fourier Transform (STFT), which may have limitations due to unambiguous Doppler frequency, and the STFT computation may be onerous in constrained embedded environments. [2]단시간 푸리에 변환과 비교하여 높은 시간 주파수 분해능의 이점을 가진 동기 추출 변환은 균열된 블레이드의 고유 주파수 이동을 더 일찍 감지할 수 있습니다. [1] 그러나 STFT(Short-Time Fourier Transform)의 시간-주파수 분해능 절충 문제가 있으며, 이는 명확한 도플러 주파수로 인해 한계가 있을 수 있으며 STFT 계산은 제한된 임베디드 환경에서 번거로울 수 있습니다. [2]
time frequency graph 시간 주파수 그래프
First, frequency domain signals and a time frequency graph are obtained by using the short-time Fourier transform and a wavelet transform to process original time domain signals simultaneously. [1] The time-frequency graph obtained by Short-time Fourier transform (STFT) is used as input, and Deep Convolutional Generative Adversarial Networks (DCGANs) is used for data enhancement. [2]먼저, 단시간 푸리에 변환과 웨이블릿 변환을 이용하여 원래의 시간 영역 신호를 동시에 처리하여 주파수 영역 신호와 시간 주파수 그래프를 구한다. [1] STFT(Short-time Fourier Transform)로 얻은 시간-주파수 그래프를 입력으로 사용하고 DCGAN(Deep Convolutional Generative Adversarial Networks)을 데이터 향상에 사용합니다. [2]
mixed mono signal 혼합 모노 신호
In the short-time Fourier transform (STFT) domain, the amplitude of the down-mixed mono signal is obtained by adding and averaging the amplitude of the multi-channel speech signals, the phase of the down-mixed mono signal is replaced by the phase of the reference channel, the STFT of the down-mixed mono signal is obtained. [1]STFT(단시간 푸리에 변환) 영역에서 다운믹스된 모노 신호의 진폭은 다채널 음성 신호의 진폭을 더하고 평균화하여 얻어지며 다운믹스된 모노 신호의 위상은 다음 식으로 대체됩니다. 기준 채널의 위상에서 다운믹스된 모노 신호의 STFT가 획득됩니다. [1]
1 f fluctuation 1 f 변동
Furthermore, we conducted a real-time analysis of the music including 1/f fluctuation using the short-time Fourier transform and evaluated the relationship between the 1/f fluctuation of the whole music analysis and the appearance rate of 1/f fluctuation. [1]또한, 단시간 푸리에 변환을 이용하여 1/f 변동을 포함한 음악에 대한 실시간 분석을 수행하여 전체 음악 분석의 1/f 변동과 1/f 변동의 출현율 사이의 관계를 평가하였다. [1]
Short Time Fourier 단시간 푸리에
In this paper, AE analysis, including single parameter analysis, correlation analysis and short time Fourier transform (STFT), are used to analyze the process of laser modified SiC. [1] Then, using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. [2] The proposed model generates scalogram and spectrogram representations by transforming preprocessed 30 s ECG segments from time domain to the frequency domain using continuous wavelet transform and short time Fourier transform, respectively. [3] Short time Fourier transform analysis showed that our telehaptic system can transmit various types of tactile stimuli, such as the shape of objects and letters, textures of fabrics, and vibration patterns with high fidelity. [4] Firstly, short time fourier transform (STFT) is applied to obtain the spectrogram image from the received signal. [5] , raw signals, and time-frequency spectra images by short time Fourier transform. [6] In the last part, a reduced short time Fourier transform of top 10 absolute maximum component AE feature sets that correlates to wear measurement data “profile depth” is used to train and test supervised neural network and CART algorithms. [7] In contrast to the traditional single window function for evaluation of short time Fourier transform (STFT), this work proposes a novel method for evaluating STFT coefficients using a combinational window function comprising of Gaussian, Lanczos and Chebyshev (GLC) windows. [8] The previous method uses SVM (Support Vector Machine) and STFT (Short Time Fourier Transform Algorithm) to process an image processing system based on stroke detection. [9] Simultaneously, through the short time Fourier transform (STFT) method, the amplitude of torque pulsation is obviously different between FRC and BRC. [10] Two spectral analysis methods: Short Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT), were then applied to get the spectrogram of the gunshot audio signal. [11] We first present a convolutive dictionary model as a generative model for the short time Fourier transform (STFTs) of PMU data streams from multiple PMUs in the presence of a power system event. [12] The proposed method is compared with four existing methods based on the wavelet transform, the short time Fourier transform, the S transform or the Hilbert-Huang transform to underline its integral performance. [13] In conventional DUET algorithm, short time Fourier transform (STFT) is utilized for extracting the TFR of speech signals. [14] In this paper, we propose a network traffic prediction method based on Short Time Fourier Transform (STFT) and traffic modeling. [15] This chapter describes an SoS approach for frequency estimation using Chirplet Transform (CT), Hough Transform (HT), and the Short Time Fourier Transform (STFT) with filtering viewpoint. [16] The Short Time Fourier Transform can be used but its time frequency precision is not optimal. [17] Firstly, the short time Fourier transform (STFT) amplitude spectrum, STFT phase spectrum, and bispectrum feature of underwater acoustic signals are extracted and form the input for the network. [18] We also applied Short Time Fourier Transform (STFT) with epoch lengths of 3. [19] A short time Fourier transform is applied to each electrode of the MI-EEG signal to generate a time-frequency image, and the parts corresponding to the본 논문에서는 단일 매개변수 분석, 상관 분석 및 STFT(short time Fourier Transform)를 포함한 AE 분석을 사용하여 레이저 개질된 SiC의 공정을 분석합니다. [1] 그런 다음 단시간 푸리에 변환을 사용하여 시간 영역의 혼합 신호를 주파수 영역으로 전송합니다. [2] 제안된 모델은 연속 웨이블릿 변환과 단시간 푸리에 변환을 각각 사용하여 시간 영역에서 주파수 영역으로 전처리된 30초 ECG 세그먼트를 변환하여 스칼로그램 및 스펙트로그램 표현을 생성합니다. [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19] MI-EEG 신호의 각 전극에 단시간 푸리에 변환을 적용하여 시간-주파수 영상을 생성하고, <inline-formula> <tex-math notation="LaTeX">$\alpha $ < /tex-math></inline-formula> 및 <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> 밴드는 가로채고, 융합되고, 가장 가까운 이웃 보간 방법에 의해 EEG 전극 맵에 추가로 배열되어 두 개의 키 밴드 이미지 시퀀스를 형성합니다. [20] nan [21] nan [22] nan [23] nan [24] nan [25] nan [26] nan [27] nan [28] 본 논문에서는 다양한 정상 또는 불량 조건에서 원시 데이터로부터 시간-주파수 표현 진동 이미지를 획득하기 위한 전처리 단계로 단시간 푸리에 변환(STFT)을 제안합니다. [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] nan [38] nan [39] nan [40] nan [41] nan [42] nan [43] nan [44] nan [45] nan [46] nan [47] QM-ML 파이프라인에서 단시간 푸리에 변환, 연속 웨이블릿 분석 및 위그너-빌 분포)를 통해 분자의 표현, 시각화 및 정방향 설계에 사용할 수 있는 강력한 기계(QM-SP-ML)를 얻습니다. [48] nan [49] nan [50]
Discrete Time Fourier 이산 시간 푸리에
Firstly, the proposed receiver works in DTFrFD which is more effective to intercept the fractional bandlimited signals, while the original MWC-based digital receiver works in discrete time Fourier domain (DTFD). [1] In this study, three digital signal processing methods—the quadrature demodulation (QD) method, Hilbert method, and sliding discrete time Fourier transform method—are analyzed for their applications in processing sensor signals and providing measurement results under gas-liquid two-phase flow condition. [2]첫째, 제안된 수신기는 부분 대역 제한 신호를 가로채기에 더 효과적인 DTFrFD에서 작동하는 반면, 원래의 MWC 기반 디지털 수신기는 DTFD(이산 시간 푸리에 도메인)에서 작동합니다. [1] 본 연구에서는 3가지 디지털 신호 처리 방법인 QD(Quadrature Demodulation) 방법, Hilbert 방법 및 슬라이딩 이산 시간 푸리에 변환 방법을 기체-액체 2상 흐름에서 센서 신호 처리 및 측정 결과 제공에 적용하기 위해 분석합니다. 상태. [2]
time fourier transform 시간 푸리에 변환
Being used with a high-performance audio classification model, the proposed fbsp-layer provides an accuracy improvement over the previously used Short-Time Fourier Transform (STFT) on standard datasets. [1] Fusing the short-time Fourier transform spectrogram of hand gesture to the image processing techniques of corner feature detection, feature descriptors, and Hamming-distance matching are the first-time, to our knowledge, employed to recognize hand gestures. [2] The spatial–temporal features extracted by short-time Fourier transform (STFT) are fed to the CADCNN, and the raw EEG signals are fed for further feature extraction. [3] In this paper, AE analysis, including single parameter analysis, correlation analysis and short time Fourier transform (STFT), are used to analyze the process of laser modified SiC. [4] Then, the echo sequence in time domain after zero interpolation is transformed to the time-frequency domain by short-time Fourier transform (STFT). [5] For this purpose, the fast Fourier transform and short-time Fourier transform functions implemented in MatLab were used. [6] The short-time Fourier transform (STFT) and Wigner–Ville distribution (WVD) are used to localize the frequency content of the dispersed wave as a function of time and to estimate the group velocity in experiments using a 1-D MIW constructed with planar resonators. [7] Then, using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. [8] Compared with short-time Fourier transform, synchroextracting transform with the advantage of high time-frequency resolution can detect the natural frequency shift of the cracked blade earlier. [9] The algorithm transforms the degraded sound in an ’image’ in the time-frequency domain via a short-time Fourier transform. [10] First, frequency domain signals and a time frequency graph are obtained by using the short-time Fourier transform and a wavelet transform to process original time domain signals simultaneously. [11] In this process, a short-time Fourier transform (STFT) is first applied to the AE signal of each sensor, and the STFT results from the different sensors are then fused to obtain a condition-invariant 2D image of cracks; this scheme is denoted as Multi-Sensors Fusion-based Time-Frequency Imaging (MSFTFI). [12] The basis is the frequency representation of signals associated with the known time Fourier transform. [13] The proposed approach feeds Shot-Time Fourier Transform (STFT) features of low-band signals to the GAN, expecting to obtain their high-band information through jointly considering content and adversarial losses. [14] One of the most commonly used representative for the input signal used to train the DDAE is the spectrogram, which is the ordered series of the short-time Fourier transform (STFT) of each frame for the input signal. [15] In this letter, based on the short-time Fourier transform (STFT) and convolutional neural network (CNN), we firstly develop a STFT-CNN method for spectrum sensing. [16] The modulated signal is analyzed by short-time Fourier transform and wavelet transform. [17] In this regard, the time-frequency analysis of AE signals during dressing through STFT (short-time Fourier transform) can contribute toward the proposal of new monitoring methodologies, thus reflecting the optimization of the grinding process. [18] Our model is based on simple log-power Short-Time Fourier Transform (STFT) spectrograms and combines them with several well-known approaches from the image domain (i. [19] The proposed model generates scalogram and spectrogram representations by transforming preprocessed 30 s ECG segments from time domain to the frequency domain using continuous wavelet transform and short time Fourier transform, respectively. [20] The properties of both the short-time Fourier transform (STFT) and the wavelet transform are combined in the S-transform. [21] This work expanded the short-time Fourier transform (STFT)- and wavelet transform (WT)-based Kurtograms and developed a hybrid signal separation operator (SSO)-spectral kurtosis computational scheme to implement Kurtogram by introducing the SSO method-SSO-based Kurtogram. [22] The short-time Fourier transform of the noisy signal was obtained, and the signal without noise was recovered using the inverse short-time Fourier transform. [23] Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. [24] Short time Fourier transform analysis showed that our telehaptic system can transmit various types of tactile stimuli, such as the shape of objects and letters, textures of fabrics, and vibration patterns with high fidelity. [25] In this paper, we consider synchrosqueezing transform based short-time fourier transform with instantaneous frequency rate of change to analyze nonlinear and nonstationary signal, called the adaptive synchrosqueezing transform (ASST). [26] In comparison to the conventional short-time Fourier transform- based technique, it has higher Fi scores and classification accuracies (with a mean of 0. [27] We introduce a non-stationarity matrix defined as the auto-correlation matrix of short-time Fourier transforms, which describes the average variation and interaction of intensity by frequency. [28] The short-time Fourier transform-based SST (FSST for short) reassigns the frequency variable to sharpen the time-frequency representation and to separate the components of a multicomponent non-stationary signal. [29] In audio processing applications, phase retrieval (PR) is often performed from the magnitude of short-time Fourier transform (STFT) coefficients. [30] In this paper, we propose a novel NIDS framework based on a deep convolution neural network that utilizes network spectrogram images generated using the short-time Fourier transform. [31] Firstly, short time fourier transform (STFT) is applied to obtain the spectrogram image from the received signal. [32] Compared with the Short-Time Fourier Transform (STFT) method, the VMD-HT method presents a higher accuracy in events localization, which indicates that the method is effective and applicable. [33] This method fuses three short-time Fourier transform time–frequency graphs disturbed by three consecutive pulse periods into a new graph as the input of the convolutional neural network (CNN). [34] This paper applies feature extraction methods in the field of voice processing to the processing of load curve data, and proposes a method based on short-time Fourier transform (STFT) and convolutional recurrent neural network (CRNN) to extract load features. [35] The Short-time Fourier Transform is used to convert the time-domain output signals of a circuit into two-dimensional circuit spectrum maps, which are further used as the ResNet input. [36] Among plenty of time–frequency analysis methods, the S transform (ST) and its extensions are widely used because of their self-adjustable flexibility, compared with the short-time Fourier transform and Gabor transform. [37] The application of the TWDFT estimator as a short-time analysis on a vibration signal of a tram gearbox shows a significantly more differentiated time-frequency analysis compared to a short-time Fourier transform (STFT). [38] We discussed a vertical synchrosqueezing transform, which is a second-order synchrosqueezing transform based on the short-time Fourier transform and compared it to the traditional short-time Fourier transform, synchrosqueezing transform, and another form of the second-order synchrosqueezing transform, the oblique synchrosqueezing transform. [39] In most considered approaches, models are trained with phase and magnitude components of the Short-Time Fourier Transform (STFT). [40] We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). [41] It represents the domain-reversed counterpart of the popular method of averaging raw spectra from short-time Fourier transforms. [42] , raw signals, and time-frequency spectra images by short time Fourier transform. [43] Under the premise of short-time Fourier transform (STFT), this method defines a new synchroextracting operator (SEO) based on high-order approximations of signal amplitude and phase. [44] A controller based on the short-time Fourier transform (STFT) was developed for the stiffness control. [45] The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. [46] The input of current fault diagnosis methods is mostly the original vibration signal or the time–frequency graph (TFG) obtained by short-time Fourier transform. [47] In the last part, a reduced short time Fourier transform of top 10 absolute maximum component AE feature sets that correlates to wear measurement data “profile depth” is used to train and test supervised neural network and CART algorithms. [48] We propose a new phase retrieval algorithm for recovering 2D discrete signals from the squared magnitudes of their short-time Fourier transform measurements. [49] The comparison with other popular frequency-domain based fault detection methods including the wavelet transform, the short-time Fourier transform, the S transform, and the existing HHT-based analysis, using amplitude frequency coefficient as detection criterion, underlines the performance of the proposed method. [50]고성능 오디오 분류 모델과 함께 사용되는 제안된 fbsp-layer는 표준 데이터 세트에서 이전에 사용된 STFT(단시간 푸리에 변환)보다 정확도가 향상되었습니다. [1] 손 제스처의 단시간 푸리에 변환 스펙트로그램을 모서리 특징 감지, 특징 설명자 및 해밍 거리 일치의 이미지 처리 기술에 융합하는 것은 우리가 아는 한 손 제스처를 인식하는 데 사용된 최초의 작업입니다. [2] STFT(short-time Fourier Transform)에 의해 추출된 공간-시간 특징은 CADCNN에 공급되고 원시 EEG 신호는 추가 특징 추출을 위해 공급됩니다. [3] 본 논문에서는 단일 매개변수 분석, 상관 분석 및 STFT(short time Fourier Transform)를 포함한 AE 분석을 사용하여 레이저 개질된 SiC의 공정을 분석합니다. [4] 그런 다음 0 보간 후 시간 영역의 에코 시퀀스는 STFT(단시간 푸리에 변환)에 의해 시간-주파수 영역으로 변환됩니다. [5] 이를 위해 MatLab에서 구현한 고속 푸리에 변환 및 단시간 푸리에 변환 기능을 사용하였다. [6] 단시간 푸리에 변환(STFT) 및 위그너-빌 분포(WVD)는 분산된 파동의 주파수 함량을 시간의 함수로 현지화하고 평면 MIW를 사용하여 실험에서 그룹 속도를 추정하는 데 사용됩니다. 공진기. [7] 그런 다음 단시간 푸리에 변환을 사용하여 시간 영역의 혼합 신호를 주파수 영역으로 전송합니다. [8] 단시간 푸리에 변환과 비교하여 높은 시간 주파수 분해능의 이점을 가진 동기 추출 변환은 균열된 블레이드의 고유 주파수 이동을 더 일찍 감지할 수 있습니다. [9] 알고리즘은 단시간 푸리에 변환을 통해 시간-주파수 영역에서 '이미지'의 저하된 사운드를 변환합니다. [10] 먼저, 단시간 푸리에 변환과 웨이블릿 변환을 이용하여 원래의 시간 영역 신호를 동시에 처리하여 주파수 영역 신호와 시간 주파수 그래프를 구한다. [11] 이 프로세스에서 STFT(단시간 푸리에 변환)가 먼저 각 센서의 AE 신호에 적용되고 다른 센서의 STFT 결과가 융합되어 균열의 조건 불변 2D 이미지를 얻습니다. 이 체계는 MSFTFI(Multi-Sensors Fusion-based Time-Frequency Imaging)로 표시됩니다. [12] nan [13] 제안된 접근 방식은 콘텐츠와 적대적 손실을 공동으로 고려하여 고대역 정보를 얻을 것으로 기대하면서 저대역 신호의 STFT(샷 시간 푸리에 변환) 기능을 GAN에 제공합니다. [14] DDAE를 훈련하는 데 사용되는 입력 신호에 대해 가장 일반적으로 사용되는 대표적인 것 중 하나는 스펙트로그램이며, 이는 입력 신호에 대한 각 프레임의 STFT(단시간 푸리에 변환)의 정렬된 시리즈입니다. [15] 이 편지에서는 STFT(단시간 푸리에 변환) 및 CNN(컨볼루션 신경망)을 기반으로 먼저 스펙트럼 감지를 위한 STFT-CNN 방법을 개발합니다. [16] 변조된 신호는 단시간 푸리에 변환 및 웨이블릿 변환으로 분석됩니다. [17] 이와 관련하여 STFT(단시간 푸리에 변환)를 통한 드레싱 중 AE 신호의 시간-주파수 분석은 새로운 모니터링 방법론의 제안에 기여할 수 있으므로 연삭 공정의 최적화를 반영할 수 있습니다. [18] 우리의 모델은 간단한 로그 전력 단시간 푸리에 변환(STFT) 스펙트로그램을 기반으로 하며 이미지 도메인(i. [19] 제안된 모델은 연속 웨이블릿 변환과 단시간 푸리에 변환을 각각 사용하여 시간 영역에서 주파수 영역으로 전처리된 30초 ECG 세그먼트를 변환하여 스칼로그램 및 스펙트로그램 표현을 생성합니다. [20] STFT(단시간 푸리에 변환)와 웨이블릿 변환의 속성은 S-변환에서 결합됩니다. [21] 본 연구에서는 STFT(Short-Time Fourier Transform)와 WT(Wavelet Transform) 기반의 Kurtogram을 확장하고, SSO 방식-SSO 기반의 Kurtogram을 도입하여 Kurtogram을 구현하기 위한 SSO(Hybrid Signal Separation Operator)-스펙트럼 첨도 계산 기법을 개발하였다. . [22] 잡음이 있는 신호의 단시간 푸리에 변환을 구하고, 역단시간 푸리에 변환을 이용하여 잡음이 없는 신호를 복구하였다. [23] 그런 다음 단시간 푸리에 변환을 사용하여 전처리된 신호를 2차원 스펙트로그램 이미지로 변환합니다. [24] nan [25] 본 논문에서는 비선형 및 비정상 신호를 분석하기 위해 순시 주파수 변화율을 이용한 동기식 푸리에 변환 기반의 단시간 푸리에 변환을 고려하여 적응형 동기식 변환(ASST)이라고 합니다. [26] 기존의 단시간 푸리에 변환 기반 기술과 비교하여 Fi 점수와 분류 정확도가 더 높습니다(평균 0. [27] 주파수에 따른 강도의 평균 변화와 상호 작용을 설명하는 단시간 푸리에 변환의 자동 상관 행렬로 정의된 비정상 행렬을 소개합니다. [28] 단시간 푸리에 변환 기반 SST(줄여서 FSST)는 주파수 변수를 재할당하여 시간-주파수 표현을 선명하게 하고 다성분 비정상 신호의 구성요소를 분리합니다. [29] 오디오 처리 응용 프로그램에서 위상 검색(PR)은 종종 STFT(단시간 푸리에 변환) 계수의 크기에서 수행됩니다. [30] 본 논문에서는 단시간 푸리에 변환을 사용하여 생성된 네트워크 스펙트로그램 이미지를 활용하는 심층 컨볼루션 신경망 기반의 새로운 NIDS 프레임워크를 제안합니다. [31] nan [32] STFT(Short-Time Fourier Transform) 방법과 비교하여 VMD-HT 방법은 이벤트 위치 파악에서 더 높은 정확도를 나타내어 이 방법이 효과적이고 적용 가능함을 나타냅니다. [33] 이 방법은 3개의 연속적인 펄스 주기에 의해 교란된 3개의 단시간 푸리에 변환 시간-주파수 그래프를 합성곱 신경망(CNN)의 입력으로 새 그래프로 융합합니다. [34] 본 논문에서는 음성 처리 분야의 특징 추출 방법을 부하 곡선 데이터 처리에 적용하고, 부하 특징을 추출하기 위해 STFT(Short-Time Fourier Transform)와 CRNN(Convolutional Recurrent Neural Network)을 기반으로 하는 방법을 제안한다. [35] 단시간 푸리에 변환은 회로의 시간 영역 출력 신호를 ResNet 입력으로 추가로 사용되는 2차원 회로 스펙트럼 맵으로 변환하는 데 사용됩니다. [36] 많은 시간-주파수 분석 방법 중에서 S 변환(ST)과 그 확장은 단시간 푸리에 변환 및 가보 변환에 비해 자체 조정 가능한 유연성으로 인해 널리 사용됩니다. [37] 트램 기어박스의 진동 신호에 대한 단시간 분석으로 TWDFT 추정기를 적용하면 단시간 푸리에 변환(STFT)에 비해 훨씬 더 차별화된 시간-주파수 분석을 보여줍니다. [38] 우리는 단시간 푸리에 변환을 기반으로 한 2차 동기 변환인 수직 동기 변환에 대해 논의하고 이를 기존의 단시간 푸리에 변환인 동기 변환 및 2차 동기 변환의 또 다른 형태인 2차 동기 변환과 비교했습니다. 비스듬한 싱크로 스퀴징 변환. [39] 대부분의 고려된 접근 방식에서 모델은 STFT(단시간 푸리에 변환)의 위상 및 크기 구성 요소로 훈련됩니다. [40] 우리는 단시간 푸리에 변환(STFT) 및 비음수 텐서/행렬 분해(NTF, NMF)를 사용하는 신호 디콘볼루션 방법과 생성적 지형 매핑 회귀(GTMR)를 사용하여 NMR 신호 및 물리적 특성을 예측하는 방법을 조사했습니다. [41] 이것은 단시간 푸리에 변환에서 원시 스펙트럼을 평균화하는 인기 있는 방법의 도메인 반전 대응물을 나타냅니다. [42] nan [43] STFT(단시간 푸리에 변환)를 전제로 이 방법은 신호 진폭 및 위상의 고차 근사를 기반으로 하는 새로운 SEO(동기 추출 연산자)를 정의합니다. [44] 강성 제어를 위해 단시간 푸리에 변환(STFT)에 기반한 컨트롤러가 개발되었습니다. [45] 원래 신호는 단시간 푸리에 변환을 사용하여 먼저 주파수 영역으로 변환되고 스펙트럼에 대한 간섭은 특정 분포를 만족하는 양방향 노이즈 마스크에 의해 실현될 수 있습니다. [46] 현재 고장 진단 방법의 입력은 대부분 원래의 진동 신호 또는 단시간 푸리에 변환에 의해 얻은 시간-주파수 그래프(TFG)입니다. [47] nan [48] 우리는 단시간 푸리에 변환 측정의 제곱 크기에서 2D 이산 신호를 복구하기 위한 새로운 위상 검색 알고리즘을 제안합니다. [49] 웨이블릿 변환, 단시간 푸리에 변환, S 변환 및 진폭 주파수 계수를 검출 기준으로 사용하는 기존 HHT 기반 분석을 포함한 다른 널리 사용되는 주파수 영역 기반 결함 검출 방법과의 비교는 제안된 성능을 강조합니다. 방법. [50]
time fourier transformation 시간 푸리에 변환
Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). [1] An analytical method for electromagnetic scattering characteristics of complex unmanned aerial vehicle targets by using hybrid multilevel fast multipole algorithm (MLFMA)-physical optics (PO) and short-time fourier transformation (STFT) is proposed in this paper. [2] An analytical method for electromagnetic scattering characteristics of complex satellite targets by using multilevel fast multipole algorithm (MLFMA) and short-time fourier transformation (STFT) is proposed in this paper. [3] Those radar data representations are typically generated on the basis of short-time Fourier transformations. [4] Structurally dependent CVWP behaviors (frequency, dephasing time, and oscillation amplitudes) were captured by femtosecond transient absorption spectroscopy, analyzed by short-time Fourier transformation, and rationalized by quantum mechanical calculations, revealing dual ISC pathways. [5] This paper first constructs two-dimensional time-frequency distribution matrices by using short-time Fourier transformation (STFT). [6] The discrete rotational symmetry of burner arrangement in a combustor makes it possible to diagonalize both the aforementioned thermoacoustic and flame transfer function matrices analytically from a spatial discrete-time Fourier transformation. [7] The light intensity received at the PD from every LED is continuously estimated after applying a short-time Fourier transformation. [8] Specifically, frequency-division short-time Fourier transformation (STFT) is performed on the data stream of continuous activities collected by a stepped-frequency continuous-wave (SFCW) radar, generating spectrograms of multiple frequencies which introduce different scattering properties and frequency resolutions. [9] This paper is focusing principally on some of signal - based methods in time - and frequency domain, such as Fast Fourier Transformation, Short Time Fourier Transformation, orbit portraits, symmetrized dot pattern method and time synchronous average. [10] The measured handling forces were analyzed using a short-time Fourier transformation, and the median frequency was calculated. [11] The movement characteristics of polarization grating varying with time can be obtained after a short-time Fourier transformation of the light signal. [12] Compared with the broadband LFM signal analysis based on temporal sampling, the proposed method avoids the use of high-speed analog to digital converters, and the instantaneous frequency acquisition realized by frequency-to-time mapping is also simplified since real-time Fourier transformation is not required. [13] Among various functions achievable by optical means, optical real-time Fourier transformation (RTFT) has received special attention because of its high processing speed, which is far beyond conventional digital signal processing methods. [14] The experimental results that were achieved by the wavelet transformation (WT) method are compared with these by short time Fourier transformation (STFT) method and they indicate that significant improvements, such as strain resolution of 1 με, spatial resolution of 5 mm, average repeatability of 4. [15]여기에서 우리는 6가지 표준 스펙트럼 추정 방법(고속 푸리에 및 연속 웨이블릿 변환, 대역통과 필터링 및 단시간 푸리에 변환으로 구성)과 6가지 연결 측정(위상 잠금 값, 가우시안-코풀라 상호 정보, 레일리 테스트, 가중치 적용)의 조합을 체계적으로 비교합니다. 쌍별 위상 일관성, 크기 제곱 일관성 및 엔트로피). [1] 복합 무인항공기 표적의 전자기 산란 특성에 대한 분석 방법은 MLFMA(Hybrid Multilevel Fast Multipole Algorithm)-PO(물리광학) 및 STFT(단시간 푸리에 변환)를 사용합니다. [2] 본 논문에서는 MLFMA(Multilevel Fast Multipole Algorithm)와 STFT(Short-Time Fourier Transformation)를 이용하여 복잡한 위성 표적의 전자기 산란 특성을 분석하는 방법을 제안한다. [3] 이러한 레이더 데이터 표현은 일반적으로 단시간 푸리에 변환을 기반으로 생성됩니다. [4] 구조적으로 종속적인 CVWP 동작(주파수, 위상차 제거 시간 및 진동 진폭)은 펨토초 과도 흡수 분광법으로 캡처하고, 단시간 푸리에 변환으로 분석하고, 양자 역학 계산으로 합리화하여 이중 ISC 경로를 나타냅니다. [5] 본 논문에서는 먼저 STFT(단시간 푸리에 변환)를 사용하여 2차원 시간-주파수 분포 행렬을 구성합니다. [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15]
time fourier transformed 시간 푸리에 변환
Photoinitiation abilities of the different photoinitiating systems were examined by the Real-Time Fourier Transformed Infrared Spectroscopy. [1] Chemical mechanisms supporting the polymerization process with these PIs are investigated by steady state photolysis, molecular orbital calculations and real-time Fourier transformed infrared spectroscopy. [2] The reactions are followed by optical pyrometric measurements, DSC (differential scanning calorimetry), RT-FTIR (real-time Fourier transformed infrared spectrometry), and CRM (confocal Raman microscopy). [3]실시간 푸리에 변환 적외선 분광법으로 다양한 광개시 시스템의 광개시 능력을 조사했습니다. [1] 이러한 PI로 중합 과정을 지원하는 화학적 메커니즘은 정상 상태 광분해, 분자 궤도 계산 및 실시간 푸리에 변환 적외선 분광법에 의해 조사됩니다. [2] nan [3]