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Solution of sparse linear complementarity problem (LCP) has been widely discussed in many applications. In this paper, we consider the ℓ p regularization problem with nonnegative constraint for sparse LCP, and propose algorithms based on the iterative reweighted method to approach a sparse solution of the LCP, and then show the convergence to the stationary point of ℓ p regularization problem. Numerical results on simulated data exhibit an excellent performance of the proposed algorithms on approaching a sparse solution of the LCP. Finally, we apply this method to the frictional and frictionless contact problems. The numerical experiments demonstrate that the contact problems can be efficiently solved by the proposed algorithm. • The ℓ p regularization problem with nonnegative constraint is proposed for sparse LCP. • The iterative reweighted algorithms are proposed with convergence guarantees. • Different merit functions for LCP are discussed in numerical experiments. • Numerical simulations demonstrate the superiority of proposed algorithms. • Application to contact problem exhibits the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

The distributed online convex optimization problem with time-varying constraints for multi-agent networks is addressed in this article. The purpose is to optimize a sequence of time-varying global cost functions defined as the accumulated values of local cost functions, also attempt to meet the requirement of a sequence of time-varying coupled constraint functions which denote the sum of local constraint functions. Cost functions and constraint functions are unknown to agents beforehand. It is supposed that each agent in the network communicates with its neighbours through a uniformly strongly connected sequence of time-varying directed communication topologies. This paper proposes the bandit distributed primal-dual mirror descent push-sum (BDPDMDPS) algorithm constructed by bandit primal-dual, mirror descent and push-sum methods. Operational performance of the presented algorithm is measured by expected regret and expected constraint violation, both of which are proved to be sublinear with respect to the total iteration span T in this paper. Finally, a numerical simulation example is shown, which confirms the results for expected regret and expected constraint violation of BDPDMDPS algorithm. [ABSTRACT FROM AUTHOR]

A continuous-time distributed algorithm based on multi-agent systems is proposed to solve the convex nonsmooth optimization with globally coupled constraints in this paper. Firstly, the relationship of equivalence between the equilibrium and the optimal solution of the system is proved by using the properties of projection operator and saddle point. In addition, the stability of the algorithm is analyzed by means of Lie derivative and set-valued LaSalle's invariance principle. In particular, the trajectory of the proposed algorithm from any initial point can converge to the global optimal solution. Finally, the effectiveness of the designed algorithm is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper studies the differential privacy-preserving problem for multi-agent systems (MASs) in the presence of antagonistic information over signed digraph. As for the structurally balanced case, an ε -differential privacy algorithm is proposed, upon which some sufficient conditions guaranteeing almost sure bipartite consensus are given. Based on the above scheme, the tradeoff between the system performance and the privacy guarantee is elaborated, and the optimal noise is also devised. Moreover, the proposed privacy preserving scheme is further applied to the scenario of structurally unbalanced graph, where a criterion with respect to almost sure stability of the considered system is derived, as well as the privacy preserving condition. This extends the balanced interaction scenario, and consequently the cooperative multi-agent systems. Finally, numerical simulations are presented to demonstrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the development and application of optimally efficient numerical methods for the simulation of vascular tumour growth. This model used involves the flow and interaction of four different, but coupled, phases which are each treated as incompressible fluids, Hubbard and Byrne (2013). A finite volume scheme is used to approximate mass conservation, with conforming finite element schemes to approximate momentum conservation and an associated equation. The principal contribution of this paper is the development of a novel block preconditioner for solving the linear systems arising from the discrete momentum equations at each time step. In particular, the preconditioned system has both a solution time and a memory requirement that is shown to scale almost linearly with the problem size. [ABSTRACT FROM AUTHOR]

In this paper, a randomized parallel algorithm is proposed to solve the distributed optimal consensus problem of multi-agent systems. Involving both the transient response and the final consensus state, the problem is described as a constrained non-separable optimization problem. Inspired by the randomized Jacobi proximal alternating direction method of multipliers, the proposed algorithm makes it possible for only a fraction of agents to solve their private subproblems in parallel at each iteration, which greatly saves computational resources and enhances running efficiency. The convergence analysis of the algorithm gives fully distributed convergence conditions. A trade-off between the convergence speed and resource savings is then obtained, where the convergence rate is estimated to be at least O 1 t . Furthermore, the algorithm can be accelerated to enjoy a convergence rate of O 1 t 2 by adaptively adjusting the auxiliary parameters properly. Numerical simulations demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comprehensive overview on various maximum power point tracking (MPPT) techniques, which have been recently designed, simulated and/or experimentally validated in the PV literature. The primary goal of each MPPT technique is to optimize the output of shaded/unshaded photovoltaic (PV) array under static and dynamic weather conditions. Though each MPPT technique has its own pros and cons, an optimized MPPT technique is characterized in many aspects like hardware and software simplicity, implementation, cost effectiveness, sensors required, popularity, accuracy and convergence speed. In this paper the rating of various MPPT methods has been done based on the benchmark P&O method. The rating criteria is separately calculated for the techniques that are capable to work in full-sun and partial shading conditions. A rule based table is set to evaluate the MPPT against the algorithm's complexity, hardware implementation, tracking speed, and steady state accuracy or detection of global maximum. Moreover, special consideration has been given to bio-inspired MPPT algorithms. The bio-inspired algorithms are compared side by side with their specific application in PV system. A tree diagram is also designed to see the emergence of partial shading algorithms over a period of time. The traits presented in this paper are novel and provide bottom-line for the researchers to select and implement an appropriate MPPT technique. Highlights • A new rating criteria to compare different MPPTs is presented. • The unique way of mapping of biological species with PV system is presented. • I-V curve tracing and voltage zones MPPTs are comprehensively discussed. [ABSTRACT FROM AUTHOR]

Abstract Dealing with high-dimensional dataset has always been an important problem and feature selection is one of useful tools. In this paper, we develop a new filter based supervised feature selection method by combining Trace Ratio Criterion of Linear Discriminant Analysis (TRC-LDA) and group sparsity regularization. The filter based supervised feature selection method is a classifier-independent method while the TRC-LDA criterion is a recently developed criterion for dimensionality reduction that can well preserve discriminative information of dataset. However, there are seldom methods by utilizing TRC-LDA criterion for feature selection. On the other hand, imposing the l2,0-norm to the projection matrix of TRC-LDA will force some rows in it to be zero while keep other rows nonzero making the index of nonzero rows to be the selected features, however, l 2,0 -nom minimizing problem is NP-hard and intractable. To solve the above problem, in this paper, we develop a new method, namely, Trace Ratio Criterion Discriminative Feature Selection (TRC-DFS), for feature selection. The proposed TRC-DFS has imposed l 2,1 -norm, i.e. an approximation of l 2,0 -norm, to the projection matrix W of TRC-LDA to achieve feature selection. As a result, the proposed TRC-DFS can both achieve feature selection as well as capture the discriminative structure of data. We also extend the proposed method with l 2, p -norm (0 < p < 1) regularization to grasp more sparse property and develop an iterative approach to calculate the optimal solution, which is rigorously proved to be converged. Extensive simulations based on several real-world datasets verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract Twin extreme learning machine (TELM) is an efficient and effective method for pattern classification, based on widely known extreme learning machine (ELM). However, TELM is mainly used to deal with supervised learning problems. In this paper, we extend TELM to handle semi-supervised learning problems and propose a novel Laplacian twin extreme learning machine (LapTELM), which simultaneously trains two related and paired semi-supervised ELMs with two nonparallel separating planes for the final classification. The proposed method exploits the geometry structure property of the unlabeled samples and incorporates it as a manifold regularization term. This allows LapTELM to reap the benefits of fully exploring the plentiful unlabeled samples while retaining the learning ability and efficiency of TELM. Moreover, the paper shows that semi-supervised and supervised TELM can form an unified learning framework. Compared with several mainstream semi-supervised learning methods, the experimental results on the synthetic and several real-world data sets verify the effectiveness and efficiency of LapTELM. [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]

• The improved PSO was employed to calibrating the micro-parameters. • Different combinations of micro-parameters of DEM model can be obtained. • More macro-parameters should be used to calibrate the micro-parameters of DEM model. The discrete element method (DEM) is commonly used for simulating the mechanical characteristics of rock materials; however, constructing a DEM model requires the specification of a number of microparameters. In this paper, to obtain the microparameters of the DEM model, the improved particle swarm optimization (PSO) calibration method was presented. Based on numerical simulation examples, the new approach is considered valid for calibrating the microparameters of the DEM model. Moreover, it is concluded that different sets of microparameters can be determined when few macroparameters are used, which indicates that the empirical formula between microparameters and macroparameters is not reliable. From the analysis of the numerical simulation results, it is suggested that more macroparameters should be used to calibrate the microparameters of the DEM model, and the corresponding numerical simulation results could be more reliable; otherwise, the generated numerical model may not accurately simulate the mechanical characteristics of rock materials. [ABSTRACT FROM AUTHOR]

This paper presents a distributed swarm system for small fixed-wing unmanned aerial vehicles (UAVs). In particular, to perform various missions with multiple UAVs that are densely gathered and collision free, a hybrid-flocking control algorithm is synthesized by using three types of control protocols: vector field guidance (for path following/loitering), augmented Cucker–Smale (ACS) model (for collective flocking behavior), and potential field (for collision avoidance). In particular, to address the issue of conflicts between different control protocols, the adaptive ACS model is proposed and the optimization problem is formulated to determine the suitable mixing weights of control protocols. We also design the transition of multiple operation modes and communication architecture for the swarm system. The system is evaluated using the proposed hybrid-flocking control algorithm by proof-of-concept real flight experiments using 18 small fixed-wing UAVs as well as extensive numerical simulations. Flight experiments are successfully performed for multiple consecutive tasks including the individual task, circular path loitering and elliptical path loitering while avoiding collisions among UAVs. • The distributed small fixed-wing UAV swarm system is designed. • The hybrid-flocking control algorithm is proposed by combining three control protocols. • The optimal mixing weight improves the mission performance. • Numerical simulations and flight experiments validate the hybrid-flocking algorithm. [ABSTRACT FROM AUTHOR]

• We firstly give an analysis solution of the proximal operator of the L1 over Linf in compressed sensing. • We construct a novel variant of fast iterative shrinkage-thresholding algorithm (FISTA) based on the Moreau envelop of the L1/Linf. • We design a specific and convergent variable-splitting scheme of the alternating direction method of multipliers (ADMM) by introducing a simple auxiliary variable. In this paper, we study minimization of the ratio of ℓ 1 and ℓ ∞ norms (ℓ 1 / ℓ ∞) as a nonconvex and sparsity-promoting metric for solving the unconstrained compressed sensing problems. To design some efficient algorithms, we derive a closed-form solution to the proximal operator of the ℓ 1 / ℓ ∞ function. With the newly variant of the shrinkage operator, we propose a novel variant of fast iterative shrinkage-thresholding algorithm (FISTA) and a specific variable-splitting scheme of the alternating direction method of multipliers (ADMM) which is guaranteed to have sub-sequential convergence. Experimentally, we construct extensive numerical simulations to demonstrate the efficiency of these two proposed approaches over the state-of-the-art algorithms in sparse recovery. [ABSTRACT FROM AUTHOR]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

In computer-aided manufacturing systems, generation strategies are classified into two main types according to the generation domain of the tool path: real space and the parametric domain. Although generation methods in the parametric domain defined using a parametric surface, such as a non-uniform rational B-spline surface, have the advantage of rapid calculation, tool path variations are limited because few methods using this type of tool path generation recognize features of the surface in the parametric domain. This study proposes a new algorithm for tool path generation that can generate machining points in the parametric domain without losing information about the distances among the corresponding points in real space. Using this proposed method, a tool path with a constant pitch in real space and an efficient tool path, such as a spiral path, can be rapidly calculated. This paper explains the theory behind this method and describes its implementation using free-form surfaces composed of a single patch. The proposed method was then expanded to surfaces composed of multiple patches. In this case, because correspondence between each surface patch is required, parametric boundary representation using a half-edge structure is also proposed in this paper. Parametric boundary representation can be used to represent connections among the outer and inner trimming boundaries of each surface patch on the parametric domain. Finally, the proposed method was implemented for two surfaces composed of multiple patches, and its effectiveness was confirmed by simulating and machining these surfaces. [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 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]

In the following paper we present a new type of optimization algorithms adapted for neural network training. These algorithms are based upon sequential operator splitting technique for some associated dynamical systems. Furthermore, we investigate through numerical simulations the empirical rate of convergence of these iterative schemes toward a local minimum of the loss function, with some suitable choices of the underlying hyper-parameters. We validate the convergence of these optimizers using the results of the accuracy and of the loss function on the MNIST, MNIST-Fashion and CIFAR 10 classification datasets. [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]

This paper examines a private distributed online convex optimization problem in which each agent strives to minimize the sum of objective functions while also tending to keep their individual objective functions confidential. We use differential privacy as the metric to safeguard each agent's privacy and offer a distributed online mirror descent technique that is differentially private. We demonstrate that for strongly convex objective functions, our proposed algorithm satisfies the expected regret bound of O ln T while maintaining differential privacy, where T is the number of iterations. The established expected regret bound matches the optimal theoretical regret bound with respect to T. We further illuminate the trade-off between the extent of privacy-preserving and the expected regret bound through numerical simulations. [ABSTRACT FROM AUTHOR]

• Improved iterative method based on Gerchberg-Saxton (GS) algorithm. • Red, green, and blue channels of images is recorded in three holograms. • Constraint that consists of modified amplitudes with a weighting factor. • Reconstruction for three layer images. Depth-division multiplexing (DDM) is distinguished for generating full-color holograms. However, this method is confronted with such issues as poor quality and severe crosstalk when reconstructing multiplane color images. In this paper, we propose an improved iterative method based on Gerchberg-Saxton (GS) algorithm to generate multiplane full-color phase-only holograms (POHs), and the information of red (R), green (G), and blue (B) channels of images is recorded in three POHs, respectively. In the iterative process, we introduce an improved hologram plane constraint that consists of modified amplitudes given a static weighting factor, and display the reconstruction results for three-layer images, which ultimately improve the quality of the reconstructed images and effectively avoid the crosstalk among different color channels. And compared with GS algorithm, our method can effectively balance the reconstruction quality of three-color channels of each image. The feasibility of the method is verified by numerical simulations and optical experiments. [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]

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]

This paper proposes a new pretension simulation algorithm and examines the pretension feasibility for a new type of cable dome: a negative Gaussian curvature cable dome. First, the paper gives a pretension simulation algorithm based on the fuzzy relationship between the prestress and internal force. Second, a negative Gaussian curvature cable dome is proposed and the corresponding experimental model is designed and pretensioned according to the simulation results from the proposed simulation algorithm. The results indicate that the pretension simulation algorithm is efficient and accurate for simulating the pretension process and guiding the practical pretension. And the results also confirm that the negative Gaussian curvature cable dome can be pretensioned successfully, thus it can be constructed as a new type of cable dome. [ABSTRACT FROM AUTHOR]

Harvesting daylight for improving building energy performance has become an important issue in recent years. Most energy simulation tools use a Daylight Factor (DF) based algorithm to capture the complexity of daylight behaviors. However, DF has a limitation for fully accounting realistic daylight conditions, and several modifications or integrations with other supporting tools were developed to compensate for this limitation. This paper uses the Kriging method to predict daylight conditions for a whole year, and couples this method with energy simulation tools. Kriging is a group of geo-statistical techniques used to interpolate the value of a random field at an unobserved location from discrete observed values at nearby locations. In previous research, the Kriging method showed promising outcomes; that result was significantly better than current daylight modeling in energy simulation tool, and very close to the results from advanced physics-based daylight simulation tool. However, Kriging also comes with certain limitations that require further investigation. One major hurdle was Kriging’s long computational time compared to current daylight models in energy simulation tools. For this reason, this paper aims to find possibilities to reduce computational time to realistic levels that can be applicable in practice. Furthermore, it proposes a method to integrate Kriging-based daylight model with an energy simulation tool that can plot hourly indoor light distribution. This proposal obtains more realistic results than current energy simulation daylight models, while reducing computational expense. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal stitching planning for two-dimensional profile measurement of large aspheric surface by applying small range profiler as the measuring machine. The originality of this paper reflects on two aspects: (1) According to the applied range of probe, the amount of overlap region and the geometric characteristic of large aspheric surface, a segmentation planning algorithm is presented to determine the segment parameters during the measuring process. Considering the measurement efficiency and stitching accuracy synthetically, the applied range of probe with ±4 mm and the amount of overlap region with 20% of segment length are regarded as the optimal parameters. (2) Based on the invariance of curvature radius of overlap region, a data matching algorithm is developed to match the adjacent segments. Through the simulation analysis, the deviation of adjacent segments is reduced to 0.03 mm with the sampling interval of 0.01 mm. Then on the basis of the simulation settings with the translation error of 1 μm and the rotation error of 30′, the accumulative stitching errors of three different curves are predicted within sub-micron level. Finally the optimal stitching method is verified by experiments of two groups of 110-mm aspheric curves. The PV (Peak-to-valley) values of the stitching errors of the two curves are in the range of ±1 μm and the standard deviations are less than 0.2 μm. [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 • 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 • 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]

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]

Highlights • A time-dependent simulation mode has been implemented in the Serpent 2 code. • The reactor physics modeling of the SPERT transients have been performed using Serpent 2. • The results of the reactor physics simulations without fuel temperature feedback are in very good agreement with the experimental data. Abstract Monte Carlo multi-physics modeling of reactor transients is being made feasible by the advancement of computer capacities. Developments have recently been made in the area of dynamic Monte Carlo methods, and corresponding new algorithms have been implemented in a number of transport codes. In particular, a time-dependent simulation mode has been implemented in the Serpent 2 code. This paper is focused on time-dependent simulations of the SPERT experiments. The results of reactor-physics simulations are presented. The modeling has been performed using Serpent 2. The results of the reactor physics simulations without fuel temperature feedback are in very good agreement with the experimental data. [ABSTRACT FROM AUTHOR]

Highlights • The dual-level control scheme can meet the frequency regulation requirement and the individual PEV charging requirements. • The consensus algorithm is applicable for distributed control areas and PEVs power coordination with limited communication: every control area/PEV only needs to exchange information among the neighboring control areas/PEVs instead of all the agents. • This method can be applied in real-time simulation. The optimal charging commands can be obtained and updated in every time step based on the current frequency deviation condition and electricity price. Abstract To coordinate plug-in electric vehicles (PEVs) to support the primary frequency control, this paper presents a dual-level consensus-based frequency control method. The upper-level control aims to minimize the frequency deviation of multi-area power system, and the lower-level control aims to minimize the frequency regulation cost and battery degradation cost for individual PEVs. The incremental cost functions are formulated based on the objectives of the dual control levels, in this case, no objective function needs to be solved, and instead, the optimal results can be achieved by reaching a consensus incremental cost among neighboring individuals. The graph theory is applied to build the communication network among neighboring control areas in upper-level control, and among neighboring PEVs in lower-level control. The consensus algorithm is applied to specify the information updating and exchange among neighboring individuals. In addition, the dynamic models of multi-area power system and PEVs are developed for controller implementation and investigation. The simulation with different case studies are conducted to validate the performance of the proposed dual-level frequency control. The simulation results show that, with limited communication, the connected PEVs can be efficiently coordinated via dual-level controller to provide primary frequency regulation service, and meanwhile meet the battery charging/discharging constraints. [ABSTRACT FROM AUTHOR]

Highlights • The modified Distributed Gradient Projection algorithm with a constant step size can handle non-strictly and convex consumer costs. • We establish a bound on the step size such that the algorithm behaves in a stable manner. • Numerical tests with the IEEE 39-bus system show that predictions are still accurate even when consumer costs are non-convex. • The algorithm is robust to model mismatch and time delays. • Its performance suffers if the dynamic response of the loads is slow. Abstract The Distributed Gradient Projection (DGP) algorithm was proposed in prior work to allow loads to provide contingency service to the grid using local noisy frequency measurements by varying their demand. Convergence of DGP was established in prior work for a decaying step size. In this paper we modify the algorithm to using a constant step size—constant step size being much more useful for practical implementation. We provide a convergence analysis of the modified algorithm, which we call DGP-C (DGP with Constant step size) and perform extensive simulations in the IEEE 39-bus test system. These studies (i) demonstrate that the DGP-C algorithm is robust to several assumptions made in the analysis and (ii) reveal which factors among the many tested (measurement noise, loads' response time, etc.) have significant effect on the algorithm's performance. [ABSTRACT FROM AUTHOR]

Abstract One of the most crucial tasks in hydrocarbon field development, it is an optimal wells placement. Simulation-based well placement optimization can be intricate to implement or computationally expensive. In this paper, a novel approach for the reduction of computational challenges by combining population-base optimization algorithm with self-adapt reservoir clusterization method to determine initial well placement position for the similar producing zones is presented. The developed methodology supported initial well placement location and allowed to shrink search space area. As a result, the computational time was decreased by 18% for particle swarm optimization and by 21% for genetic algorithm. Additionally, the project profitability was increased by 0.24% in case of genetic algorithm utilization and 0.59% for particle swarm optimization, what confirms the necessity for precise well placement localization. The average convergence factor for the employed algorithms increased from 18.43% to 60.78% for genetic algorithm and from 41.71% to 46.96% for particle swarm algorithm. An additional advantage of the developed clustering method is a notable reduction in time to reach high solution diversity in particle swarm algorithm. Increasing SOM training time for the large data set is negligible in comparison with full reservoir simulation run. The proposed methodology is based on the typical numerical model without requirement of additional dataset or streamline and can be easily transferred from field to field. The power and the workflow utility are demonstrated by the results of significant improvement for the algorithm's convergence rate and time. Highlights • Novel self-adapt methodology to enhance well placement optimization was developed. • Significant reduction of optimization time was observed. • Artificial Kohonen neural network was implemented to solve complex engineering problem. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with the decentralized optimal tracking control problem of large-scale interconnected systems with constrained-input. The large-scale interconnected systems are firstly transformed to several nominal isolated subsystems. Then, nominal isolated subsystems tracking problem is solved via integral reinforcement learning (IRL) method. It is proved that the solved optimal controllers ensure the boundedness of the original systems tracking error. The actor-critic neural network (NN) technique is used to approximate the critic cost and control policy to implement the IRL algorithm. The least squares approach is employed to solve the weights of actor-critic NN by using only system data. A simulation example is provided to verify the effectiveness of the controllers by comparing with the controllers without considering constrained-input. [ABSTRACT FROM AUTHOR]

Abstract Model predictive control strategies have been applied successfully when controlling solar plants. If the control algorithm uses a linear model associated only to an operating point, when the plant is working far from the design conditions, the performance of the controller may deteriorate. In this paper, a gain scheduling model predictive control strategy is designed for the Fresnel collector field located at the Escuela Superior de Ingenieros de Sevilla. Simulation results are provided comparing the proposed strategy with another linear MPC controller showing a better performance. Furthermore, two real tests are presented showing the effectiveness of the proposed strategy. Highlights • Advances control techniques play an important role in operating solar plants. • Simple linear control strategies do not perform well in the entire operation range. • Thus, adaptative or nonlinear control strategies are needed. • A gain scheduling GPC is developed. • Simulations and real tests show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the windup problem in active vibration control (AVC) is studied systematically. Instead of evaluating the performance of several anti-windup compensators implemented on independent abstract simulation problems, a unified benchmark setup in active-damping control (ADC) is used. The investigated anti-windup schemes (analysis and synthesis) are adapted to the disturbance rejection control. Large attention is given to capture the similarities and differences of the methods in dealing with the windup event in a practical context. Therefore, instead of categorizing the methods into static and non-static methods or model recovery and direct linear anti-windup schemes, a logical route is followed to highlight the significance of each method. The mathematical interpretations of the methods are provided for the vibration engineer while delivering forthright implementation algorithms for AVC. The tackled methods are unified on a state space model obtained from the frequency-domain subspace system identification approach. Practical issues that may raise for each technique are mentioned, and detained guidelines are provided for tuning each algorithm. Finally, in order to compare the compensated system's performance, comprehensive time-domain studies are carried out by separating the transient response of the compensated systems to three modes: linear mode, where the actuation nonlinearity is inactive; the nonlinear mode, where the windup event is in progress, and finally, the output mismatch rejection mode, where the windup incident is over, but performance degradation is still present. Highlights • A unified framework for anti-windup compensator design is provided. • The analysis and synthesis process are adapted for active vibration control problem. • Detailed experimental investigations are carried out for extraction of the true goal of anti-windup compensators. [ABSTRACT FROM AUTHOR]

Highlights • The Tobit Kalman filtering problem is studied for discrete time-varying systems. • Non-Gaussian Levy and time-correlated additive measurement noises are considered. • The measurement differencing method and Levy-Ito theorem are utilized. • A modified recursive filtering algorithm is designed where the unknown noise covariances are carefully calculated. • Simulation results are provided to illustrate the effectiveness of the proposed filter. Abstract The Tobit Kalman filter (TKF) is a powerful tool in solving the state estimation problem for linear systems with censored measurements. This paper is concerned with the Tobit Kalman filtering problem for discrete time-varying systems subject to non-Gaussian Lévy and time-correlated additive measurement noises. By referencing to the measurement differencing method, the time-correlation of the measurement noises is transformed into the cross-correlation between the equivalent measurement noise and the process noise. Then, by resorting to the Lévy-Ito theorem, the non-Gaussian Lévy measurement noises are transformed into equivalent Gaussian noises with unknown covariances. Based on the transformed Gaussian measurement noises, a modified recursive TKF is designed where the unknown noise covariances are carefully calculated. Simulation results are provided to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

Highlights • An efficient algorithm in terms of convergence rate and resource usage to calibrate microscopic traffic models. • A robust algorithm in terms of the probability of finding the global optimum solution in the different calibration configurations. • Considering dependency between variables by learning and sampling from a copula-based probabilistic model. • Possibility of extracting knowledge with regard to the calibration problem through analyzing the probabilistic models that are constructed during the evolution process. Abstract The importance of calibration of microscopic traffic models as the main core of traffic simulation software results from the need for more realistic traffic behaviors. The latent essence of several parameters in such models as well as the uncertainties resulting from the noise in the data, make the process of calibration much more complex. Usually, the calibration process is formulated as an optimization problem. Selecting the appropriate solution algorithm due to nonlinear and non-convex nature of the problem is crucial. The importance of the issue is more significant when the matter of calibrating the medium or large-scale simulation model is considered. This is mainly due to the expensive cost that running the simulation models impose. Therefore, applying the current algorithms in which finding the appropriate solutions requires a large number of simulation runs is not deemed suitable. In this paper, an estimation of distribution algorithm based on copula theory has been suggested. In contrast with traditional solution algorithms, in the proposed algorithm complex interaction between parameters of a model has been considered by constructing and sampling from a copula-based probabilistic model. Copulas are functions that describe the dependence structure of a set of random variables and connect multivariate distribution functions to one-dimensional marginal distribution functions. The results indicate that applying an explicit probabilistic model based on copula helps the estimation of distribution algorithm to explore the search space more effectively and efficiently as well as provides the possibility of extracting the knowledge with regard to the structure of the calibration problem through analyzing the probabilistic models that are constructed during the evolution process. Furthermore, this new algorithm has been compared with the genetic algorithm and kernel-based cross-entropy method on synthetic and real trajectory data. The results confirm that the proposed algorithm is more efficient in terms of convergence rate, resource usage and more robust in terms of the probability of finding the global optimal solution. [ABSTRACT FROM AUTHOR]

Abstract In the scope of linear anisotropic elasticity, the fourth-order elasticity tensor or tetrad has to be identified. This can be done either by measurements or by numerical simulations. An important task is then to identify a given tetrad, probably with some experimental or numerical scattering, with one of the symmetry classes. For this purpose one needs a distance function between a given tetrad and the class of all tetrads with a particular symmetry, which is zero if the tetrad obeys this symmetry, or non-zero otherwise. In this paper we present a fast method to solve these problems. We firstly introduce the 8th order projectors that map any stiffness tetrad into the part that is invariant under the action of a specific fixed symmetry group. For this purpose we consider the seven out of the eight symmetry classes that are distinguishable in linear elasticity. Secondly, since the symmetry axes of the specific stiffness tensor under consideration is generally not aligned with the used tensorial basis of the projector, we need to rotate the sample stiffness. The optimal orientation is obtained when the distance between the rotated stiffness and the rotated and projected stiffness is minimal. Thus, we need to apply only linear mappings and minimize over three Euler angles. The latter is quite simple, as the domain of the Euler angles is periodic, and the number of local minima is limited. This procedure has the advantage that it is applicable in an algorithmic manner, and does not require an a priori identification of symmetry planes, symmetry axes or component symmetries, which are only apparent under special choices for the tensorial basis. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the adaptive consensus problem of multi-agent nonlinear systems with odd impulsive sequence. To reduce the required information transmitted among the nodes, the distributed impulsive control protocol is designed. Considering the unknown system parameters in real system, the adaptive control technique is also used and some novel sufficient consensus conditions are derived to achieve the consensus of multi-agent nonlinear systems. The proposed odd impulsive control can obtain larger control region than the common impulsive consensus method with fixed impulsive instants, which is more flexible and reliable in practical application. Finally, two simulations are given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]