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To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi‐strategy self‐optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full‐rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition‐based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy–Gaussian hybrid variation strategy based on similarity and density is devised to achieve self‐learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine‐exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed. [ABSTRACT FROM AUTHOR]

Automated formal verification is often based on the Counterexample-Guided Abstraction Refinement (CEGAR) approach. Many variants of CEGAR have been developed over the years as different problem domains usually require different strategies for efficient verification. This has lead to generic and configurable CEGAR frameworks, which can incorporate various algorithms. In our paper we propose six novel improvements to different aspects of the CEGAR approach, including both abstraction and refinement. We implement our new contributions in the Theta framework allowing us to compare them with state-of-the-art algorithms. We conduct an experiment on a diverse set of models to address research questions related to the effectiveness and efficiency of our new strategies. Results show that our new contributions perform well in general. Moreover, we highlight certain cases where performance could not be increased or where a remarkable improvement is achieved. [ABSTRACT FROM AUTHOR]

The paper presents algorithms for parameter identification of linear vessel models being in force for the current operating point of a ship. Advantages and disadvantages of gradient and genetic algorithms in identifying the model parameters are discussed. The study is supported by presentation of identification results for a nonlinear model of a drilling vessel. [ABSTRACT FROM AUTHOR]

In this paper, an identification scheme for sandwich systems with a dead zone is proposed. In this scheme, a so-called combinational input is proposed to transform the sandwich model with a dead zone into a model with separated parameters. Then, the recursive general identification algorithm can be applied to the parameter-estimation, since the transformed model has a form that is linearized in coefficients. Finally, the proposed method is applied to the identification of an X–Y moving positioning stage. [ABSTRACT FROM PUBLISHER]

In this paper, we derive a mathematical model for dual-rate systems and present a stochastic gradient identification algorithm to estimate the model parameters and an output estimation algorithm to compute the intersample outputs based on the dual-rate input-output data directly. Moreover, we investigate convergence properties of the parameter and intersample estimation, and we test the proposed algorithms with example systems, including an experimental water-level system. [ABSTRACT FROM AUTHOR]

This paper presents methods of modeling and predicting face recognition (FR) system performance based on analysis of similarity scores. We define the performance of an FR system as its recognition accuracy, and consider the intrinsic and extrinsic factors affecting its performance. The intrinsic factors of an FR system include the gallery images, the FR algorithm, and the tuning parameters. The extrinsic factors include mainly query image conditions. For performance modeling, we propose the concept of "perfect recognition," based on which a performance metric is extracted from perfect recognition similarity scores (PRSS) to relate the performance of an FR system to its intrinsic factors. The PRSS performance metric allows tuning FR algorithm parameters offline for near optimal performance. In addition, the performance metric extracted from query images is used to adjust face alignment parameters online for improved performance. For online prediction of the performance of an FR system on query images, features are extracted from the actual recognition similarity scores and their corresponding PRSS. Using such features, we can predict online if an individual query image can be correctly matched by the FR system, based on which we can reduce the incorrect match rates. Experimental results demonstrate that the performance of an FR system can be significantly improved using the presented methods. [ABSTRACT FROM AUTHOR]

Ring signature is a kind of digital signature which can protect the identity of the signer. Certificateless public key cryptography not only overcomes key escrow problem but also does not lose some advantages of identity-based cryptography. Certificateless ring signature integrates ring signature with certificateless public key cryptography. In this paper, we propose an efficient certificateless ring signature; it has only three bilinear pairing operations in the verify algorithm. The scheme is proved to be unforgeable in the random oracle model. [ABSTRACT FROM AUTHOR]

We present an algorithm for the identification of the relaxation kernel in the theory of diffusion systems with memory (or of viscoelasticity), which is linear, in the sense that we propose a linear Volterra integral equation of convolution type whose solution is the relaxation kernel. The algorithm is based on the observation of the flux through a part of the boundary of a body. The identification of the relaxation kernel is ill posed, as we should expect from an inverse problem. In fact, we shall see that it is mildly ill posed, precisely as the deconvolution problem which has to be solved in the algorithm we propose. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Developing a model based digital human meridian system is one of the interesting ways of understanding and improving acupuncture treatment, safety analysis for acupuncture operation, doctor training, or treatment scheme evaluation. In accomplishing this task, how to construct a proper model to describe the behavior of human meridian systems is one of the very important issues. From experiments, it has been found that the hysteresis phenomenon occurs in the relations between stimulation input and the corresponding response of meridian systems. Therefore, the modeling of hysteresis in a human meridian system is an unavoidable task for the construction of model based digital human meridian systems. As hysteresis is a nonsmooth, nonlinear and dynamic system with a multi-valued mapping, the conventional identification method is difficult to be employed to model its behavior directly. In this paper, a neural network based identification method of hysteresis occurring in human meridian systems is presented. In this modeling scheme, an expanded input space is constructed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. For this purpose, a modified hysteretic operator is proposed to handle the extremum-missing problem. Then, based on the constructed expanded input space with the modified hysteretic operator, the so-called Extreme Learning Machine (ELM) neural network is utilized to model hysteresis inherent in human meridian systems. As hysteresis in meridian system is a dynamic system, a dynamic ELM neural network is developed. In the proposed dynamic ELM neural network, the output state of each hidden neuron is fed back to its own input to describe the dynamic behavior of hysteresis. The training of the recurrent ELM neural network is based on the least-squares algorithm with QR decomposition. [ABSTRACT FROM AUTHOR]

The main purpose of the paper was to present a new approach to the mathematical modelling of ciliary movements. This approach is based on the solution of the inverse problem of the dynamics, which is described by Lagrange's equations for the system of successively hinged rigid rods. In this case, as is well known, the generalized forces may be found as the time functions. It is proposed to represent these functions by the functions of generalized coordinates and velocities and also of the model parameters, whose values are determined as a solution of the parametric optimization problem. Besides, a special algorithm of ciliary movement control was elaborated. This algorithm is based on the hypothesis of variation of the equilibrium positions for cilia during one cycle of beating. The numerical results are in a good agreement with the cilia movements observed in Paramecium multimicronucleatum. [ABSTRACT FROM AUTHOR]

In this paper, a simplified system identification procedure is developed to investigate the dynamic characteristics of base-isolated buildings equipped with lead-rubber bearings (LRBs). The multistory superstructure is assumed to be linear on the account of substantial reduction in seismic forces due to the installation of LRBs for which a bilinear hysteretic model is considered. The hysteretic model is in turn characterized by a backbone curve by which the multivalued restoring force is transformed into a single-valued function. With the introduction of backbone curves, the system identification analysis of inelastic structures is simplified to a large extent. The proposed algorithm extracts individually the physical parameters of each floor and the bearing system that are considered useful information in the structural health monitoring. A numerical example is conducted to demonstrate the feasibility of using the proposed technique for physical-parameter identification of partially inelastic based-isolated buildings. [ABSTRACT FROM AUTHOR]

This paper presents a short review of the state-of-the-art methods to identify bridge deck flutter derivatives and proposes a new algorithm to simultaneously extract the aeroelastic coefficients from free-vibration section-model tests, which is based on the improvement of the unifying least-squares (ULS) method and is therefore called modified unifying least-squares method. The advantages with respect to ULS are the faster and better convergence and the improvement in accuracy due to the introduction of weighting factors in the unifying error function. The method has been validated through numerically simulated noisy signals and experimental heaving and pitching time histories for two different bridge deck cross sections: a single-box and a multiple-box girder section model. The analysis of the artificial signals shows that a few system parameters are very difficult to be identified due to the fact that the problem is strongly ill-conditioned. Nevertheless, all the diagonal and off-diagonal components of the stiffness and damping matrices which significantly contribute to the output of the system are correctly estimated. The improvement with respect to other methods is extensively discussed. For the wind-tunnel test cases the accuracy of the identification procedure is evaluated through the comparison between measured signals and those simulated through the estimated mechanical and aerodynamic system parameters with very satisfactory results. With respect to many previous attempts of validation, this approach clearly shows the degree of accuracy that can be expected from the identification algorithm. Finally, for the considered test cases the linear model which stands behind the method seems to be an acceptable approximation of the physics of the phenomenon. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity and transition dynamics between different operating points of the high purity distillation column process, it is difficult to use a single model for modeling such a process. Therefore, the multiple model based approach is introduced for modeling the high purity distillation column plant under the framework of the expectation maximization (EM) algorithm. In this paper, autoregressive exogenous (ARX) models are adopted to construct the local models of this chemical process at different operating points, and the EM algorithm is used for identification of local models as well as the probability that each local model takes effect. The global model is obtained by aggregating the local models using an exponential weighting function. Finally, the simulation performed on the high purity distillation column demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We introduce the notion of a centroidal locating set of a graph G, that is, a set L of vertices such that all vertices in G are uniquely determined by their relative distances to the vertices of L. A centroidal locating set of G of minimum size is called a centroidal basis, and its size is the centroidal dimension [ABSTRACT FROM AUTHOR]

This article deals with identification of cascade dynamic systems including backlash. A new form of backlash description is leading to mathematical models of the cascade system with output backlash and the sandwich system with internal backlash, which have all the model parameters separated. The identification based on these models is solved as a quasi-linear problem using iterative algorithms with internal variable estimation. More illustrative examples are included. [ABSTRACT FROM AUTHOR]

The lithium-ion battery is a complicated non-linear system with multi electrochemical processes including mass and charge conservations as well as electrochemical kinetics. The calculation process of the electrochemical model depends on an in-depth understanding of the physicochemical characteristics and parameters, which can be costly and time-consuming. We investigated the electrochemical modeling, reduction, and identification methods of the lithium-ion battery from the electrode-level to the system-level. A reduced 9th order linear model was proposed using electrode-level physicochemical modeling and the cell-level mathematical reduction method. The data-driven predictor-based subspace identification algorithm was presented for the estimation of lithium-ion battery model in the system-level. The effectiveness of the proposed modeling and identification methods was validated in an experimental study based on LiFePO4 cells. The accuracy and dynamic characteristics of the identified model were found to be much more likely related to the operating State of Charge (SOC) range. Experimental results showed that the proposed methods perform well with high precision and good robustness in the SOC range of 90% to 10%, and the tracking error increases significantly within higher (100–90%) or lower (10–0%) SOC ranges. Moreover, to achieve an optimal balance between high-precision and low complexity, statistical analysis revealed that the 6th, 3rd, and 5th order battery model is the optimal choice in the SOC range of 90% to 100%, 90% to 10%, and 10% to 0%, respectively. [ABSTRACT FROM AUTHOR]

Summary: In this work, we propose a highly efficient binary tree‐based anti‐collision algorithm for radio frequency identification (RFID) tag identification. The proposed binary splitting modified dynamic tree (BS‐MDT) algorithm employs a binary splitting tree to achieve accurate tag estimation and a modified dynamic tree algorithm for rapid tag identification. We mathematically evaluate the performance of the BS‐MDT algorithm in terms of the system efficiency and the time system efficiency based on the ISO/IEC 18000‐6 Type B standard. The derived mathematical model is validated using computer simulations. Numerical results show that the proposed BS‐MDT algorithm can provide the system efficiency of 46% and time system efficiency of 74%, outperforming all other well‐performed algorithms. [ABSTRACT FROM AUTHOR]