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In this paper, the Halanay inequality is generalized into the time-varying version under simple and natural conditions, without constant to bound the time-varying coefficients. The solution of the underlying inequality is estimated by virtue of the feasible function pair associated with the coefficients and delays. Corollaries are given for typical special cases. Some approaches are illustrated for the searching of the initial feasible pairs, and an iterative procedure is proposed for the optimization of the feasible pairs. The usage and superiority of the obtained result are exhibited by numerical examples with simulations. As a base, the comparison principle for the functional differential inequalities and equations with arbitrary time-varying delays is studied firstly. As an application of the main result, the stability and control of the stochastic systems with time-varying coefficients and delays are investigated, with the main concern to the time variance. An asymptotic stability theorem and a criterion are established, and an interesting control strategy with unbounded time delay, namely the pantograph delay sampled data based feedback, is proposed. The st

In Special Relativity it is well established that relatively moving observers measure different times for an observed event. However, it is always assumed that relatively moving observers agree on their relative velocity, expressed as v/c, even though velocity is a function of time. Special Relativity can be reinterpreted by adding an assumption that space is invariant. Relatively moving observers will then find different relative velocities and time variance will be shown to be a result of how measurements are referenced at the quantum level., PDF export from a Maple document

Searching and containing dynamic target like oil spillage in the ocean is a challenging task due to the natural time variance of the spread of the oil. The use of cooperative multi-marine vehicle systems in a cluttered environment for this purpose poses difficulties in sustaining formation pattern to pursue and contain the leakage. In this article, in order to provide realistic setup for the industrial applications of multi-marine vehicles systems, we present a novel approach for collision-free distributed formation control for a network of underactuated surface vessels (USVs). The proposed approach comprises two layers: A distributed coordination layer and a local fixed-time neural network control layer. In the first layer, formation leaders accomplish a specified formation configuration while tracking a desired trajectory from a tracking leader. The second control layer is to robustly drive the real USVs with parametric and nonparametric uncertainties to track their corresponding formation leaders. Because only parts of the formation leaders can acquire the states of the tracking leader, a distributed fixed-time estimator is proposed to obtain accurate estimations of the desired information for each USV in the network. Next, in order to effectively maneuver in cluttered environment, local path replanning-based repulsive potential function technique is proposed for each USV in the group formation to act on the formation leaders trajectories. Further, redesigned adaptive neural networks are integrated to compensate the model uncertainties. The stability of the proposed controller is verified by the Lyapunov direct method. Simulation studies of a hexagon formation are presented to illustrate the effectiveness of the proposed approach.

Peninjauan prestasi proyek peningkatan jaringan irigasi Tukad Petanu masih menggunakan konsep manajemen biaya tradisional sehingga evaluasi berdasarkan konsep nilai hasil perlu dilakukan. Tujuan penelitian adalah menganalisis kinerja biaya dan waktu proyek didasarkan konsep earned value analysis. Penelitian menggunakan deskriptif kuantitatif dengan earned value analysis. Berdasarkan hasil analisa, bulan ke-1 sampai bulan ke-8 nilai Budgeted Cost Work Performance (BCWP) ada di atas Budgeted Cost Work Schedule (BCWS) dan berakhir di titik yang sama di bulan ke-9 sehingga proyek dikategorikan berjalan lebih cepat 6,52% dari waktu rencana dan berakhir sesuai waktu rencana. Nilai Actual Cost Work Performance (ACWP) berada di bawah BCWP sehingga biaya aktual lebih kecil dari biaya rencana yaitu 21,14% dari biaya rencana. Analisis varians biaya dan waktu berdasarkan komulatif bernilai positif (+), menunjukkan pekerjaan dilaksanakan lebih cepat dari jadwal dan biaya yang dikeluarkan lebih kecil dari anggaran. Indeks prestasi kinerja biaya dan waktu berdasarkan komulatif memiliki nilai rata-rata Cost Performance Index (CPI) 1,21 dan rata-rata Schedule Performance Index (SPI) 1,02 sehingga proyek berjalan lebih cepat dari waktu rencana dan biaya lebih kecil dari anggaran rencana.

The widespread integration of electric vehicles (EVs) into the electrical grid creates a new opportunity for frequency regulation. In this article, to deal with the penetration of intermittent renewable energy and the time variance of system model, an online evolutionary mechanism is developed for the electrical-grid- electric-vehicle system. With a real-time decoder consisting of the long-short-term memory (LSTM) array, the dispatch center is upgraded from a passive executor to an intelligent analyst, which extracts the rolling features from multiple time scales. Based on the high-dimension decoding information from the LSTM array, a deep neural network (DNN) array is then embedded to provide strategic dispatch commands learning from the evolving memory. The whole decoder-dispatch framework is then upgraded with a unified online adaption technique to achieve gradient optimization and weight evolution. The proposed evolutionary structure is validated on a frequency management system to demonstrate its superior performance.

Transportation time information plays an important role in transportation and logistics. Variability in transportation time is obvious in any mode of transport due to unpredictable system parameters and their dynamic behavior. Significant work has been done in transportation on road and rail modes but the area is still open and hot for maritime being less explored so far. This paper, in maritime perspective, defines the transportation time as time taken for all the activities from loading at source harbor till it’s unloading at the destination and so the return path. As uncertainties can be handled effectively by statistical methods, it is used for transportation time appraisal. The paper presents the methodology for maritime transportation reliability assessment and illustrates the same with an application to a case of Stena Baltica ferry operation in Europe. This paper uniquely combines quality and reliability tools in maritime studies and the obtained results are validated through the simulation. Further a leading link is taken to resolve the problem reducing the uncertainty level in the voyage by bringing process capability study into the investigation. Appropriate investigation and analyses with respect to transportation time variance reduction is also carried out in the paper.

The availability of automated data for urban metro systems allows operators to accurately measure journey time reliability. However, there remains limited understanding of the causes of journey time variance and how journey time performance can be improved. In this paper, we present a semiparametric regression modelling framework to determine the underlying drivers of journey time variance in urban metro systems, using the London Underground as a case study. We merge train location and passenger trip data to decompose total journey times into three constituent parts: access times as passengers enter the system, on-train times, and egress times as passengers exit at their destinations. For each journey time component, we estimate non-linear functional relationships which we then use to derive elasticity estimates of journey times with respect to service supply and demand factors, including operational and physical characteristics of metros as well as passenger demand and passenger-specific travel characteristics. We find that the static fixed physical characteristics of stations and routes have the greatest influence on journey time, followed by train speeds, and headways, for which the average elasticities of total journey time are −0.54 and 0.05, respectively. The results of our analysis could inform operators about where potential interventions should be targeted in order to improve journey time performance.

International audience; We study a parabolic Lotka-Volterra equation, with an integral term representing competition, and time periodic growth rate. This model represents a trait structured population in a time periodic environment. After showing the convergence of the solution to the periodic periodic solution of the problem, we study the influence of different factors on the mean limit population. As this quantity is the opposite of a certain eigenvalue, we are able to investigate the influence of the diffusion rate, the period length and the time variance of the environment fluctuations. We also give biological interpretation of the results in the framework of cancer, if the model represents a cancerous cells population under the influence of a periodic treatment. In this framework, we show that the population might benefit from a intermediate rate of mutation.

Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. Here we develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics., Temporal networks in which interaction events are distributed heterogeneously in time are complex to model. Unicomb et al. propose an analytical framework for the analysis of cascading dynamics in such networks, relevant for spin interactions, epidemic spreading, and language dynamics.

The electromagnetic environment in buildings is complex and fluctuating in time. We can distinguish three time scales: short, mid and long term time-variance. In this paper we measure the time-varying behavior of a complex electromagnetic environment over the course of a 24-hour period. We show that this measurement provides extra information on top of short time-variance data, necessary to assess the risk for EMI in a complex EM environment; the risk-based EMC approach.

Recurring batch data pipelines are a staple of the modern enterprise-scale data warehouse. As a data warehouse scales to support more products and services, a growing number of interdependent pipelines running at various cadences can give rise to periodic resource bottlenecks for the cluster. This resource contention results in pipelines starting at unpredictable times each day and consequently variable landing times for the data artifacts they produce. The variability gets compounded by the dependency structure of the workload, and the resulting unpredictability can disrupt the project workstreams which consume this data. We present Clockwork, a delay-based global scheduling framework for data pipelines which improves landing time stability by spreading out tasks throughout the day. Whereas most scheduling algorithms optimize for makespan or average job completion times, Clockwork's execution plan optimizes for stability in task completion times while also targeting predefined pipeline SLOs. We present this new problem formulation and design a list scheduling algorithm based on its analytic properties. We also discuss how we estimate the resource requirements for our recurring pipelines, and the architecture for integrating Clockwork with Dataswarm, Facebook's existing data workflow management service. Online experiments comparing this novel scheduling algorithm and a previously proposed greedy procrastinating heuristic show tasks complete almost an hour earlier on average, while exhibiting lower landing time variance and producing significantly less competition for resources in the cluster.

Starting from the time variance and uncertainty of accidental discharge, this paper describes the probability of the occurrence of the “normal-accident” alternate state for a risk source using the Markov state transfer model, simulates the behaviour of pollutants in rivers using the hydrodynamic and water quality models for non-conservative substances, and tracks the transport path of pollutants in rivers using the water quality model for conservative substances. The above models are coupled with the Sequential Monte Carlo algorithm, and the risk probability analysis model for sudden water pollution in the plain river network is established and applied to the Yixing river network. The results show that (a) the risk probability of exceeding ammonia nitrogen standard (PES of ammonia nitrogen) is lower in the upper reaches and higher in the middle and lower reaches; (b) dynamic changes in pollutant concentration lead to different changes in the PES of ammonia nitrogen in each reach; (c) the differences in the simulated PES values between the sudden scheme and the stable scheme (NPES of ammonia nitrogen) in the upper and middle reaches show a patchy distribution of high and low values, which are related to the risk source location, the water movement direction and the concentration change in the reach after accepting pollutant loads from the risk sources; (d) the NPES of ammonia nitrogen in the lower reaches results from the coupling effect caused by accidental discharges from multiple risk sources; and (e) the different effects of the lower boundary hydrological conditions on the upstream water inflow lead to the different coupling effect on the water quality probability of sections in the downstream area.

Control of continuous microwave heating system (CMHS) is truly a complex problem with time variance, uncertainty and nonlinearity, which becomes prohibitive to use a conventional model-based approach. To overcome this, a novel data-based optimal temperature tracking control is designed for CMHS in this paper. In order to obtain the complex dynamics of CMHS, a neural network model is first constructed driven by process data. After transforming the original temperature tracking problem into an error regulation problem, adaptive dynamic programming is introduced to deal with the regulation problem as well as to decrease operation cost. The design and operation of this controller depend mainly on the online data, and minor prior knowledge is required. Simulation results show that the proposed method can effectively control the CMHS in terms of temperature tracking and energy utilization.

Spatio-temporal patterns of drought from 1961 to 2013 over the Beijing-Tianjin-Hebei (BTH) region of China were analyzed using the Palmer Drought Severity index (PDSI) based on 21 meteorological stations. Overall, changes in the mean-state of drought detected in recent decades were due to decreases in precipitation and potential evapotranspiration. The Empirical Orthogonal Functions (EOF) method was used to decompose drought into spatio-temporal patterns, and the first two EOF modes were analyzed. According to the first leading EOF mode (48.5%), the temporal variability (Principal Components, PC1) was highly positively correlated with annual series of PDSI (r=+0.99). The variance decomposition method was further applied to explain the inter-decadal temporal and spatial variations of drought relative to the total variation. We find that 90% of total variance was explained by time variance, and both total and time variance dramatically decreased from 1982 to 2013. The total variance was consistent with extreme climate events at the inter-decadal scale (r=0.71, p

Based on clinical observations of severe episodic memory (EM) impairment in dementia of Alzheimer's disease (AD), a brief, computerized EM test was developed for AD patient evaluation. A continuous recognition task (CRT) was chosen because of its extensive use in EM research. Initial experience with this computerized CRT (CCRT) showed patients were very engaged in the test, but AD patients had marked failure in recognizing repeated images. Subsequently, the test was administered to audiences, and then a two-minute online version was implemented (http://www.memtrax.com). The online CCRT shows 50 images, 25 unique and 25 repeats, which subjects respectively either try to remember or indicate recognition as quickly as possible. The pictures contain 5 sets of 5 images of scenes or objects (e.g., mountains, clothing, vehicles, etc.). A French company (HAPPYneuron, SAS) provided the test for 2 years, with these results. Of 18,477 individuals, who indicated sex and age 21-99 years and took the test for the first time, 18,007 individuals performed better than chance. In this group, age explained 1.5% of the variance in incorrect responses and 3.5% of recognition time variance, indicating considerable population variability. However, when averaging for specific year of age, age explained 58% of percent incorrect variance and 78% of recognition time variance, showing substantial population variability but a major age effect. There were no apparent sex effects. Further studies are indicated to determine the value of this CCRT as an AD screening test and validity as a measure of EM impairment in other clinical conditions.

Active fingerprinting is an effective penetration testing technique to know about vulnerability of hosts against security threats and network as a whole. Sometimes firewalls may block fingerprinting packets, hence making the probes infeasible. Measured Round Trip Time (RTTm) is a benign number that can be obtained from communication based on legitimate non malicious packets. In this paper, RTTm has been used along with other timers namely Smoothened Round-trip Time (SRTT), Round-trip Time Variance (RTTVar), Retransmission Time Out (RTO) and Scantime for pattern recognition and association analysis with the aid of cross-correlations. Experimental relationship among these timers are derived to back-up existing theoretical knowledge. A novel method to estimate IP-ID Sequence classes and network-traffic intensity based on these timers has been proposed. Results show that the model can be used to accurately derive (about 100% accuracy) active fingerprinting components IP-ID sequences and link traffic estimation. Analytical results obtained by this study can help in designing high-performance realistic networks and dynamic congestion control techniques.

Signal priority controls have long been viewed as one of the viable strategies to minimize bus delays at intersections so as to reduce the travel time variability or increase headway stability. Such control strategies, however, are often not effective for arterials serving heavy bus flows, because frequent signal priority calls will inevitably disrupt the arterial signal plan and incur excessive delays to general traffic. To overcome such deficiencies, this paper presents a bus-based signal progression model for arterials with heavy bus flows as in most major cities in Asia and Europe. The proposed model features its properties of accounting for the impacts of average bus dwell time, its variance, and the bus stop capacity. The impacts of various upstream traffic flow rates and signal plans on the available bus progression band have also been included in computing the optimal bus-progression offsets. Extensive simulation experiments have confirmed the effectiveness of the computed bus progression bands under various traffic congestion levels. The proposed bus-based pre-timed signal system can serve as a base plan for real-time operations of priority control for bus progression if the congestion level on the arterial or dwelling time variance as well as traffic queues have exceeded the operational capacity of the bus-based progression system.

Parametric roll resonance is an unstable phenomenon that can lead a ship into dangerous situations such as possibly capsizing or loss of cargo. Container ships are particularly susceptible to parametric roll due to the time variance of the metacentric height. Recent incidents have reinvigorated the conversation concerning parametric roll which in turn has caused many to wonder what types of early stage design tools could be developed to provide parametric roll design guidance. This paper presents several predictive modeling techniques that were used to investigate if parametric roll leading indicators can possibly be identified and then considered in early stage design to avoid parametric roll resonance. This paper will utilize four predictive models that utilize and are trained by the parametric roll data created through the execution of a nonlinear non-homogenous damped Mathieu equation, subject to stochastic stiffness and forcing. The ultimate goal of the execution of these techniques is to expand the understanding the potential for parametric roll in early stage design.

Most present-day resonant systems, throughout physics and engineering, are characterized by a strict time-reversal symmetry between the rates of energy coupled in and out of the system, which leads to a trade-off between how long a wave can be stored in the system and the system’s bandwidth. Any attempt to reduce the losses of the resonant system, and hence store a (mechanical, acoustic, electronic, optical, or of any other nature) wave for more time, will inevitably also reduce the bandwidth of the system. Until recently, this time-bandwidth limit has been considered fundamental, arising from basic Fourier reciprocity. In this work, using a simple macroscopic, fiber-optic resonator where the nonreciprocity is induced by breaking its time-invariance, we report, in full agreement with accompanying numerical simulations, a time-bandwidth product (TBP) exceeding the ‘fundamental’ limit of ordinary resonant systems by a factor of 30. We show that, although in practice experimental constraints limit our scheme, the TBP can be arbitrarily large, simply dictated by the finesse of the cavity. Our results open the path for designing resonant systems, ubiquitous in physics and engineering, that can simultaneously be broadband and possessing long storage times, thereby offering a potential for new functionalities in wave-matter interactions.

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject’s task is the same from trial to trial.

Speech activity detection (SAD), which often rests on the fact that the noise is "more" stationary than speech, is particularly challenging in non-stationary environments, because the time variance of the acoustic scene makes it difficult to discriminate speech from noise. We propose two approaches to SAD, where one is based on statistical signal processing, while the other utilizes neural networks. The former employes sophisticated signal processing to track the noise and speech energies and is meant to support the case for a resource efficient, unsupervised signal processing approach. The latter introduces a recurrent network layer that operates on short segments of the input speech to do temporal smoothing in the presence of non-stationary noise. The systems are tested on the Fearless Steps challenge, which consists of the transmission data from the Apollo-11 space mission. The statistical SAD achieves comparable detection performance to earlier proposed neural network based SADs, while the neural network based approach leads to a decision cost function of 1.07% on the evaluation set of the 2020 Fearless Steps Challenge, which sets a new state of the art., Accepted to Interspeech 2020

Extended Cellular Automata (XCA) represent one of the most known parallel computational paradigm for the modeling and simulation of complex systems on stenciled structured grids. However, the formalism does not perfectly lend itself to the modeling of multiple automata were two or more models co-evolve by interchanging information and by synchronizing during the dynamic evolution of the system. Here we propose the Extended Cellular Automata Network (XCAN) formalism, an extension of the original XCA paradigm in which different automata are described by means of a graph, with vertices representing automata and inter-relations modeled by a set of edges. The formalism is applied to the modeling of a theoretical 2D/3D coupled system, where space/time variance and synchronization aspects are pointed out.

In this paper, we apply flexible data-driven analysis methods on large-scale mass transit data to identify areas for improvement in the engineering and operation of urban rail systems. Specifically, we use data from automated fare collection (AFC) and automated vehicle location (AVL) systems to obtain a more precise characterisation of the drivers of journey time variance on the London Underground, and thus an improved understanding of delay. Total journey times are decomposed via a probabilistic assignment algorithm, and semiparametric regression is undertaken to disentangle the effects of passenger-specific travel characteristics from network-related factors. For total journey times, we find that network characteristics, primarily train speeds and headways, represent the majority of journey time variance. However, within the typically twice as onerous access and egress time components, passenger-level heterogeneity is more influential. On average, we find that intra-passenger heterogeneity represents 6% and 19% of variance in access and egress times, respectively, and that inter-passenger effects have a similar or greater degree of influence than static network characteristics. The analysis shows that while network-specific characteristics are the primary drivers of journey time variance in absolute terms, a nontrivial proportion of passenger-perceived variance would be influenced by passenger-specific characteristics. The findings have potential applications related to improving the understanding of passenger movements within stations, for example, the analysis can be used to assess the relative way-finding complexity of stations, which can in turn guide transit operators in the targeting of potential interventions.

When performing electrochemical impedance spectroscopy (EIS) measurements on a system, we must make sure it fulfills certain conditions. One of them is that it should be stationary that is to say, steady-state and time-invariant. Commonly studied systems are time-variant, for example a corroding electrode or a battery under operation. A corroding electrode sees its polarization resistance decrease with time. A passivating electrode sees its polarization resistance increase with time. These phenomena cause a deformation of the Nyquist impedance at low frequencies. This result was first simulated and validated by experimental measurements on a corroding steel sample undergoing uniform cor­rosion. The effect of performing impedance measurements on a discharging battery was also shown. Several methods are available to check and correct time-variance. The non-stationary distortion (NSD) indicator is used to separate valid and invalid data samples and the so called “4D impedance” method can easily produce instantaneous impedance data.

The present study analyzes the learning in a coincident timing task with force perturbation. We aimed to verify whether a predictable load (constant spring) applied to hand movements could facilitate learning and, thus, performance improvement with respect to movements without any external load and an unpredictable load to perform a coincident timing task with a few number of repetitions (n = 28) under acquisition and transfer phases. The results showed that the group with a predictable load had a significant better performance with lower percentage of errors and smaller time variance in the acquisition and transfer phase. The groups with no load and unpredictable load had a similar performance in the transfer phase. It can be concluded that adding a predictable force to the coincident timing task results in performance improvement. Therefore, learning to reach a target at a correct time could be improved with the application of predictable external loads.

In this paper, we design time–frequency-localized two-band orthogonal wavelet filter banks using convex semidefinite programming (SDP). The sum of the time variance and frequency variance of the filter is used to formulate a real symmetric positive definite matrix for joint time–frequency localization of filters. Time–frequency-localized orthogonal low-pass filter with specified length and regularity order is designed. For nonmaximally regular two-band filter banks of length twenty, it is found that, as we increase the regularity order, the solution of the SDP converges to the filters with time–frequency product (TFP) almost same as the Daubechies maximally regular filter of length twenty. Unlike the class of Daubechies maximally regular minimum phase wavelet filter banks, a rank minimization algorithm in a SDP is employed to obtain mixed-phase low-pass filters with TFP of the filters as well as the scaling and wavelet function better than the equivalent two-band Daubechies filter bank.

Recent research has found that hydrologic modeling over decadal time periods often requires time variant model parameters. Most prior work has focused on assessing time variance in model parameters conceptualizing watershed features and functions. In this paper, we assess whether adding a time variant scalar to potential evapotranspiration (PET) can be used in place of time variant parameters. Using the HBV hydrologic model and four different simple but common PET methods (Hamon, Priestly-Taylor, Oudin, and Hargreaves), we simulated 60+ years of daily discharge on four rivers in New York state. Allowing all ten model parameters to vary in time achieved good model fits in terms of daily NSE and long-term water balance. However, allowing single model parameters to vary in time – including a scalar on PET – achieved nearly equivalent model fits across PET methods. Overall, varying a PET scalar in time is likely more physically consistent with known biophysical controls on PET as compared to varying parameters conceptualizing innate watershed properties related to soil properties such as wilting point and field capacity. This work suggests that the seeming need for time variance in innate watershed parameters may be due to overly simple evapotranspiration formulations that do not account for all factors controlling evapotranspiration over long time periods.

Different from conventional phased-array providing only angle-dependent beampattern, frequency diverse array (FDA) produces angle-range-dependent and time-variant transmit beampattern. Existing investigations show that FDA offers improved performance in interference suppression and target localization, but the time-variant beampattern will bring interferences to subsequent matched filtering. More seriously, the range-dependent signal phase may be canceled out in the filtering process. In fact, traditional single-channel receiver does not fully exploit the multicarrier feature in FDA signals. In this paper, we propose a multichannel matched filtering structure with considering the time-variance property for receiving pulsed-FDA signals. A coherent pulsed-FDA radar signal model to deal with the angle-range-dependent and time-variance problem is devised under additive colored Gaussian noise scenarios, followed by the corresponding waveform design principle. Moreover, closed-form expressions of the output signal-to-interference-plus-noise ratio and Cramer–Rao bounds for angle and range are derived. The proposed receiver design approach and corresponding theoretical performance derivations are verified by extensive numerical results.

The increasing amount of data recorded during power system operations and recently developed data-driven methods make online sensitivity identification (SI) a possibility. However, due to the inherent properties of power systems—nonlinearity, time variance, and collinearity—the effective data that carry the sensitivity information are insufficient. Consequently, the online SI information collected with existing methods may result in unexpected estimates. In this paper, a sufficient effective data condition that guarantees the success of online SI is proposed. The inherent properties of power systems and their impacts on this condition are then investigated. A series of metrics to qualify online whether the data meet the condition is put forward to assess the online SI results. A method is also proposed to select the effective data to improve the online computational efficiency. Finally, the findings and methods are validated in an eight-generator 36-node bus system with operations data recorded from actual power systems.

This research includes answers from 324 main contractor representatives and 256 clients for a survey in Sweden, 2014. The literature review covers project management success in construction projects. A statistical correlation method is used to select the features that are strongly correlated with three performance indicators: cost variance, time variance and client- and contractor satisfaction. A linear regression prediction model is presented. The conclusion is an identification of the most correlating factors to project performance, and that human related factors in the project life cycle have higher impact on project success than the external factors and technical aspects of buildings.

This study aims to evaluate the robustness of a low volume mixed model production line under different operating conditions. A real production line that builds six different aircraft heat exchangers is modelled, simulated and analysed under different operating conditions. A number of experiments are conducted in order to assess the effect of dispatching rules and disturbances related to reworks, and processing time variance on tardiness robustness. A penalty function to quantitatively assess tardiness is defined based on lead time at capacity and it is used to measure the robustness of the system. The results of the assessment are then discussed in order to give some practical guidance to production planners with controlling the line in the face of uncertainty or disturbances similar to those evaluated in the study.

Bat species in the rhinolophid and hipposiderid families can deform the shapes of their noseleaves (ultrasonic emission baffles) in synchrony with emission of the biosonar pulses. Prior work has shown that these physical deformations of a diffracting baffle can add a time-variant quality to the emitted pulses. Whereas conventional sonar emitters can be characterized by gain values that are functions of direction (e.g. azimuth and elevation) and frequency only, the biosonar emission characteristics in these bat species is a function of direction, frequency, and time. Prior work has indicated that the unique emission time variance seen in the bats could support the encoding of additional sensory information. In the work presented here, a biomimetic noseleaf has been used to collect a densely sampled representation of a time-variant emission characteristics. This data set has been analyzed using advanced (nearest-neighbor based) estimators for information theoretic measures such as mutual information and relative entropy. The findings from this analysis suggest three hypotheses regarding the nature of these biomimetic time-variant emitter characteristics: (i) its complexity reflects all independent physical dimensions (ii) coding capacity along the time and frequency dimensions does not differ substantially, and (iii) sensory information is accumulated continuously along a time-variant pulse.

We developed a general method to generate populations of artificial spike trains (ASTs) that match the statistics of recorded neurons. The method is based on computing a Gaussian local rate function of the recorded spike trains, which results in rate templates from which ASTs are drawn as gamma distributed processes with a refractory period. Multiple instances of spike trains can be sampled from the same rate templates. Importantly, we can manipulate rate-covariances between spike trains by performing simple algorithmic transformations on the rate templates, such as filtering or amplifying specific frequency bands, and adding behavior related rate modulations. The method was examined for accuracy and limitations using surrogate data such as sine wave rate templates, and was then verified for recorded spike trains from cerebellum and cerebral cortex. We found that ASTs generated with this method can closely follow the firing rate and local as well as global spike time variance and power spectrum. The method is primarily intended to generate well-controlled spike train populations as inputs for dynamic clamp studies or biophysically realistic multicompartmental models. Such inputs are essential to study detailed properties of synaptic integration with well-controlled input patterns that mimic the in vivo situation while allowing manipulation of input rate covariances at different time scales.

A major challenge for future wide area measurement systems is how to efficiently monitor and control the power system, which requires accurate and real-time tracking of the phase, frequency, and Power Factor (PF) of the system. The presence of inter and sub harmonics, especially in a distribution system caused by widely used non-linear power loads, rectifiers, and inverters, will distort estimation of phase/frequency and make it difficult to track the voltage, current and frequency variations. In this paper we present a new approach to identify and track the harmonics, sub-harmonics and inter-harmonics, as well as observe their impact on the power system. We propose a two-stage processing approach that consists of a subspace-based estimation method to detect and identify all harmonic components, followed by a low-complexity fast tracking algorithm to monitor frequency variations of voltage and current signals in real-time with great accuracy. The simulation results show that the proposed approach can provide highly reliable estimation and fast tracking of the harmonic components, while avoiding the impact of time variance.

© 2018 IEEE. This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.

Processing and analysis of time-varying systems and associated non-stationary signals are of great practical importance. The issue of quantifying the amount of time variance and amount of non-stationarity has gained importance recently. This work is also an effort in that direction. In particular, in this paper, we define two measures, one for the degree of time variance ( MDTV ) in the context of linear periodically time-varying systems (LPTV) and second for the degree of non-stationarity ( MDNOST ) of cyclostationary processes as they naturally arise when a wide-sense stationary (WSS) process passes through an LPTV system. While MDTV is a system property, on the other hand, MDNOST is a signal property. This paper also tries to find a relationship between the amount of time variation of a system with the amount of non-stationarity it generates. In this light, two concepts are analyzed for two different scenarios. First, we consider a system consisting of an upsampler followed by a linear time-invariant (LTI) filter and second a dual rate system (DRS). Both these systems are LPTV. We have shown that the MDTV generated by both LPTV systems is the same as the MDNOST of their output process through simulations and rigorous mathematical proofs.

Background Recent years have seen an increased interest in electrohysterogram (EHG) signals as a means to evaluate the synchronization of uterine contractions. Several studies have pointed out that the quality of signal processing – and hence the interpretation of measurement results – is affected significantly by the choice of measurement technique and the presence of non-stationary frequency content in EHG signals. To our knowledge, the effect of time variance on the quality of EHG signal processing has never been fully investigated. How best to process EHG signals with the goal of distinguishing labor-induced contractions from their harmless, pre-labor cousins, remains an open question. Method Our methodology is based on three pillars. The first consists of a new method for EHG preprocessing in which we apply a second-order Butterworth filter to retain only the EHG fast-wave, low-frequency band (FWL), then use a bivariate piecewise stationary pre-segmentation (bPSP) algorithm to segment the EHG signal into stationary parts. The second pillar addresses the estimation of connectivity and directionality using three methods: nonlinear correlation coefficient (h2), general synchronization (H), and Granger causality (GC). The third pillar is related to signal classification and discrimination between pregnancy and labor using receiver operating curves (ROC) and connectivity and direction maps. For this purpose, we analyze the impact of four factors on data processing efficiency: i) method of connectivity detection, ii) effect of piecewise stationary segmentation preprocessing, iii) retained frequency content and iv) electrode configuration used for EHG recording (bipolar vs. unipolar). Results Our results show that piecewise signal segmentation and filtering considerably improves classification performance and statistical significance for some connectivity methods, in particular the h2. To this end we propose a new approach (detailed below) for h2 called Filtered-Windowed (FW) h2 that better highlights the differences between pregnancy and labor in the connectivity matrix and directionality maps. Conclusions This is the first comparative study of the effects of multiple processing factors on connectivity measurement efficiency. Our results indicate that appropriate preprocessing can improve the differentiation of pregnancy and labor-induced contraction signals and may lead to innovative applications in the prevention of preterm labor.