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Hale, Dave. "Dynamic warping of seismic images". GEOPHYSICS 78, nr 2 (1.03.2013): S105—S115. http://dx.doi.org/10.1190/geo2012-0327.1.
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The problem of estimating relative time (or depth) shifts between two seismic images is ubiquitous in seismic data processing. This problem is especially difficult where shifts are large and vary rapidly with time and space, and where images are contaminated with noise or for other reasons are not shifted versions of one another. A new solution to this problem requires only simple extensions of a classic dynamic time warping algorithm for speech recognition. A key component of that classic algorithm is a nonlinear accumulation of alignment errors. By applying the same nonlinear accumulator repeatedly in all directions along all sampled axes of a multidimensional image, I obtain a new and effective method for dynamic image warping (DIW). In tests where known shifts vary rapidly, this new method is more accurate than methods based on crosscorrelations of windowed images. DIW also aligns seismic reflectors well in examples where shifts are unknown, for images with differences not limited to time shifts.
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Gao, Wenlei, i Mauricio D. Sacchi. "Multicomponent seismic data registration by nonlinear optimization". GEOPHYSICS 83, nr 1 (1.01.2018): V1—V10. http://dx.doi.org/10.1190/geo2017-0105.1.
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Mapping PS-wave data to the PP-wave time domain is a critical step before joint PP- and PS-wave data interpretation and inversion. Registration techniques are often constrained by having access to a known [Formula: see text] ratio. When an accurate [Formula: see text] ratio is not provided, one can solve the problem of seismic data registration by minimizing the difference between the PP-wave and the warped PS-wave data with a smoothing constraint applied on the warping function. To avoid undesirable foldings or rapid changes in the warped PS-wave image, we require a warping function that is monotonic and smooth. We invert the [Formula: see text] ratio directly from PP- and PS-wave data instead of estimating it from the warping function. Seismic data registration is posed as a constrained nonlinear optimization problem. Furthermore, we represent the [Formula: see text] ratio by spline functions and adopt a parameterization that guarantees monotonic warping functions. Our parameterization in terms of splines significantly reduces the number of unknowns of our problem, and the convergence to a smooth monotonic solution is guaranteed.
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Son, Nguyen Thanh. "Pattern matching under dynamic time warping for time series prediction". Tạp chí Khoa học 15, nr 3 (20.09.2019): 148. http://dx.doi.org/10.54607/hcmue.js.15.3.146(2018).
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Time series forecasting based on pattern matching has received a lot of interest in the recent years due to its simplicity and the ability to predict complex nonlinear behavior. In this paper, we investigate into the predictive potential of the method using k-NN algorithm based on R*-tree under dynamic time warping (DTW) measure. The experimental results on four real datasets showed that this approach could produce promising results in terms of prediction accuracy on time series forecasting when comparing to the similar method under Euclidean distance.
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Xu, Qingyu, Hongju Chen, Shaoqing Ye, Yongming Zeng, Hongmei Lu i Zhimin Zhang. "Standardization of Raman spectra using variable penalty dynamic time warping". Analytical Methods 13, nr 30 (2021): 3414–23. http://dx.doi.org/10.1039/d1ay00541c.
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Kwong, S., Q. H. He, K. F. Man, K. S. Tang i C. W. Chau. "Parallel Genetic-Based Hybrid Pattern Matching Algorithm for Isolated Word Recognition". International Journal of Pattern Recognition and Artificial Intelligence 12, nr 05 (sierpień 1998): 573–94. http://dx.doi.org/10.1142/s0218001498000348.
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Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path; and (ii) finding the K-best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW. In this paper, a Parallel Genetic Time Warping (PGTW) is proposed to solve the above said problems. A database extracted from the TIMIT speech database of 95 isolated words is set up for evaluating the performance of the PGTW. In the database, each of the first 15 words had 70 different utterances, and the remaining 80 words had only one utterance. For each of the 15 words, one utterance is arbitrarily selected as the test template for recognition. Distance measure for each test template to the utterances of the same word and to those of the 80 words is calculated with three different time warping algorithms: TTS, PGTW and Sequential Genetic Time Warping (SGTW). A Normal Distribution Model based on Rabiner23 is used to evaluate the performance of the three algorithms analytically. The analyzed results showed that the PGTW had performed better than the TTS. It also showed that the PGTW had very similar results as the SGTW, but about 30% CPU time is saved in the single processor system.
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Zhang, Yuxin, Yoshikazu Miyanaga i Constantin Siriteanu. "Robust Speech Recognition with Dynamic Time Warping and Nonlinear Median Filter". Journal of Signal Processing 16, nr 2 (2012): 147–57. http://dx.doi.org/10.2299/jsp.16.147.
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Chelidze, D., i J. P. Cusumano. "Phase space warping: nonlinear time-series analysis for slowly drifting systems". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364, nr 1846 (28.07.2006): 2495–513. http://dx.doi.org/10.1098/rsta.2006.1837.
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A new general dynamical systems approach to data analysis is presented that allows one to track slowly evolving variables responsible for non-stationarity in a fast subsystem. The method is based on the idea of phase space warping , which refers to the small distortions in the fast subsystem's phase space that results from the slow drift, and uses short-time reference model prediction error as its primary measurement of this phenomenon. The basic theory is presented and the issues associated with its implementation in a practical algorithm are discussed. A vector-tracking version of the procedure, based on smooth orthogonal decomposition analysis, is applied to the study of a nonlinear vibrating beam experiment in which a crack propagates to complete fracture. Our method shows that the damage evolution is governed by a scalar process, and we are able to give real-time estimates of the current damage state and identify the governing damage evolution model. Using a final recursive estimation step based on this model, the time to failure is continuously and accurately predicted well in advance of actual failure.
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Stoykov, S., i S. Margenov. "Nonlinear Vibrations of 3D Laminated Composite Beams". Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/892782.
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A model for 3D laminated composite beams, that is, beams that can vibrate in space and experience longitudinal and torsional deformations, is derived. The model is based on Timoshenko’s theory for bending and assumes that, under torsion, the cross section rotates as a rigid body but can deform longitudinally due to warping. The warping function, which is essential for correct torsional deformations, is computed preliminarily by the finite element method. Geometrical nonlinearity is taken into account by considering Green’s strain tensor. The equation of motion is derived by the principle of virtual work and discretized by thep-version finite element method. The laminates are assumed to be of orthotropic materials. The influence of the angle of orientation of the laminates on the natural frequencies and on the nonlinear modes of vibration is presented. It is shown that, due to asymmetric laminates, there exist bending-longitudinal and bending-torsional coupling in linear analysis. Dynamic responses in time domain are presented and couplings between transverse displacements and torsion are investigated.
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Chen, Shuangquan, Song Jin, Xiang-Yang Li i Wuyang Yang. "Nonstretching normal-moveout correction using a dynamic time warping algorithm". GEOPHYSICS 83, nr 1 (1.01.2018): V27—V37. http://dx.doi.org/10.1190/geo2016-0673.1.
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Normal-moveout (NMO) correction is one of the most important routines in seismic processing. NMO is usually implemented by a sample-by-sample procedure; unfortunately, such implementation not only decreases the frequency content but also distorts the amplitude of seismic waveforms resulting from the well-known stretch. The degree of stretch increases with increasing offset. To minimize severe stretch associated with far offset, we use a dynamic time warping (DTW) algorithm to achieve an automatic dynamic matching NMO nonstretch correction, which does not handle crossing events and convoluted events such as thin layers. Our algorithm minimizes the stretch through an automatic static temporal correction of seismic wavelets. The local static time shifts are obtained using a DTW algorithm, which is a nonlinear optimization method. To mitigate the influence of noise, we evaluated a multitrace window strategy to improve the signal-to-noise ratio of seismic data by obtaining a more precise moveout correction at far-offset traces. To illustrate the effectiveness of our algorithm, we first applied our method to synthetic data and then to field seismic data. Both tests illustrate that our algorithm minimizes the stretch associated with far offsets, and the method preserves the amplitude fidelity.
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Chelidze, David, i Ming Liu. "Reconstructing slow-time dynamics from fast-time measurements". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, nr 1866 (18.10.2007): 729–45. http://dx.doi.org/10.1098/rsta.2007.2124.
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This paper considers a dynamical system subjected to damage evolution in variable operating conditions to illustrate the reconstruction of slow-time (damage) dynamics using fast-time (vibration) measurements. Working in the reconstructed fast-time phase space, phase space warping-based feature vectors are constructed for slow-time damage identification. A subspace of the feature space corresponding to the changes in the operating conditions is identified by applying smooth orthogonal decomposition (SOD) to the initial set of feature vectors. Damage trajectory is then reconstructed by applying SOD to the feature subspace not related to the changes in the operating conditions. The theory is validated experimentally using a vibrating beam, with a variable nonlinear potential field, subjected to fatigue damage. It is shown that the changes in the operating condition (or the potential field) can be successfully separated from the changes caused by damage (or fatigue) accumulation and SOD can identify the slow-time damage trajectory.
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Yang, Chan-Yun, Pei-Yu Chen, Te-Jen Wen i Gene Eu Jan. "IMU Consensus Exception Detection with Dynamic Time Warping—A Comparative Approach". Sensors 19, nr 10 (14.05.2019): 2237. http://dx.doi.org/10.3390/s19102237.
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A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-sensitive microelectronic system for the realization of remote motion abnormality detection. In combination with an inertial measurement unit (IMU), the algorithm is potentially applicable for remotely monitoring patients who are at risk of certain exceptional motions. The fixed interval signal sampling mechanism has normally been adopted when devising motion detection systems; however, dynamically capturing the particular motion patterns from the IMU motion sensor can be difficult. To this end, the DTW algorithm, as a kind of nonlinear pattern-matching approach, is able to optimally align motion signal sequences tending towards time-varying or speed-varying expressions, which is especially suitable to capturing exceptional motions. Thus, this paper evaluated this kind of abnormality detection using the proposed DTW algorithm on the basis of its theoretical fundamentals to significantly enhance the viability of the methodology. To validate the methodological viability, an artificial neural network (ANN) framework was intentionally introduced for performance comparison. By incorporating two types of designated preprocessors, i.e., a DFT interpolation preprocessor and a convolutional preprocessor, to equalize the unequal lengths of the matching sequences, two kinds of ANN frameworks were enumerated to compare the potential applicability. The comparison eventually confirmed that the direct template-matching DTW is excellent in practical application for the detection of time-varying or speed-varying abnormality, and reliably captures the consensus exceptions.
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Gonzalez-Lopez, Jose A., Alejandro Gomez-Alanis, José L. Pérez-Córdoba i Phil D. Green. "Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping". Applied Sciences 12, nr 3 (23.01.2022): 1167. http://dx.doi.org/10.3390/app12031167.
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In this paper, we propose a novel algorithm called multiview temporal alignment by dependence maximisation in the latent space (TRANSIENCE) for the alignment of time series consisting of sequences of feature vectors with different length and dimensionality of the feature vectors. The proposed algorithm, which is based on the theory of multiview learning, can be seen as an extension of the well-known dynamic time warping (DTW) algorithm but, as mentioned, it allows the sequences to have different dimensionalities. Our algorithm attempts to find an optimal temporal alignment between pairs of nonaligned sequences by first projecting their feature vectors into a common latent space where both views are maximally similar. To do this, powerful, nonlinear deep neural network (DNN) models are employed. Then, the resulting sequences of embedding vectors are aligned using DTW. Finally, the alignment paths obtained in the previous step are applied to the original sequences to align them. In the paper, we explore several variants of the algorithm that mainly differ in the way the DNNs are trained. We evaluated the proposed algorithm on a articulatory-to-acoustic (A2A) synthesis task involving the generation of audible speech from motion data captured from the lips and tongue of healthy speakers using a technique known as permanent magnet articulography (PMA). In this task, our algorithm is applied during the training stage to align pairs of nonaligned speech and PMA recordings that are later used to train DNNs able to synthesis speech from PMA data. Our results show the quality of speech generated in the nonaligned scenario is comparable to that obtained in the parallel scenario.
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Dong, Yongle, Fan Zhang, Xuan Li, Lifang Zhang, Jia Yu, Yongmei Mao i Guanglong Jiang. "Nonlinear Load Harmonic Prediction Method Based on Power Distribution Internet of Things". Scientific Programming 2021 (24.05.2021): 1–12. http://dx.doi.org/10.1155/2021/9978900.
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A large number of nonlinear loads have an impact on the stable operation of the power system. To solve this problem, this article proposes a nonlinear load harmonic prediction method based on the architecture of Power Distribution Internet of Things. Firstly, this method integrates the characteristics of edge computing technology and Power Distribution Internet of Things technology and proposes a Power Distribution Internet of Things framework applied to nonlinear load harmonic prediction, which provides top-level design for subsequent harmonic prediction methods of Power Distribution Internet of Things; then, considering the electrical characteristics of the typical nonlinear load, the mathematical model of nonlinear load data is constructed based on the harmonic coupling admittance matrix model on the edge side. At the same time, a nonlinear load harmonic prediction model based on dynamic time warping and long-term and short-term memory network (DTW-LSTM) is established in the cloud computing center to realize high accuracy and high real-time prediction and analysis of nonlinear load harmonics. Finally, the simulation results based on the general data set show that the MAE evaluation index of the proposed method is less than 5% in the experimental group, which shows good generalization ability, and has some advantages over the current method in operation efficiency.
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Thode, Aaron, Julien Bonnel, Margaux Thieury, Aileen Fagan, Chris Verlinden, Dana Wright, Catherine Berchok i Jessica Crance. "Using nonlinear time warping to estimate North Pacific right whale calling depths in the Bering Sea". Journal of the Acoustical Society of America 141, nr 5 (maj 2017): 3059–69. http://dx.doi.org/10.1121/1.4982200.
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IHRKE, MATTHIAS, HECKE SCHROBSDORFF i J. MICHAEL HERRMANN. "RECURRENCE-BASED ESTIMATION OF TIME-DISTORTION FUNCTIONS FOR ERP WAVEFORM RECONSTRUCTION". International Journal of Neural Systems 21, nr 01 (luty 2011): 65–78. http://dx.doi.org/10.1142/s0129065711002651.
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We introduce an approach to compensate for temporal distortions of repeated measurements in event-related potential research. The algorithm uses a combination of methods from nonlinear time-series analysis and is based on the construction of pairwise registration functions from cross-recurrence plots of the phase-space representations of ERP signals. The globally optimal multiple-alignment path is approximated by hierarchical cluster analysis, i.e. by iteratively combining pairs of trials according to similarity. By the inclusion of context information in form of externally acquired time markers (e.g. reaction time) into a regularization scheme, the extracted warping functions can be guided near paths that are implied by the experimental procedure. All parameters occurring in the algorithm can be optimized based on the properties of the data and there is a broad regime of parameter configurations where the algorithm produces good results. Simulations on artificial data and the analysis of ERPs from a psychophysical study demonstrate the robustness and applicability of the algorithm.
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Ramos, Marlon D., Rigobert Tibi, Christopher J. Young i Erica L. Emry. "Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms". Seismic Record 5, nr 1 (1.01.2025): 97–106. https://doi.org/10.1785/0320240038.
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Abstract The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excitation, which can be frequency-dependent and may not make use of the full waveform. In this article, we explore whether phase and amplitude distances derived from dynamic time warping (DTW) and elastic shape analysis (ESA) can inform event discrimination. We demonstrate the ability to distinguish underground point sources using synthetic waveforms calculated for a 1D Earth model and various source mechanisms. We then apply the method to recorded data from events in the Korean Peninsula, which includes declared nuclear explosions, a collapse event, and naturally occurring earthquakes. Phase and amplitude distances derived from DTW and ESA are then used to classify the event types via dendrogram and k-nearest-neighbor clustering analyses. Using information from the full waveform, we show how different underground sources can be distinguished at regional distances. We highlight the potential of these nonlinear alignment algorithms for discrimination and comment on ways we can extend the framework presented here.
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Mansor, Nur Syazwin, i Norhaiza Ahmad. "Spatial grouping of homogeneous river flow process in Johor". Malaysian Journal of Fundamental and Applied Sciences 13, nr 4-1 (5.12.2017): 405–7. http://dx.doi.org/10.11113/mjfas.v13n4-1.845.
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This study identifies the spatial grouping of homogeneous river flow process on eight rivers in Johor based on river discharge records of 28 years. A clustering approach using a nonlinear dissimilarity measure called Dynamic Time Warping (DTW) is used to detect the similarity between these eight rivers. The clustering results validated by an internal validity measure, C-index shows two distinct groups of rivers: Cluster 1 consist of Sungai Lenik, Sungai Segamat, Sungai Bekok, and Sungai Muar; Cluster 2 consist of Sungai Sayong, Sungai Lenggor, Sungai Johor, and Sungai Kahang. This two-cluster solution are stable and interpretable with reference to spatial variations and can be distinguish by their geographical location in the peninsular.
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Aizpuru, Judit, Annina Karolin Kemmer, Jong Woo Kim, Stefan Born, Peter Neubauer, Mariano N. Cruz Bournazou i Tilman Barz. "Fitting nonlinear models to continuous oxygen data with oscillatory signal variations via a loss based on Dynamic Time Warping*". IFAC-PapersOnLine 55, nr 7 (2022): 216–21. http://dx.doi.org/10.1016/j.ifacol.2022.07.447.
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Bonnel, Julien, Aaron Thode, Dana Wright i Ross Chapman. "Nonlinear time-warping made simple: A step-by-step tutorial on underwater acoustic modal separation with a single hydrophone". Journal of the Acoustical Society of America 147, nr 3 (marzec 2020): 1897–926. http://dx.doi.org/10.1121/10.0000937.
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Thode, Aaron, Julien Bonnel, Margaux Thieury, Aileen Fagan, Christopher M. Verlinden, Dana Wright, Jessica Crance i Catherine L. Berchok. "Using nonlinear time warping to estimate North Pacific right whale calling depths and propagation environment in the Bering Sea". Journal of the Acoustical Society of America 142, nr 4 (październik 2017): 2711–12. http://dx.doi.org/10.1121/1.5014894.
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Xu, Jianchao, Bo Diao, Quanquan Guo, Yinghua Ye, Y. L. Mo i Tianmin Zhou. "Parametric Study on Mixed Torsional Behavior of U-Shaped Thin-Walled RC Girders". Advances in Civil Engineering 2018 (15.11.2018): 1–18. http://dx.doi.org/10.1155/2018/3497390.
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Nowadays, U-shaped thin-walled concrete girders have been widely applied in the urban construction of rail viaducts in China as well as worldwide. However, the mixed torsional behaviors of these structures are not well understood. In this paper, the mixed torsional behaviors of the U-shaped thin-walled RC girders are theoretically analyzed, and a method predicting failure modes and ultimate torques is proposed. Nonlinear FE models based on ABAQUS to simulate the mixed torsional behaviors are built and calibrated with the test results. Parametric studies considering three crucial parameters (boundary condition, span length-section height ratio, and ratio of longitudinal bars to stirrups) are conducted based on both the above suggested calculating method and the FE modeling. The calculated and the simulated results agree well with each other and with the test results. It is found that the failure modes of the U-shaped thin-walled RC girders under torsion are influenced by all the three parameters. Three kinds of failure modes are observed: flexural failures dominated by warping moment, shear failures caused by warping torque and circulatory torque, and flexural-shear failures in the cases where flexural failure and shear failure appear almost at the same time.
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Palmieri, Flavio, Pedro Gomis, José Esteban Ruiz, Dina Ferreira, Alba Martín-Yebra, Esther Pueyo, Juan Pablo Martínez, Julia Ramírez i Pablo Laguna. "Nonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study". Sensors 21, nr 8 (12.04.2021): 2710. http://dx.doi.org/10.3390/s21082710.
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Background: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([K+]) variations (Δ[K+]) out of normal ranges. Therefore, a reliable method for continuous, noninvasive monitoring of [K+] is crucial. The morphology of the T-wave in the electrocardiogram (ECG) reflects Δ[K+] and two time-warping-based T-wave morphological parameters, dw and its heart-rate corrected version dw,c, have been shown to reliably track Δ[K+] from the ECG. The aim of this study is to derive polynomial models relating dw and dw,c with Δ[K+], and to test their ability to reliably sense and quantify Δ[K+] values. Methods: 48-hour Holter ECGs and [K+] values from six blood samples were collected from 29 ESRD-HD patients. For every patient, dw and dw,c were computed, and linear, quadratic, and cubic fitting models were derived from them. Then, Spearman’s (ρ) and Pearson’s (r) correlation coefficients, and the estimation error (ed) between Δ[K+] and the corresponding model-estimated values (Δ^[K+]) were calculated. Results and Discussions: Nonlinear models were the most suitable for Δ[K+] estimation, rendering higher Pearson’s correlation (median 0.77 ≤r≤ 0.92) and smaller estimation error (median 0.20 ≤ed≤ 0.43) than the linear model (median 0.76 ≤r≤ 0.86 and 0.30 ≤ed≤ 0.40), even if similar Spearman’s ρ were found across models (median 0.77 ≤ρ≤ 0.83). Conclusion: Results support the use of nonlinear T-wave-based models as Δ[K+] sensors in ESRD-HD patients.
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Hong, Jeong-Hyo, Tianyu Yu, Zixuan Chen, Soo-Jeong Park i Yun-Hae Kim. "Improvement of flexural strength and compressive strength by heat treatment of PLA filament for 3D-printing". Modern Physics Letters B 33, nr 14n15 (28.05.2019): 1940025. http://dx.doi.org/10.1142/s0217984919400256.
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Poly-lactic Acid (PLA) is an environmentally friendly material with better stability in heat shrinkage than Acrylonitrile Butadiene Styrene (ABS), such as warping in 3D printing. This study focused on the enhancement of the mechanical properties of PLA filament for 3D printers through different heat treatment temperature and heat exposure time of PLA samples. The results showed that the highest flexural strength was recorded in the PLA sample that went through heat treatment at [Formula: see text] and heat exposure time of 300 s. And it tended to decrease with temperature and time after this point. But it has higher flexural strength than neat PLA. The compressive strength showed the highest compressive strength through heat treatment at [Formula: see text] for 600 s. Because compressive strength has no threshold limit temperature in experimental temperature, compressive strength showed a tendency to increase with increasing heat exposure time and high temperature at same condition. This result showed that the heat treatment process affects the flexural strength and compressive strength and can be improved upon using appropriate heat treatment conditions.
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Gruttmann, F., i W. Wagner. "An advanced shell model for the analysis of geometrical and material nonlinear shells". Computational Mechanics 66, nr 6 (10.09.2020): 1353–76. http://dx.doi.org/10.1007/s00466-020-01905-2.
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AbstractIn this paper layered shells subjected to static loading are considered. The displacements of the Reissner–Mindlin theory are enriched by a an additional part. These so-called fluctuation displacements include warping displacements and thickness changes. They lead to additional terms for the material deformation gradient and the Green–Lagrangian strain tensor. Within a nonlinear multi-field variational formulation the weak form of the boundary value problem accounts for the equilibrium of stress resultants and couple resultants, the local equilibrium of stresses, the geometrical field equations and the constitutive equations. For the independent shell strains an ansatz with quadratic shape functions is chosen. This leads to a significant improved convergence behaviour especially for distorted meshes. Elimination of a set of parameters on element level by static condensation yields an element stiffness matrix and residual vector of a quadrilateral shell element with the usual 5 or 6 nodal degrees of freedom. The developed model yields the complicated three-dimensional stress state in layered shells for elasticity and elasto-plasticity considering geometrical nonlinearity. In comparison with fully 3D solutions present 2D shell model requires only a fractional amount of computing time.
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Wei, Zheng Zheng, i Fu Zhang Wang. "Research on Seasonal Index Based on Dynamic Clustering of the Daily Railway Passenger Flow Title". Advanced Materials Research 981 (lipiec 2014): 966–71. http://dx.doi.org/10.4028/www.scientific.net/amr.981.966.
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The railway passenger flow is greatly impacted by different months and weeks of the season, and the impact is periodic. Accurate evaluation of the seasonal index for predicting the railway passenger flow is of key importance. Based on this background, the paper proposes an algorithm for calculating the seasonal index which is impacted by both months and weeks. The railway passenger flow between different OD(Origination Destination) is affected by months and weeks quite different. Therefore the paper focuses on the method for effective calculation of the month index and week index on the basis of time series clustering. When adopting hierarchical cluster, general Euclidean distance and its expansion used as a similarity metric is widely applied in time series comparison, however, this distance measurement is not robust enough for the processed data. Dynamic time warping is a pattern matching algorithm based on nonlinear dynamic programming technique. It is applied to calculate month and week index to get seasonal index that defined in this paper, which has good application value for predicting the passenger flow.
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Wiafe, Sir-Lord, Ashkan Faghiri, Zening Fu, Robyn Miller, Adrian Preda i Vince D. Calhoun. "The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity". Imaging Neuroscience 2 (2024): 1–23. http://dx.doi.org/10.1162/imag_a_00187.
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Abstract In neuroimaging research, understanding the intricate dynamics of brain networks over time is paramount for unraveling the complexities of brain function. One approach commonly used to explore the dynamic nature of brain networks is functional connectivity analysis. However, while functional connectivity offers valuable insights, it fails to consider the diverse timescales of coupling between different brain regions. This gap in understanding leaves a significant aspect of brain dynamics unexplored in neuroimaging research. We propose an innovative approach that delves into the dynamic coupling/connectivity timescales of brain regions relative to one another, focusing on how brain region couplings stretch or shrink over time, rather than relying solely on functional connectivity measures. Our method introduces a novel metric called “warping elasticity,” which utilizes dynamic time warping (DTW) to capture the temporal nuances of connectivity. Unlike traditional methods, our approach allows for (potentially nonlinear) dynamic compression and expansion of the time series, offering a more intricate understanding of how coupling between brain regions evolves. Through the adaptive windows employed by the DTW method, we can effectively capture transient couplings within varying connectivity timescales of brain network pairs. In extensive evaluations, our method exhibits high replicability across subjects and diverse datasets, showcasing robustness against noise. More importantly, it uncovers statistically significant distinctions between healthy control (HC) and schizophrenia (SZ) groups through the identification of warp elasticity states. These states are cluster centroids, representing the warp elasticity across subjects and time, offering a novel perspective on the dynamic nature of brain connectivity, distinct from conventional metrics focused solely on functional connectivity. For instance, controls spend more time in a warp elasticity state characterized by timescale stretching of the visual domain relative to other domains, suggesting disruptions in the visual cortex. Conversely, patients show increased time spent in a warp elasticity state with stretching timescales in higher cognitive areas relative to sensory regions, indicative of prolonged cognitive processing of sensory input. Overall, our approach presents a promising avenue for investigating the temporal dynamics of brain network interactions in functional magnetic resonance imaging (fMRI) data. By focusing on the elasticity of connectivity timescales, rather than adhering to functional connectivity metrics, we pave the way for a deeper understanding of neuropsychiatric disorders in neuroscience research.
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Abdulsada, Abdullah, i Nasir Selman. "Robust digital voltage mode control of buck converter with enhanced tracking performance". Al-Qadisiyah Journal for Engineering Sciences 17, nr 2 (30.06.2024): 158–64. http://dx.doi.org/10.30772/qjes.2024.145861.1076.
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The digital voltage controller of power converters is used because it has many advantages over its analog counterparts. Thus, in this paper, a controller of digital voltage mode is designed and analyzed for a non-isolated DC-DC buck converter in continuous conduction mode (CCM). First, an analog controller is designed using a Bode plot to achieve the desired stability margins in the frequency domain. Next, the analog controller is discretized using bilinear transformation with the pre-warping method. The characteristics of the discretized compensator can be maintained close to the corresponding analog compensator as long as a proper sampling time is selected. The digital control scheme is simulated with a nonlinear DC-DC buck converter model in MATLAB/Simulink to investigate the controller performance. The simulation results demonstrated the validation of the proposed digital voltage-mode control design approach. The developed digital controller eliminates DC error by tracking the reference voltage, achieving a fast transient response, maintaining specified stability margins, and effectively rejecting large disturbances.
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Zhu, Junjiang, Bo Yuan, Yibai Mao, Ping Zhou, Qingchao Sun i Guozhen Fang. "A Thermomechanical Model for Time-Varying Deformations of Spigot Interference Connection under Shrink-Fitting Assembly". Machines 11, nr 11 (26.10.2023): 994. http://dx.doi.org/10.3390/machines11110994.
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The interference fit connection structure is widely employed in various industries. Different from the conventional connection structure, the aero-engine rotor connection has a spigot-bolt structure. The quality of the shrink-fitting assembly process directly affects the final assembly performance of the rotor. The complexity of the connection structure and the time-varying thermal deformation bring great challenges in analyzing the formation patterns of connection performance. However, existing methods of interference connection analysis are mainly used in the wide range of interference connection structures, which makes them difficult to apply in low height interference connection for aero-engine rotors. This paper introduces a thermomechanical interference fit pressure model. First, a theoretical model for interference fit pressure considering temperature-variable loads is established to obtain the time-varying pressure during the assembly process. Second, a finite element model is established to explore the influence of interference and temperature loads on the spigot pressure and the mounting edge deformation. Finally, the experiments validate the accuracy of both the theoretical model and the finite element analysis. The results indicate that during the shrink-fitting assembly process, the interference fit pressure exhibits a nonlinear evolution trend, and the warping deformation of the mounting edge is a result of the combined influence of temperature and interference fit pressure. The law found in this paper has an application prospect in the process parameter setting of shrink-fitting assembly for special structures.
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Shoukry, Samir N. "Backcalculation of Thermally Deformed Concrete Pavements". Transportation Research Record: Journal of the Transportation Research Board 1716, nr 1 (styczeń 2000): 64–72. http://dx.doi.org/10.3141/1716-08.
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Nonlinear explicit three-dimensional finite element (3-D FE) modeling is used to investigate the performance of the falling weight deflectometer (FWD) test in the evaluation of layer moduli of jointed plain concrete pavements (JPCP) subjected to nonlinear thermal gradient through the slab thickness. Concrete slab separation from the base, in-plane friction at the concrete-base interface, the gravitational forces, and the interface characteristics between dowel bars and surrounding concrete are all represented in the 3-D FE model. Experimental verification of the model is obtained through comparison of the 3-D FE generated response to ( a) the FWD measured deflection basin and ( b) the measured response of an instrumented rigid pavement section located in Ohio to a loaded truck moving at 21.8 m/s (48 mph). Several cases of linear and nonlinear thermal gradients are applied to the model, and deflection basins are obtained. Two backcalculation programs, MODULUS 5.0 and EVERCALC 4.0, are used for prediction of the layer moduli in each case, and the values are compared. The results indicate that thermal curling of the slab due to negative thermal gradient has little effect on the accuracy of backcalculated moduli. Warping of the slab due to positive thermal gradient greatly influences the measured FWD deflection basin and leads to significant errors in the backcalculated moduli. These errors may be minimized if the time an FWD test is conducted falls between the late afternoon and midmorning (from 5:30 p.m. to 9:30 a.m. during summer in West Virginia).
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Zhang, Liang, Qinglei Du i Hui Chen. "Matched power-frequency–modulated signal transform and its application in bat call signal analysis". Journal of the Acoustical Society of America 156, nr 4 (1.10.2024): 2596–605. http://dx.doi.org/10.1121/10.0032394.
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Bat call signal analysis is an important research topic, which is meaningful for bat species identification, and the design of various biomimetic systems. In addition to the commonly used methods in the time-frequency domain, the fractional Fourier transform (FRFT) is a valuable signal processing tool, as it is a generalization of the Fourier transform. However, the FRFT is constrained to the analysis of the linear frequency modulated–like bat call signal, while the modulation of the harmonics in a bat call is often nonlinear. For this reason, this paper proposes an integral transform, named matched power-frequency–modulated (PFM) signal transform (MPST), which is also the generalization of the Fourier transform, more precisely, a time-warping Fourier transform. As with the limitation of FRFT, the MPST is constrained to the analysis of the PFM-like bat call with the instantaneous frequency defined as an approximate power function abut time, in which the power can be an arbitrary positive integer or a fraction. The applications of MPST on the PFM-modeled bat call analysis are mainly parameter estimation and harmonic separation, and the performance is fully validated using the recordings of the feeding buzzes, social calls, and distress calls from the European bats.
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Kim, Yooil, Kyong-Hwan Kim i Yonghwan Kim. "Analysis of Hydroelasticity of Floating Shiplike Structure in Time Domain Using a Fully Coupled Hybrid BEM-FEM". Journal of Ship Research 53, nr 01 (1.03.2009): 31–47. http://dx.doi.org/10.5957/jsr.2009.53.1.31.
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A fully coupled time-domain ship hydroelasticity problem focusing on a springing phenomenon is considered in this study using a hybrid boundary element method (BEM) finite element method (FEM) scheme. The fluid domain surrounding a flexible vessel is handled with a boundary element method adopting a higher-order B-spline Rankine panel method. The structural domain is modeled by a finite element method relying on the one-dimensional beam element, which is able to capture the coupling effect between torsion and bending as well as warping distortion. Coupling between the two subdomains is realized by the Newton method in which an exact Jacobian matrix is derived by solving both fluid and structure tangent problems. The calculation is repeated until the solution reaches convergence. Thanks to the positive aspects of this implicit scheme, numerical instability related to the time integration can be avoided without relying on infinite frequency added mass, which is inevitable when an explicit scheme is used. Moreover, a direct integration scheme, such as the Newmark-β method, for structural problems can be used, and this formulation can be easily extended to the case with structural nonlinear effect, such as large deformation. The developed computer program is validated through comparison with published experimental data, ending up with good correspondence between the two results. Validation is also achieved through a comparative study on rigid body motion with an existing six degrees of freedom (6-DOF) ship motion program.
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Abbas, Ghulam, Muhammad Usman Asad, Jason Gu, Salem Alelyani, Valentina E. Balas, Mohammad Rashid Hussain, Umar Farooq, Ahmed Bilal Awan, Ali Raza i Chunqi Chang. "Multivariable Unconstrained Pattern Search Method for Optimizing Digital PID Controllers Applied to Isolated Forward Converter". Energies 14, nr 1 (25.12.2020): 77. http://dx.doi.org/10.3390/en14010077.
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Most of the traditional PID tuning methods are heuristic in nature. The heuristic approach-based tuned PID controllers show only nominal performance. In addition, in the case of a digital redesign approach, mapping of the heuristically-designed continuous-time PID controllers into discrete-time PID controllers and in case of the direct digital design approach, mapping of the continuous-time plant (forward converter) into the discrete-time plant, results in frequency distortion (or warping). Besides this, nonlinear elements such as ADC and DAC, and delay in the digital control loop deteriorate the control performance. There is a need to tune conventionally-designed digital controllers to enhance performance. This paper proposes optimized discrete-time PID controllers for a forward DC–DC converter operating in continuous conduction mode (CCM). The considered conventional digital PID controllers designed on the basis of the digital redesign and direct digital approaches are tuned by one of the multivariable unconstrained pattern search methods named Hooke–Jeeves (H–J) search method to ensure excellent output voltage regulation performance against the changes in input voltage and load current. Numerical results show that the H–J-based optimized PID compensated forward converter system shows tremendous improvement in performance compared to its unoptimized counterpart and simulated annealing (SA)-based compensated system, thus justifying the applicability of the H–J method for enhancing the performance.
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Rutkowska, Aleksandra, Magdalena Szyszko i Mariusz Próchniak. "Consumer and Professional Inflation Expectations – Properties and Mutual Dependencies". Comparative Economic Research. Central and Eastern Europe 27, nr 3 (30.09.2024): 93–116. http://dx.doi.org/10.18778/1508-2008.27.23.
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Inflation expectations are a crucial variable for central banks. However, empirically examining their properties is challenging. This paper juxtaposes the properties of consumer and professional expectations. It also assesses the degree of forward- and backward-lookingness and the information content of expectations. We apply entropy-based measures (common information and mutual common information) to capture nonlinear dependencies and dynamic time warping to account for different lags in the relationships. The study covers 12 inflation-targeting economies from the European region. The results suggest that in most countries, professionals are more forward-looking, and consumers follow professionals. Both groups of economic agents present expectations that are aligned in terms of information content. However, cross-country differences occur. These results imply that, from the central bank’s point of view, communication and practices designed to shape expectations, even if understood mostly by specialists, are effective also for consumers. The novelty of this study lies in its use of alternative methods to tackle the formation and dependencies between heterogeneous expectations. This avoids the drawbacks of a standard approach and allows broader conclusions to be drawn.
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Lu, Yikang, Xiaoyue Wang, Mengjie Wu, Lei Shi i Junpyo Park. "Effects of species vigilance on coexistence in evolutionary dynamics of spatial rock–paper–scissors game". Chaos: An Interdisciplinary Journal of Nonlinear Science 32, nr 9 (wrzesień 2022): 093116. http://dx.doi.org/10.1063/5.0103247.
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Recognizing surrounding situations, such as enemy attacks, which can be realized by predator–prey relationships, is one of the common behaviors of the population in ecosystems. In this paper, we explore the relationship between such species’ behavior and biodiversity in the spatial rock–paper–scissors game by employing the ecological concept “vigilance.” In order to describe the vigilance process, we adopt a multiplex structure where two distinct layers describe virtual and physical interactions. By investigating the process of evolution in species, we also found that species with different vigilance go together. In addition, by utilizing the dynamic time warping method, we found that species with the same vigilance have consistent behavior, but species with different vigilance have diverse behavior. Our findings may lead to broader interpretations of mechanisms promoting biodiversity via vigilance in species ecosystems.
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Luo, Jing, Chenguang Yang, Hang Su i Chao Liu. "A Robot Learning Method with Physiological Interface for Teleoperation Systems". Applied Sciences 9, nr 10 (22.05.2019): 2099. http://dx.doi.org/10.3390/app9102099.
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The human operator largely relies on the perception of remote environmental conditions to make timely and correct decisions in a prescribed task when the robot is teleoperated in a remote place. However, due to the unknown and dynamic working environments, the manipulator’s performance and efficiency of the human-robot interaction in the tasks may degrade significantly. In this study, a novel method of human-centric interaction, through a physiological interface was presented to capture the information details of the remote operation environments. Simultaneously, in order to relieve workload of the human operator and to improve efficiency of the teleoperation system, an updated regression method was proposed to build up a nonlinear model of demonstration for the prescribed task. Considering that the demonstration data were of various lengths, dynamic time warping algorithm was employed first to synchronize the data over time before proceeding with other steps. The novelty of this method lies in the fact that both the task-specific information and the muscle parameters from the human operator have been taken into account in a single task; therefore, a more natural and safer interaction between the human and the robot could be achieved. The feasibility of the proposed method was demonstrated by experimental results.
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Yang, Wei, i Xiaolin Tang. "Numerical Analysis for Heat Transfer Laws of a Wet Multi-disk Clutch During Transient Contact". International Journal of Nonlinear Sciences and Numerical Simulation 18, nr 7-8 (20.12.2017): 599–613. http://dx.doi.org/10.1515/ijnsns-2017-0081.
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AbstractThis study aims to solve the transmission failure caused by warping and crackling on frictional and steel disks of a wet multi-disk clutch. Thus, a dynamic model is proposed to describe the transient contact process of the disks, as well as the heat transfer laws, during the engagement of the clutch using the commercial software MSC/NASTRAN. Moreover, the heat transfer laws, which varies with engagement time within a contact area, is investigated. On the basis of these results, some rules for heat conduction in a wet multi-disk clutch are developed. By measuring the pressure and temperature of the inlet and outlet of the clutch, the correctness of the theoretical analysis method is indirectly verified.
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Yazgaç, Bilgi Görkem, i Mürvet Kırcı. "Fractional-Order Calculus-Based Data Augmentation Methods for Environmental Sound Classification with Deep Learning". Fractal and Fractional 6, nr 10 (29.09.2022): 555. http://dx.doi.org/10.3390/fractalfract6100555.
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In this paper, we propose two fractional-order calculus-based data augmentation methods for audio signals. The first approach is based on fractional differentiation of the Mel scale. By using a randomly selected fractional derivation order, we are warping the Mel scale, therefore, we aim to augment Mel-scale-based time-frequency representations of audio data. The second approach is based on previous fractional-order image edge enhancement methods. Since multiple deep learning approaches treat Mel spectrogram representations like images, a fractional-order differential-based mask is employed. The mask parameters are produced with respect to randomly selected fractional-order derivative parameters. The proposed data augmentation methods are applied to the UrbanSound8k environmental sound dataset. For the classification of the dataset and testing the methods, an arbitrary convolutional neural network is implemented. Our results show that fractional-order calculus-based methods can be employed as data augmentation methods. Increasing the dataset size to six times the original size, the classification accuracy result increased by around 8.5%. Additional tests on more complex networks also produced better accuracy results compared to a non-augmented dataset. To our knowledge, this paper is the first example of employing fractional-order calculus as an audio data augmentation tool.
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Zhou, Shenghan, Xingxing Xu, Yiyong Xiao, Wenbing Chang, Silin Qian i Xing Pan. "Remaining Useful Life Prediction with Similarity Fusion of Multi-Parameter and Multi-Sample Based on the Vibration Signals of Diesel Generator Gearbox". Entropy 21, nr 9 (3.09.2019): 861. http://dx.doi.org/10.3390/e21090861.
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The prediction of electrical machines’ Remaining Useful Life (RUL) can facilitate making electrical machine maintenance policies, which is important for improving their security and extending their life span. This paper proposes an RUL prediction model with similarity fusion of multi-parameter and multi-sample. Firstly, based on the time domain and frequency domain extraction of vibration signals, the performance damage indicator system of a gearbox is established to select the optimal damage indicators for RUL prediction. Low-pass filtering based on approximate entropy variance (Aev) is introduced in this process because of its stability. Secondly, this paper constructs Dynamic Time Warping Distance (DTWD) as a similarity measurement function, which belongs to the nonlinear dynamic programming algorithm. It performed better than the traditional Euclidean distance. Thirdly, based on DTWD, similarity fusion of multi-parameter and multi-sample methods is proposed here to achieve RUL prediction. Next, the performance evaluation indicator Q is adopted to evaluate the RUL prediction accuracy of different methods. Finally, the proposed method is verified by experiments, and the Multivariable Support Vector Machine (MSVM) and Principal Component Analysis (PCA) are introduced for comparative studies. The results show that the Mean Absolute Percentage Error (MAPE) of the similarity fusion of multi-parameter and multi-sample methods proposed here is below 14%, which is lower than MSVM’s and PCA’s. Additionally, the RUL prediction based on the DTWD function in multi-sample similarity fusion exhibits the best accuracy.
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Abidi, Nada, Raimo Franke, Peter Findeisen i Frank Klawonn. "Alignment of time-resolved data from high throughput experiments". Journal of Bioinformatics and Computational Biology 14, nr 06 (grudzień 2016): 1650030. http://dx.doi.org/10.1142/s021972001650030x.
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To better understand the dynamics of the underlying processes in cells, it is necessary to take measurements over a time course. Modern high-throughput technologies are often used for this purpose to measure the behavior of cell products like metabolites, peptides, proteins, [Formula: see text]RNA or mRNA at different points in time. Compared to classical time series, the number of time points is usually very limited and the measurements are taken at irregular time intervals. The main reasons for this are the costs of the experiments and the fact that the dynamic behavior usually shows a strong reaction and fast changes shortly after a stimulus and then slowly converges to a certain stable state. Another reason might simply be missing values. It is common to repeat the experiments and to have replicates in order to carry out a more reliable analysis. The ideal assumptions that the initial stimulus really started exactly at the same time for all replicates and that the replicates are perfectly synchronized are seldom satisfied. Therefore, there is a need to first adjust or align the time-resolved data before further analysis is carried out. Dynamic time warping (DTW) is considered as one of the common alignment techniques for time series data with equidistant time points. In this paper, we modified the DTW algorithm so that it can align sequences with measurements at different, non-equidistant time points with large gaps in between. This type of data is usually known as time-resolved data characterized by irregular time intervals between measurements as well as non-identical time points for different replicates. This new algorithm can be easily used to align time-resolved data from high-throughput experiments and to come across existing problems such as time scarcity and existing noise in the measurements. We propose a modified method of DTW to adapt requirements imposed by time-resolved data by use of monotone cubic interpolation splines. Our presented approach provides a nonlinear alignment of two sequences that neither need to have equi-distant time points nor measurements at identical time points. The proposed method is evaluated with artificial as well as real data. The software is available as an R package tra (Time-Resolved data Alignment) which is freely available at: http://public.ostfalia.de/klawonn/tra.zip .
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Luo, Ziwei, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu i Siwei Lyu. "Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 2 (28.06.2022): 1917–25. http://dx.doi.org/10.1609/aaai.v36i2.20086.
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Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods need to iteratively optimize an objective function via a specific deformation model along with meticulous parameter tuning, but which have limited capabilities in registering images with large deformations. While deep learning-based methods can learn the complex mapping from input images to their respective deformation field, it is regression-based and is prone to be stuck at local minima, particularly when large deformations are involved. To this end, we present Stochastic Planner-Actor-Critic (spac), a novel reinforcement learning-based framework that performs step-wise registration. The key notion is warping a moving image successively by each time step to finally align to a fixed image. Considering that it is challenging to handle high dimensional continuous action and state spaces in the conventional reinforcement learning (RL) framework, we introduce a new concept `Plan' to the standard Actor-Critic model, which is of low dimension and can facilitate the actor to generate a tractable high dimensional action. The entire framework is based on unsupervised training and operates in an end-to-end manner. We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations. Our empirical results highlight that our work achieves consistent, significant gains and outperforms state-of-the-art methods.
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Tian, Liping, Liangqin Chen, Zhimeng Xu i Zhizhang (David) Chen. "A People-Counting and Speed-Estimation System Using Wi-Fi Signals". Sensors 21, nr 10 (16.05.2021): 3472. http://dx.doi.org/10.3390/s21103472.
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Counting the number of people and estimating their walking speeds are essential in crowd control and flow. In this work, we propose a system that uses prevalent Wi-Fi signals to identify the number of people entering and leaving a room through a door. It selects the best subcarrier of Wi-Fi signals and applies the Hampel filter to remove outlier information first. Then, it employs a double threshold method to determine the start and end times of entering or leaving. Afterward, it compares the detected signals with the precollected database using the dynamic time-warping algorithm and determines the number of people. It uses a variance threshold method to identify the states of entering or leaving. It also employs a nonlinear fitting approach to calculate the walking speeds. The experiments show that, in a large empty laboratory, the accuracy rates in determining the number of people are 100% for one person, 81% for two persons, and 95% for three persons. In a small office, the accuracy rates for detecting the number of people are 98% for one or two persons, 82% for three persons, 93% for four, and 75% for five persons. For the walking speed estimation, the accuracy rate for a speed error of less than 0.2410 m/s is 75% for a single person.
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Lozano, Antonio, Cristina Soto-Sánchez, Javier Garrigós, J. Javier Martínez, J. Manuel Ferrández i Eduardo Fernández. "A 3D Convolutional Neural Network to Model Retinal Ganglion Cell’s Responses to Light Patterns in Mice". International Journal of Neural Systems 28, nr 10 (grudzień 2018): 1850043. http://dx.doi.org/10.1142/s0129065718500430.
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Deep Learning offers flexible powerful tools that have advanced our understanding of the neural coding of neurosensory systems. In this work, a 3D Convolutional Neural Network (3D CNN) is used to mimic the behavior of a population of mice retinal ganglion cells in response to different light patterns. For this purpose, we projected homogeneous RGB flashes and checkerboards stimuli with variable luminances and wavelength spectrum to mimic a more naturalistic stimuli environment onto the mouse retina. We also used white moving bars in order to localize the spatial position of the recorded cells. Then recorded spikes were smoothed with a Gaussian kernel and used as the output target when training a 3D CNN in a supervised way. To find a suitable model, two hyperparameter search stages were performed. In the first stage, a trial and error process allowed us to obtain a system that is able to fit the neurons firing rates. In the second stage, a systematic procedure was used to compare several gradient-based optimizers, loss functions and the model’s convolutional layers number. We found that a three layered 3D CNN was able to predict the ganglion cells firing rates with high correlations and low prediction error, as measured with Mean Squared Error and Dynamic Time Warping in test sets. These models were either competitive or outperformed other models used already in neuroscience, as Feed Forward Neural Networks and Linear-Nonlinear models. This methodology allowed us to capture the temporal dynamic response patterns in a robust way, even for neurons with high trial-to-trial variable spontaneous firing rates, when providing the peristimulus time histogram as an output to our model.
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Pan, Nan, Xin Shen, Xiaojue Guo, Min Cao i Dilin Pan. "Study on intelligent anti–electricity stealing early-warning technology based on convolutional neural networks". Journal of Intelligent & Fuzzy Systems 40, nr 4 (12.04.2021): 7993–99. http://dx.doi.org/10.3233/jifs-189621.
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In recent years, electricity stealing has been repeatedly prohibited, and as the methods of stealing electricity have become more intelligent and concealed, it is growing increasingly difficult to extract high-dimensional data features of power consumption. In order to solve this problem, a correlation model of power-consumption data based on convolutional neural networks (CNN) is established. First, the original user signal is preprocessed to remove the noise. The user signal with a fixed signal length is then intercepted and the parallel class labelled. The segmented user signals and corresponding labels are input into the convolutional neural network for training, and the trained convolutional neural network is then used to detect and classify the test user signals. Finally, the actual steal leak dataset is used to verify the effectiveness of this algorithm, which proves that the algorithm can effectively carry out anti–-electricity stealing by warning of abnormal power consumption behavior. There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm.
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Shen, Zhiyuan Shen, Xiaowei Wang i Qiuxiang Wu. "A Novel 4D Track Prediction Approach Combining Empirical Mode Decomposition with Nonlinear Correlation Coefficient". South Florida Journal of Development 2, nr 4 (19.08.2021): 5208–19. http://dx.doi.org/10.46932/sfjdv2n4-019.
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The accuracy of 4D track prediction plays an important role to solve the prominent contradiction between the rapid development of air transport industry and the limited resources of airspace. The conventional 4D track prediction based on the aerospace dynamic model is usually inaccurate since of weather influence and air traffic controller (ATC) factor. In this paper, an entirely data-driven nominal flight height profile prediction approach combing empirical mode decomposition (EMD) with nonlinear correlation coefficient (NCC) is proposed. Firstly, the historical tracks are implemented on EMD individually. Then according to a procedure similar to leave-one-out cross validation (LOOCV), the physical meanings of different intrinsic mode functions (IMFs) obtained by EMD are analyzed to corresponding to the various flight information. For a specified flight, the similarities between different dates are measured by NCC. Finally, a predicted nominal trajectory is obtained by summing a series of selected IMFs with a regression weight under least square optimization framework. It is demonstrated that the proposed method shows a higher prediction performance when comparing with the state of the art method named as nearest neighbor classification with dynamic time warping (DTW).
La precisión de la predicción de la pista 4D desempeña un papel importante para resolver la importante contradicción entre el rápido desarrollo de la industria del transporte aéreo y los recursos limitados del espacio aéreo. La predicción convencional de la pista 4D basada en el modelo dinámico aeroespacial suele ser inexacta debido a la influencia de las condiciones meteorológicas y el factor del controlador de tráfico aéreo (ATC). En este trabajo, se propone un enfoque de predicción del perfil de altura de vuelo nominal totalmente basado en datos que combina la descomposición empírica de modos (EMD) con el coeficiente de correlación no lineal (NCC). En primer lugar, las pistas históricas se implementan en la EMD individualmente. A continuación, de acuerdo con un procedimiento similar al de la validación cruzada sin intervención (LOOCV), se analizan los significados físicos de las diferentes funciones de modo intrínseco (IMF) obtenidas por la EMD para que correspondan a las diversas informaciones de vuelo. Para un vuelo específico, se miden las similitudes entre las distintas fechas mediante NCC. Por último, se obtiene una trayectoria nominal predicha mediante la suma de una serie de FMI seleccionadas con un peso de regresión en el marco de la optimización de mínimos cuadrados. Se demuestra que el método propuesto muestra un mayor rendimiento de predicción en comparación con el método más avanzado denominado clasificación de vecinos más cercanos con deformación temporal dinámica (DTW).
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Vital, Welington, Ramon Silva, Luciano M. Bezerra, Cynthia M. Oliveira, Cleirton A. S. Freitas i Jorge Bonilla. "Experimental and Numerical Analysis for Eccentricity Solution in Double-Layer Space Truss". Buildings 14, nr 3 (26.02.2024): 608. http://dx.doi.org/10.3390/buildings14030608.
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This paper shows an extensive study on the Typical Connections used in Double-Layer Space Truss. For this structural system, the ends of the bars are flattened to connect the bars. However, the flattening process results in a highly plastic stamping zone susceptible to warping with the appearance of two eccentricities, one of which causes rotation of the connection, with the presence of a bending moment with local rupture resulting in progressive collapse of the entire coverage system, as already evidenced in several countries. Therefore, eccentricity in this paper is studied and an analytical solution to the problem is presented through the use of a new device called a spacer. Furthermore, a preliminary study with complex numerical simulation was carried out with nonlinear analysis in ABAQUS were evaluated. For this study, nine space trusses were experimentally tested with reinforcement spacer in reduced trusses. After confirming the efficiency of the spacer proposal, another six space trusses were tested in the laboratory, this time, on a full-scale. In this study, two types of spacers were evaluated, one made of USI SAC 350 steel and another cheaper one made from recycled tires from heavy vehicles with multiple filaments of steel and nylon wires in the rubber layers. The two devices presented very close resistance capacity values, with a resistance gain of approximately 30% in relation to connections without reinforcement, with structural failure characterized by buckling of the bars. Finally, a numerical study of space trusses with spacers was developed. In practical design terms, from these FE simulations it was possible to determine the normal stresses for different spacers applied in the different modeled spans.
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Mahgoub, Mohamed, Yasir Bashir, Andy Anderson Bery i Abdelwahab Noufal. "Four-Dimension Seismic Analysis in Carbonate: A Closed Loop Study". Applied Sciences 12, nr 19 (21.09.2022): 9438. http://dx.doi.org/10.3390/app12199438.
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Four-dimensional seismic analysis is an effective reservoir surveillance tool to track the changes of fluid and pressure phases in the oil and gas reservoirs over time of the baseline and monitoring seismic acquisition. In practice, the 4D seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. Therefore, 4D seismic analysis is a model for integrating various disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. In this study, we started the 4D seismic workflow with a 1D well-based 4D feasibility study to detect the likelihood of 4D signals before performing 4D seismic co-processing of the baseline and monitoring surveys starting from the seismic field data of both datasets. As part of a full 4D seismic co-processing of the baseline and monitor surveys, 4D seismic metric attributes were analyzed over the survey area to measure the improvement in seismic acquisition repeatability for the scarce 1994 baseline seismic and the 2014 monitor seismic survey. For the monitor survey, a 4D time-trace shift was performed using the baseline survey as a reference to measure the time shifts between the baseline and monitor surveys at 20-year intervals. The 4DFour-dimensional dynamic trace warping was followed by a 4D seismic inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contaminations, and differences in dynamic amplitude range between the baseline and monitor seismic surveys. We then examined the relationship between well logs and seismic volumes by predicting a volume of log properties at the well locations of the seismic volume. In this method, we computed a possibly nonlinear operator that can predict well logs based on the properties of adjacent seismic data. We then tested a Deep Feed Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using the baseline and monitoring seismic inverted data. The objective of trying such a deep machine learning approach was to predict the density and porosity of both the baseline and the monitoring seismic data to validate the accuracy of the 4D seismic inversion and evaluate the changes in reservoir properties over a time-lapse of 20 years of production from 1994 to 2014. Finally, we matched the 4D seismic signal with changes in reservoir production properties, investigating the mechanism underlying the observed 4D signal. It was found that the detectability of 4D signals is primarily related to changes in fluid saturation and pressure changes in the reservoir, which increased from 1994 to 2014. This innovative closed-loop research proved that the low repeatability of seismic acquisition can be compensated by optimal 4D seismic co-processing with a complete integration workflow to assess the reliability of the 4D seismic observed signal.
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Shiwei, Xie, Xu Ming, Yuan Xiao i Gao Mingyu. "A Method of Velocity Estimation for an Underwater Moving Source in Wave-Number Domain Based on The Warping Transform". Journal of Physics: Conference Series 2419, nr 1 (1.01.2023): 012078. http://dx.doi.org/10.1088/1742-6596/2419/1/012078.
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Abstract When a single hydrophone is stationary and the underwater acoustic source is moving linearly at a constant velocity at the same depth, the phase of the received sound pressure signal in the frequency domain varies nonlinearly with time. Phase linearization can be achieved by warping transformation, so a method based on warping transformation is proposed to estimate the motion parameters of the source based on warping transformation. Firstly, the warping operators under different motion parameters are constructed and then used to resample the received sound pressure. Secondly, the corresponding wave-number spectrum is obtained by the Fourier transform of the resampled sound pressure. Finally, only when the search value of the motion parameter matches the real value, the energy magnitude of the wave-number spectrum concentrating in the concerned interval is maximum. Thus, the estimation of motion parameters is achieved. Simulation data verify the effectiveness of the method. Compared with the traditional Minimum Mean Square Error (MMSE) method, the proposed method has better performance in velocity estimation at low speeds and low frequencies.
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Yang, Guangyu, i Shuyan Xia. "Weighted Dynamic Time Warping for Time Series". International Journal of Bifurcation and Chaos 33, nr 13 (październik 2023). http://dx.doi.org/10.1142/s0218127423501493.
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Recurrence network is a typical time series analysis method. However, irregular sampling may overshadow the dynamic features characterized by traditional recurrence network method, which makes the method ineffective. This paper introduces dynamic time warping method to determine the distance between time series segments. The method can match the features of time series segments and reduce the distortion of distances caused by irregular sampling. For points in sparse sampling area, many characteristics may not be collected and thus the matching results are unreliable. To overcome this problem, we further propose weighted dynamic time warping method which assigns small weights to matching points with sparse local sampling so as to reduce their influence on distance. A recurrence network constructed by weighted dynamic time warping method can effectively capture underlying nonlinear and nonstationary system dynamics from irregular sampling time series. The effectiveness of the proposed method is illustrated by two case studies: the discrimination of different dynamic behaviors and detection of system parameter perturbation from time series of Rössler system.
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Saha, Soumadeep, i Awadhesh Narayan. "Nonlinear Hall effect in Rashba systems with hexagonal warping". Journal of Physics: Condensed Matter, 18.08.2023. http://dx.doi.org/10.1088/1361-648x/acf1eb.
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Abstract Rashba spin-orbit coupled systems are an important class of materials noted for diverse fundamental and applied phenomena. Recently, the emergence of non-linear Hall effect under conditions of time-reversal symmetry has been discovered in materials with broken inversion symmetry. In this work, we study the second- and third-order Hall response in Rashba systems with hexagonal warping. Starting with a low-energy model, we obtain the analytic expressions and discover the unique dipole profile in Rashba systems with hexagonal warping. Furthermore, we extend the analysis using a realistic tight-binding model. Next, we predict the existence of a third-order Hall effect in these systems, and calculate the Berry connection polarizability tensor analytically. We also show how the model parameters affect the third-order conductivity. Our predictions can help in the experimental realization of Berry curvature multipole physics in Rashba materials with hexagonal warping, and provide a new platform for engineering the non-linear Hall effects.
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Diehl, Joscha, i Richard Krieg. "FRUITS: feature extraction using iterated sums for time series classification". Data Mining and Knowledge Discovery, 14.09.2024. http://dx.doi.org/10.1007/s10618-024-01068-1.
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AbstractWe introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier. These features are intrinsically nonlinear, capture chronological information, and, under certain settings, are invariant to a form of time-warping. We achieve competitive results, both in accuracy and speed, on the UCR archive. We make our code available at https://github.com/irkri/fruits.