Width Of Time Window Research Articles

Intelligent identification for working-cycle stages of excavator based on main pump pressure

Abstract An identification of the working stages of the excavator is used as the basis to realize a staged energy-saving control and reduce the fuel consumption. However, existing methods based on computer vision and multi-sensor information fusion technologies have limitations in practical applications. In this paper, an intelligent identification method for the working-cycle stages of an excavator is proposed based on the relationship between the working stages and the main pump pressure waveform. Three machine learning algorithms, a support vector machine (SVM), back propagation neural network (BPNN), and logistic regression (LR), were used to establish the intelligent recognition model. In addition, an intelligent calibration system was established to reduce the effects of human-induced factors and pressure fluctuations on the accuracy when using the original main pump pressure signals. The relationships among the identification accuracy, time window width, and signal sampling frequency were also studied. The results show that the library for support vector machine (LIBSVM) model outperforms the BPNN and LR, recording an accuracy of 94.64% when using the test set and an accuracy of 93.82% with an intelligent calibration system when using the original main pump pressure signals. The optimal time window width and signal sampling frequency are 0.5 s and 20 Hz, respectively. The proposed method is a practical approach to realize the real-time supervision of the working stage of an excavator, providing theoretical support for a staged energy-saving control.

Optimization of receiving window width of the correlation receiver for radiofrequency identification marker localization

The radiofrequency identification (RFID) technology is widely used in modern industry to identify and localize the final manufactured products and their parts. This article analyses and optimizes the localization process of special RFID transponders – markers used to mark the position and type of the underground facility networks (pipes, cables, etc.). The analysis of electric circuits representing the system consisting of the marker and the localization device is performed by numerical solution of the corresponding equations. The results of the numerical solution are then used for calculation of the analytical description of the waveform received as response from the excited marker. The constants obtained from the analytical form of the solution are then used as input parameters for optimization of time window width in the correlation receiver of the marker responses. The optimization is focused on the maximization of the signal-to-noise ratio in the receiving time window. The theoretical calculations are completed by the processing of real signals recorded by an oscilloscope from the localization device where the correlation receiver is planned to apply.

A robust optimization for a home health care routing and scheduling problem with consideration of uncertain travel and service times

In today’s competitive environment, one of the most critical objectives for Home Health Care (HHC) companies is to meet the demand of patients in a timely fashion. According to the feedback from HHC companies, caregivers have to deal with some uncertainties when carrying out a given schedule to visit their patients. However, a majority of the previous work only considers the deterministic models which ignore the uncertainties, and solutions obtained by these deterministic models are usually less robust in case of any possible changes in practical situations. Inspired by this point, in this work, we formulate a model for an HHC Routing and Scheduling Problem with taking into account uncertain travel and service times, from the perspective of Robust Optimization (RO). Specifically, the non-deterministic variables are defined based on the theory of budget uncertainty, and then the arrival time of each caregiver is rewritten as a complicated recursive function. After that, Gurobi Solver, Simulated Annealing, Tabu Search, and Variable Neighborhood Search are adapted to solve the model respectively. Finally, a series of experiments have been performed to validate the proposed models and algorithms. Experimental results from Monte Carlo simulation highlight the strength of considering uncertainties when modeling the problem. Additional, the influences of other characters in instances, like the width of time-window, distributed location have also been empirically analyzed. Finally, the comparison performed between the solutions obtained by the stochastic model and the RO model also demonstrates the advantage of the RO model. This work provides a valuable framework for HHC companies to make a robust schedule when arranging the caregivers.

The order scheduling problem of product-service system with time windows

A product-service system (PSS) is a system offering a mix of tangible products and intangible services to meet the personalized needs of customers. This paper studies a PSS order scheduling problem with time windows (PSSOSPTW) for a PSS provider composed of a manufacturing plant and a service centre. In this problem, the product of each PSS order is first produced by the manufacturing plant, and then the installation service is provided by the service centre. A mathematical model for the PSSOSPTW is presented to minimize the total storage and tardiness cost. To solve this NP-hard problem, an idle time insertion algorithm is proposed to obtain the optimal start and completion times of production and service of PSS orders for a given order sequence. Three metaheuristics based on iterated local search, simulated annealing and tabu search are developed to search for the best sequence through incorporating the idle time insertion algorithm. All metaheuristics utilize an adapted version of the NEH heuristic to generate initial solutions, and apply the idea of variable neighbourhood search to find local optimum. The performance of the proposed algorithms is evaluated by comparison, and the effects of some instance factors are also examined. The results show that our algorithms perform very similarly in general but quite differently in various situations. Besides, the problem size, tardiness factor, due date range factor and time window width range factor have noticeable impact on their performance.

Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings

The energy management of buildings currently offers a powerful opportunity to enhance energy efficiency and reduce the mismatch between the actual and expected energy demand, which is often due to an anomalous operation of the equipment and control systems. In this context, the characterisation of energy consumption patterns over time is of fundamental importance. This paper proposes a novel methodology for the characterisation of energy time series in buildings and the identification of infrequent and unexpected energy patterns. The process is based on an enhanced Symbolic Aggregate approXimation (SAX) process, and it includes an optimised tuning of the time window width and of the symbol intervals according to the building energy behaviour. The methodology has been tested on the whole electrical load of buildings for two case studies, and its flexibility and robustness have been confirmed. In order to demonstrate the implications for a preliminary diagnosis, some unexpected trends of the total electrical load have also been discussed in a post-mining phase, using additional datasets related to heating and cooling electrical energy needs.The process can be used to support stakeholders in characterising building behaviour, to define appropriate energy management strategies, and to send timely alerts based on anomaly detection outcomes.

A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study

BackgroundMissing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection. Traditional multiple imputation (MI) methods (fully conditional specification (FCS) and multivariate normal imputation (MVNI)) treat repeated measurements of the same time-dependent variable as just another ‘distinct’ variable for imputation and therefore do not make the most of the longitudinal structure of the data. Only a few studies have explored extensions to the standard approaches to account for the temporal structure of longitudinal data. One suggestion is the two-fold fully conditional specification (two-fold FCS) algorithm, which restricts the imputation of a time-dependent variable to time blocks where the imputation model includes measurements taken at the specified and adjacent times. To date, no study has investigated the performance of two-fold FCS and standard MI methods for handling missing data in a time-varying covariate with a non-linear trajectory over time – a commonly encountered scenario in epidemiological studies.MethodsWe simulated 1000 datasets of 5000 individuals based on the Longitudinal Study of Australian Children (LSAC). Three missing data mechanisms: missing completely at random (MCAR), and a weak and a strong missing at random (MAR) scenarios were used to impose missingness on body mass index (BMI) for age z-scores; a continuous time-varying exposure variable with a non-linear trajectory over time. We evaluated the performance of FCS, MVNI, and two-fold FCS for handling up to 50% of missing data when assessing the association between childhood obesity and sleep problems.ResultsThe standard two-fold FCS produced slightly more biased and less precise estimates than FCS and MVNI. We observed slight improvements in bias and precision when using a time window width of two for the two-fold FCS algorithm compared to the standard width of one.ConclusionWe recommend the use of FCS or MVNI in a similar longitudinal setting, and when encountering convergence issues due to a large number of time points or variables with missing values, the two-fold FCS with exploration of a suitable time window.

Assessing Customer Service Reliability in Route Planning with Self-Imposed Time Windows and Stochastic Travel Times

This paper presents mathematical models and solution methods for assigning time windows to customers in vehicle routing problems with stochastic travel times. The goal is to design minimum-cost routes in terms of traveling distance and to simultaneously minimize the expected penalty costs associated with the assignment of time windows. Focus is given on how to evaluate the flexibility of time windows and to incorporate service reliability into the routing plans. Two mathematical models are proposed for the stochastic time window assignment problem that treat the travel times as continuous and discrete random variables, and take into account lateness and earliness penalties, penalties for the time window widths, and chance constraints to model the probability of serving each customer within the assigned time window so as to meet service reliability requirements. A hierarchical solution approach is proposed. At the first level, the master vehicle routing problem is solved via an adaptive large neighborhood search metaheuristic to optimize the routing cost, while at the second level the subordinate time window assignment subproblems are solved optimally. Various computational experiments are conducted to assess the performance of the proposed algorithmic framework, to understand time window flexibility, to study the trade-off between service reliability and routing costs, and to demonstrate the effectiveness and applicability of optimal time window assignment policies in an urban transportation environment with uncertain travel times.

Time-resolved spectroscopy at 19,000 lines per second using a CMOS SPAD line array enables advanced biophotonics applications.

A SPAD-based line sensor fabricated in 130 nm CMOS technology capable of acquiring time-resolved fluorescence spectra (TRFS) in 8.3 milliseconds is presented. To the best of our knowledge, this is the fastest time correlated single photon counting (TCSPC) TRFS acquisition reported to date. The line sensor is an upgrade to our prior work and incorporates: i) parallelized interface from sensor to surrounding circuitry enabling high line rate to the PC (19,000 lines/s) and ii) novel time-gating architecture where detected photons in the OFF region are rejected digitally after the output stage of the SPAD. The time-gating architecture was chosen to avoid electrical transients on the SPAD high voltage supplies when gating is achieved by excess bias modulation. The time-gate has an adjustable location and time window width allowing the user to focus on time-events of interest. On-chip integrated center-of-mass (CMM) calculations provide efficient acquisition of photon arrivals and direct lifetime estimation of fluorescence decays. Furthermore, any of the SPC, TCSPC and on-chip CMM modes can be used in conjunction with the time-gating. The higher readout rate and versatile architecture greatly empower the user and will allow widespread applications across many techniques and disciplines. Here we focused on 3 examples of TRFS and time-gated Raman spectroscopy: i) kinetics of chlorophyll A fluorescence from an intact leaf; ii) kinetics of a thrombin biosensor FRET probe from quenched to fluorescence states; iii) ex vivo mouse lung tissue autofluorescence TRFS; iv) time-gated Raman spectroscopy of toluene at 3056 cm-1 peak. To the best of our knowledge, we detect spectrally for the first time the fast rise in fluorescence lifetime of chlorophyll A in a measurement over single fluorescent transient.

An improved car-following model with multiple preceding cars’ velocity fluctuation feedback

In order to explore and evaluate the effects of velocity variation trend of multiple preceding cars used in the Cooperative Adaptive Cruise Control (CACC) strategy on the dynamic characteristic, fuel economy and emission of the corresponding traffic flow, we conduct a study as follows: firstly, with the real-time car-following (CF) data, the close relationship between multiple preceding cars’ velocity fluctuation feedback and the host car’s behaviors is explored, the evaluation results clearly show that multiple preceding cars’ velocity fluctuation with different time window-width are highly correlated to the host car’s acceleration/deceleration. Then, a microscopic traffic flow model is proposed to evaluate the effects of multiple preceding cars’ velocity fluctuation feedback in the CACC strategy on the traffic flow evolution process. Finally, numerical simulations on fuel economy and exhaust emission of the traffic flow are also implemented by utilizing VT-micro model. Simulation results prove that considering multiple preceding cars’ velocity fluctuation feedback in the control strategy of the CACC system can improve roadway traffic mobility, fuel economy and exhaust emission performance.

Detection and localization of change points in temporal networks with the aid of stochastic block models

A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.

リアルタイムスペクトル解析法における多段階解析による周波数分解能の改善

In recent years, analytical method in which the length of time-window for time-localization can be established optionally was shown by the authors. If you decide the time-window width which is in inverse proportion to the frequency, The function becomes the same as the elastic function of the mother wavelet of wavelet transform, and the same effective analysis as wavelet transform is carried out. Since the processing of this analysis is independent almost completely, approximately complete parallel processing is possible. Analysis in real time is carried out by introducing parallel processing. In this analytical method, we use a sine wave and cosine wave of plural periods, and so we call these waves a cutting out wave and call the plural periods a number of periods. We use inner product of the cutting out wave and the signal. The number of periods determines the size of the width of time-window. If width of time-window is enlarged, frequency resolution will improve, but time resolution falls. It is impossible to improve the frequency resolution and the time resolution simultaneously. This is the uncertainty principle. In this analyzing method, it's analyzed by many signal detection unit. At first a signal is performed by all signal detection units, and I call this first stage analysis. Next we analyze the wave that appears in each signal detection unit in first stage by using the same signal detection unit again. This is second-stage analysis . This is repeated and plural stage analyses are conducted. This is multi-stage analysis. It is shown that the frequency resolution can be improved in spite of uncertainty principle by this multistage analysis.

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints.

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting-state FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks suggesting that they could be capturing consciousness-related processes such as mind wandering.

Characteristics of the spatio-temporal network of cattle movements in France over a 5-year period

A good knowledge of the specificities of the animal trade network is highly valuable to better control pathogen spread on a large regional to transnational scale. Because of their temporal dynamical nature, studying multi-annual datasets is particularly needed to investigate whether structural patterns are stable over the years. In this study, we analysed the French cattle movement network from 2005 to 2009 for different spatial granularities and temporal windows, with the three-fold objective of exploring temporal variations of the main network characteristics, computing proxies for pathogen spread on this network, which accounts for its time-varying properties and identifying specificities related to the main types of animals and farms (dairy versus beef). Network properties did not qualitatively vary among different temporal and spatial granularities. About 40% of the holdings and 80% of the communes were directly interconnected. The width of the aggregation time window barely impacted normalised distributions of indicators. A period of 8–16 weeks would suffice for robust estimation of their main trends, whereas longer periods would provide more details on tails. The dynamic nature of the network could be seen through the small overlap between consecutive networks with 65% of common active nodes for only 3% of common links over 2005–2009. To control pathogen spread on such a network, by reducing the largest strongly connected component by more than 80%, movements should be prevented from 1 to 5% of the holdings with the highest centrality in the previous year network. The analysis of breed-wise and herd-wise subnetworks, dairy, beef and mixed, reveals similar trends in temporal variation of average indicators and their distributions. The link-based backbones of beef subnetworks seem to be more stable over time than those of other subnetworks. At a regional scale, node reachability accounting for time-respecting paths, as proxy of epidemic burden, is greater for a dairy region than for a beef region. This highlights the importance of considering local specificities and temporal dynamics of animal trade networks when evaluating control measures of pathogen spread.

Computing an optimal time window of audiovisual integration in focused attention tasks: illustrated by studies on effect of age and prior knowledge

The concept of a "time window of integration" holds that information from different sensory modalities must not be perceived too far apart in time in order to be integrated into a multisensory perceptual event. Empirical estimates of window width differ widely, however, ranging from 40 to 600ms depending on context and experimental paradigm. Searching for theoretical derivation of window width, Colonius and Diederich (Front Integr Neurosci 2010) developed a decision-theoretic framework using a decision rule that is based on the prior probability of a common source, the likelihood of temporal disparities between the unimodal signals, and the payoff for making right or wrong decisions. Here, this framework is extended to the focused attention task where subjects are asked to respond to signals from a target modality only. Evoking the framework of the time-window-of-integration (TWIN) model, an explicit expression for optimal window width is obtained. The approach is probed on two published focused attention studies. The first is a saccadic reaction time study assessing the efficiency with which multisensory integration varies as a function of aging. Although the window widths for young and older adults differ by nearly 200ms, presumably due to their different peripheral processing speeds, neither of them deviates significantly from the optimal values. In the second study, head saccadic reactions times to a perfectly aligned audiovisual stimulus pair had been shown to depend on the prior probability of spatial alignment. Intriguingly, they reflected the magnitude of the time-window widths predicted by our decision-theoretic framework, i.e., a larger time window is associated with a higher prior probability.

Estimation of genuine and random synchronization in multivariate neural series

Synchronization is an important mechanism that helps in understanding information processing in a normal or abnormal brain. In this paper, we propose a new method to estimate the genuine and random synchronization indexes in multivariate neural series, denoted as GSI (genuine synchronization index) and RSI (random synchronization index), by means of a correlation matrix analysis and surrogate technique. The performance of the method is evaluated by using a multi-channel neural mass model (MNMM), including the effects of different coupling coefficients, signal to noise ratios (SNRs) and time-window widths on the estimation of the GSI and RSI. Results show that the GSI and the RSI are superior in description of the synchronization in multivariate neural series compared to the S-estimator. Furthermore, the proposed method is applied to analyze a 21-channel scalp electroencephalographic recording of a 35 year-old male who suffers from mesial temporal lobe epilepsy. The GSI and the RSI at different frequency bands during the epileptic seizure are estimated. The present results could be helpful for us to understand the synchronization mechanism of epileptic seizures.

Windowed nearest neighbour method for mining spatio-temporal clusters in the presence of noise

In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process.

The multi-shift vehicle routing problem with overtime

In this paper, we study a new variant of the vehicle routing problem (VRP) with time windows, multi-shift, and overtime. In this problem, a limited fleet of vehicles is used repeatedly to serve demand over a planning horizon of several days. The vehicles usually take long trips and there are significant demands near shift changes. The problem is inspired by a routing problem in healthcare, where the vehicles continuously operate in shifts, and overtime is allowed. We study whether the tradeoff between overtime and other operational costs such as travel cost, regular driver usage, and cost of unmet demands can lead to a more efficient solution. We develop a shift dependent (SD) heuristic that takes overtime into account when constructing routes. We show that the SD algorithm has significant savings in total cost as well as the number of vehicles over constructing the routes independently in each shift, in particular when demands are clustered or non-uniform. Lower bounds are obtained by solving the LP relaxation of the MIP model with specialized cuts. The solution of the SD algorithm on the test problems is within 1.09–1.82 times the optimal solution depending on the time window width, with the smaller time windows providing the tighter bounds.

Forward and adjoint quasi‐geostrophic models of the geomagnetic secular variation

We introduce a quasi‐geostrophic model of core dynamics, which aims at describing core processes on geomagnetic secular variation timescales. It extends the formalism of Alfvén torsional oscillations by incorporating nonzonal motions. Within this framework, the magnetohydrodynamics takes place in the equatorial plane; it involves quadratic magnetic quantities, which are averaged along the direction of rotation of the Earth. In addition, the equatorial flow is projected on the core‐mantle boundary. It interacts with the magnetic field at the core surface, through the radial component of the magnetic induction equation. That part of the model connects the dynamics and the observed secular variation, with the radial component of the magnetic field acting as a passive tracer. We resort to variational data assimilation to formally construct the relationship between model predictions and observations. Variational data assimilation seeks to minimize an objective function by computing its sensitivity to its control variables. The sensitivity is efficiently calculated after integration of the adjoint model. We illustrate that framework with twin experiments, performed first in the case of the kinematic core flow inverse problem and then in the case of Alfvén torsional oscillations. In both cases, using the adjoint model allows us to retrieve core state variables which, while taking part in the dynamics, are not directly sampled at the core surface. We study the effect of several factors on the solution (width of the assimilation time window, amount and quality of data), and we discuss the potential of the model to deal with real geomagnetic observations.

The effect of configuration parameters of time-frequency maps in the detection of intra-QRS electrical transients of the signal-averaged electrocardiogram: Impact in clinical diagnostic performance

Time-frequency maps of signal-averaged electrocardiogram based on a short time Fourier transform (STFT) technique analysis was employed to assess the presence of high frequency electrical transients (turbulence) and arrhythmia risk assessment. The optimal configuration set of STFT variables aiming at risk stratification for sustained monomorphic ventricular tachycardia (SMVT) is still undetermined. Different configuration variables, including analyzing time-window widths, starting positions, relative displacements, and zero-padding for STFT time-frequency maps constructions were combined on each analyzing averaged signal from 18 healthy controls and 18 subjects presenting inducible SMVT. Spectral turbulence analysis (STA) was, thus, carried out according to conventional procedures. The optimal configuration set of variables for STA was obtained by assessing the total diagnostic accuracy of all combinations of parameters. The optimal diagnostic performance was found at 86% total diagnostic accuracy as compared to 56% using previous defined normality thresholds (p=0.01). Present configuration set of variables is distinctive from previously defined set of variables and improves risk stratification.

Separation of very hydrophobic analytes by micellar electrokinetic chromatography. III. Characterization and optimization of the composition of the separation electrolyte using carbon number equivalents

Four coefficients are suggested to characterize the separation properties of separation electrolytes in MEKC. These coefficients are the electrophoretic mobility of the pseudostationary phase (PSP), the electroosmotic mobility, and two parameters which can be obtained via the Martin equation using the retention data of the members of a suitable homologous series. These four coefficients are used for the characterization of separation electrolytes applicable to the separation of very hydrophobic analytes (log P(OW) = 3-4) with SDS as PSP and to quantitatively describe the influence of the buffer additives ACN and urea for the fine-tuning of retention factors and the additive calcium chloride for the fine-tuning of the width of the migration time window. Together with carbon number equivalents (N*(C)) as analyte descriptors the suggested coefficients provide a tool for the fast optimization of the composition of the separation electrolyte. The proposed method optimization scheme is applied to the separation of ingredients of essential oils. Predicted resolutions are compared to experimental results.