Time Instants Research Articles

Semantic segmentation of longitudinal thermal images for identification of hot and cool spots in urban areas

This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas. An infrared observatory was operated over a few months to collect thermal images of different types of buildings on the educational campus of the National University of Singapore. A subset of the thermal image dataset was used to train state-of-the-art deep learning models to segment various urban features such as buildings, vegetation, sky, and roads. It was observed that the U-Net segmentation model with ‘resnet34’ CNN backbone has the highest mIoU score of 0.99 on the test dataset, compared to other models such as DeepLabV3, DeeplabV3+, FPN, and PSPnet. The masks generated using the segmentation models were then used to extract the temperature from thermal images and correct for differences in the emissivity of various urban features. Further, various statistical measures of the temperature extracted using the predicted segmentation masks are shown to closely match the temperature extracted using the ground truth masks. Finally, the masks were used to identify hot and cool spots in the urban feature at various instances of time. This forms one of the very few studies demonstrating the automated analysis of thermal images, which can be of potential use to urban planners for devising mitigation strategies for reducing the urban heat island (UHI) effect, improving building energy efficiency, and maximizing outdoor thermal comfort.

On a Positional Control Problem for a Nonlinear Equation with Distributed Parameters

We consider a guaranteed control problem for a nonlinear distributed equation of diffusion type. The problem is essentially to construct a feedback control algorithm ensuring that the solution of a given equation tracks the solution of a similar equation subjected to an unknown disturbance. The case in which a discontinuous unbounded function can be a feasible disturbance is studied. We solve the problem under conditions of inaccurate measurement of solutions of each of the equations at discrete instants of time and indicate a solution algorithm robust under information noise and calculation errors.

Validation of an algorithm to assess regular and irregular gait using inertial sensors in healthy and stroke individuals.

Studies using inertial measurement units (IMUs) for gait assessment have shown promising results regarding accuracy of gait event detection and spatiotemporal parameters. However, performance of such algorithms is challenged in irregular walking patterns, such as in individuals with gait deficits. Based on the literature, we developed an algorithm to detect initial contact (IC) and terminal contact (TC) and calculate spatiotemporal gait parameters. We evaluated the validity of this algorithm for regular and irregular gait patterns against a 3D optical motion capture system (OMCS). Twenty healthy participants (aged 59±12 years) and 10 people in the chronic phase after stroke (aged 61±11 years) were equipped with 4 IMUs: on both feet, sternum and lower back (MTw Awinda, Xsens) and 26 reflective makers. Participants walked on an instrumented treadmill for 2 minutes (i) with their preferred stride lengths and (ii) once with irregular stride lengths (±20% deviation) induced by light projected stepping stones. Accuracy of the algorithm was evaluated on stride-by-stride agreement of IC, TC, stride time, length and velocity with OMCS. Bland-Altman-like plots were made for the spatiotemporal parameters, while differences in detection of IC and TC time instances were shown in histogram plots. Performance of the algorithm was compared between regular and irregular gait with a linear mixed model. This was done by comparing the performance in healthy participants in the regular vs irregular walking condition, and by comparing the agreement in healthy participants with stroke participants in the regular walking condition. For each condition at least 1,500 strides were included for analysis. Compared to OMCS, IMU-based IC detection in both groups and condition was on average 9-17 (SD ranging from 7 to 35) ms, while IMU-based TC was on average 15-24 (SD ranging from 12 to 35) ms earlier. When comparing regular and irregular gait in healthy participants, the difference between methods was 2.5 ms higher for IC, 3.4 ms lower for TC, 0.3 cm lower for stride length, and 0.4 cm/s higher for stride velocity in the irregular walking condition. No difference was found on stride time. When comparing the differences between methods between healthy and stroke participants, the difference between methods was 7.6 ms lower for IC, 3.8 cm lower for stride length, and 3.4 cm/s lower for stride velocity in stroke participants. No differences were found on differences between methods on TC detection and stride time between stroke and healthy participants. Small irrelevant differences were found on gait event detection and spatiotemporal parameters due to irregular walking by imposing irregular stride lengths or pathological (stroke) gait. Furthermore, IMUs seem equally good compared to OMCS to assess gait variability based on stride time, but less accurate based on stride length.

Assist-As-Needed rehabilitation using velocity field for upper limb exoskeleton

Assist-As-Needed (AAN) rehabilitation in related works typically limits subjects to perform rehabilitation tasks with particular time instants. This paper proposes a velocity field based on an active-assistive control system to solve this issue. The active intention of motion of the subjects are extracted via a Kalman filter constructed on the basis of an interactive torque observer. Then, given a task motion pattern devised to provide subjects the time-decoupled assistance, our system can first automatically produce a position-coupled velocity field. Afterwards, a method is proposed to integrate the active and assistive motion based on the subjects’ performance and involvement so that eventually they will actively perform the task in a more accurate manner. The stability of the proposed system is verified by Lyapunov analysis. The experiment results show that the execution time and the subjects’ exertion are reduced when performing given tasks compared with the related work. To sum up, the hereby developed system not only can maintain the subjects’ active intention but also can assist them to run the task accurately.

A data-driven epidemic model with human mobility and vaccination protection for COVID-19 prediction

Epidemiological models allow for quantifying the dynamic characteristics of large-scale outbreaks. However, capturing detailed and accurate epidemiological information often requires consideration of multiple kinetic mechanisms and parameters. Due to the uncertainty of pandemic evolution, such as pathogen variation, host immune response and changes in mitigation strategies, the parameter evaluation and state prediction of complex epidemiological models are challenging. Here, we develop a data-driven epidemic model with a generalized SEIR mechanistic structure that includes new compartments, human mobility and vaccination protection. To address the issue of model complexity, we embed the epidemiological model dynamics into physics-informed neural networks (PINN), taking the observed series of time instances as direct input of the network to simultaneously infer unknown parameters and unobserved dynamics of the underlying model. Using actual data during the COVID-19 outbreak in Australia, Israel, and Switzerland, our model framework demonstrates satisfactory performance in multi-step ahead predictions compared to several benchmark models. Moreover, our model infers time-varying parameters such as transmission rates, hospitalization ratios, and effective reproduction numbers, as well as calculates the latent period and asymptomatic infection count, which are typically unreported in public data. Finally, we employ the proposed data-driven model to analyze the impact of different mitigation strategies on COVID-19.

Averaging for switched impulsive systems with pulse width modulation

Linear switched impulsive systems (SIS) are characterized by ordinary differential equations as modes dynamics and state jumps at the switching time instants. The presence of possible jumps in the state makes nontrivial the application of classical averaging techniques. In this paper we consider SIS with pulse width modulation (PWM) and we propose an averaged model whose solution approximates the moving average of the SIS solution with an error which decreases with the multiple of the switching period and by decreasing the PWM period. The averaging result requires milder assumptions on the system matrices with respect to those needed by the previous averaging techniques for SIS. The interest of the proposed model is strengthened by the fact that it reduces to the classical averaged model for PWM systems when there are no jumps in the state. The theoretical results are verified through simulations obtained by considering a switched capacitor electrical circuit.

Distributed estimation and control over mobile sensor networks with jointly connected topology: Event-triggered approach

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples.

How to Make the Stress Relaxation Experiment for Polymers More Informative.

Different viscoelastic models and characteristics are commonly used to describe, analyze, compare and improve the mechanical properties of polymers. A time-dependent linear relaxation modulus next to frequency-domain storage and loss moduli are the basic rheological material functions of polymers. The exponential Maxwell model and the exponential stretched Kohlrausch-Williams-Watts model are, probably, the most known linear rheological models of polymers. There are different identification methods for such models, some of which are dedicated to specific models, while others are general in nature. However, the identification result, i.e., the best model, always depends on the specific experimental data on the basis of which it was determined. When the rheological stress relaxation test is performed, the data are composed of the sampling instants used in the test and on the measurements of the relaxation modulus of the real material. To build a relaxation modulus model that does not depend on sampling instants is a fundamental concern. The problem of weighted least-squares approximation of the real relaxation modulus is discussed when only the noise-corrupted time-measurements of the relaxation modulus are accessible for identification. A wide class of models, that are continuous, differentiable and Lipschitz with respect to parameters, is considered for the relaxation modulus approximation. The main results concern the models that are selected asymptotically as the number of measurements tends to infinity. It is shown that even when the true relaxation modulus description is completely unknown, the approximate optimal model parameters can be derived from the measurement data that are obtained for sampling instants that are selected randomly due to the appropriate randomization introduced whenever certain conditions regarding the adopted class of models are satisfied. It is shown that the most commonly used stress relaxation models, the Maxwell and Kohlrausch-Williams-Watts models, satisfy these conditions. Since the practical problems of the identification of relaxation modulus models are usually ill posed, Tikhonov regularization is applied to guarantee the stability of the regularized solutions. The approximate optimal model is a strongly consistent estimate of the regularized model that is optimal in the sense of the deterministic integral weighted square error. An identification algorithm leading to the best regularized model is presented. The stochastic-type convergence analysis is conducted for noise-corrupted relaxation modulus measurements, and the exponential convergence rate is proved. Numerical studies for different models of the relaxation modulus used in the polymer rheology are presented for the material described by a bimodal Gauss-like relaxation spectrum. Numerical studies have shown that if appropriate randomization is introduced in the selection of sampling instants, then optimal regularized models of the relaxation modulus being asymptotically independent of these time instants can be recovered from the stress relaxation experiment data. The robustness of the identification algorithm to measurement noises was demonstrated both by analytical and numerical analyses.

Sequential Anomaly Detection Under Sampling Constraints

The problem of sequential anomaly detection is considered, where multiple data sources are monitored in real time and the goal is to identify the “anomalous” ones among them, when it is not possible to sample all sources at all times. A detection scheme in this context requires specifying not only when to stop sampling and which sources to identify as anomalous upon stopping, but also which sources to sample at each time instance until stopping. A novel formulation for this problem is proposed, in which the number of anomalous sources is not necessarily known in advance and the number of sampled sources per time instance is not necessarily fixed. Instead, an arbitrary lower bound and an arbitrary upper bound are assumed on the number of anomalous sources, and the fraction of the expected number of samples over the expected time until stopping is required to not exceed an arbitrary, user-specified level. In addition to this sampling constraint, the probabilities of at least one false alarm and at least one missed detection are controlled below user-specified tolerance levels. A general criterion is established for a policy to achieve the minimum expected time until stopping to a first-order asymptotic approximation as the two familywise error rates go to zero. Moreover, the asymptotic optimality is established of a family of policies that sample each source at each time instance with a probability that depends on past observations only through the current estimate of the subset of anomalous sources. This family includes, in particular, a novel policy that requires minimal computation under any setup of the problem.

Solute imbibition in paper strip: Pore-scale insights into the concentration-dependent permeability

Capillary wicking in a thicker gel blot microfluidics paper has been investigated through a combination of an analytical framework, experiments, and numerical simulations. The primary objectives of this work are to investigate the concentration-dependent wicking process inside thicker microfluidic paper and to estimate the concentration-dependent permeability using both theoretical models and experimental data. An additional goal is to estimate the parameters for saturation-dependent flow modeling in thicker microfluidic paper. To comprehend the wicking phenomenon on thicker gel blot paper, a series of experiments employing aqueous food dye solutions at varying concentrations has been conducted. In order to calculate the temporal wicking length analytically, the Brinkman-extended Darcy equation is implemented. By modifying the permeability expression for a simple rectangular unidirectional fiber cell and pure liquid, the expression of effective permeability for the analytical framework has also been introduced. The concentrations of the food dye solutions appear to have a substantial influence on the wicking phenomenon. Effective permeability and wicking length have been found to follow a decreasing pattern at lower concentrations while both increase at higher values. Intriguingly, employing a microfluidics paper with a relatively greater thickness facilitates the visualization of the fluid front. This phenomenon is identified by the formation of an acute angle at intermediate time instants, while the fluid front angle assumes an angle nearly ∼90° during smaller and higher time instants. In order to evaluate the saturation-dependent capillary pressure and permeability, the empirical correlation of concentration-dependent Brooks and Corey parameters is additionally determined experimentally. These parameters are subsequently employed in numerical simulations to illustrate the saturation-dependent flow field using Richards’ equation. Furthermore, numerical simulations based on these estimated model parameters have been conducted, and it turns out that the saturation field has an excellent agreement with the experimental results. The results of the current study can be used to design low-cost paper-based diagnostic devices for usage in healthcare and environmental applications.

HyNTP: A Distributed Hybrid Algorithm for Time Synchronization

This paper presents a distributed hybrid algorithm that synchronizes the time and rate of a set of clocks connected over a network. Clock measurements of the nodes are given at aperiodic time instants and the controller at each node uses these measurements to achieve synchronization. Due to the continuous and impulsive nature of the clocks and the network, we introduce a hybrid system model to effectively capture the dynamics of the system and the proposed hybrid algorithm. Moreover, the hybrid algorithm allows each agent to estimate the skew of its internal clock in order to allow for synchronization to a common timer rate. We provide sufficient conditions guaranteeing synchronization of the timers, exponentially fast, with robustness. Numerical results illustrate the synchronization property induced by the proposed algorithm as well as its performance against comparable algorithms from the literature.

An Inference Mechanism for Proactive Service Migration at the Edge

The coexistence of the Internet of Things and Edge Computing aims to offer a processing infrastructure close to end users that will improve the performance of applications and limit the latency in the provision of services. Services are adopted to assist in the execution of tasks imposed by the requests of end users/applications. The implementation of an effective framework for services management in the distributed edge nodes is necessary to achieve the aforementioned goals. The discussed framework ought to address the trade-off between overheads related to services migration/replication and data transmission. In this paper, we propose a proactive statistical model for allocating the available services upon the observed demand and supporting edge nodes to decide when and where it is necessary to migrate/replicate them. Our aim is to place services at locations where an increased demand is observed, however, under the uncertainty about the future evolution of the incoming requests. We elaborate on the evaluation of the proposed model and provide a comparative assessment with relevant schemes adopting real datasets. Our experimental validation demonstrates that our approach reinforces the heterogeneous engaged edge nodes to correctly infer the time instance and the location when/where services should be migrated/replicated to meet the dynamics of their demand. The interesting is that the proposed model achieves encouraging outcomes when it is adopted to cope with the mobility of users.

Model-Free Predictive Current Control of Five-Phase PMSM Drives

Model predictive control is highly dependent on accurate models and the parameters of electric motor drives. Multiphase permanent magnet synchronous motors (PMSMs) contain nonlinear parameters and mutual cross-coupling dynamics, resulting in challenges in modeling and parameter acquisition. To lessen the parameter dependence of current predictions, a model-free predictive current control (MFPCC) strategy based on an ultra-local model and motor outputs is proposed for five-phase PMSM drives. The ultra-local model is constructed according to the differential equation of current. The inherent relation between the parameters in the predictive current model and the ultra-local model is analyzed in detail. The unknowns of the ultra-local model are estimated using the motor current and voltage at different time instants without requiring motor parameters or observers. Moreover, space vector modulation technology is employed to minimize the voltage tracking error. Finally, simulations and experiments are conducted to verify the effectiveness of the MFPCC with space vector modulation. The results confirm that the proposed method can effectively eliminate the impact of motor parameters and improve steady-state performance. Moreover, this control strategy demonstrates good robustness against load variations.

A novel hybrid time-variant reliability analysis method through approximating bound-most-probable point trajectory

In the engineering field, time-variant reliability analysis (TRA) is used to measure the safety level of structures under time-variant uncertainties. Lacking in information or data, some uncertainties cannot be directly quantified as stochastic models, which results in the simultaneous existence of aleatory and epistemic uncertainties in most of problems. In general, stochastic and interval models are respectively used to describe aleatory and epistemic uncertainties. For the hybrid TRA (HTRA) problem considering the two kinds of uncertainties, the existing method needs to excessively evaluate the original time-variant limit-state function, which is too expensive for engineering problems. To address this issue, we propose the concept of bound-most-probable point trajectory (BMPPT) which can be used to construct the approximation of the limit-state hyper-surface. Moreover, we develop a HTRA method based on approximating BMPPT which can further improve the computational efficiency. First, based on time discretization, we transform the HTRA problem into a time-independent series-system reliability problem which can be solved by searching the bound-most-probable point (BMPP) at all discrete time instants. Then, with the BMPPT, the lower and upper bounds of the time-variant limit-state function are linearized into two Gaussian processes. Finally, the expansion optimal linear estimation and Monte Carlo simulation are performed to estimate the time-variant reliability. To avoid excessive BMPP searches, the active learning Kriging is used to approximate the BMPPT. Two numerical examples including a cantilever beam, and a 10-bar truss, and two engineering applications of the solid rocket engine shell and the rocket inter-stage structure are investigated, and the results reveal that the proposed method can solve the HTRA problems with high accuracy and efficiency.

Latching control: A wave energy converter inspired vibration control strategy

Latching control is a phase optimization control mechanism for improving the energy conversion efficiency of a wave energy converter (WEC). This paper, for the first time, extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed in this paper by integrating conventional tuned mass dampers (TMD) and the latching mechanism. First, the theoretical models of WEC and TMD are compared. Second, the mathematical model of an LMD installed on a single-degree-of-freedom structure is established, and three latching control strategies with different latching time instants, latching durations, unlatching time instants, and required information feedback are tested. Subsequently, the performance of the LMD is evaluated through the comparison with the detuned and optimally tuned TMDs. The characteristics of the latching control force are analyzed in terms of phase, component, energy dissipation, and damping enhancement effect. Lastly, the feasibility and effectiveness of the proposed LMD with three different latching strategies are proven. The latching control can reduce the stiffness component and increase the damping component of the control force, thereby optimizing the phase lag between the control force and the structural response. The proposed latching mechanism and strategies for vibration control can provide an innovative solution to improve the effectiveness of high-frequency detuned dampers, facilitate the design of long-period dampers, and develop adaptive semi-active dampers in the future.

Computing differential operators of the particle velocity in moving particle clouds using tessellations

We propose finite-time measures to compute the divergence, the curl and the velocity gradient tensor of the point particle velocity for two- and three-dimensional moving particle clouds. For this purpose, a tessellation of the particle positions is performed to assign a volume to each particle. We introduce a modified Voronoi tessellation which overcomes some drawbacks of the classical construction. Instead of the circumcenter we use the center of gravity of the Delaunay cell for defining the vertices. Considering then two subsequent time instants, the dynamics of the volume can be assessed. Determining the volume change of tessellation cells yields the divergence of the particle velocity. Reorganizing the various velocity coefficients allows computing the curl and even the velocity gradient tensor. The helicity of particle velocity can be likewise computed and swirling motion of particle clouds can be quantified. First we assess the numerical accuracy for randomly distributed particles. We find a strong Pearson correlation between the divergence computed with the modified tessellation, and the exact value. Moreover, we show that the proposed method converges with first order in space and time in two and three dimensions. Then we consider particles advected with random velocity fields with imposed power-law energy spectra. We study the number of particles necessary to guarantee a given precision. Finally, applications to fluid and inertial particles advected in three-dimensional fully developed isotropic turbulence show the utility of the approach for real world applications to quantify self-organization in particle clouds and their vortical or even swirling motion.

A barrier method for contact avoiding particles in Stokes flow

Rigid particles in a Stokesian fluid experience an increasingly strong lubrication resistance as particle gaps narrow. Numerically, resolving these lubrication forces comes at an intractably large cost, even for moderate system sizes. Hence, it can typically not be guaranteed that artificial particle collisions and overlaps do not occur in a dynamic simulation, independently of the choice of method to solve the Stokes equations. In this work, the potentially large set of non-overlap constraints, in terms of the Euclidean distance between boundary points on disjoint particles, are efficiently represented via a barrier energy. We solve for the minimum magnitudes of repelling contact forces and torques between any particle pair in contact to correct for overlaps by enforcing a zero barrier energy at the next time level, given a contact-free configuration at a previous instance in time. Robustness for the method is illustrated using a multiblob method to solve the mobility problem in Stokes flow, applied to suspensions of spheres, rods and boomerang shaped particles. Collision free configurations are obtained at all instances in time, and considerably larger time-steps can be taken than without the technique. The effect of the contact forces on the collective order of a set of rods in a background flow that naturally promote particle interactions is also illustrated.

Optical thickness measurement of occluded samples by lens-less Fourier transform digital holography, thermal loading, and machine learning.

Thickness measurements of objects, especially transparent and semi-transparent objects, are essential for their characterization and identification. However, in the case of occluded objects, the optical thickness determination becomes difficult, and an indirect way must be devised. Thermal loading of the objects changes their opto-thermal properties, which will be reflected as a change in their optical thickness. The key to quantifying such occluded objects lies in collecting these opto-thermal signatures. This could be achieved by imaging the changes occurring to a probe wavefront passing through the object while it is being thermally loaded. Digital holographic interferometry is an ideal tool for observing phase changes, as it can be used to compare wavefronts recorded at different instances of time. Lens-less Fourier transform digital holographic imaging provides the phase information from a single Fourier transform of the recorded hologram and can be used to quantify occluded phase objects. Here we describe a technique for the measurement of change in optical thickness of thermally loaded occluded phase samples using lens-less Fourier transform digital holography and machine learning. The advantage of the proposed technique is that it is a single shot, lens-less imaging modality for quasi-real-time quantification of phase samples behind thin occlusions.

A Subdomain uncertainty-guided Kriging method with optimized feasibility metric for time-dependent reliability analysis

Active learning strategy combined with single-loop Kriging method has attracted much attention for Time-dependent Reliability Analysis (TRA). Large sample pool across all time instants is required to comprehensively cover the failure surface. Identifying training samples with most existing methods requires assessing the response of the entire sample pool, leading to high time costs. Similarly, to assist in identifying critical sampling regions, evaluating failure probability with a large sample pool in each iteration is also time-consuming. Additionally, higher-order uncertainty information is typically ignored, impairing the sampling efficiency. To address these issues, a Subdomain Uncertainty-guided Kriging (SUK) Method is proposed. Stochastic processes are first equivalently converted to random variables. By simultaneously sampling random variables and time parameter, an equivalent sample pool with significantly reduced size is generated for adaptive sampling. With a concise subdomain uncertainty assessment function, critical sampling region, i.e. sensitive subdomain, is distinguished efficiently. By comprehensively considering both the expectation and standard deviation of feasibility function, a novel Optimized Feasibility Metric (OFM) is then proposed for active learning. The proportion of misclassified samples is analytically deduced as stopping criterion. Finally, comparison results on four examples demonstrate the good performances of the proposed subdomain uncertainty-guided Kriging method.

Using demand response to improve power system small-signal stability

With the increase of uncertain and intermittent renewable energy supply on the grid, the power system has become more vulnerable to instability. In this paper, we develop a demand response strategy to improve power system small-signal stability. We pose the problem as an optimization problem wherein the total demand-responsive load is held constant at each time instance but shifted between different buses to improve small-signal stability, which is measured by small-signal stability metrics that are functions of subsets of the system’s eigenvalues, such as the smallest damping ratio. To solve the problem, we use iterative linear programming and generalized eigenvalue sensitivities. We demonstrate the approach via a case study that uses the IEEE 14-bus system. Our results show that shifting the load between buses, can improve a small-signal stability margin. We explore the use of models of different fidelity and find that it is important to include models of the automatic voltage regulators and power system stabilizers. In addition, we show that load shifting can achieve similar improvements to generation shifting and better improvement than simply tuning power system stabilizers.