Gotowa bibliografia na temat „Time-frequency Representations (TFRs)”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Time-frequency Representations (TFRs)”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Time-frequency Representations (TFRs)"
1
Bačnar, David, Nicoletta Saulig, Irena Petrijevčanin Vuksanović i Jonatan Lerga. "Entropy-Based Concentration and Instantaneous Frequency of TFDs from Cohen’s, Affine, and Reassigned Classes". Sensors 22, nr 10 (13.05.2022): 3727. http://dx.doi.org/10.3390/s22103727.
Pełny tekst źródłaStreszczenie:
This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigned classes of time–frequency representations (TFRs). This study provides detailed insight into the theory behind the selected TFRs belonging to these classes. Extensive numerical simulations were performed with examples that illustrate the behavior of the analyzed TFR classes in the joint time–frequency domain. The methods were applied both on synthetic and real-life non-stationary signals. The obtained results were assessed with respect to time–frequency concentration (measured by the Rényi entropy), instantaneous frequency (IF) estimation accuracy, cross-term presence in the TFRs, and the computational cost of the TFRs. This study gives valuable insight into the advantages and limitations of the analyzed TFRs and assists in selecting the proper distribution when analyzing given non-stationary signals in the time–frequency domain.
2
Pan, M. Ch, P. Sas i H. Van Brussel. "Machine Condition Monitoring Using Signal Classification Techniques". Journal of Vibration and Control 9, nr 10 (październik 2003): 1103–20. http://dx.doi.org/10.1177/107754603030683.
Pełny tekst źródłaStreszczenie:
Two signal classification approaches, based on Wigner-Ville distribution and extended symmetric Itakura distance, are proposed to post-process the time-frequency representations (TFRs) of vibration signatures, with the final aim to arrive at an automated procedure of machine condition monitoring. Three synthetical signals are used to evaluate and compare the classification performance of these techniques. Some related computation issues, such as characters of different TFRs and weighted window length, are discussed. Experimental case studies, joint fault diagnosis, are realized.
3
MASRI, PAUL, ANDREW BATEMAN i NISHAN CANAGARAJAH. "A review of time–frequency representations, with application to sound/music analysis–resynthesis". Organised Sound 2, nr 3 (listopad 1997): 193–205. http://dx.doi.org/10.1017/s1355771898009042.
Pełny tekst źródłaStreszczenie:
Analysis–resynthesis (A–R) systems gain their flexibility for creative transformation of sound by representing sound as a set of musically useful features. The analysis process extracts these features from the time domain signal by means of a time–frequency representation (TFR). The TFR provides an intermediate representation of sound that must make the features accessible and measurable to the rest of the analysis. Until very recently, the short-time Fourier transform (STFT) has been the obvious choice for time–frequency representation, despite its limitations in terms of resolution. Recent and ongoing developments are providing several alternative schemes that allow for a more considered choice of TFR. This paper reviews these contemporary approaches in comparison with the more classical ones and with reference to their applicability, merits and shortcomings for application to sound analysis. (Where they have been successfully applied, details are provided.) The techniques reviewed include linear, bilinear and higher-order spectra, nonparametric and parametric methods and some sound-model-specific TFRs.
4
Padovese, L. R., N. Martin i F. Millioz. "Time—frequency and time-scale analysis of Barkhausen noise signals". Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 223, nr 5 (30.04.2009): 577–88. http://dx.doi.org/10.1243/09544100jaero436.
Pełny tekst źródłaStreszczenie:
Carrying out information about the microstructure and stress behaviour of ferromagnetic steels, magnetic Barkhausen noise (MBN) has been used as a basis for effective non-destructive testing methods, opening new areas in industrial applications. One of the factors that determines the quality and reliability of the MBN analysis is the way information is extracted from the signal. Commonly, simple scalar parameters are used to characterize the information content, such as amplitude maxima and signal root mean square. This paper presents a new approach based on the time—frequency analysis. The experimental test case relates the use of MBN signals to characterize hardness gradients in a AISI4140 steel. To that purpose different time—frequency (TFR) and time-scale (TSR) representations such as the spectrogram, the Wigner-Ville distribution, the Capongram, the ARgram obtained from an AutoRegressive model, the scalogram, and the Mellingram obtained from a Mellin transform are assessed. It is shown that, due to nonstationary characteristics of the MBN, TFRs can provide a rich and new panorama of these signals. Extraction techniques of some time—frequency parameters are used to allow a diagnostic process. Comparison with results obtained by the classical method highlights the improvement on the diagnosis provided by the method proposed.
5
Zhang, Guanghui, Xueyan Li i Fengyu Cong. "Objective Extraction of Evoked Event-Related Oscillation from Time-Frequency Representation of Event-Related Potentials". Neural Plasticity 2020 (19.12.2020): 1–20. http://dx.doi.org/10.1155/2020/8841354.
Pełny tekst źródłaStreszczenie:
Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. In most previous studies, the calculation of the regions of evoked EROs of interest is commonly based on a predefined time window and a frequency range given by the experimenter, which tends to be subjective. Additionally, evoked EROs sometimes cannot be fully extracted using the conventional time-frequency analysis (TFA) because they may be overlapped with each other or with artifacts in time, frequency, and space domains. To further investigate the related neuronal processes, a novel approach was proposed including three steps: (1) extract the temporal and spatial components of interest simultaneously by temporal principal component analysis (PCA) and Promax rotation and project them to the electrode fields for correcting their variance and polarity indeterminacies, (2) calculate the time-frequency representations (TFRs) of the back-projected components, and (3) compute the regions of evoked EROs of interest on TFRs objectively using the edge detection algorithm. We performed this novel approach, conventional TFA, and TFA-PCA to analyse both the synthetic datasets with different levels of SNR and an actual ERP dataset in a two-factor paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic datasets results indicated that N2-theta and P3-delta oscillations can be stably detected from different SNR-simulated datasets using the proposed approach, but, by comparison, only one oscillation was obtained via the last two approaches. Furthermore, regarding the actual dataset, the statistical results for the proposed approach revealed that P3-delta was sensitive to the waiting time but not for that of the other approaches. This study manifested that the proposed approach could objectively extract evoked EROs of interest, which allows a better understanding of the modulations of the oscillatory responses.
6
Gaviria, Carlos A., i Luis A. Montejo. "Optimal Wavelet Parameters for System Identification of Civil Engineering Structures". Earthquake Spectra 34, nr 1 (luty 2018): 197–216. http://dx.doi.org/10.1193/092016eqs154m.
Pełny tekst źródłaStreszczenie:
Damage-induced changes in structure dynamic properties are commonly tracked with time-frequency representations (TFRs). One of the most widely accepted tools for determining a TFR is the continuous wavelet transform (CWT). The success of CWT analysis is highly dependent on selecting the most appropriate values for the parameters that define the mother wavelet. This article presents a detailed analytical and numerical study to select optimal wavelet parameters using the complex Morlet wavelet (CMOR) and the Gabor wavelet. The results obtained suggest that it is possible to define optimal parameter values based on identification target, instantaneous frequency, or average damping ratio. This reduces the computational cost of a reliable CWT analysis when compared with currently employed iterative methodologies based on minimal Shannon entropy criteria.
7
Li, B., P.-L. Zhang, Z.-J. Wang, S.-S. Mi i D.-S. Liu. "Application of S transform and morphological pattern spectrum for gear fault diagnosis". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, nr 12 (6.07.2011): 2963–72. http://dx.doi.org/10.1177/0954406211408781.
Pełny tekst źródłaStreszczenie:
Time–frequency representations (TFR) have been intensively employed for analysing vibration signals in gear fault diagnosis. However, in many applications, TFR are simply utilized as a visual aid to detect gear defects. An attractive issue is to utilize the TFR for automatic classification of faults. A key step for this study is to extract discriminative features from TFR as input feature vector for classifiers. This article contributes to this ongoing investigation by applying morphological pattern spectrum (MPS) to characterize the TFR for gear fault diagnosis. The S transform, which combines the separate strengths of the short-time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals from gear. Then, the MPS scheme is applied to extract the discriminative features from the TFR. The promise of MPS is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experiment results demonstrate the MPS to be a satisfactory scheme for characterizing TFRs for an accurate classification of gear faults.
8
DJEBBARI, ABDELGHANI, i F. BEREKSI-REGUIG. "SMOOTHED-PSEUDO WIGNER–VILLE DISTRIBUTION OF NORMAL AND AORTIC STENOSIS HEART SOUNDS". Journal of Mechanics in Medicine and Biology 05, nr 03 (wrzesień 2005): 415–28. http://dx.doi.org/10.1142/s0219519405001552.
Pełny tekst źródłaStreszczenie:
In this paper, we are interested in the acquisition and the time-frequency analysis of the Phonocardiogram (PCG) signal. The interactive software "PCG Recorder" we implemented in MATLAB, drives the sound card of a personal computer for acquisition purposes. Normal and abnormal heart sounds were acquired with 16 bits resolution and at high sampling frequencies; the value 2 kHz was selected as sampling rate to avoid spectral aliasing. For each patient, additional information like the age, the gender, the weight as well as the auscultation area can be introduced within the saved data file. The aortic, the tricuspid, the mitral and the pulmonic areas are considered for the acquisition task. The Smoothed-Pseudo Wigner–Ville Distribution (SPWVD) yield adequate Time-Frequency Representations (TFRs) of such non-stationary signal as heart sounds. Moreover, by taking into account the corresponding auscultation area for each obtained TFR, we adopt exclusion reasoning to attribute each burst to its origins within the myocardium. Furthermore, the alternating functioning of heart valves and cavities in systole and diastole was characterized in the time and frequency domains. Aortic stenosis heart sounds were involved in our study in a view to confirm their pathological nature towards the normal heart sounds findings. Indeed, the weakened S1 and S2 heart sounds and the strong systolic ejection murmur which dominates the overall systole, confirm our hypotheses. Thus, modulations laws relating to the systolic ejection of blood through the stenosed orifice were characterized by means of the reliable SPWVD approach. A third heart sound (S3) which is an indicator of the presence of systolic dysfunction and the elevated filling pressure for aortic stenosis lesion was also characterized.
9
Bhuiyan, Moinuddin, Eugene V. Malyarenko, Mircea A. Pantea, Dante Capaldi, Alfred E. Baylor i Roman Gr Maev. "Time-Frequency Analysis of Clinical Percussion Signals Using Matrix Pencil Method". Journal of Electrical and Computer Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/274541.
Pełny tekst źródłaStreszczenie:
This paper discusses time-frequency analysis of clinical percussion signals produced by tapping over human chest or abdomen with a neurological hammer and recorded with an air microphone. The analysis of short, highly damped percussion signals using conventional time-frequency distributions (TFDs) meets certain difficulties, such as poor time-frequency localization, cross terms, and masking of the lower energy features by the higher energy ones. The above shortcomings lead to inaccurate and ambiguous representation of the signal behavior in the time-frequency plane. This work describes an attempt to construct a TF representation specifically tailored to clinical percussion signals to achieve better resolution of individual components corresponding to physical oscillation modes. Matrix Pencil Method (MPM) is used to decompose the signal into a set of exponentially damped sinusoids, which are then plotted in the time-frequency plane. Such representation provides better visualization of the signal structure than the commonly used frequency-amplitude plots and facilitates tracking subtle changes in the signal for diagnostic purposes. The performance of our approach has been verified on both ideal and real percussion signals. The MPM-based time-frequency analysis appears to be a better choice for clinical percussion signals than conventional TFDs, while its ability to visualize damping has immediate practical applications.
10
MASRI, PAUL, ANDREW BATEMAN i NISHAN CANAGARAJAH. "The importance of the time–frequency representation for sound/music analysis–resynthesis". Organised Sound 2, nr 3 (listopad 1997): 207–14. http://dx.doi.org/10.1017/s1355771898009054.
Pełny tekst źródłaStreszczenie:
The time–frequency representation (TFR) is the initial stage of analysis in sound/music analysis–resynthesis (A–R) systems. Given a time-domain waveform, the TFR makes temporal and spectral detail available to the remainder of the analysis, so that the component features may be extracted. The resulting ‘feature set’ must represent the sound as completely as the original time-domain signal, if the A–R system is to be capable of effective transformation and good synthesis sound quality. Therefore the system as a whole is reliant upon the TFR to make the sound components detectable, separable and measurable. Yet the standard TFR to-date is the short-time Fourier transform (STFT), of which the shortcomings, in terms of resolution, are well recognised. The purpose of this paper is to demonstrate the importance of the TFR to system function and system design. Poor feature extraction is shown to result from the use of inappropriate TFRs, whose underlying assumptions and expectations do not match those of the system. Existing models are used as case studies, with examples of performance for different sound types. A philosophy for A–R system design that includes TFR design is presented and a methodology for implementing it is proposed.
Rozprawy doktorskie na temat "Time-frequency Representations (TFRs)"
1
Viswanath, G. "Robustness And Localization In Time-Varying Spectral Estimation". Thesis, 1997. http://etd.iisc.ernet.in/handle/2005/1814.
Pełny tekst źródłaCzęści książek na temat "Time-frequency Representations (TFRs)"
1
Gupta, Priti, i Vijay Kumar. "Measuring of Time-Frequency Representation (TFR) Content – Using the Kapur’s Entropies". W Communications in Computer and Information Science, 381–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14834-7_36.
Pełny tekst źródła
2
Roy, Nabamita Banerjee, i Kesab Bhattacharya. "Application of ST for Time Frequency Representations (TFRs) of Different Electrical Signals". W Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults, 37–50. CRC Press, 2021. http://dx.doi.org/10.1201/9780367431143-4.
Pełny tekst źródła
3
Marks II, Robert J. "Time-Frequency Representations". W Handbook of Fourier Analysis & Its Applications. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195335927.003.0014.
Pełny tekst źródłaStreszczenie:
The Fourier transform is not particularly conducive in the illustration of the evolution of frequency with respect to time. A representation of the temporal evolution of the spectral content of a signal is referred to as a time-frequency representation (TFR). The TFR, in essence, attempts to measure the instantaneous spectrum of a dynamic signal at each point in time. Musical scores, in their most fundamental interpretation, are TFR’s. The fundamental frequency of the note is represented by the vertical location of the note on the staff. Time progresses as we read notes from left to right. The musical score shown in Figure 9.1 is an example. Temporal assignment is given by the note types. The 120 next to the quarter note indicates the piece should be played at 120 beats per minute. Thus, the duration of a quarter note is one half second. The frequency of the A above middle C is, by international standards, 440 Hertz. Adjacent notes notes have a ratio of 21/12. The note, A#, for example, has a frequency of 440 × 21/12 = 466.1637615 Hertz. Middle C, nine half tones (a.k.a. semitones or chromatic steps) below A, has a frequency of 440 × 2−9/12 = 261.6255653 Hertz. The interval of an octave doubles the frequency. The frequency of an octave above A is twelve half tones, or, 440 × 212/12 = 880 Hertz. The frequency spacings in the time-frequency representation of musical scores such as Figure 9.1 are thus logarithmic. This is made more clear in the alternate representation of the musical score in Figure 9.2 where time is on the horizontal axis and frequency on the vertical. At every point in time where there is no rest, a frequency is assigned. To make chords, numerous frequencies can be assigned to a point in time. Further discussion of the technical theory of western harmony is in Section 13.1.
Streszczenia konferencji na temat "Time-frequency Representations (TFRs)"
1
Pan, Min-chun. "Mechanical Noise Identification Using Time-Frequency Representations". W ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21005.
Pełny tekst źródłaStreszczenie:
Abstract Three computation schemes of time-frequency representations (TFRs) have been developed and implemented to identify different components of mechanical noise originated from the transmission system of electrical vehicles. This study explores the close relationships between three TFRs, i.e. the spectrogram based on windowed Fourier transform (WFT), the Wigner-Ville distribution (WVD), and the smoothed WVD (SWVD). One main purpose is to pursue the efficiency of computing the SWVD of a dynamic signature. The revised scheme can tremendously reduce the computation time to a scale of around 1/90, compared with the original scheme. To assess the validation of these TFR schemes, firstly, four synthetic signals are designed and processed. Secondly, the developed TFRs are applied to distinguish different spectral components of transmission noise, and identify their sources. This study takes an electrical scooter with a continuous velocity transmission (CVT) system as a test bench. The CVT-belt noise, helical-gear whine noise, and fan noise can be clearly identified via the processing of the TFRs. These obtained conclusions can be used as references for machine element modification to improve annoying noise.
2
Liu, Zhiliang, Yaqiang Jin i Ming J. Zuo. "Time-Frequency Representation Based on Robust Local Mean Decomposition". W ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-65184.
Pełny tekst źródłaStreszczenie:
Fourier transform based frequency representation makes an underlying assumption of stationarity and linearity for the target signal whose spectrum is to be computed, and thus it is unable to track time varying characteristics of non-stationary signals that also widely exist in the physical world. Time-frequency representation (TFR) is a technique to reveal useful information included in the signals, and thus the TFR methods are very attractive to the scientific and engineering world. Local mean decomposition (LMD) is a TFR technique used in many fields, e.g. machinery fault diagnosis. Similar to Hilbert-Huang transform, it is an alternative approach to demodulate amplitude-modulation (AM) and frequency-modulation (FM) signals into a set of components, each of which is the product of an instantaneous envelope signal and a pure FM signal. TFR can then be derived by the instantaneous envelope signal and the pure FM signal. However, LMD based TFR technique still has two limitations, i.e. the end effect and the mode mixing problems. Solutions for the two limitations greatly depend on three critical parameters of LMD that are boundary condition, envelope estimation, and sifting stopping criterion. Most reported studies aiming to improve performance of LMD have focused on only one parameter a time, and thus they ignore the fact that the three parameters are not independent to each other, and all of them are needed to address the end effect and the mode mixing problems in LMD. In this paper, a robust optimization approach is proposed to improve performance of LMD through an integrated framework of parameter selection in terms of boundary condition, envelope estimation, and sifting stopping criterion. The proposed optimization approach includes three components. First, the mirror extending method is employed to deal with the boundary condition problem. Second, moving average is used as the smooth algorithm for envelope estimation of local mean and local magnitude in LMD. The fixed subset size is the only parameter that usually needs to be predefined with a prior knowledge. In this step, a self-adaptive method based on the statistics theory is proposed to automatically determine a fixed subset size of moving average for accurate envelope estimation. Third, based on the first and the second steps, a soft sifting stopping criterion is proposed to enable LMD to achieve a self-adaptive stop for each sifting process. In this last step, we define an objective function that considers both global and local characteristics of a target signal. Based on the objective function, a heuristic mechanism is proposed to automatically determine the optimal number of sifting iterations in the sifting process. Finally, numerical simulation results show the effectiveness of the robust LMD in terms of mining time-frequency representation information.
3
Liu, Libin, i Ming J. Zuo. "Copula-Based Time-Frequency Distribution Analysis for Planetary Gearbox Fault Detection". W ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68060.
Pełny tekst źródłaStreszczenie:
Linear and bilinear time-frequency distributions (TFDs) have been employed in planetary gearbox fault diagnosis. For linear TFDs, there is a trade-off between the time localization and frequency resolution and the spectrogram may not have correct energy marginals. For bilinear TFDs, they cannot be interpreted as an energy distribution because of the existence of possible negative values even though they are designed for energy density representation. To overcome these shortcomings, TFDs based on copula theory have been reported in the literature. In this paper, we analyze two simulated data sets using linear TFD and copula-based TFD. The results show that the constructed copula-based TFD has desirable properties of being positive, free from cross-term interference, having high time-frequency resolution and matching well with true marginals. The copula-based TFD is also able to locate fault-induced impulses by vertical lines over a certain frequency range in the time-frequency domain. Consequently, this study confirms the advantages of the copula-based TFD as an energy distribution and its capability in fault detection for planetary gearboxes.
4
Huang, Huan, Natalie Baddour i Ming Liang. "Algorithm for Multiple Time-Frequency Curve Extraction From Time-Frequency Representation of Vibration Signals for Bearing Fault Diagnosis Under Time-Varying Speed Conditions". W ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67171.
Pełny tekst źródłaStreszczenie:
Bearing fault diagnosis under constant operational condition has been widely investigated. Monitoring the bearing vibration signal in the frequency domain is an effective approach to diagnose a bearing fault since each fault type has a specific Fault Characteristic Frequency (FCF). However, in real applications, bearings are often running under time-varying speed conditions which makes the signal non-stationary and the FCF time-varying. Order tracking is a commonly used method to resample the non-stationary signal to a stationary signal. However, the accuracy of order tracking is affected by many factors such as the precision of the measured shaft rotating speed and the interpolation methods used. Therefore, resampling-free methods are of interest for bearing fault diagnosis under time-varying speed conditions. With the development of Time-Frequency Representation (TFR) techniques, such as the Short-Time Fourier Transform (STFT) and wavelet transform, bearing fault characteristics can be shown in the time-frequency domain. However, for bearing fault diagnosis, instantaneous time-frequency characteristics, i.e. Time-Frequency (T-F) curves, have to be extracted from the TFR. In this paper, an algorithm for multiple T-F curve extraction is proposed based on a path-optimization approach to extract T-F curves from the TFR of the bearing vibration signal. The bearing fault can be diagnosed by matching the curves to the Instantaneous Fault Characteristic Frequency (IFCF) and its harmonics. The effectiveness of the proposed algorithm is validated by experimental data collected from a faulty bearing with an outer race fault and a faulty bearing with an inner race fault, respectively.
5
Shakir, Huzefa, i Won-Jong Kim. "Discrete-Time Closed-Loop Model Identification of Fixed-Structure Unstable Multivariable Systems". W ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41834.
Pełny tekst źródłaStreszczenie:
This paper presents improved empirical representations of a general class of open-loop unstable systems using closed-loop system identification. A multi-axis magnetic-levitation (maglev) nanopositioning system with an extended translational travel range is used as a test bed to verify the closed-loop system-identification method proposed in this paper. A closed-loop identification technique employing the Box-Jenkins (BJ) method and a known controller structure is developed for model identification and validation. Direct and coupling transfer functions (TFs) are then derived from the experimental input-output time sequences and the knowledge of controller dynamics. A persistently excited signal with a frequency range of [0, 2500] Hz is used as a reference input. An order-reduction algorithm is applied to obtain TFs with predefined orders, which give a close match in the frequency range of interest without missing any significant plant dynamics. The entire analysis is performed in the discrete-time domain in order to avoid any errors due to continuous-to-discrete-time conversion and vice versa. Continuous-time TFs are used only for order-reduction and performance analysis of the identified plant TFs. Experimental results in the time as well as frequency domains verified the accuracy of the plant TFs and demonstrated the effectiveness of the closed-loop identification and order-reduction methods.
6
Comandur, Vinodhini, Karen Feigh, J. V. i Robert Walters. "Development and Assessment of Flight Lead Cue for Real-time Guidance and Pilot Workload Reduction in Rotorcraft Shipboard Recovery". W Vertical Flight Society 76th Annual Forum & Technology Display. The Vertical Flight Society, 2020. http://dx.doi.org/10.4050/f-0076-2020-16398.
Pełny tekst źródłaStreszczenie:
Helicopter shipboard launch and recovery are some of the most challenging operations to date, owing to the pilot workload associated with the tasks. A variety of environmental conditions such as random deck motion, heavy sea states and unsteady aerodynamic interactions can be attributed to the same. This paper highlights the development and assessment of a visual flight lead cue for real-time guidance and pilot workload reduction. For a chosen approach-turnland maneuver, the pilot workload is assessed using data from pilot-in-the-loop (PIL) flight simulations. Quantitative metrics based on Time-Frequency Representation (TFR) are used to evaluate pilot workload and the analysis of workload in the presence and absence of the cue is presented. Furthermore, the deviations from the desired path are studied using root-mean-square error (RMSE) for the cue on/off cases. The relation between pilot workload and path following is analyzed to determine the efficacy of the cue provided.