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The purpose of this paper is to propose an improved differential evolution algorithm (DEA) suitable for motor’s model identification.

The mutation operation of the standard DEA is improved, and the adaptive coefficient is designed to adjust the optimization process.

The application of motor model identification shows that the proposed improved DEA is more robust, with higher modeling accuracy and efficiency, and is more suitable for motor identification modeling applications. Compared with the ultrasonic motor model established by using particle swarm algorithm, the model established in this paper has higher precision.

This paper explores an improved DEA suitable for motor identification modeling. The algorithm can not only obtain the optimal solution but also effectively reduce the iterative generations and time required in the process of optimization identification.

The purpose of this paper is to introduce a method for stiffness modeling, identification and updating of collaborative robots (cobots). This method operates in real-time and with high precision and can eliminate the modeling error between the actual stiffness model and the theoretical stiffness model.

To simultaneously ensure the computational efficiency and modeling accuracy of the stiffness model, this method introduces the finite element substructure method (FESM) into the virtual joint method (VJM). The stiffness model of the cobots is built by integrating several 6-degree of freedom virtual joints that represent the elastic deformation of the cobot modules, and the stiffness matrices of these modules can be identified and obtained by the FESM. A model-updating method is proposed to identify stiffness influence coefficients, which can eliminate the modeling error between the actual prototype model and the theoretical finite element model.

The average relative error and the cycle time of the proposed method are approximately 6.14% and 1.31 ms, respectively. Compared with other stiffness modeling methods, this method not only has high modeling accuracy in high dexterity poses but also in low dexterity poses.

A hybrid stiffness modeling method is introduced to integrate the modeling accuracy of the FESM into the VJM. Stiffness influence coefficients are proposed to eliminate the modeling error between the theoretical and actual stiffness models.

With organizations hiring from increasingly diverse labor markets, this study aims to examine the implications of newcomers’ individual differentiation for their group identification. The paper proposes and tests a self-verification process in which individual differentiation predicts group identification through role innovation under positive social feedback on innovation (moderated mediation). Simultaneously, a self-categorization pathway is examined of the indirect negative influence of individual differentiation on group identification through role modeling (mediation).

Survey data were collected at three time points from 161 UK university alumni.

The analyses support a self-verification pathway: newcomers with high individual differentiation report higher group identification via role innovation only when they receive positive feedback on their innovative actions. However, there was no support for a self-categorization pathway, with no indirect relationship found between individual differentiation and group identification via role modeling.

HR practitioners and managers who are responsible for helping newcomers adjust should consider newcomers’ individual differentiation. Specifically, newcomers with high individual differentiation may more successfully navigate their transition and identify with their workgroup when given appropriate support, such as positive social feedback on their innovative actions.

The study extends organizational socialization research by focusing on when newcomers with high individual differentiation may experience group identification. The findings highlight the important role of positive social feedback on group identification; this suggests a potential means by which newcomers with high individual differentiation can settle successfully.

This paper aims to build an accurate mathematical model which is necessary for control design and attitude estimation of a miniature unmanned rotorcraft and its subsequent conversion to an autonomous vehicle.

Frequency-domain system identification of a small-size flybar-less remote controlled helicopter is carried out based on the input–output data collected from flight tests of the instrumented vehicle. A complete six degrees of freedom quasi-steady dynamic model is derived for hover and cruise flight conditions.

The veracity of the developed model is ascertained by comparing the predicted model responses to the actual responses from flight experiments and from statistical measures. Dynamic stability analysis of the vehicle is carried out using eigenvalues and eigenvectors. The identified model represents the vehicle dynamics very well in the frequency range of interest.

The model needs to be augmented with additional terms to represent the high-frequency dynamics of the vehicle.

Control algorithms developed using the first principles model can be easily reconfigured using the identified model, because the model structure is not altered during identification.

This paper gives a practical solution for model identification and stability analysis of a small-scale flybar-less helicopter. The estimated model can be easily used in developing control algorithms.

The purpose of this paper is to develop a hybrid algorithm using differential evolution (DE) and prediction error modeling (PEM) for identification of small-scale autonomous helicopter state-space model.

In this study, flight data were collected and analyzed; MATLAB-based system identification algorithm was developed using DE and PEM; parameterized state-space model parameters were estimated using the developed algorithm and model dynamic analysis.

The proposed hybrid algorithm improves the performance of the PEM algorithm in the identification of an autonomous helicopter model. It gives better results when compared with conventional PEM algorithm inside MATLAB toolboxes.

This study is applicable to only linearized state-space model.

The identification algorithm is expected to facilitate the required model development for model-based control design for autonomous helicopter development.

This study presents a novel hybrid algorithm for system identification of an autonomous helicopter model.

The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy (SMA) actuators, as one of the most applicable types of actuators in engineering and industry. The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators.

To address the requirements of designing a pragmatic identification system, the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion. First, the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model. At the next step, they adopt a computationally efficient metaheuristic algorithm to elicit the optimum operating parameters of the gray box identifier.

Applying the proposed hybrid identifier framework allows the authors to find out its advantages in modeling the behavior of SMA actuator. Through different experiments, the authors conclude that the proposed identifier can be used for identification of highly nonlinear dynamic behavior of SMA actuators. Furthermore, by extending the conclusions and expounding the obtained results, one can easily infer that such a hybrid method may be conveniently applied to model other engineering phenomena that possess dynamic nonlinear reactions. Based on the exerted experiments and implementing the method, the authors come to the conclusion that integrating the power of metaheuristic exploration/exploitation with gray box identifier results a predictive paradigm that much more computationally efficient as compared with black box identifiers such as neural networks. Additionally, the derived gray box method has a higher degree of preference over the black box identifiers, as it allows a manipulated expert to extract the knowledge of the system at hand.

The originality of the research paper is twofold. From the practical (engineering) point of view, the authors built a prototype biased-spring SMA actuator and carried out several experiments to ascertain and validate the parameters of the model. From the computational point of view, the authors seek for designing a novel identifier that overcomes the main flaws associated with the performance of black-box identifiers that are the lack of a mean for extracting the governing knowledge of the system at hand, and high computational expense pertinent to the structure of black-box identifiers.

Business process (BP) reengineering is defined as reinventing BPs either structurally or technically to achieve dramatic improvements in performance. In any business process reengineering (BPR) project, process modeling is used to reason about problems found in existing (as-is) process and helps to design target (to-be) process. BP model notation is a widely accepted standard for process modeling. “Expressiveness” and “missing formal semantics” are two problems reported to its modeling practices. In existing studies, solutions to these problems are also proposed but still have certain limitations. The paper aims to discuss this issue.

In proposed methodology, a meta-model is formally defined that is composed of commonly used modeling elements and their well-formedness rules to check for syntactic and structural correctness of process models. Proposed solution also check semantics of process models and allows to compare as-is and to-be process models for gap identification which is another important aspect of BPR. To achieve the first goal, Z specification is used to provide formal specifications of modeling constructs and their rules and Z3 (an SMT solver) is used for comparisons and verifying properties.

Proposed method addresses both “expressiveness” and “missing formal semantics” of BPR models. The results of its evaluation clearly indicate that using formally specified meta-model, BPR model is syntactically and structurally correct. Moreover, formal modeling of BPs in Z3 helped to compare processes and to check control flow properties.

Although the proposed method is tested on an example that is widely used in BPR literature, the example is only covering modeling elements which are part of the proposed subset and are reported in literature as frequently used modeling elements. A separate detailed study is required to test it on more complex systems.

Specifying process models using Z specification and Z3 solver requires certain expertise.

The proposed method adds value to BPR body of knowledge as it proposes a method to ensure structural and syntactic correctness of models, highlighting the importance of verifying run time properties and providing a direction toward comparing process models for gap analysis.

The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

The purpose of the present study was to evaluate the role of context in the identification of learning disabilities (LD) within the responsiveness-to-intervention (RTI) model. In Study 1, using a sample of students with and without LD (N=167) and data from a reading assessment, we tested whether the decision making regarding literacy disabilities is significantly different if we take into account variability within the schools and school characteristics. Initially a logistic multilevel model was fit to the data to assess prevalence rates of LD identification. The validity of these estimates was substantiated by bootstrapping the sample's parameters using 1,000 replications and by evidencing negligible bias parameters. Subsequently, the relationship between reading ability and LD identification was established by means of a multilevel model including random effects. The significant slopes linking reading to LD identification (i.e., fluency and overall reading ability ratings by teachers) were predicted by cross-level interactions involving schools' location (rural, urban, and suburban). The results of Study 1 demonstrated the moderating role of school context, as the slopes linking fluency and reading achievement to LD placement were moderated by the area in which a school was located. Study 2 was designed to present a relative discrepancy identification model by taking into account information from the school (i.e., district). Using 29 students from one district, whose writing ability was evaluated three times within the semester, comparisons were made between a specific low-ability student and the rest of his/her class. Through fitting a multilevel model in which within-student and between-student variance was assessed, Study 2 demonstrated that the specific pattern of responsiveness of a target student can be tested against the norm of his/her school district in order to have a more sensitive relative criterion of what constitutes both responsiveness and the norm. Thus, by utilizing a multilevel framework that involves school characteristics into our assessment we demonstrated that decision making is much more informative and likely more “accurate” under the RTI model. Certainly more research is needed to verify the usefulness and applicability of the proposed “relative slope-difference discrepancy model.”

The purpose of this paper is to apply the novel instantaneous power flow balance (IPFB)-based identification strategy to a specific practical situation like nonlinear lap joints having single and double bolts. The paper also investigates the identification performance of the proposed power flow method over conventional acceleration-matching (AM) methods and other methods in the literature for nonlinear identification.

A parametric model of the joint assembly formulated using generic beam element is used for numerically simulating the experimental response under sinusoidal excitations. The proposed method uses the concept of substructure IPFB criteria, whereby the algebraic sum of power flow components within a substructure is equal to zero, for the formulation of an objective function. The joint parameter identification problem was treated as an inverse formulation by minimizing the objective function using the Particle Swarm Optimization (PSO) algorithm, with the unknown parameters as the optimization variables.

The errors associated with identified numerical results through the instantaneous power flow approach have been compared with the conventional AM method using the same model and are found to be more accurate. The outcome of the proposed method is also compared with other nonlinear time-domain structural identification (SI) methods from the literature to show the acceptability of the results.

In this paper, the concept of IPFB-based identification method was extended to a more specific practical application of nonlinear joints which is not reported in the literature. Identification studies were carried out for both single-bolted and double-bolted lap joints with noise-free and noise-contamination cases. In the current study, only the zone of interest (substructure) needs to be modelled, thus reducing computational complexity, and only interface sensors are required in this method. If the force application point is outside the substructure, there is no need to measure the forcing response also.