Stationary Segments Research Articles

Localization of Turn Points in the Rhythmic Movement of Sound Image

The localization of start and turn points in rhythmic sound movement created through the modeling of binaural beats (BB) was investigated. The BB-modeled broadband stimuli consisted of stationary initial and final segments with a section of cyclic motion between them. Spatial effects were induced by changes in the interaural time difference (ITD). During the experiment, subjects assessed the position of the movement trajectory ends or the position of reference points using a graphic tablet. It was discovered that the perception of rhythmic movement of the sound image was significantly influenced by the integrative ability of the binaural auditory system. The results indicated that with instantaneous switching between stationary segments, the perceived positions of the trajectory ends (start point and turn point) matched the positions of the reference points. Conversely, the smooth movement between the same extreme values showed a displacement of the trajectory ends: the turn points were localized further from the reference points compared to the start points, at all trajectory positions in space. Localization of the trajectory end crucially depended on the time that the sound had stayed near the turning point. These patterns were expressed stronger in the central area of the acoustic space compared to the periphery.

Noise-Adaptive State Estimators with Change-Point Detection.

Aiming at tracking sharply maneuvering targets, this paper develops novel variational adaptive state estimators for joint target state and process noise parameter estimation for a class of linear state-space models with abruptly changing parameters. By combining variational inference with change-point detection in an online Bayesian fashion, two adaptive estimators-a change-point-based adaptive Kalman filter (CPAKF) and a change-point-based adaptive Kalman smoother (CPAKS)-are proposed in a recursive detection and estimation process. In each iteration, the run-length probability of the current maneuver mode is first calculated, and then the joint posterior of the target state and process noise parameter conditioned on the run length is approximated by variational inference. Compared with existing variational noise-adaptive Kalman filters, the proposed methods are robust to initial iterative value settings, improving their capability of tracking sharply maneuvering targets. Meanwhile, the change-point detection divides the non-stationary time sequence into several stationary segments, allowing for an adaptive sliding length in the CPAKS method. The tracking performance of the proposed methods is investigated using both synthetic and real-world datasets of maneuvering targets.

Design and evaluation of a pneumatic actuation unit for a wasp-inspired self-propelled needle.

Transperineal laser ablation is a minimally invasive thermo-ablative treatment for prostate cancer that requires the insertion of a needle for accurate optical fiber positioning. Needle insertion in soft tissues may cause tissue motion and deformation, resulting in tissue damage and needle positioning errors. In this study, we present a wasp-inspired self-propelled needle that uses pneumatic actuation to move forward with zero external push force, thus avoiding large tissue motion and deformation. The needle consists of six parallel 0.25-mm diameter Nitinol rods driven by a pneumatic actuation system. The pneumatic actuation system consists of Magnetic Resonance (MR) safe 3D-printed parts and off-the-shelf plastic screws. A self-propelled motion is achieved by advancing the needle segments one by one, followed by retracting them simultaneously. The advancing needle segment has to overcome a cutting and friction force, while the stationary needle segments experience a friction force in the opposite direction. The needle self-propels through the tissue when the friction force of the five stationary needle segments overcomes the sum of the friction and cutting forces of the advancing needle segment. We evaluated the prototype's performance in 10-wt% gelatin phantoms and ex vivo porcine liver tissue inside a preclinical Magnetic Resonance Imaging (MRI) scanner in terms of the slip ratio of the needle with respect to the phantom or liver tissue. Our results demonstrated that the needle was able to self-propel through the phantom and liver tissue with slip ratios of 0.912-0.955 and 0.88, respectively. The prototype is a promising step toward the development of self-propelled needles for MRI-guided transperineal laser ablation as a method to treat prostate cancer.

Enhancing rock and soil hazard monitoring in open-pit mining operations through ultra-short-term wind speed prediction

Wind speed exacerbates challenges associated with rock stability, introducing factors such as heightened erosion and the possibility of particle loosening. This increased sensitivity to erosion can result in material displacement, thereby compromising the overall stability of rock layers within the open-pit mining site. Therefore, accurate wind speed predictions are crucial for understanding the impact on rock stability, ensuring the safety and efficiency of open-pit mining operations. While most existing studies on wind speed prediction primarily focus on making overall predictions from the entire wind speed sequence, with limited consideration for the stationarity characteristics of the sequence, This paper introduces a novel approach for effective monitoring and early warning of geotechnical hazards. Our proposed method involves dividing wind speed data into stationary and non-stationary segments using the sliding window average method within the threshold method, validated by the Augmented Dickey-Fuller test. Subsequently, we use temporal convolutional networks (TCN) with dilated causal convolution and long short-term memory to predict the stationary segment of wind speed, effectively improving prediction accuracy for this segment. For the non-stationary segment, we implement complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to reduce sequence complexity, followed by TCN with an attention mechanism (ATTENTION) to forecast wind speed one step ahead. Finally, we overlay the predictions of these two segments to obtain the final prediction. Our proposed model, tested with data from an open-pit mining area in western China, achieved promising results with an average absolute error of 0.14 knots, mean squared error of 0.05 knots2, and root mean squared error of 0.20 knots. These findings signify a significant advancement in the accuracy of short-term wind speed prediction. This advancement not only enables the rapid assessment and proactive response to imminent risks but also contributes to geotechnical hazard monitoring in open-pit mining operations.

Experimental design for a novel co-flow jet airfoil

The Co-flow Jet (CFJ) technology holds significant promise for enhancing aerodynamic efficiency and furthering decarbonization in the evolving landscape of air transportation. The aim of this study is to empirically validate an optimized CFJ airfoil through low-speed wind tunnel experiments. The CFJ airfoil is structured in a tri-sectional design, consisting of one experimental segment and two stationary segments. A support rod penetrates the airfoil, fulfilling dual roles: it not only maintains the structural integrity of the overall model but also enables the direct measurement of aerodynamic forces on the test section of the CFJ airfoil within a two-dimensional wind tunnel. In parallel, the stationary segments are designed to effectively minimize the interference from the lateral tunnel walls. The experimental results are compared with numerical simulations, specifically focusing on aerodynamic parameters and flow field distribution. The findings reveal that the experimental framework employed is highly effective in characterizing the aerodynamic behavior of the CFJ airfoil, showing strong agreement with the simulation data.

Arrival-time detection with histogram distance for acoustic emission signals

Arrival-time picking is a fundamental step in time-of-arrival (TOA)-based localisation in current and future acoustic emission (AE), microseismic and seismic localisation systems. The accurate detection of TOA is of great importance for high localisation accuracy. This work presents a histogram distance-based method for TOA detection of elastic wave signals. The authors treat an original elastic waveform that includes the arrival as two different locally stationary segments, namely the intervals following and preceding arrival. To determine the optimal separation of these two stationary segments, ie the arrival time, histograms of these intervals are calculated and a measure of the distance between them modified from the Bhattacharyya coefficient is proposed. The performance of the proposed method is evaluated using TOA picking for AE. The method is shown to provide accurate and robust picks on AE signals under various signal-to-noise ratios. To evaluate the adaptability of the method to other TOA picking scenarios, it is applied to detect the seismic P-phase. The detection accuracy is adequate and errors are constrained to within a few seconds. Factors that influence the detection accuracy are discussed. The results suggest that the proposed method has the potential to detect TOA in various fields.

Capacitive Linear Displacement Sensors With High Accuracy and Long Range Absolute Positioning Capability Based on a Splicing Technique

Considerable progress has been made in the development of absolute capacitive linear displacement sensors based on time grating that are capable of providing highly accurate absolute position measurements in conjunction with easy fabrication using standard printed circuit board (PCB) technology. The working range of currently available sensor designs has, however, been rather limited. The present work addresses this issue by greatly expanding the measurement range via a technique for splicing multiple identical stationary rulers with individual measurement lengths of 600 mm. Here, absolute positioning is realized in a manner similar to that of Vernier calipers using stationary segments composed of periodic rectangular excitation electrodes with orthogonal excitation signals and a moving component composed of sinusoidal-shaped induction electrodes. The sensor wiring is greatly simplified by transferring the induction signals output by the moving ruler to the stationary rulers via special transmitting and receiving electrodes. Splicing errors are eliminated by applying two sets of induction electrodes, including a front sensing unit (FSU) and a rear sensing unit (RSU) along the measurement direction on the moving ruler. The performance of the proposed design is evaluated experimentally using a two-segment prototype sensor fabricated by PCB technology. The results demonstrate that interference between the FSU and RSU generates a first harmonic error. This is then addressed by increasing the distance between the FSU and RSU, and enhancing the shielding layer between the two units. This yields a measurement accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm 3.5~\mu \text{m}$ </tex-math></inline-formula> over the full 988 mm measurement range of the sensor.

Equivalence relations and [formula omitted] distances between time series with application to the Black Summer Australian bushfires

This paper introduces a new framework of algebraic equivalence relations between time series and new distance metrics between them, then applies these to investigate the Australian “Black Summer” bushfire season of 2019–2020. First, we introduce a general framework for defining equivalence between time series, heuristically intended to be equivalent if they differ only up to noise. Our first specific implementation is based on using change point algorithms and comparing statistical quantities such as mean or variance in stationary segments. We thus derive the existence of such equivalence relations on the space of time series, such that the quotient spaces can be equipped with a metrizable topology. Next, we illustrate specifically how to define and compute such distances among a collection of time series and perform clustering and additional analysis thereon. Then, we apply these insights to analyze air quality data across New South Wales, Australia, during the 2019–2020 bushfires. There, we investigate structural similarity with respect to this data and identify locations that were impacted anonymously by the fires relative to their location. This may have implications regarding the appropriate management of resources to avoid gaps in the defense against future fires.

Estimation of the variance function in structural break autoregressive models with non‐stationary and explosive segments

In this article, we consider estimating the innovation variance function when the conditional mean model is characterised by a structural break autoregressive model, which exhibits multiple unit root, explosive and stationary collapse segments, allowing for behaviour often seen in financial data where bubble and crash episodes are present. Estimating the variance function normally proceeds in two steps: estimating the conditional mean model, then using the residuals to estimate the variance function. In this article, a non‐parametric approach is proposed to estimate the complicated parametric conditional mean model in the first step. The approach turns out to provide a convenient solution to the problem and achieve robustness to any structural break features in the conditional mean model without the need of estimating them parametrically. In the second step, kernel‐smoothed squares of the truncated first‐step residuals are shown to consistently estimate the variance function. In Monte Carlo simulations, we show that our proposed method performs very well in the presence of explosive and stationary collapse segments compared with the popular rolling standard deviation estimator that is commonly used in economics and finance. As an empirical illustration of our new approach, we apply the volatility estimator to recent Bitcoin data.

Change point detection-based simulation of nonstationary sub-hourly wind time series

In this paper, we present a wind speed simulation method by detecting change points in multivariate nonstationary wind speed time series data. The change point detection method identifies changes in the covariance structure and decomposes the nonstationary multivariate time series into stationary segments. Parametric and nonparametric techniques are also provided to model and simulate new time series within each stationary segment. The proposed simulation approach retains the statistical properties of the original time series (as obtained from a Numerical Weather Prediction system) and therefore, can be employed for simulation-based analysis of power systems planning and operations problems. We demonstrate the capabilities of the change point detection method through computational experiments conducted on wind speed time series at five-minute resolution. We also conduct experiments on the economic dispatch problem to illustrate the impact of nonstationarity in wind generation on conventional generation and location marginal prices.

Two-stage data segmentation permitting multiscale change points, heavy tails and dependence

The segmentation of a time series into piecewise stationary segments is an important problem both in time series analysis and signal processing. In the presence of multiscale change points with both large jumps over short intervals and small jumps over long intervals, multiscale methods achieve good adaptivity but require a model selection step for removing false positives and duplicate estimators. We propose a localised application of the Schwarz criterion, which is applicable with any multiscale candidate generating procedure fulfilling mild assumptions, and establish its theoretical consistency in estimating the number and locations of multiple change points under general assumptions permitting heavy tails and dependence. In particular, combined with a MOSUM-based candidate generating procedure, it attains minimax rate optimality in both detection lower bound and localisation for i.i.d. sub-Gaussian errors. Overall competitiveness of the proposed methodology compared to existing methods is shown through its theoretical and numerical performance.

Recrystallization boundary migration in the 3D heterogeneous microstructure near a hardness indent

Boundary migration during recrystallization in the heterogeneous microstructure near a hardness indent in lightly rolled pure Al was followed in 3D. The microstructure after multiple steps of ex-situ annealing was examined using synchrotron white-beam differential-aperture X-ray microscopy, supplemented by scanning electron microscopy. Heterogeneous recrystallization boundary migration was observed and analyzed in terms of driving force and boundary characteristics. The results reveal very similar local stored energies and boundary misorientations for the migrating and stationary boundary segments, whereas the grain boundary normals differ significantly. Effects of grain boundary mobility and deformation microstructure morphology on the migration are discussed.

Influence of Boundary Layer Skew on the Tip Leakage Vortex of an Axial Compressor Stator

Abstract For an axial compressor stator with tip gap, the boundary layer (BL) in the hub endwall region has a significant influence on the development and progression of the tip leakage vortex. Herein, the so-called BL skew, which develops through relative motion of the hub, is of particular interest. Therefore, experimental and numerical investigations of a single axial compressor stator row with varying tip gap height (tip gap height/chord length =2.0%|5.4%|6.7%) have been conducted. Comparing cases with rotating or stationary hub endwall segments upstream of the examined vanes allowed to determine the effect of the skewed and un-skewed inflow BL. The steady-state flow fields up- and downstream of the stator row were measured using five-hole pressure probes. For validation and to improve the understanding of the existing flow phenomena, 3D-Reynolds-averaged Navier–Stokes (RANS) CFD simulations using a commercial flow solver were carried out. Furthermore, analog cases with no tip gap were examined and considered in the comparisons to extend the knowledge on this BL characteristic. The results show that the BL skew has a major influence on the trajectory and size of the tip leakage vortex for the cases with tip clearance. The effect of reduction of the produced losses decreases with increasing tip gap height.

Structural Clustering of Volatility Regimes for Dynamic Trading Strategies

ABSTRACT We develop a new method to find the number of volatility regimes in a nonstationary financial time series by applying unsupervised learning to its volatility structure. We use change point detection to partition a time series into locally stationary segments and then compute a distance matrix between segment distributions. The segments are clustered into a learned number of discrete volatility regimes via an optimization routine. Using this framework, we determine the volatility clustering structure for financial indices, large-cap equities, exchange-traded funds and currency pairs. Our method overcomes the rigid assumptions necessary to implement many parametric regime-switching models while effectively distilling a time series into several characteristic behaviours. Our results provide a significant simplification of these time series and a strong descriptive analysis of prior behaviours of volatility. Finally, we create and validate a dynamic trading strategy that learns the optimal match between the current distribution of a time series and its past regimes, thereby making online risk-avoidance decisions at present.

Robust sequential design for piecewise-stationary multi-armed bandit problem in the presence of outliers

ABSTRACT The multi-armed bandit (MAB) problem studies the sequential decision making in the presence of uncertainty and partial feedback on rewards. Its name comes from imagining a gambler at a row of slot machines who needs to decide the best strategy on the number of times as well as the orders to play each machine. It is a classic reinforcement learning problem which is fundamental to many online learning problems. In many practical applications of the MAB, the reward distributions may change at unknown time steps and the outliers (extreme rewards) often exist. Current sequential design strategies may struggle in such cases, as they tend to infer additional change points to fit the outliers. In this paper, we propose a robust change-detection upper confidence bound (RCD-UCB) algorithm which can distinguish the real change points from the outliers in piecewise-stationary MAB settings. We show that the proposed RCD-UCB algorithm can achieve a nearly optimal regret bound on the order of , where T is the number of time steps, K is the number of arms and S is the number of stationary segments. We demonstrate its superior performance compared to some state-of-the-art algorithms in both simulation experiments and real data analysis. (See https://github.com/woaishufenke/MAB_STRF.git for the codes used in this paper.)

SEQUENTIAL MONITORING OF CHANGES IN DYNAMIC LINEAR MODELS, APPLIED TO THE U.S. HOUSING MARKET

We propose a sequential monitoring scheme to find structural breaks in dynamic linear models. The monitoring scheme is based on a detector and a suitably chosen boundary function. If the detector crosses the boundary function, a structural break is detected. We provide the asymptotics for the procedure under the null hypothesis of stability. The consistency of the procedure is also proved. We derive the asymptotic distribution of the stopping time under the change point alternative. Monte Carlo simulation is used to show the size and the power of our method under several conditions. As an example, we study the real estate markets in Boston and Los Angeles, and at the national U.S. level. We find structural breaks in the markets, and we segment the data into stationary segments. It is observed that the autoregressive parameter is increasing but stays below 1.

Fatigue assessment of non-stationary random loading in the frequency domain by a quasi-stationary Gaussian approximation

The power spectral density (PSD) is a fundamental technique of random vibration fatigue providing an effective statistical characterization that can be processed by linear systems theory and load spectrum estimators. This lays the basis for a statistical-based fatigue assessment. While the PSD assembles a full stochastic characterization of stationary Gaussian loading, for loading subjected to changing operational, environmental, and excitational conditions, it provides no means of a fluctuating spectral density. Therefore, the PSD neither qualifies to characterize varying loads, nor reproduces comparable stress amplitudes to a referencing non-stationary excitation following a statistical-based stress analysis. Consequently, this paper employs the non-stationarity matrix to characterize the varying evolution of realistic loading and proposes a system of equations that decomposes this characterization into stationary Gaussian portions. The fundamental idea is to approximate realistic loading by abstracting a series of stationary segments, whose assembly in return embodies a full statistical characterization. The resulting quasi-stationary load definition better reflects the fatigue damage potential of realistic, non-stationary loading and allows to implement load spectrum estimators, ensuing computationally efficient and statistically robust structural lifetime predictions. Further, quasi-stationary load definitions are utilized to advance the concept of damage-equivalent statistical load definitions to be independent of a specific Miner exponent.

Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series

This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk toward zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography. Supplementary materials for this article are available online.

Towards viable flow simulations of small-scale rotors and blade segments

The paper focuses on the possibilities of adequately simulating complex flow fields that appear around small-scale propellers of multicopter aircraft. Such unmanned air vehicles (UAVs) are steadily gaining popularity for their diverse applications (surveillance, communication, deliveries, etc.) and the need for a viable (i.e. usable, satisfactory, practical) computational tool is also surging. From an engineering standpoint, it is important to obtain sufficiently accurate predictions of flow field variables in a reasonable amount of time so that the design process can be fast and efficient, in particular the subsequent structural and flight mechanics analyses. That is why more or less standard fluid flow models, e.g. Reynolds-averaged Navier?Stokes (RANS) equations solved by the finite volume method (FVM), are constantly being employed and validated. On the other hand, special attention must be given to various flow peculiarities occurring around the blade segments shaped like airfoils since these flows are characterized by small chords (length-scales), low speeds and, therefore, low Reynolds numbers (Re) and pronounced viscous effects. The investigated low-Re flows include both transitional and turbulent zones, laminar separation bubbles (LSBs), flow separation, as well as rotating wakes, which require somewhat specific approaches to flow modeling (advanced turbulence models, fine spatial and temporal scales, etc). Here, the conducted computations (around stationary blade segments as well as rotating rotors), closed by different turbulence models, are presented and explained. Various qualitative and quantitative results are provided, compared and discussed. The main possibilities and obstacles of each computational approach are mentioned. Where possible, numerical results are validated against experimental data. The correspondence between the two sets of results can be considered satisfactory (relative differences for the thrust coefficient amount to 15%, while they are even lower for the torque coefficient). It can be concluded that the choice of turbulence modeling (and/or resolving) greatly affects the final output, even in design operating conditions (at medium angles-of-attack where laminar, attached flow dominates). Distinctive flow phenomena still exist, and in order to be adequately simulated, a comprehensive modeling approach should be adopted.

Reliability of wrist‐worn accelerometry devices and algorithms for sleep detection

AbstractBackgroundWearable devices have shown great potential for obtaining measures of real world evidence in clinical trials, but standardization and variability between different devices remain one of the barriers for systematic deployment. In this investigation, we present a neural network algorithm for sleep detection, Deep Learning Sleep (DLS), and compare its reliability across different datasets and two widely used sleep algorithms.MethodA publically available dataset TUD (Borazio; ICHI 2014, N=45) and the CONTEXT dataset collected in Montpellier, IXI (Wolz; Alzheimer’s &amp; Dementia 2017, N=45) were used to compare algorithm performance. Each dataset provided one‐night accelerometry data alongside polysomnography (PSG) per participant. DLS performance was compared against two other algorithms for sleep segment detection; Cole‐Kripke, CK (Cole; Sleep 1992), and estimation of stationary segments or ESS (Borazio; ICHI 2014). Then, cross‐validations were implemented with the datasets; e.g. training with dataset TUD and testing on dataset IXI, and vice versa.ResultThe cross‐validations experiments showed no significant differences in performance (sleep detection: 85‐89% sensitivity, 43‐45% specificity and 72‐74% accuracy) for the DLS algorithm. On the contrary, CK and ESS showed significant differences in their performances across experiments, and CK showed the highest difference for specificity (mean specificity = 27‐52%), demonstrating that CK and ESS algorithms are susceptible to different devices and/or data sources (Figure 1).ConclusionThe CK and ESS algorithms showed susceptibility to different devices or data sources (e.g. clinical groups) and this raises questions about their reliability. In conclusion, we presented a deep learning based algorithm (DLS) that showed high robustness against different data sources as well as high accuracy when compared against the gold standard PSG.