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In this paper, we present computer simulation results concerning interdiffusion of fully compatible components in symmetric binary (AB) polymer mixtures in solutions. The simulation is performed in two dimensions using the algorithm based on the dynamic lattice liquid model. The solvent molecules are taken into account explicitly. The evolution of the concentration profiles in time at an interface is studied for chain lengths N=2,4,8,16 for three polymer concentrations [lowercase_phi_synonym]=0.1,0.5,0.9. The tracer diffusion coefficients for polymer chains and for the solvent are obtained by monitoring the mean square displacements of their center of mass. The relationships between coefficients of interdiffusion and self-diffusion are tested. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

In a cognitive radio network, cooperative communications between a primary user (PU) and a second user (SU) may be able to significantly improve the spectrum utilization, and thus, the network performance. To be specific, the PU can select a number of SUs as its relays to cooperatively transmit its data. In turn, these relays can be granted access to the licensed channel of the PU to transmit their data. In this paper, an effective cooperation strategy for SUs is presented. We formulate the problem of cooperative relay selection as a coalition formation game, and develop a utility function based on the game. The utility function considers various factors such as transmission power and noise level. With the utility function, a distributed coalition formation algorithm is proposed, which can be used by SUs to decide whether to join or leave a coalition. Such a decision is based on whether it can increase the maximal coalition utility value. We rigorously prove that our proposed coalition formation algorithm can terminate and reach a stable state. Finally, this paper demonstrates that the proposed scheme is able to enhance the network throughput via a simulation study. [ABSTRACT FROM AUTHOR]

Compressive sensing theory has attracted widespread attention in recent years and sparse signal reconstruction has been widely used in signal processing and communication. This paper addresses the problem of sparse signal recovery especially with non-Gaussian noise. The main contribution of this paper is the proposal of an algorithm where the negentropy and reweighted schemes represent the core of an approach to the solution of the problem. The signal reconstruction problem is formalized as a constrained minimization problem, where the objective function is the sum of a measurement of error statistical characteristic term, the negentropy, and a sparse regularization term, ℓp-norm, for 0 < p < 1. The ℓp-norm, however, leads to a non-convex optimization problem which is difficult to solve efficiently. Herein we treat the ℓp-norm as a serious of weighted ℓ1-norms so that the sub-problems become convex. We propose an optimized algorithm that combines forward-backward splitting. The algorithm is fast and succeeds in exactly recovering sparse signals with Gaussian and non-Gaussian noise. Several numerical experiments and comparisons demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Accurate location information is essential for mobile systems such as wireless sensor networks. A location-aware sensor network generally includes two types of nodes: Sensors whose locations to be determined and anchors whose locations are known a priori. For range-based cooperative positioning, sensors’ locations are deduced from anchor-to-sensor and sensor-to-sensor range measurements. Positioning accuracy depends on the network parameters such as network connectivity and size. This paper provides a generalized theory that quantitatively characterizes such a relation between network parameters and positioning accuracy. We use the average degree as a connectivity metric and use geometric dilution of precision (DOP) to quantify positioning accuracy. Under the assumption that nodes are randomly deployed, we prove a novel lower bound on expectation of average geometric DOP (LB-E-AGDOP) and derives a closed-form formula that relates LB-E-AGDOP to only three parameters: Average anchor degree, average sensor degree, and number of sensor nodes. The formula shows that positioning accuracy is approximately inversely proportional to the average degree, and a higher ratio of average anchor degree to average sensor degree yields better positioning accuracy. Furthermore, the paper shows a strong connection between LB-E-AGDOP and the best achievable accuracy. Finally, we demonstrate the theory via numerical simulations with three different random graph models. [ABSTRACT FROM PUBLISHER]

Recent studies reveal that both phase aberration and reverberation play a major role in degrading ultrasound image quality. We previously developed an algorithm for suppressing clutter, but we have not yet tested it in the context of aberrated wavefronts. In this paper, we evaluate our previously reported algorithm, called aperture domain model image reconstruction (ADMIRE), in the presence of phase aberration and in the presence of multipath scattering and phase aberration. We use simulations to investigate phase aberration corruption and correction in the presence of reverberation. As part of this paper, we observed that ADMIRE leads to suppressed levels of aberration. In order to accurately characterize aberrated signals of interest, we introduced an adaptive component to ADMIRE to account for aberration, referred to as adaptive ADMIRE. We then use ADMIRE, adaptive ADMIRE, and conventional filtering methods to characterize aberration profiles on in vivo liver data. These in vivo results suggest that adaptive ADMIRE could be used to better characterize a wider range of aberrated wavefronts. The aberration profiles’ full-width at half-maximum of ADMIRE, adaptive ADMIRE, and postfiltered data with 0.4- mm^-1 spatial cutoff frequency are 4.0 ± 0.28 mm, 2.8 ± 1.3 mm, and 2.8 ± 0.57 mm, respectively, while the average root-mean square values in the same order are 16 ± 5.4 ns, 20 ± 6.3 ns, and 19 ± 3.9 ns, respectively. Finally, because ADMIRE suppresses aberration, we perform a limited evaluation of image quality using simulations and in vivo data to determine how ADMIRE and adaptive ADMIRE perform with and without aberration correction. [ABSTRACT FROM AUTHOR]

Simulated ultrasound data is a valuable tool for development and validation of quantitative image analysis methods in echocardiography. Unfortunately, simulation time can become prohibitive for phantoms consisting of a large number of point scatterers. The COLE algorithm by Gao et al. is a fast convolution-based simulator that trades simulation accuracy for improved speed. We present highly efficient parallelized CPU and GPU implementations of the COLE algorithm with an emphasis on dynamic simulations involving moving point scatterers. We argue that it is crucial to minimize the amount of data transfers from the CPU to achieve good performance on the GPU. We achieve this by storing the complete trajectories of the dynamic point scatterers as spline curves in the GPU memory. This leads to good efficiency when simulating sequences consisting of a large number of frames, such as B-mode and tissue Doppler data for a full cardiac cycle. In addition, we propose a phase-based subsample delay technique that efficiently eliminates flickering artifacts seen in B-mode sequences when COLE is used without enough temporal oversampling. To assess the performance, we used a laptop computer and a desktop computer, each equipped with a multicore Intel CPU and an NVIDIA GPU. Running the simulator on a high-end TITAN X GPU, we observed two orders of magnitude speedup compared to the parallel CPU version, three orders of magnitude speedup compared to simulation times reported by Gao et al. in their paper on COLE, and a speedup of 27000 times compared to the multithreaded version of Field II, using numbers reported in a paper by Jensen. We hope that by releasing the simulator as an open-source project we will encourage its use and further development. [ABSTRACT FROM AUTHOR]

In the area of active traffic management, new technologies provide opportunities to improve the use of current infrastructure. Vehicles equipped with in-car communication systems are capable of exchanging messages with the infrastructure and other vehicles. This new capability offers many opportunities for traffic management. This paper presents a novel merging assistant strategy that exploits the communication capabilities of intelligent vehicles. The proposed control requires the cooperation of equipped vehicles on the main carriageway in order to create merging gaps for on-ramp vehicles released by a traffic light. The aim is to reduce disruptions to the traffic flow created by the merging vehicles. This paper focuses on the analytical formulation of the control algorithm, and the traffic flow theories used to define the strategy. The dynamics of the gap formation derived from theoretical considerations are validated using a microscopic simulation. The validation indicates that the control strategy mostly developed from macroscopic theory well approximates microscopic traffic behaviour. The results present encouraging capabilities of the system. The size and frequency of the gaps created on the main carriageway, and the space and time required for their creation are compatible with a real deployment of the system. Finally, we summarise the results of a previous study showing that the proposed merging strategy reduces the occurrence of congestion and the number of late-merging vehicles. This innovative control strategy shows the potential of using intelligent vehicles for facilitating the merging manoeuvre through use of emerging communications technologies. [ABSTRACT FROM AUTHOR]

In a previous paper, the authors studied the limits of probabilistic prediction in nonlinear time series analysis in a perfect model scenario, i.e., in the ideal case that the uncertainty of an otherwise deterministic model is due to only the finite precision of the observations. The model consisted of the symbolic dynamics of a measure-preserving transformation with respect to a finite partition of the state space, and the quality of the predictions was measured by the so-called ignorance score, which is a conditional entropy. In practice, though, partitions are dispensed with by considering numerical and experimental data to be continuous, which prompts us to trade off in this paper the Shannon entropy for the differential entropy. Despite technical differences, we show that the core of the previous results also hold in this extended scenario for sufficiently high precision. The corresponding imperfect model scenario will be revisited too because it is relevant for the applications. The theoretical part and its application to probabilistic forecasting are illustrated with numerical simulations and a new prediction algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a general framework is proposed for the analysis and characterization of observability and diagnosability of finite state systems. Observability corresponds to the reconstruction of the system’s discrete state, while diagnosability corresponds to the possibility of determining the past occurrence of some particular states, for example faulty states. A unifying framework is proposed where observability and diagnosability properties are defined with respect to a critical set, i.e. a set of discrete states representing a set of faults, or more generally a set of interest. These properties are characterized and the involved conditions provide an estimation of the delay required for the detection of a critical state, of the precision of the delay estimation and of the duration of a possible initial transient where the diagnosis is not possible or not required. Our framework makes it possible to precisely compare some of the observability and diagnosability notions existing in the literature with the ones introduced in our paper, and this comparison is presented. [ABSTRACT FROM AUTHOR]

This paper reviews and advances a data science framework for capturing and communicating critical information regarding the evolution of material structure in spatiotemporal multiscale simulations. This approach is called the MKS (Materials Knowledge Systems) framework, and was previously applied successfully for capturing mainly the microstructure-property linkages in spatial multiscale simulations. This paper generalizes this framework by allowing the introduction of different basis functions, and explores their potential benefits in establishing the desired process-structure-property (PSP) linkages. These new developments are demonstrated using a Cahn-Hilliard simulation as an example case study, where structure evolution was predicted three orders of magnitude faster than an optimized numerical integration algorithm. This study suggests that the MKS localization framework provides an alternate method to learn the underlying embedded physics in a numerical model expressed through Green’s function based influence kernels rather than differential equations, and potentially offers significant computational advantages in problems where numerical integration schemes are challenging to optimize. With this extension, we have now established a comprehensive framework for capturing PSP linkages for multiscale materials modeling and simulations in both space and time. [ABSTRACT FROM AUTHOR]

Background: With the invention of fitness trackers, it has been possible to continuously monitor a user's biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user's "activeness", and investigates the feasibility in modeling and predicting the long-term activeness of the user.Methods: The dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures-as well as a deep neural network and a simple regression model-were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user's activeness falls below a certain threshold.Results: A preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user's activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user's future activeness with precision, for example, a trained RNN model could predict-with the precision of 84%-when the user would be less active within the next hour given the latest 15 min of his activeness data.Conclusions: This paper defines and investigates the notion of a user's "activeness", and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user. [ABSTRACT FROM AUTHOR]

Psychological factors affect the movement of people in the competitive or panic mode of evacuation, in which the density of pedestrians is relatively large and the distance among them is small. In this paper, a crowd is divided into groups according to their social relations to simulate the actual movement of crowd evacuation more realistically and increase the attractiveness of the group based on social force model. The force of group attraction is the synthesis of two forces; one is the attraction of the individuals generated by their social relations to gather, and the other is that of the group leader to the individuals within the group to ensure that the individuals follow the leader. The synthetic force determines the trajectory of individuals. The evacuation process is demonstrated using the improved social force model. In the improved social force model, the individuals with close social relations gradually present a closer and coordinated action while following the leader. In this paper, a grouping algorithm is proposed based on grid density and relationship via computer simulation to illustrate the features of the improved social force model. The definition of the parameters involved in the algorithm is given, and the effect of relational value on the grouping is tested. Reasonable numbers of grids and weights are selected. The effectiveness of the algorithm is shown through simulation experiments. A simulation platform is also established using the proposed grouping algorithm and the improved social force model for crowd evacuation simulation. [ABSTRACT FROM AUTHOR]

A hybrid analytical/numerical conducted an electromagnetic interference (EMI) modeling approach, which adopts both numerical method and analytical method, is proposed in this paper. In this model, the noise source is calculated directly with the original data from oscilloscopes. The noise path at different operating status is extracted from the original converter topology and its analytical expression is then derived. Also, according to the EMI receiver detector mode, the algorithm of the EMI receiver is proposed in this paper. Peak (PK), average (AV), and quasipeak (QP) values of the EMI can then be calculated to analyze the impact of each relevant component. The analytical method is provided to carry out PK, AV, and QP simulations accurately and fast. The simulation and experimental results for common mode leakage currents validate this EMI model. Furthermore, two analysis examples are given to illustrate how this model is applied for engineers. [ABSTRACT FROM AUTHOR]

In a previous paper, Duchêne and Rigo introduced the notion of invariance for take-away games on heaps. Roughly speaking, these are games whose rulesets do not depend on the position. Given a sequence S of positive tuples of integers, the question of whether there exists an invariant game having S as set of P -positions is relevant. In particular, it was recently proved by Larsson et al. that if S is a pair of complementary Beatty sequences, then the answer to this question is always positive. In this paper, we show that for a fairly large set of sequences (expressed by infinite words), the answer to this question is decidable. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based optimal control algorithm for the clamp switch of a zero-voltage switching (ZVS) bidirectional DC-DC converter. The bidirectional DC-DC converter (BDC) can accomplish the ZVS operation using the clamp switch. The minimum current for the ZVS operation is maintained, and the inductor current is separated from the input and output voltages by the clamp switch in this topology. The clamp switch can decrease the inductor current ripple, switching loss, and conduction loss of the system. Therefore, the optimal control of the clamp switch is significant to improve the efficiency of the system. This paper proposes a model-based optimal control algorithm using phase shift in a micro-controller unit. The proposed control algorithm is demonstrated by the results of PSIM simulations and an experiment conducted in a 1-kW ZVS BDC system. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed-time distributed Nash equilibrium seeking (DNES) problem for multi-agent noncooperation games. Based on the distributed motion-planning method and the gradient search, a class of prescribed-time DNES algorithms are developed for first and second-order multi-agent systems, respectively. They may ensure each player' s action converges to the Nash equilibrium (NE) point at a prescribed settling time in advance. Further, for the case when the velocity information of each agent is not available, an observer-based prescribed-time DNES algorithm is extended. Compared with existing works, the convergence time of proposed prescribed-time DNES algorithms in this paper can be assigned in advance by users according to task requirements. Besides, the designed DNES algorithms are sampled without continuous measurements, which means players can update their actions at each sampling moment to avoid the increasing cost brought by continuous information interaction among players. Finally, the effectiveness of algorithms proposed in this paper is verified by some numerical simulations. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) give users the opportunity to access different access points(APs), which will simultaneously affect user performance and system performance, so user association in HetNets plays a critical role in enhancing the load balancing and the system sum-throughput of networks. Meanwhile, the incremental sum-throughput currently fails to meet the escalating data demands. Besides, ensuring fairness amongst users constitutes another urgent issue in the radio resource management (RRM) of HetNets. What is more, few works consider the maximum service user number constraint in femtocell access points (FAPs). To solve the aforementioned problem, this paper associates users to APs by considering system sum-throughput and fairness at the same time in HetNets under a maximum service user number constraint of FAPs; accordingly, the user association problem is formulated. By releasing constraint, the optimal user association algorithm is obtained by Lagrangian function, and based on this optimal solution, a low complexity suboptimal user association algorithm is proposed. At last, this paper investigates the relationship between system sum-throughput and maximum service user number of FAPs. Numerical simulation results show that the proposed algorithm can improve sum-throughput and fairness at the same time at a specific maximum service user number of FAPs. [ABSTRACT FROM AUTHOR]

This paper investigates the parameter estimation problem for multivariate output-error systems perturbed by autoregressive moving average noises. Since the identification model has two different kinds of parameters, a vector and a matrix, the gradient algorithm cannot be used directly. Therefore, we decompose the original system model into two sub-models and proceed the identification problem by the collaboration between the two sub-models. By employing the gradient search and determining the optimal step-sizes, we present an auxiliary model based two-stage projection algorithm. However, in order to alleviate the sensitivity to the noise, we reselect the step-sizes and derive the auxiliary model based two-stage stochastic gradient (AM-2S-SG) algorithm. Based on the AM-2S-SG algorithm, an auxiliary model based two-stage multi-innovation stochastic gradient algorithm is proposed to generate more accurate estimates. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Coarse-graining of complex networks is a hot topic in network science. Coarse-grained networks are required to preserve the topological information or dynamic properties of the original network. Some effective coarse-graining methods have been proposed, while an urgent problem is how to obtain coarse-grained network with optimal scale. In this paper, we propose an extraction algorithm (EA) for optimal coarse-grained networks. Numerical simulation for EA on four kinds of networks and performing Kuramoto model on optimal coarse-grained networks, we find our algorithm can effectively obtain the optimal coarse-grained network. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by α-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilize computer simulation algorithm in MATLAB to visualize the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modeling structure of stochastic differential equations. [ABSTRACT FROM AUTHOR]

Clustered survival data in the presence of cure has received increasing attention. In this paper, we consider a semiparametric mixture cure model which incorporates a logistic regression model for the cure fraction and a semiparametric regression model for the failure time. We utilize Archimedean copula (AC) models to assess the strength of association for both susceptibility and failure times between susceptible individuals in the same cluster. Instead of using the full likelihood approach, we consider a composite likelihood function and a two-stage estimation procedure for both marginal and association parameters. A Jackknife procedure that takes out one cluster at a time is proposed for the variance estimation of the estimators. Akaike information criterion is applied to select the best model among ACs. Simulation studies are performed to validate our estimating procedures, and two real data sets are analyzed to demonstrate the practical use of our proposed method. [ABSTRACT FROM AUTHOR]

This research is based on the Derivative/Integration of the Power Spectrum for the reduction of noise in a periodic signal. The performed procedure, joined to a convolution process, results in the detection of the original values of amplitude, phase and frequency of the spectral components of the periodic signal. In this paper, second-order statistics foundations are presented and the validation of the proposed algorithm is exposed in both theoretical and practical sense. [ABSTRACT FROM AUTHOR]

Numerical methods that approximate the solution of the Vlasov-Poisson equation by a low-rank representation have been considered recently. These methods can be extremely effective from a computational point of view, but contrary to most semi-Lagrangian or Eulerian Vlasov solvers, they do not conserve mass and momentum, neither globally nor in respecting the corresponding local conservation laws. This can be a significant limitation for intermediate and long time integration. In this paper we propose a numerical algorithm that overcomes some of these difficulties and demonstrate its utility by presenting numerical simulations. [ABSTRACT FROM AUTHOR]

Of the wide palette of 5G features, ultra-wide bandwidth and large-scale antenna configuration are regarded as the essential enabling technology components for millimeter wave (mmWave) communication. Accurate knowledge of delay and angle information of multipath components is essential for many applications in mmWave systems. There is a strong need for a low computation-cost channel estimation algorithm for such systems, where typically adopted far-field and narrowband assumptions might be violated. In this paper, a generic yet novel beamspace uniform circular array (UCA) beamforming algorithm with successive cancellation scheme is proposed to jointly detect the impinging azimuth angle and delay of each multipath component. The proposed algorithm is computationally cheap and it works for ultra-wideband UCA systems in the near-field conditions as well as the traditional narrowband systems in the far-field conditions. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed algorithm, compared to the state-of-art works. [ABSTRACT FROM AUTHOR]

In this paper, we consider numerical approximations for solving the anisotropic Cahn–Hilliard model. We combine the Scalar Auxiliary Variable (SAV) approach with the stabilization technique to arrive at a novel Stabilized-SAV approach, where three linear stabilization terms, which are shown to be crucial to remove the oscillations caused by the anisotropic coefficient, are added to enhance the stability while keeping the required accuracy. The schemes are very easy-to-implement and fast in the sense that all nonlinear terms are treated in a semi-explicit way, and one only needs to solve three decoupled linear equations with constant coefficients at each time step. We further prove the unconditional energy stabilities rigorously and present numerous 2D and 3D numerical simulations to demonstrate the stability and accuracy. • We propose a novel Stabilized-SAV approach for solving the anisotropic Cahn–Hilliard model. • The scheme is linear, second-order accurate, provably unconditionally energy stable, and non-iterative. • Three crucial linear stabilization terms are added to remove oscillations caused by anisotropy. • One only need to solve three decoupled linear equations at each time step. • We further prove the unconditional energy stabilities rigorously and present numerous simulations. [ABSTRACT FROM AUTHOR]

In the context of Intermittently Connected Mobile Networks, researchers have proposed different collaborative routing schemes with fundamental objectives to maximize packets delivery, and minimise latency through improving peers' routing decisions. One promising approach adopted by researchers to improve peers' routing decisions is a trust-based routing mechanism. Most current trust-based protocols in the literature enable peers to make routing decisions based on trust relationships that manifest among peers but with less concern about the peers' attributes. This further made the design of efficient, collaborative routing protocols a challenging task due to the high dynamics of peers' characteristics and mobility. In this paper, we hypothesise that in a trust-based routing protocol, it will be additional trust evaluation reliability if peers can take into consideration their partners attributes' status in addition to the trust relationships. We propose DATM protocol; a trust-based scheme to enforce collaborative behaviour in wireless mobile networks taking into consideration the peers' attributes for an efficient routing scheme. We introduce a personalised similarity algorithm for peers' attributes modelling as a scaling factor for trust evaluation. The relative comparison of the DATM and PROPHET protocol using simulation shows the improved performance of our proposed model in terms of messages delivery, messages overhead and average latency. [ABSTRACT FROM AUTHOR]

The principal function of circuit breakers (CBs) is to isolate a portion of the power network from the rest of it in a timely manner following an opening command. Any failure in the opening operation of CBs, especially following a fault condition, will almost certainly result in a catastrophic event. Therefore, the issue of failure detection of CBs is essential and has a vital role in power system protection. This paper presents a novel power-based algorithm for failure detection of CBs. The power loss of CBs due to an arcing event increases as the arcing time gets longer. The arcing time of CBs increases with defective operations originating from any failure or malfunction for different reasons. The proposed method uses the power difference between the input and output terminals of CBs to calculate the power loss due to an arcing event, which can then be used to estimate the arcing energy. A trip signal is initiated whenever the estimated arcing energy for a single opening operation reaches a predefined trip level, which shows a failure in CB operation. This predefined trip value is related to the integration of the maximum power capacity of the CB during an opening operation time. Computer simulation studies conducted to analyze the performance of the proposed technique demonstrate that the algorithm is capable of discriminating between normal and abnormal arcing energies and thus identifying malfunctions or failures in the opening operations of CBs. [ABSTRACT FROM AUTHOR]

The protection characteristics used for the detection of power systems’ oscillations are adjusted according to the recommendations of the equipment manuals and validated through transient stability studies. Once the characteristics are adjusted, the parameters do not change when topological changes occur in the system, which opens up the possibility of malfunction of the protection equipment, either by not considering special cases of failure during the study or by errors in the system. This paper describes the modeling and implementation of a virtual out-of-step protection algorithm based on traditional, unconventional methods. The proposed framework allows the user to implement a customized out-of-step protection algorithm and analyze a real fault event recorded in a COMTRADE file. Furthermore, the programming advantages include the possibility to import COMTRADE files from specific simulations and assess the adequate settings of the protective relay. The result brings a computational tool compatible with different simulation programs capable of reproducing stable and unstable power swing events through playback techniques. By assessing critical cases in which the omission of the tripping is reflected in a generator slip condition, this study proposes an alternative with the instantaneous power theory to analyze the oscillation conditions in the definite time frame. [ABSTRACT FROM AUTHOR]

A synchronous reluctance motor drive exhibits an inherent unlimited speed range capability; in fact, the maximum speed of this kind of machine is bounded only by mechanical limits. In order to exploit this peculiar characteristic of the drive while assuring the lowest power losses, a proper control algorithm has to be used. In particular, while the working speed is increasing, the control must be able to command the reluctance motor under the maximum torque per ampere condition at first, into the flux-weakening region, and then on the maximum torque per voltage trajectory. To this purpose, a novel voltage control loop is presented in this paper. It is combined with a field-oriented current control and profitably exploits a polar coordinate representation of the current reference to operate the reluctance machine in compliance with current and voltage rating constraints in all the different operating conditions. Additional merits of the proposed scheme are the ease of implementation and the simple design. The stability of the proposed control algorithm is proved through a small signal analysis and simulations and experimental tests confirm its effectiveness. [ABSTRACT FROM AUTHOR]

Highlights • Two incidence matrices are proposed for complete modeling of distribution systems. • A review of various distributed generations is provided in power flow studies. • An efficient power flow algorithm for distribution systems is proposed. • Constant power-voltage buses are handled by a new in-program sensitivity matrix. • Accuracy, performance, and robustness of the model are validated by simulations. Abstract Real-time operation, analysis and control of active distribution systems (DSs) mainly rely on accurate power flow (PF) results. Almost all decisions are made considering PF calculations. On the other hand, efficiency of PF algorithm should be updated to comply with new smart grid paradigms which affect traditional management methods of DSs. A new graph theory based PF algorithm is proposed in this paper for active electrical DSs including various distributed generation (DG) models, three phase equipment and meshed topologies. At first, two incidence matrices are proposed for modeling of DSs. These matrices could successfully handle meshed topologies and unbalanced networks. They could also be modified to consider various three phase equipment such as transformers and voltage regulators. Moreover, a comprehensive study of various modeling methods of DGs in PF studies is presented. In order to handle DG models, a new sensitivity matrix is proposed for in-algorithm calculation of reactive power injection of PV buses. Since, the proposed incidence matrices provide a decoupled model of the network, any topology and characteristic change could be simply applied in this PF algorithm. The proposed PF algorithm was tested in several simulations in various test systems. The results of accuracy test, performance test, and robustness test show capability of the proposed PF algorithm for handling various situations in different PF calculations. [ABSTRACT FROM AUTHOR]

Highlights • A three-phase phase-locked loop algorithm is introduced. • Inherent immunity to subharmonics and interharmonics is achieved. • An adaptive filter is employed to improve disturbance rejection. • A proper comparison with similar approaches is presented. Abstract This paper introduces a synchronization algorithm for the detection of the fundamental positive-sequence component in three-phase systems. The structure is based on the correlation of the input signal with a complex signal generated from the association of an adaptive filter and a phase-locked loop (PLL) method previously presented in literature. Good disturbance rejection is then achieved, in particular to interharmonics and subharmonics. Simulation and experimental results are presented and discussed in order to demonstrate the performance of the proposed solution when compared with that regarding dual second-order generalized integrator frequency locked loop (DSOGI-FLL), which is often considered to present good disturbance rejection. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We proposed and test a DSE based algorithm to detect traveling waves in transmission lines. • Performance was validated with transient simulations and playback testing using Bergeron and frequency dependent parameter models. • The method was compared to legacy protections with TWs differentiator smoother and impedance based relays. • The method showed a successful performance with different sampling rates (lower sampling rates) which is one of the main contributions. • The method was also assessed with different types of fault with the EMTP case study and a real Colombian overhead transmission line. Abstract A dynamic state estimation (DSE) approach is proposed to improve the accuracy to identify the front-wave arrival times of a two-terminal traveling wave protection scheme for a transmission line with low sampling rates. The solution is implemented as an offline method aiming to use existing infrastructure measurements. The work presents the principles of traveling wave (TW) theory and legacy protection schemes with this methodology. The proposed method requires GPS synchronized measurements at both ends of the line and a high fidelity model of the protected line. The paper presents comparisons between the differentiator smoother for arrival time identification, traditional impedance based fault location using 1 or 2 terminals and the proposed DSE-TW. The performance is also assessed with the Bergeron model and the frequency dependent parameter model of the transmission line. Numerical simulations and laboratory tests demonstrate that the method can correctly identify the front-wave arrival time at each end with better accuracy and different sampling rates. This work illustrates the advantages of DSE-TW such as high accuracy of arrival time identification, the minimum error of fault location and better performance with different (lower) sampling rates. [ABSTRACT FROM AUTHOR]

Highlights • Simulation of microwave rotating heating is realized by ALE method. • The simulation results compared with conventional methods is more accurate and efficient. • This method can be used for microwave heating simulation of other food movements. Abstract In a particular condition, moving elements are typically used to improve heating uniformity during microwave heating. However, due to the complexity of mathematical models, the simulation of moving elements during microwave heating process remains a challenge. In this paper, a novel continuous algorithm based on Arbitrary Lagrangian-Eulerian method is developed to compute this heating process. By introducing the ALE method, the mesh of moving domain are set as a function of space coordinate and time, the mesh of surrounding domain is free deformation under the action of the moving domain, ensuring the accuracy and continuity of calculation by remesh the deformed configuration. A 2-D model and a 3-D model with a rotating turntable are used to illustrate the proposed method in detail. The change of temperature was measured by experiments to verify the rationality and accuracy of the proposed method. This method can be used in microwave heating simulation of other food movements and assist microwaveable food product development. [ABSTRACT FROM AUTHOR]

Background: Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes. Results: In this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis identified a significant number of gene sets related to breast cancer or other types of cancer that are enriched in this set of 268 genes. Network analysis of the kidney cancer data in the TCGA database with ProGAdNet also identified a set of genes involved in network changes, and the majority of the top genes identified have been reported in the literature to be implicated in kidney cancer. These results corroborated that the gene sets identified by ProGAdNet were very informative about the cancer disease status. A software package implementing the ProGAdNet, computer simulations, and real data analysis is available as Additional file 1. Conclusion: With its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions. [ABSTRACT FROM AUTHOR]

Optimization is the process of achieving the best solution for a problem. LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method. PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples. Also, SVM parameters are optimized for Parkinson's disease data by combining CS and PSO. The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better. The hybrid model shows that the accuracy of the performance achieved is 97.4359%. Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall, F1 score, false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), negative predictive value (NPV), and Matthews' correlation coefficient (MCC) parameters. [ABSTRACT FROM AUTHOR]

In this paper, a novel scheme is proposed to optimize the effectiveness of the device-to-device (D2D) enabled vehicular communications (D2D-V), which are underlaid in cellular networks, where the uplink channel allocated to one cellular user (CU) is reused by the multiple D2D-V links. To develop a well-functioning D2D-V system, the sum rate of all D2D-V links is necessary to be maximized, and also the reliability of the co-channel CU to infrastructure has to be guaranteed with the CU interference constraint. Unlike the traditional statical D2D systems, the D2D-V system suffers with high mobility and channel uncertainty. Therefore, the CU interference constraint is formulated as a probabilistic function by the Bernstein approximation, the constraint attempts to address the mobile channel fluctuations. Besides, the tractable approximate constraint is reformulated as two separable structure, this reformulated constraint attempts to determine the near-optimal solutions more efficiently. Since the objective function with a logarithmic form is nonconvex, successive convex approximation is applied to transform the nonconvex problem into the convex problem. The dual decomposition is used to determine the near-optimal solutions. After the problem is reformulated, a distributed robust power control algorithm is proposed to perform the chance-constrained optimization. Numerical simulations are used to evaluate the performance of the proposed scheme. The simulation results show the impacts of system performance when applying the proposed scheme in vehicular environments with high mobility. The proposed optimization schemes are further verified by comparing with the existing methods. [ABSTRACT FROM AUTHOR]

Hybrid analog and digital beamforming (HBF) has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. It well balances the demand for sufficient beamforming gains to overcome the propagation loss and the desire to reduce the hardware cost and power consumption. In this paper, the mean square error (MSE) is chosen as the performance metric to characterize the transmission reliability. Using the minimum sum-MSE criterion, we investigate the HBF design for broadband mmWave transmissions. To overcome the difficulty of solving the multi-variable design problem, the alternating minimization method is adopted to optimize the hybrid transmit and receive beamformers alternatively. Specifically, a manifold optimization-based HBF algorithm is first proposed, which directly handles the constant modulus constraint of the analog component. Its convergence is then proved. To reduce the computational complexity, we then propose a low-complexity general eigenvalue decomposition-based HBF algorithm in the narrowband scenario and three algorithms via the eigenvalue decomposition and orthogonal matching pursuit methods in the broadband scenario. A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to a faster convergence. Furthermore, we extend the sum-MSE-based design to that with weighted sum-MSE, which is then connected to the spectral efficiency-based design. Simulation results show that the proposed HBF algorithms achieve a significant performance improvement over existing ones and perform close to full-digital beamforming. [ABSTRACT FROM AUTHOR]

Orienteering problem (OP) is a routing problem, where the aim is to generate a path through set of nodes, which would maximize total score and would not exceed the budget. In this paper, we present an extension of classic OP—Orienteering Problem with Functional Profits (OPFP), where the score of a specific point depends on its characteristics, position in the route, and other points in the route. For solving OPFP, we developed an open-source framework for solving orienteering problems, which utilizes four core components of OP in its modular architecture. Fully-written in Go programming language our framework can be extended for solving different types of tasks with different algorithms; this was demonstrated by implementation of two popular algorithms for OP solving—Ant Colony Optimization and Recursive Greedy Algorithm. Computational efficiency of the framework was shown through solving four well-known OP types: classic Orienteering Problem (OP), Orienteering Problem with Compulsory Vertices (OPCV), Orienteering Problem with Time Windows (OPTW), and Time Dependent Orienteering Problem (TDOP) along with OPFP. Experiments were conducted on a large multi-source dataset for Saint Petersburg, Russia, containing data from Instagram, TripAdvisor, Foursquare and official touristic website. Our framework is able to construct touristic paths for different OP types within few seconds using dataset with thousands of points of interest. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication has been regarded as a promising technique for content dissemination in the fifth generation wireless network. To improve the efficiency of content dissemination, in this paper, we propose a content dissemination mechanism with proactive content fetching (PCF) and full-duplex (FD) D2D communication, referred to as PCF-FD mechanism. This mechanism is characterized by the proactive content requests of partial users that don't request contents originally. FD communication is utilized to establish more D2D links and increase the area spectral efficiency (ASE). Based on stochastic geometry, we derive the recursive formula of content caching probability from the evolutionary perspective. In addition, the expressions of successful offloading probability (SOP), ASE and average energy consumption are also obtained. Computer experiments show that the analytical results match well with simulation results and our scheme outperforms the conventional scheme without PCF and FD communication by 45% in terms of SOP in a scenario with small user density and light load. [ABSTRACT FROM AUTHOR]

Highlights • Introducing modified versions of ERA and fast ERA to overcome their deficiencies in real life applications. • Introducing theoretical foundations for modified ERA and modified fast ERA. • Analyzing the theoretical results using the experimental tests. • Extracting the real parameters of the structural system. Abstract The eigensystem realization algorithm (ERA) and fast ERA are two popular methods of structural-system identification; however, these two powerful methods have deficiencies when employed in real-life identification projects. The main deficiencies of the ERA and fast ERA methods are that, when the order of the structural system is not known or is infinite, the sample time is not appropriate and/or initial condition is not zero, the identification results using these algorithms are not correct. This paper introduces a theoretical foundation called the modified ERA (mERA) and modified fast ERA (mfERA) to tackle these deficiencies. The simulation and experimental results are presented to demonstrate the validity of these analytical developments. [ABSTRACT FROM AUTHOR]

There are some problems on multi-user downlink power allocation and beamforming in a cognitive radio network (CRN) which works in the underlay mode. They are the high complexity of the conventional SDR algorithm. the limited applicability and ignoring the interferences of the primary network (PN) to the secondary user(SU) , etc. Aiming at these problems, this paper firstly added the term of interference of the PN to the SU to the CRN model and formulated the optimization problem. Then based on the duality of uplink-downlink, it obtained the simple and fast iterative algorithm by introducing virtual power, transforming the optimization problem into the problem of uplink power allocation and beamforming. And it analyzed the convergence of the algorithm to obtain the convergent conditions. It also reckoned the algorithm complexity. The result demonstrates that iterative algorithm is superior to the SDR algorithm. Numerical simulation results show that the algorithm conveiges faster, and that the variation of transmitting power of the primary base station (PBS) could affect the feasibility region. They also show that the increasing of transmitting power of the PBS could lead to the downlink power increasing. The influences are notable. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a two-stage optimization method for a coupled capacity planning and operation problem, cast within the economical operation of Regional Integrated Energy System. The first stage optimization of the proposed model represents a regional integrated energy system planner whose purpose is to minimize its energy and environmental cost, while the second stage is an operation problem whose primary role is to achieve the optimal operation scheme of the system. The regional integrated energy system planner pursues best interests by co-optimizing the capacity configuration and power output of individual energy supply module, while the regional integrated energy system maximizes the installed capacity of renewable energy sources and minimizes the environmental costs. To illustrate the advantage of the proposed method, the NSGA-II algorithm and the mixed integer linear programming method are implemented to solve the model based on simulation. Besides, application of the optimization method proposed to the energy infrastructure of a regional integrated energy system in China is discussed, and the results obtained through simulation are compared to the bi-level optimization objectives. The results show that the proposed method is economical and effective in practical application. Highlights • A bi-level optimization model for integrated energy system is presented. • A decentralized optimization method is proposed for solving capacity planning. • Accuracy and economy of capacity planning are improved. • The economic efficiency of the RIES is improved. [ABSTRACT FROM AUTHOR]

The detection of groups of individuals is attracting the attention of many researchers in diverse fields, from automated surveillance to human-computer interaction, with a growing number of approaches published every year. Unexpectedly, the evaluation metrics for this problem are not consolidated, with some measures inherited from the people detection field, other from clustering, other designed specifically for a particular approach, thus lacking in generalization and making the comparisons between different approaches hard to be carried out. Moreover, most of the existent metrics are scarcely expressive, addressing groups as they are atomic entities, ignoring that they may have different cardinalities, and that group detection approaches may fail in capturing the exact number of individuals that compose it. This paper fills this gap presenting the GROup DEtection (GRODE) metrics, which formally define precision and recall on the groups, including the group cardinality as a variable. This gives the possibility to investigate aspects never considered so far, such as the tendency of a method of over- or under-segmenting, or of better dealing with specific group cardinalities. The GRODE metrics have been evaluated first on controlled scenarios, where the differences with alternative metrics are evident. Then, the metrics have been applied to eight approaches of group detection, on eight public datasets, providing a fresh-new panorama of the state-of-the-art, discovering interesting strengths and pitfalls of the recent approaches. [ABSTRACT FROM AUTHOR]