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The aim of this paper is to establish the existence, uniqueness and asymptotic behaviour of a strong regular solution for a class of nonlinear equations of reaction–diffusion nonlocal type with moving boundaries: u t - a ∫ Ω t u ( x , t ) dx u xx = f ( x , t ) , ( x , t ) ∈ Q t , u ( α ( t ) , t ) = u ( β ( t ) , t ) = 0 , t > 0 , u ( x , 0 ) = u 0 ( x ) , x ∈ Ω 0 = ] α ( 0 ) , β ( 0 ) [ , where Q t is a bounded non-cylindrical domain defined by Q t = ( x , t ) ∈ R 2 : α ( t ) < x < β ( t ) , for all 0 < t < T . Moreover, we study the properties of the solution and implement a numerical algorithm based on the Moving Finite Element Method (MFEM) with polynomial approximations of any degree, to solve this class of problems. Some numerical tests are investigated to evaluate the performance of our Matlab code based on the MFEM and illustrate the exponential decay of the solution. [ABSTRACT FROM AUTHOR]

Abstract: The bin-normalized frequency-domain block LMS (FBLMS) algorithm has low computational burden and potential fast convergence; however, it suffers from a biased steady-state solution when the reference signal lags behind the desired signal or the adaptive filter is of insufficient length. This paper proposes a unified framework for the FBLMS algorithm, which can be used to comprehensively analyze its steady-state behavior. Furthermore, a modified FBLMS algorithm with guaranteed optimal steady-state performance is proposed based on the framework. Simulations are carried out to demonstrate the benefit of the proposed algorithm. [Copyright &y& Elsevier]

This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.). [ABSTRACT FROM AUTHOR]

This paper presents an approach for verifying the correctness of the feasibility theorem on the deadline driven scheduler (DDS) with the axiom system of Propositional Projection Temporal Logic (PPTL). To do so, the deadline driven scheduling algorithm is modeled by an MSVL (Modeling, Simulation and Verification Language) program and the feasibility theorem is formulated by PPTL formulas with two parts: a necessary part and a sufficient part. Then, several lemmas are abstracted and proved by means of the axiom system of PPTL. With the help of the lemmas, two parts of the theorem are deduced respectively. This case study convinces us that some real-time properties of systems can be formally verified by theorem proving using the axiom system of PPTL. [ABSTRACT FROM AUTHOR]

Daylight is a restricted resource in urban contexts. Rooms situated in an urban context often have a significant proportion of the sky and the sun blocked out by the urban building mass. The reduced direct daylight potential makes daylight reflected from outdoor surfaces an important daylight source to the room. It is therefore important to be able to take into account the daylight reflections from the urban environment in early design stage. This paper describes a simplified method that uses a combination of ray-tracing, the luminous exitance method and the concept of the urban canyon to represent daylight levels in rooms situated in an urban setting. The method is implemented in the daylight algorithm of an existing building simulation tool capable of making rapid integrated daylight and thermal simulation. Comparisons with the more sophisticated lighting tool Radiance show a maximum relative error of 17% but it is often much lower. The accuracy of this approach is therefore considered to be adequate for the early stages of the building design process. The results from integrated daylight and thermal simulations are presented to illustrate how the tool can be used to investigate the impact of urban canyon parameters on indoor environment and energy performance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of control design and implementation for a nonlinear marine vessel manoeuvring model. The authors consider a highly nonlinear vessel 4 DOF model as the basis of this work. The control algorithm here proposed consists of a combination of two methodologies: (i) an iteration technique that approximates the original nonlinear model by a sequence of linear time varying equations whose solution converge to the solution of the original nonlinear problem and (ii) a lead compensation design in which for each of the iterated linear time varying system generated, the controller is optimized at each time on the interval for better tracking performance. The control designed for the last iteration is then applied to the original nonlinear problem. Simulations and results here presented show a good performance of the approximation methodology and also an accurate tracking for certain manoeuvring cases under the control of the designed lead controller. The main characteristic of the nonlinear system's response is the reduction of the settling time and the elimination of the steady state error and overshoot. [ABSTRACT FROM AUTHOR]

In this paper the total focusing method, the so called gold standard in classical beamforming, is compared with the widely used time-reversal MUSIC super resolution technique in terms of its ability to resolve closely spaced scatterers in a solid. The algorithms are tested with simulated and experimental array data, each containing different noise levels. The performance of the algorithms is evaluated in terms of lateral resolution and sensitivity to noise. It is shown that for the weak noise situation (SNR>20dB), time-reversal MUSIC provides significantly enhanced lateral resolution when compared to the total focusing method, breaking the diffraction limit. However, for higher noise levels, the total focusing method is shown to be robust, whilst the performance of time-reversal MUSIC is degraded. The influence of multiple scattering on the imaging algorithms is also investigated and shown to be small. [ABSTRACT FROM AUTHOR]

Trans-disciplinary hydrologic models oriented toward practical questions must be accompanied by accurate parameterization techniques. This paper describes the effects of different choices in the integration of various data sources on outcomes of the model Process-based Adaptive Watershed Simulator coupled with the Community Land Model (PAWS + CLM). Using our Hierarchical Stochastic Selection method, the represented land use percentages are much closer to the raw dataset, and lead to a 26% difference in carbon flux from that of the traditional dominant classes method. River bed elevations extracted using a novel algorithm agree well with the groundwater table and significantly increase baseflow contribution to streams relative to a coarse-DEM-based model. The inclusion of additional information in the soil pedotransfer functions drastically shifts ET, Net Primary Production and recharge. These results indicate that judicious treatment of input data has strong impacts on hydrologic and ecosystem fluxes. We emphasize the need to report details of data integration procedures. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned about the distributed consensus problem for general sampled-data control multi-agent systems without or with a leader under control inputs missing over some sampling intervals. For these two cases, a distributed adaptive dynamical consensus algorithm is proposed based only on the relative information of network structure. Under the assumption that the dynamical network contains a directed spanning tree, some sufficient consensus conditions are induced under a switched model. These obtained consensus criteria are independent of dynamical network topology, namely, they are valid for both undirected and directed topology. Besides, they do not rely on the spectra and the eigenvalue of Laplacian matrix. Furthermore, some quantitative relations included in dynamical systems, such as sampling period, admissible control inputs missing rate and exponential reaching rate of the consensus performance, are established. Finally, simulation examples are given to show the effectiveness of the proposed results. [Copyright &y& Elsevier]

Abstract: Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced to initialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjust the network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods. [Copyright &y& Elsevier]

Abstract: During the design, maintenance and decommissioning of nuclear facilities, nuclear radiation protection is an important part. In recent years, researchers have explored a lot of radiation protection approach, and some radiation protection approach have been applied in practice, such as visualization technique of radiation environment, path-planning method, robotics and etc., in these techniques the path-planning in radiation environment technology has become an important radiation protection measure. In this paper, we addressed a staff walking path-planning approach in radiation environment based on particle swarm algorithm and introduced some key technologies of path-planning in radiation environment. To obtain the optimal walking route to verify the operation of the proposed method, we carried out the simulation experiment in which dose and distance were as decision factors. The experiment results represented the probability and the effectiveness of path-planning in radiation environment based on particle swarm algorithm. [Copyright &y& Elsevier]

Wireless devices integrating more and more features in ever-smaller packages have become integral part of everyone's daily life. These systems have seen a continuous push to profit from digital signal processing techniques, in which signals are no longer traditional continuous amplitude and/or phase-modulated carriers, but became pulsed waveforms where the amplitude and/or phase are coded in some on–off digital scheme. This on–off scenario has been addressing new challenges to the circuit-level simulation of such systems. Having this in mind this paper aims to propose an innovative time–frequency simulation technique based on a switch mode algorithm, which is specially conceived for the efficient numerical simulation of RF circuits whose stimuli are intermittently turned on and off for unknown periods of time. At present, to simulate this category of circuits commercial tools have no choice than to make use of full conventional SPICE algorithms. This paper suggests an alternative way, which can result in significant speedups, without perceptible loss in accuracy. [ABSTRACT FROM AUTHOR]

• Discrete event simulation models for improving healthcare processes performances. • Process mining techniques: gaining insights into business processes functioning. • Timely approaches required for modeling hospital processes from event logs. • Essence of process mining: a strongly connection between logs and process models. The purpose of this paper is to show how the knowledge of Process Mining techniques can provide a robust premise to build a Discrete Event Simulation (DES) model of a healthcare process. In order to analyze some specific processes of an ophthalmology ward of a large Italian hospital, ProM6 framework was implemented, which supports process mining techniques in form of plug-ins; the plug-ins process data, in form of Event Log, in order to extract information about the process. The DES model based on such information was run via a commercial tool, Simul8, which allows building sophisticated process models. A timely algorithm was then deployed to adapt the ProM6 information to the DES model. Following, a conformance analysis was conducted by comparing the original Event Log, and the Simulation tool data, taking into account the main case-related model attributes (routing probabilities profile, time perspective, and resources perspective). The paper aims at developing the line of inquiry for what concerns the deployment of approaches to set forth the link between Process mining and Simulation modeling. [ABSTRACT FROM AUTHOR]

• This paper summarizes several variable kernel width maximum correntropy criterion (MCC) algorithms, and discusses the basic principles of these algorithms. A close relationship between this algorithms and LMS algorithm is analyzed and established. • Then a new statistics variable kernel width MCC algorithm is proposed (SVKW-MCC) on the basis of previous variable kernel width algorithms. • SVKW-MCC algorithm is proposed to address the shortcomings of some well-known variable kernel width algorithm. The SVKW-MCC algorithm use statistics method to compute the kernel width and eliminates the abnormal errors caused by impulsive noise by statistical method. • The stability and steady-state mean-square performance of the proposed algorithm is analyzed and verified by experiments. Since the maximum correntropy criterion (MCC) algorithm with a constant kernel width leads to the trade-off problem between the convergence rate and steady-state misalignment, various adaptive kernel width MCC algorithms were derived to solve this problem. However, the superior performances of these algorithms depend mainly on specific data range, or have complicated calculation and parameter setting. Thus, this paper proposes a statistics variable kernel width MCC (SVKW-MCC) algorithm to overcome these problems. Specifically, the proposed algorithm calculates the mean and variances of the errors signal, and then the proposed algorithm removes these data that significantly deviate from the mean value of errors signal, moreover, the new mean and variance are recalculated after removing these abnormal data, subsequently, the new kernel width is calculated by the new variance and mean. Simulation results in system identification and echo cancellation scenarios show that the proposed algorithm outperforms the existing variable kernel width methods. Moreover, the stability and steady-state mean-square performance of the proposed algorithm is analyzed and verified by experiments. More importantly, the new method involves no extra free parameters and does not depend on the specific application data range, so the proposed algorithm has a very good application prospect. [ABSTRACT FROM AUTHOR]

Abstract: In order to simulate the wheel wear behaviour of freight cars, the rail vehicle's multi-body dynamical model and wheel wear simulation programme were combined as a wheel wear simulation tool in this paper. Multi-body dynamical models of China's heavy haul freight cars such as the C80, C80H, C70 and C70H, were built in SIMPACK software and the track system was built based on the China's Ring-line. For the wheel/rail rolling contact, the semi-Hertzian method and FASTSIM algorithm were applied to solve the normal and tangential problem respectively. The shapes of worn wheel profiles in the simulation agree well with the field measurements, but wear rates from the simulation are larger than those found in field measurements. This discrepancy is contributed to two factors. The first factor is the wheel material. The CL60 wheel steel used in China's freight car, is harder than the BS11 wheel steel used in Zobory's wheel/rail wear experiments. The second factor is the impact of a material's elastic shear deformation on the slip velocity in contact patch. This impact will increase the wear rate and was considered in the present wear simulation. [Copyright &y& Elsevier]

Abstract: This paper presents a stochastic model for the individual-activation-factor proportionate normalized least-mean-square (IAF-PNLMS) adaptive algorithm operating under correlated Gaussian input data. The proposed approach uses the contragredient transformation to obtain an analytical solution for the normalized autocorrelation-like matrices arising from the model development. Model expressions describing the learning curve and the second-order moment of the weight-error vector for the IAF-PNLMS algorithm are derived taking into account the time-varying characteristic of the gain distribution matrix. As a consequence, the obtained model predicts very well the algorithm behavior for both transient and steady-state phases. Through simulation results, considering different operating scenarios, the accuracy of the proposed model is attested (via learning curve) for both complex- and real-valued input data. [Copyright &y& Elsevier]

• A novel robust mixed-norm algorithm with affine projection (APRMNA) is proposed. • The APRMNA based on the generalized maximum correntropy criterion (APRMNA-GMC) is proposed. • The simplified APRMNA-GMC (S-APRMNA-GMC) is proposed. In this paper, a novel adaptive filtering algorithm combining both affine projection (AP) method and robust mixed-norm algorithm (RMNA) is proposed, which is called APRMNA. The AP method has the feature of fast convergence speed under colored inputs and the RMNA exhibits stable performance against noise interference. The proposed APRMNA algorithm not only combines the advantages of both AP and RMNA but also utilizes the ℓ 2 -norm constraint on the weight vector to avoid matrix inversion. Then, applying the generalized maximum correntropy (GMC) criterion to APRMNA, we also develop the APRMNA-GMC. Finally, a simplified version of the proposed APRMNA-GMC (S-APRMNA-GMC) is derived to reduce the computation complexity. Numerical simulations for system identification show that the proposed algorithms outperform other AP-type algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing provides a new infrastructure for the research of complex system simulation (CSS). However, insufficient computing resource allocation results in lower performance of a CSS application. On the other hand, excessive computing resource allocation will lead to the increase of simulation communication overhead and simulation synchronous computing. Therefore, accurate computing resource prediction is important to achieve optimal scheduling for CSS applications in the cloud environment. In this paper, a computing resource prediction approach based on ensemble learning has been proposed, which includes a cloud computing resource prediction framework and an intelligent ensemble algorithm. The framework with three–level architecture (simulation as a service, cloud computing resource predictor, and cloud computing resource pool) can provide computing resources to deal with the demands of the simulation applications. The intelligent ensemble algorithm uses an Accuracy and Relative Error-based Pruning algorithm to ensure the effective ensemble of base models (support vector machine, decision tree, and k-nearest neighbor). To improve the performance of the intelligent ensemble algorithm, a Feature Capability-based forward search Feature Selection algorithm is introduced to reduce redundancy between features. The experiments are presented to demonstrate that the intelligent ensemble algorithm can achieve higher accuracy by 4%-20% when compared with existing resource prediction models such as Regressive Ensemble Approach for Prediction, Bayesian, Linear Regression, Random Forest, and Fuzzy Neural Network. [ABSTRACT FROM AUTHOR]

• The Downhill-Simplex-Algorithm is applicable to the inverse material parameter determination • Selection of the optimization parameters influences the determined results • Adaptive Downhill-Simplex-Algorithm reduces the computational effort • Uniqueness of Johnson-Cook material model parameters is not given for machining simulations Modeling the machining process by means of simulation techniques became more and more popular within the last decades. The increasing use of simulation techniques can be attributed to the enhancing computational performance. To accurately model the machining process, different input models are required. Among them, the material model has a crucial impact on the quality of the simulated results. To determine the underlying material model parameters, an inverse procedure has been established, where the material model parameters are adjusted within a chip formation simulation until a reasonable match between simulations and experiments is achieved. However, the procedure of the inverse determination requires high computational efforts and is not robust. This paper presents a novel approach to determine material model parameters inversely from FE-machining simulations by means of the Downhill-Simplex-Algorithm. An effect study was conducted to investigate the influence of the initial simplex, the boundary conditions, and the underlying parameters of the Downhill-Simplex-Algorithm. Further, the validated procedure has been extended to consider multiple cutting conditions and an adaptive step-size has been implemented into the algorithm. The results of the procedure were validated by re-identification of process observables from the target simulations and it was aimed to re-identify the material model parameters of the target simulations. Based on the algorithm, a robust and systematical method has been developed to determine material model parameters inversely from the machining process. [ABSTRACT FROM AUTHOR]

• Frequency-adaptive vector control of a grid-connected PV system. • MSOGI-FLL synchronization algorithm. • Low-Voltage Ride-Through (LVRT) capability with improved limitation of the amplitude of the three-phase inverter currents. • Controller Hardware-in-the-Loop (CHIL) simulation technique. The low-voltage ride-through service is carried out in this paper according to the voltage profile described by the IEC 61400-21 European normative when short-duration voltage sags happen, and some instantaneous reactive power is delivered to the grid in accordance with the Spanish grid code; the mandatory limitation of the amplitude of the three-phase inverter currents to its nominal value is carried out with a novel control strategy, in which a certain amount of instantaneous constant active power can also be delivered to the grid when small or moderate voltage sags happen. A Multiple second order generalized integrator frequency-locked loop synchronization algorithm is employed in order to estimate the system frequency without harmonic distortions, as well as to output the positive- and the negative- sequence of the αβ quantities of the three-phase grid voltages when balanced and unbalanced voltage sags happen in a frequency- adaptive scheme. The current control is carried out in the stationary reference frame, which guarantees the cancellation of the harmonic distortions in the utility grid currents using a Harmonic compensation structure, and the implementation of a constant active power control in order to protect the DC link capacitor from thermal stresses avoiding the appearance of large harmonic distortions at twice the fundamental frequency in the DC link voltage. A case study of a three-phase single-stage grid-connected PV system with a maximum apparent power about 500 kVA is tested with several simulations using MATLAB/SIMULINK firstly, and secondly, with some experiments using the Controller hardware-in-the-loop (CHIL) simulation technique for several types of voltage sags in order to do the final validation of the control algorithms. [ABSTRACT FROM AUTHOR]