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Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms' convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm. [ABSTRACT FROM AUTHOR]

Objective: Bioinformatics analyses have been becoming important research tools in the field of life science since 2000. New tools have been developed for the detection of allergens in food sources. This study investigated potential allergen proteins similar to the endochitinase class 1(Pers a 1) protein found in avocado using bioinformatics analysis tools. Materials and Methods: The potential allergens were examined with four different in silico tools: Clustal W for multiple sequence comparison, Swiss-MODEL for homology modeling, ProtParam for comparing protein parameters, and AlgPred for allergen prediction. Results: In silico analyses revealed high rates of similarities in protein sequences among the studied species. The highest percent identity was found in Cinnamomum micranthum f. kanehirae as 97.55 while the lowest percent identity was found in Diospyros kaki as 70.23. It is very important to note that 9 of the 10 potential allergen species investigated are plats consumed in daily life and these species were identified as allergens. According to the algorithm of BLAST search on allergen representative peptides (ARPs), ARPs were found in eight potential allergen proteins except for Prunus avium and Citrus unshiu. The ARP sequence related to Persea americana is LQLTWNYNYGAAGNSIGFNGLSNP. Conclusion: People who are allergic to avocado (Persea americana) may be advised to be careful while consuming foods such as Phoenix dactylifera, Allium sativum, Citrus unshiu, Diospyros kaki, and Spinacia oleracea. The present paper can be considered as a model study for understanding allergens in foods by using in silico tools. [ABSTRACT FROM AUTHOR]

BACKGROUND: Nowadays, workplace violence is found to be a mental health hazard and considered a crucial topic. The collaboration between robots and humans is increasing with the growth of Industry 4.0. Therefore, the first problem that must be solved is human-machine security. Ensuring the safety of human beings is one of the main aspects of human-robotic interaction. This is not just about preventing collisions within a shared space among human beings and robots; it includes all possible means of harm for an individual, from physical contact to unpleasant or dangerous psychological effects. OBJECTIVE: In this paper, Non-linear Adaptive Heuristic Mathematical Model (NAHMM) has been proposed for the prevention of workplace violence using security Human-Robot Collaboration (HRC). Human-Robot Collaboration (HRC) is an area of research with a wide range of up-demands, future scenarios, and potential economic influence. HRC is an interdisciplinary field of research that encompasses cognitive sciences, classical robotics, and psychology. RESULTS: The robot can thus make the optimal decision between actions that expose its capabilities to the human being and take the best steps given the knowledge that is currently available to the human being. Further, the ideal policy can be measured carefully under certain observability assumptions. CONCLUSION: The system is shown on a collaborative robot and is compared to a state of the art security system. The device is experimentally demonstrated. The new system is being evaluated qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

In competitive global markets, it is important to meet customer demands on multiple priorities such as price, quality, customisation and quick delivery. This paper investigates the problems of part input sequencing and scheduling in flexible manufacturing systems in a mass customisation/mass personalisation (MC/MP) environment. Both robot and machine scheduling rules using a state-dependent part input sequencing algorithm are investigated. Simulation experiments and statistical analyses are carried out. Effective rules are identified. The results show interactions between robot scheduling and machine scheduling in the MC/MP environment. Further research suggestions are provided. [ABSTRACT FROM PUBLISHER]

Wheeled mobile robots (WMRs) are often used for maintenance of round pipes or ducts, which can typically be represented as a cylindrical workspace. Working in round pipes or ducts, kinematic models of WMRs are different from those applying on a plane and thus pose significant challenges in terms of kinematic analysis and motion control. To address these challenges, the kinematic properties of WMRs in a cylindrical workspace are analyzed in this paper. First, we discuss the kinematic properties of a single wheel in a cylindrical workspace. Then, we analyze the geometric constraints of WMRs in round pipes or ducts with analytical geometry. Based on these analyses, kinematic properties of WMRs in cylindrical workspaces are discussed with screw theory. A control law based on biaxial clinometer information is proposed, and it enables the robot to move horizontally in round pipes or ducts. Finally, the motion of a single wheel purely rolling in a cylindrical workspace is simulated. Experiments using a car-like mobile robot moving in round ducts are carried out to show the feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Improving tractive effort is a very complex task in locomotive design. It requires the development of not only mechanical systems but also power systems, traction machines and traction algorithms. At the initial design stage, traction algorithms can be verified by means of a simulation approach. A simple single wheelset simulation approach is not sufficient because all locomotive dynamics are not fully taken into consideration. Given that many traction control strategies exist, the best solution is to use more advanced approaches for such studies. This paper describes the modelling of a locomotive with a bogie traction control strategy based on a co-simulation approach in order to deliver more accurate results. The simplified and advanced modelling approaches of a locomotive electric power system are compared in this paper in order to answer a fundamental question. What level of modelling complexity is necessary for the investigation of the dynamic behaviours of a heavy-haul locomotive running under traction? The simulation results obtained provide some recommendations on simulation processes and the further implementation of advanced and simplified modelling approaches. [ABSTRACT FROM AUTHOR]

The problem of determining the failure rate of a series system according to the failure rates of the component elements is solved in the literature, a reference in the field being the work [4]. There is no direct relationship between the failure rate of a parallel system and the failure rates of the component elements. In this paper we will deduce an exact relation between the failure rate of a parallel system and the failure rates of the component elements, their reliability and the reliability of the system. Starting from the exact relationship we will deduce a simpler relation of the failure rate of a parallel system by studying the asymptotic failure rate behaviour. [ABSTRACT FROM AUTHOR]

This paper deals with the stability problem of selected induction motor speed estimators in the case of the regenerating mode operation. Two effective solutions are taken into consideration: adaptive full-order observer and model reference adaptive system-type estimator based on the current flux model and stator current estimation. The two estimation algorithms are compared in detail, namely their mathematical models, theoretical stability analysis, unstable operation areas, and stability improvement methods. The speed adaptation mechanism is extended with an additional shift angle in both cases. The theoretical analysis and simulation test results are validated by wide experimental tests. [ABSTRACT FROM PUBLISHER]

Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for simulation-based inference in many fields but designing and identifying good MCMC samplers is still an open question. This paper introduces a novel MCMC algorithm, namely, Nested Adaptation MCMC. For sampling variables or blocks of variables, we use two levels of adaptation where the inner adaptation optimizes the MCMC performance within each sampler, while the outer adaptation explores the space of valid kernels to find the optimal samplers. We provide a theoretical foundation for our approach. To show the generality and usefulness of the approach, we describe a framework using only standard MCMC samplers as candidate samplers and some adaptation schemes for both inner and outer iterations. In several benchmark problems, we show that our proposed approach substantially outperforms other approaches, including an automatic blocking algorithm, in terms of MCMC efficiency and computational time. [ABSTRACT FROM AUTHOR]

In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases. [ABSTRACT FROM AUTHOR]

This paper proposes an effective multiobjective estimation of distribution algorithm (MoEDA) which solves the bi-criteria stochastic job-shop scheduling problem with the uncertainty of processing time. The MoEDA proposal minimizes the expected average makespan and the expected total tardiness within a reasonable amount of computational time. With the framework of proposed MoEDA, the probability model of the operation sequence is estimated firstly. For sampling the processing time of each operation with the Monte Carlo methods, allocation method is used to decide the operation sequence, and then the expected makespan and total tardiness of each sampling are evaluated. Subsequently, updating mechanism of the probability models is proposed according to the best solutions to obtain. Finally, for comparing with some existing algorithms by numerical experiments on the benchmark problems, we demonstrate the proposed effective estimation of distribution algorithm can obtain an acceptable solution in the aspects of schedule quality and computational efficiency. [ABSTRACT FROM AUTHOR]

Paper discusses a mathematical model describing the vibrations of a linear oscillator forced by a random series of impulses. The study aims at checking how precisely the distributions of values of the impulses forcing the vibrations of an oscillator can be differentiated. The analysis was carried out in the MatLab environment with the use of hierarchical clustering algorithms of unsupervised machine learning, for samples generated from computer simulation. The time series are non-stationary. The studies showed that high precision could be achieved in distinguishing two very similar distributions forcing the vibrations, on the basis of an analysis of the two first moments calculated from the movement. [ABSTRACT FROM AUTHOR]

Recent contributions to the financial econometrics literature exploit high-frequency (HF) data to improve models for daily asset returns. This paper proposes a new class of dynamic extreme value models that profit from HF data when estimating the tails of daily asset returns. Our realized peaks-over-threshold approach provides estimates for the tails of the time-varying conditional return distribution. An in-sample fit to the S&P 500 index returns suggests that HF data convey information on daily extreme returns beyond that included in low frequency (LF) data. Finally, out-of-sample forecasts of conditional risk measures obtained with HF measures outperform those obtained with LF measures. [ABSTRACT FROM AUTHOR]

Background: Although Pneumatic Artificial Muscle (PAM) has a promising future in rehabilitation robots, it's difficult to realize accurate position control due to its highly nonlinear properties.Objective: This paper deals with position control of PAM.Methods: To describe the hysteresis inside PAM, a polynomial based phenomenological function is developed. Based on the phenomenological model for PAM and analysis of pressure dynamics within PAM, an adaptive cascade controller is proposed. Both outer loop and inner loop employ BP Neural Network tuned PID algorithm. The outer loop is to handle high nonlinearities and unmodeled dynamics of PAM, while the inner loop is responsible for nonlinearities caused by pressure dynamics.Results: Experimental results show high tracking accuracy as compared with a convention PID controller.Conclusion: The proposed controller is effective in improving performance of PAM and will be implemented in a rehabilitation robot. [ABSTRACT FROM AUTHOR]

Magnetic tracking technology is emerging to provide an occlusion-free tracking scheme for the estimation of full pose (position and orientation) of various instruments. This brings substantial benefits for intracorporeal applications, such as for tracking of flexible or wireless endoscopic devices, and thus is significant for further computer-assisted diagnosis, interventions, and surgeries. Toward efficient magnetic tracking, a 6-D magnetic localization and orientation method is proposed in this paper. An annular permanent magnet is mounted on the exterior surface of a capsule. With a magnetic sensor array, the magnetic field can be measured and the capsule’s 3-D location and 3-D orientation information can be estimated based on proposed closed-form analytical model of annular magnet and particle swarm optimization algorithm. Magnetic dipole model and Levenberg–Marquardt algorithm are used to improve the speed and accuracy of estimation. Extensive simulation experiments show that the localization and orientation method works well with good position and orientation accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the problem of estimating the model parameters of hemodynamic model under low frequency interference, a new model parameter estimation algorithm (CSCKS) is proposed in this paper for the hemodynamic model with low frequency interference. The algorithm is based on SCKS algorithm to improve, its estimation effect is obviously better than the existing SCKS algorithm and DEM algorithm. We simulate the proposed algorithm and some other traditional algorithms to evaluate the estimation performance of CSCKS proposed in this paper under different Signal to Interference Ratio (SIR) and different integration steps, and through the simulation results, the estimated results of the algorithm were analyzed and compared. [ABSTRACT FROM AUTHOR]

The block inverse iteration and block Newton’s methods proposed by the authors of this paper for computing invariant pairs of regular linear matrix pencils are generalized to the case of regular nonlinear matrix pencils. Numerical properties of the proposed methods are demonstrated with a typical quadratic eigenproblem. [ABSTRACT FROM AUTHOR]

This paper presents a context-aware computer aided inbetweening (CACAI) technique that interpolates planar strokes to generate inbetween frames from a given set of key frames. The inbetweening is context-aware in the sense that not only the stroke’s shape but also the context (i.e., the neighborhood of a stroke) in which a stroke appears are taken into account for the stroke correspondence and interpolation. Given a pair of successive key frames, the CACAI automatically constructs the stroke correspondence between them by exploiting the context coherence between the corresponding strokes. Meanwhile, the construction algorithm is able to incorporate the user’s interaction with ease and allows the user more effective control over the correspondence process than existing stroke matching techniques. With a one-to-one stroke correspondence, the CACAI interpolates the shape and context between the corresponding strokes for the generation of intermediate frames. In the interpolation sequence, both the shape of individual strokes and the spatial layout between them are well retained such that the feature characteristics and visual appearance of the objects in the key frames can be fully preserved even when complex motions are involved in these objects. We have developed a prototype system to demonstrate the ease of use and effectiveness of the CACAI. [ABSTRACT FROM AUTHOR]

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Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor. [ABSTRACT FROM AUTHOR]

Loopholes exist in nearly every regime, which leads to invalidity and instability in the running process of a regime. In this paper, we propose a new framework based on agent method and game theory to find loopholes in a regime, which contains two players with their clear payoffs. Following the principle of "winners stay, losers change", agents make their own choices among strategies, and their choices can affect the payoffs of other players, resulting in a dynamic equilibrium that has obvious features in decision path, which can be easily found in the final figures after simulation. We further exploit typical cases, such as prisoner's dilemma, anti-coordination game, coordination game and harmony, of which the results have been mathematical proved, to illustrate the validity of our method. Finally, we make some regimes in this paper based on the classical case, "Boxed Pigs", and find the loopholes in these regimes. By utilizing the framework proposed in this paper, managers are able to detect possible problems in advance at the time when they make regimes, which helps reduce the loss of costs caused by management and improve team relations. [ABSTRACT FROM AUTHOR]

The use of RFID technology in complex and distributed environments often leads to a multi-domain RFID system, in which trust establishment among entities from heterogeneous domains without past interaction or prior agreed policy, is a challenge. The current trust management mechanisms in the literature do not meet the specific requirements in multi-domain RFID systems. Therefore, this paper analyzes the special challenges on trust management in multi-domain RFID systems, and identifies the implications and the requirements of the challenges on the solutions to the trust management of multi-domain RFID systems. A multi-domain trust management model is proposed, which provides a hierarchical trust management framework include a diversity of trust evaluation and establishment approaches. The simulation results and analysis show that the proposed method has excellent ability to deal with the trust relationships, better security, and higher accuracy rate. [ABSTRACT FROM AUTHOR]

Background: Bivariate random-effects models represent a widely accepted and recommended approach for meta-analysis of test accuracy studies. Standard likelihood methods routinely used for inference are prone to several drawbacks. Small sample size can give rise to unreliable inferential conclusions and convergence issues make the approach unappealing. This paper suggests a different methodology to address such difficulties.Methods: A SIMEX methodology is proposed. The method is a simulation-based technique originally developed as a correction strategy within the measurement error literature. It suits the meta-analysis framework as the diagnostic accuracy measures provided by each study are prone to measurement error. SIMEX can be straightforwardly adapted to cover different measurement error structures and to deal with covariates. The effortless implementation with standard software is an interesting feature of the method.Results: Extensive simulation studies highlight the improvement provided by SIMEX over likelihood approach in terms of empirical coverage probabilities of confidence intervals under different scenarios, independently of the sample size and the values of the correlation between sensitivity and specificity. A remarkable amelioration is obtained in case of deviations from the normality assumption for the random-effects distribution. From a computational point of view, the application of SIMEX is shown to be neither involved nor subject to the convergence issues affecting likelihood-based alternatives. Application of the method to a diagnostic review of the performance of transesophageal echocardiography for assessing ascending aorta atherosclerosis enables overcoming limitations of the likelihood procedure.Conclusions: The SIMEX methodology represents an interesting alternative to likelihood-based procedures for inference in meta-analysis of diagnostic accuracy studies. The approach can provide more accurate inferential conclusions, while avoiding convergence failure and numerical instabilities. The application of the method in the R programming language is possible through the code which is made available and illustrated using the real data example. [ABSTRACT FROM AUTHOR]

This paper is concerned with numerical simulations for the GBrownian motion (defined by S. Peng in Stochastic Analysis and Applications, 2007, 541-567). By the definition of the G-normal distribution, we first show that the G-Brownian motions can be simulated by solving a certain kind of Hamilton-Jacobi-Bellman (HJB) equations. Then, some finite difference methods are designed for the corresponding HJB equations. Numerical simulation results of the G-normal distribution, the G-Brownian motion, and the corresponding quadratic variation process are provided, which characterize basic properties of the G-Brownian motion. We believe that the algorithms in this work serve as a fundamental tool for future studies, e.g., for solving stochastic differential equations (SDEs)/stochastic partial differential equations (SPDEs) driven by the G-Brownian motions. [ABSTRACT FROM AUTHOR]

In the process of k-coverage of the target node, there will be a lot of data redundancy forcing the phenomenon of congestion which reduces network communication capability and coverage, and accelerates network energy consumption. Therefore, this paper proposes a novel energy balanced k-coverage control algorithm based on probability model (EBKCCA). The algorithm constructs the coverage network model by using the positional relationship between the nodes. By analyzing the network model, the coverage expected value of nodes and the minimum number of nodes in the monitoring area are given. In terms of energy consumption, this paper gives the proportion of energy conversion functions between working nodes and neighboring nodes. By using the function proportional to schedule low energy nodes, we achieve the energy balance of the whole network and optimizing network resources. The last simulation experiments indicate that this algorithm can not only improve the quality of network coverage, but also completely inhibit the rapid energy consumption of node, and extend the network lifetime. [ABSTRACT FROM AUTHOR]

When conducting recurrent event data analysis, it is common to assume that the covariate processes are observed throughout the follow-up period. In most applications, however, the values of time-varying covariates are only observed periodically rather than continuously. A popular ad-hoc approach is to carry forward the last observed covariate value until it is measured again. This simple approach, however, usually leads to biased estimation. To tackle this problem, we propose to model the covariate effect on the risk of the recurrent events through jointly modeling the recurrent event process and the longitudinal measures. Despite its popularity, estimation of the joint model with binary longitudinal measurements remains a challenge, because the standard linear mixed effects model approach is not appropriate for binary measures. In this paper, we postulate a Markov model for the binary covariate process and a random-effect proportional intensity model for the recurrent event process. We use a Markov chain Monte Carlo algorithm to estimate all the unknown parameters. The performance of the proposed estimator is evaluated via simulations. The methodology is applied to an observational study designed to evaluate the effect of Group A streptococcus on pharyngitis among school children in India. [ABSTRACT FROM AUTHOR]

Based on the proposed higher order gradient quasi-continuum model, the numerical investigations of the basic mechanical properties and deformation behaviors of human red blood cell (RBC) membrane under large deformation at room temperature (i.e., 300 K) are carried out in the present paper. The results show that RBC membrane is a nonlinear hyperelastic material. The mechanical properties of RBC membrane is dominated by isotropic nature at the stage of initial deformation, however, its anisotropic material properties emerge clearly with the loading increasing. The out-of-plane wrinkling of RBC membrane upon shear loading can be reproduced numerically. With the use of the so-called higher order Cauchy-Born rule as the kinematic description, the bending stiffness of RBC membrane can be considered conveniently. [ABSTRACT FROM AUTHOR]

As peer-to-peer networks gather and share large sets of computing resources, their aggregate energy consumption is an important problem to be addressed, given the economic and environmental impact of energy production and use. This is particularly true for file sharing peer-to-peer networks, considered the vast number of nodes participating to these systems. To address such problem, this paper proposes a peer-to-peer file sharing model organized in two logical layers. The lower layer is composed of a set of peers providing the files to be shared. The upper layer includes a set of super peers organized to form a distributed hash table, whose purpose is indexing files and peers, including their availability status. Following a sleep-and-wake approach, energy saving is achieved by letting peers cyclically switch between normal and sleep modes, where the time passed in normal mode by a peer depends on the number of files it provides. An energy-saving algorithm and an associated file search and retrieval strategy are proposed within this model. Simulation results show that more than 50% of energy can be saved using the proposed model and that more than 80% of file downloads start with no additional delay compared with a network in which all peers are always powered on. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The paper introduces methods of dynamic games for automation of ship control in the collision situation, the game control processes in marine navigation and the fundamental mathematical model of the game ship control. First, state equations, control and state constraints and then control goal function in the form of payments: the integral payment and the final one, have been defined. Multi-stage positional, and multi-step matrix, non-cooperative and cooperative, game and optimum control algorithms for a collision situation, have been presented. Te considerations have been illustrated with an exemplary computer simulation of algorithms to determine a safe own ship's trajectory in the process of passing the ships encountered in Kattegat Strait. [ABSTRACT FROM AUTHOR]

System identification provides many convenient and useful methods for engineering modelling. This study targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the coloured noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then the filtered system is decomposed into several subsystems and a filtering-based partially-coupled generalised extended stochastic gradient algorithm is developed via the coupling concept. In contrast to the multivariable generalised extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples. [ABSTRACT FROM AUTHOR]

Objective: With the overall goal to harmonize prospective effectiveness assessment of active safety systems, the specific objective of this study is to identify and evaluate sources of variation in virtual precrash simulations and to suggest topics for harmonization resulting in increased comparability and thus trustworthiness of virtual simulation-based prospective effectiveness assessment. Methods: A round-robin assessment of the effectiveness of advanced driver assistance systems was performed using an array of state-of-the-art virtual simulation tools on a set of standard test cases. The results were analyzed to examine reasons for deviations in order to identify and assess aspects that need to be harmonized and standardized. Deviations between results calculated by independent engineering teams using their own tools should be minimized if the research question is precisely formulated regarding input data, models, and postprocessing steps. Results: Two groups of sources of variations were identified; one group (mostly related to the implementation of the system under test) can be eliminated by using a more accurately formulated research question, whereas the other group highlights further harmonization needs because it addresses specific differences in simulation tool setups. Time-to-collision calculations, vehicle dynamics, especially braking behavior, and hit-point position specification were found to be the main sources of variation. Conclusions: The study identified variations that can arise from the use of different simulation setups in assessment of the effectiveness of active safety systems. The research presented is a first of its kind and provides significant input to the overall goal of harmonization by identifying specific items for standardization. Future activities aim at further specification of methods for prospective assessments of the effectiveness of active safety, which will enhance comparability and trustworthiness in this kind of studies and thus contribute to increased traffic safety. [ABSTRACT FROM AUTHOR]

An output constrained control with input delay is proposed for a central heating system. Due to the delay of signal transmission and valves opening time, an input delay is considered into the system and an auxiliary system is employed to handle this issue by converting the delayed input into a delay-free one. Moreover, to ensure the output supply water temperature within a limited range, Barrier Lyapunov algorithm is involved to achieve desired control accuracy. Finally, external disturbance and model uncertainty are incorporated into the dynamic system and neural networks (NN) are trained in an online fashion for the compensation. The stability of the control system is guaranteed through rigorous Lyapunov analysis and the excellent control performance over traditional PID control is demonstrated via numerical simulation study. [ABSTRACT FROM AUTHOR]

The leader-follower consensus problem of second-order multiagent systems with both absent velocity measurement and time delay is considered. First of all, the consensus protocol is designed by introducing an auxiliary system to compensate for the unavailability of the velocity information. Then, time delay is incorporated into the consensus protocol and two cases with, respectively, constant time delay and time-varying delay are investigated. For the case of constant time delay, Lyapunov-Razumikhin theorem is deployed to obtain the sufficient conditions that guarantee the stability of the consensus algorithm. For the case of time-varying delay, the sufficient conditions are also derived by resorting to the Lyapunov-Razumkhin theorem and linear matrix inequalities (LMIs). Various numerical simulations demonstrate the correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

Abstract The estimation of a hazardous contaminant unknown source characteristics (i.e., rate and location) in a complex urban environment using efficient inverse modelling techniques is a challenging problem that involves advanced computational fluid dynamics combined with appropriate mathematical algorithms. In this paper we further assess our recently proposed inverse source term estimation method (Efthimiou et al., 2017, Atmos. Environ., 170, 118–129) by applying it in two wind tunnel experiments simulating atmospheric flow and tracer dispersion following a stationary release in realistic urban settings, namely Michelstadt and Complex Urban Terrain Experiment (CUTE). The method appears to be robust and to predict with encouraging accuracy the source location and emission rate for both wind tunnel experiments. Highlights • Evaluation of an inverse source term identification method. • Variational, 2-steps method: (1) source coordinates (2) emission rate estimation. • Successful validation through simulations of Michelstadt and CUTE wind tunnel experiments. • The performance of the method varies with the examined scenario. [ABSTRACT FROM AUTHOR]

This paper presents the results of a model-based predictive control (MPC) design for a quadrotor aerial vehicle with a suspended load. Unlike previous works, the controller takes into account the hanging payload dynamics, the dynamics in three-dimensional space, and the vehicle rotation, achieving a good balance between fast stabilization times and small swing angles. The mathematical model is based on the Euler–Lagrange formulation and considers the dynamics of the vehicle, the cable, and the load. Then, the mathematical model is represented as an input-affine system to obtain the linear model for the control design. A constrained MPC strategy was designed and compared with an unconstrained MPC and an algorithm from the literature for the case of study. The constraints to be considered include the limits on the swing angles and the quadrotor position. The constrained control algorithm was constructed to stabilize the aerial vehicle. It aims to track a trajectory reference while attenuating the load swing, considering a maximum swing range of ± 10 ∘ . Numerical simulations were carried out to validate the control strategy. [ABSTRACT FROM AUTHOR]

The harmonic voltage issue becomes a challenge for a distributed generation system. Considering that droop control is the most common control algorithm used in the distributed system, a virtual harmonic power droop strategy which aims to mitigate the harmonic voltage is proposed in this paper. First, the conventional droop control is analyzed. Based on that concept, the virtual power algorithm is introduced. Second, the output harmonic voltage issue and the mathematical model of the inverter are presented. In addition, the second-order generalized integrator is briefly discussed. Third, taking into consideration the algorithms and models presented, a virtual harmonic power droop strategy is proposed to implement the harmonic voltage mitigation. In this algorithm, signals in fundamental frequency and harmonic frequency are separated with the help of second-order generalized integrators. Unlike the conventional voltage–current dual loop structure which is used to mitigate system harmonics, this method only needs the virtual power feedback to mitigate the harmonic voltage. Based on these features, the system's control structure is simplified. Simulation and experimental results verified the harmonic voltage mitigation ability of the proposed strategy. [ABSTRACT FROM AUTHOR]

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

Objective: Dynamic ambulance redeployment policies tend to introduce much more flexibilities in improving ambulance resource allocation by capitalizing on the definite geospatial-temporal variations in ambulance demand patterns over the time-of-the-day and day-of-the-week effects. A novel modelling framework based on the Approximate Dynamic Programming (ADP) approach leveraging on a Discrete Events Simulation (DES) model for dynamic ambulance redeployment in Singapore is proposed in this paper.Methods: The study was based on the Singapore's national Emergency Medical Services (EMS) system. Based on a dataset comprising 216,973 valid incidents over a continuous two-years study period from 1 January 2011-31 December 2012, a DES model for the EMS system was developed. An ADP model based on linear value function approximations was then evaluated using the DES model via the temporal difference (TD) learning family of algorithms. The objective of the ADP model is to derive approximate optimal dynamic redeployment policies based on the primary outcome of ambulance coverage.Results: Considering an 8min response time threshold, an estimated 5% reduction in the proportion of calls that cannot be reached within the threshold (equivalent to approximately 8000 dispatches) was observed from the computational experiments. The study also revealed that the redeployment policies which are restricted within the same operational division could potentially result in a more promising response time performance. Furthermore, the best policy involved the combination of redeploying ambulances whenever they are released from service and that of relocating ambulances that are idle in bases.Conclusion: This study demonstrated the successful application of an approximate modelling framework based on ADP that leverages upon a detailed DES model of the Singapore's EMS system to generate approximate optimal dynamic redeployment plans. Various policies and scenarios relevant to the Singapore EMS system were evaluated. [ABSTRACT FROM AUTHOR]

In the literature of modern psychometric modeling, mostly related to item response theory (IRT), the fit of model is evaluated through known indices, such as χ2, M2, and root mean square error of approximation (RMSEA) for absolute assessments as well as Akaike information criterion (AIC), consistent AIC (CAIC), and Bayesian information criterion (BIC) for relative comparisons. Recent developments show a merging trend of psychometric and machine learnings, yet there remains a gap in the model fit evaluation, specifically the use of the area under curve (AUC). This study focuses on the behaviors of AUC in fitting IRT models. Rounds of simulations were conducted to investigate AUC's appropriateness (e.g., power and Type I error rate) under various conditions. The results show that AUC possessed certain advantages under certain conditions such as high-dimensional structure with two-parameter logistic (2PL) and some three-parameter logistic (3PL) models, while disadvantages were also obvious when the true model is unidimensional. It cautions researchers about the dangers of using AUC solely in evaluating psychometric models. [ABSTRACT FROM AUTHOR]

Discrete hard fault is always tested in existing node selection methods for analog circuit diagnosis. Actually, analog component parameter changes continuously and output node voltages distribute in a continuous voltage interval. In this paper, an novel test node selection method is proposed for continuous parameter shifting (CPS) fault. Firstly, CPS faults are sampled by parameter scan simulation in a single test frequency. Collected node voltages are seen as a data set in a statistical distribution. Secondly, ambiguous faults are identified according to the independent distributions of all CPS faults. The independence of CPS fault sample is deduced by Kruskal-Wallis non-parametric testing. Then, new fault dictionaries are generated for each test node according to ambiguous interval. The proposed fault dictionary represents the mutual independence of each pair of CPS faults. Finally, as fault dictionaries are considered as connected graphs, the optimal test nodes are selected based on an improved depth first search (DFS) algorithm. The effectiveness of method is verified by testing linear and nonlinear circuits. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model of steady-state reaction-diffusion (RD) model for estimating the concentration of species is discussed. We have applied a new wavelet-based operational matrix of derivative method to obtain the approximate solutions for nonlinear RDEs. The proposed method is a powerful and easy-to-use analytical tool for linear and nonlinear problems. Some illustrative examples are given to validate our results with exact solutions. Satisfactory agreement with the exact solution is noticed. Moreover, the use of Legendre wavelets is found to be simple, accurate, efficient and requires small computation costs. [ABSTRACT FROM AUTHOR]

The time synchronous averaging (TSA) is a well-known technique used for early detection of bearing failure in electrical machines. This method is very efficient if the characteristic default frequency is perfectly known. In this article, a reduced frequency model, derived from ESPRIT algorithm, is used to provide a very accurate estimation of the fault frequency. The precision obtained on this frequency allows to apply TSA algorithm under optimal conditions. The proposed method is tested on simulated and real vibration signals for inner and outer ring faults. Finally, a fault indicator is proposed to discriminate the healthy case from the faulty one. [ABSTRACT FROM AUTHOR]

The article presents the discussion on competitiveness being a determining factor for the survival of businesses. Topics include sciences research to combine experimental approaches with theoretical modelling tools from systems theory, physics, engineering, computer science; and situation driven organizations on the search for simulation models.

We are witnessing the emergence of a new paradigm in the modeling of material structures . It stems from the digitization of manufacturing and is fueled by advances in additive manufacturing and material science. This paper strives to provide a critical examination of this new paradigm in a historical and technological context and to show that it requires non-trivial extensions and generalizations of the classical theoretical foundation and algorithmic solutions originally developed for solid modeling. Specifically, it requires new models and data-intensive representations for materials, physical behavior, and manufacturing processes across multiple scales. In particular, we argue that most computational tasks that support traditional and emerging manufacturing may be formulated systematically and addressed in terms of relations ( conversions , synthesis , change propagation updates , verification , and other harmonization activities) among four views (manifestations) of an engineered artifact: Functional , which captures the design constraints and tolerances on shape, properties, and behavior; Designed , which represents a toleranced design that satisfies these constraints; Planned , which defines a manufacturing process plan; Simulated , which models the expected outcome of the process plan; and a Real sample set of physical artifacts produced by executing the process plan on a particular manufacturing technology. Based on this formulation, we outline important directions for a research agenda aimed at enabling, driving, and amplifying further advances in digital design and manufacturing. [ABSTRACT FROM AUTHOR]

Background: Efficient mid-range wireless power transfer between transmitter and the receiver has been achieved based on the magnetic resonant coupling method. The influence of electromagnetic field on the human body due to resonant wireless power transfer system (RWPT) should be taken into account during the design process of the system.Objective: To analyze the transfer performance of the RWPT system and the change rules of the specific absorption rate (SAR) in the human head model due to the RWPT system.Methods: The circuit-field coupling method for a RWPT system with consideration of the displacement current was presented. The relationship between the spiral coil parameters and transfer performance was studied. The SAR in the human head model was calculated under two different exposure conditions.Results: A system with output power higher than 10 W at 0.2 m distance operating at a frequency of approximately 1 MHz was designed. The FEM simulation results show the peak SAR value is below the safety limit which appeared when the human head model is in front of the transmitter.Conclusion: The simulation results agreed well with the experimental results, which verified the validity of the analysis and design. [ABSTRACT FROM AUTHOR]

Problem of frequency identification performance improvement for a single-tone sinusoidal signal is solved via construction of an adaptive filters cascade. The cascade consists of adaptive band-pass filters tuned by estimates of the frequency provided by a given identification algorithm. Stability of the cascade is studied and boundedness of trajectories is proven with Lyapunov analysis under certain assumption on identification algorithm. Numerical simulations are given illustrating improved identification performance for different identification algorithms. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A new dynamic programming based parallel algorithm adapted to on-board heterogeneous computers for simulation based trajectory optimization is studied in the context of "high-performance sailing". The algorithm uses a new discrete space of continuously differentiable functions called the multi-splines as its search space representation. A basic version of the algorithm is presented in detail (pseudo-code, time and space complexity, search space auto-adaptation properties). Possible extensions of the basic algorithm are also described. The presented experimental results show that contemporary heterogeneous on-board computers can be effectively used for solving simulation based trajectory optimization problems. These computers can be considered micro high performance computing (HPC) platforms--they offer high performance while remaining energy and cost efficient. The simulation based approach can potentially give highly accurate results since the mathematical model that the simulator is built upon may be as complex as required. The approach described is applicable to many trajectory optimization problems due to its black-box represented performance measure and use of OpenCL. [ABSTRACT FROM AUTHOR]

The progress in the development of computing machinery and software leads to new opportunities in design and research of different hardware. One of the popular instruments for scientists and engineers is MATLAB/Simulink software package which allows not only making complex mathematical calculations but also various hardware simulations. Simulink library contains a wide spectrum of computer devices and modules models. However, the existing data bank content in some cases does not correspond with the software user requirements. The present paper describes the opportunities of MATLAB/Simulink resources expansion by building extra computer models based on S-Function module usage. [ABSTRACT FROM AUTHOR]