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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.

This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes.

A system identification method based on the support vector machine (SVM) is developed and applied to the nonlinear dynamics of an aeroelastic aircraft. In the proposed non-parametric gray-box method, force and moment coefficients are estimated based on the state variables, flight conditions and control commands. Then, flight parameters are estimated using aircraft equations of motion. Nonlinear system identification is performed using the SVM network by minimizing errors between the calculated and estimated force and moment coefficients. To that end, a least squares algorithm is used as the training rule to optimize the generalization bound given for the regression.

The results confirm that the SVM is successful at the aircraft system identification. The precision of the SVM model is preserved when the models are excited by input commands different from the training ones. Also, the generalization of the SVM model is acceptable at non-trained flight conditions within the trained flight conditions. Considering the precision and generalization of the model, the results indicate that the SVM is more successful than the well-known methods such as artificial neural networks.

In this paper, both the simulated and real flight data of the F/A-18 aircraft are used to provide aeroelastic models for its lateral-directional dynamics.

This paper proposes a non-parametric system identification method for aeroelastic aircraft based on the SVM method for the first time. Up to the author’s best knowledge, the SVM is not used for the aircraft system identification or the aircraft parameter estimation until now.

To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness.

The paper is based on system theory and statistics, and it designs the main line of “relevant theoretical analysis – identification of indicators – expert assignment and quality inspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis, relevant factor analysis and physical process description are used to clarify the comprehensive evaluation problem and the correlation mechanism. Second, the system structure analysis, hierarchical decomposition and indicator set identification are used to complete the initial establishment of the indicator system. Third, based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliability and validity test is used to perform single-index assignment correction and consistency test is used for KENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis.

Compared with the traditional index system construction method, the optimization process used in the study standardizes the process of index establishment, reduces subjectivity and randomness, and enhances objectivity and scientificity.

The innovation point and value of the paper are embodied in three aspects. First, the system design process of the combined indicator system, the multi-dimensional index screening and system optimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second, the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment are analyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment, conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance of multiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.

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 this paper is to review maintenance strategies within the healthcare domain and to discuss practical needs as gaps between research and practice.

The paper systematically categorizes the published literature on clinical maintenance optimization and then synthesizes it methodically.

This study highlights the significant issues relevant to the application of dependability analysis in healthcare maintenance, including the quantitative and qualitative criteria taken into account, data collection techniques and applied approaches to find the solution. Within each category, the gaps and further research needs have been discussed with respect to both an academic and industrial perspective.

It is worth mentioning that medical devices are becoming more and more numerous, various and complex. Although, they are often affected by environmental disturbances, sharp technological development, stochastic and uncertain nature of operations and degradation and the integrity and interoperability of the supportability system, the associated practices related to asset management and maintenance in healthcare are still lacking. Therefore, the literature review of applied based research on maintenance subject is necessary to reveal the holistic issues and interrelationships of what has been published as categorized specific topics.

The paper presents a comprehensive review that will be useful to understand the maintenance problem and solution space within the healthcare context.

This paper aims to report an interview study investigating knowledge protection practices in a collaborative research and innovation project centred around the semi-conductor industry. The authors explore which and how knowledge protection practices are applied and zoom in on a particular one to investigate the perspective of three stakeholders which collaborate: the SUPPLIER of a specialised machine, the APPLIER of this machine and a SCHOLAR who collaborates with both, in an effort to develop a grey-box model of the machine and its operation.

A total of 33 interviews have been conducted in two rounds: 30 interviews explore knowledge protection practices applied across a large project. Qualitative content analysis is applied to determine practices not well covered by the research community. A total of three follow-up interviews inspect one specific collaboration case of three partners. Quotes from all interviews are used to illustrate the participants’ viewpoints and motivation.

SCHOLAR and APPLIER communicate using a data-centric knowledge protection practice, in that concrete parameter values are sensitive and hidden by communicating data within a wider parameter range. This practice balances the benefit that all three stakeholders have from communicating about specifics of machine design and operations. The grey-box model combines engineering knowledge of both SUPPLIER and APPLIER.

The line of thought described in this study is applicable to comparable collaboration constellations of a SUPPLIER of a machine, an APPLIER of a machine and a SCHOLAR who analyses and draws insights out of data.

The paper fills a research gap by reporting on applied knowledge protection practices and characterising a data-centric knowledge protection practice around a grey-box model.

This paper presents a new simplified text of some concepts of pansystems methodology and related applications to pedagogy, methods of teaching, study and creation, including certain principles of operations research, systems theory, cybernetics, etc.

For a lightweight and accurate description of bearing temperature, this paper aims to present an efficient semi-empirical model with oil–air two-phase flow and gray-box model.

First, the role of lubricant/coolant in bearing temperature was discussed separately, and the gray-box models on the heat convection inside a bearing cavity were also created. Next, the bearing node setting scheme was optimized. Consequently, a novel semi-empirical two-phase flow thermal grid for high-speed angular contact ball bearings was planned. With this model, the thermal network for the selected motored spindle was built, and the numerical solutions for bearing temperature rise were obtained and contrasted with the experimental values for validation. The polynomial interpolation on test data, meanwhile, was also performed to help us observe the temperature change trend. Finally, the simulations based on the current models of bearings were implemented, whose corresponding results were also compared with our research work.

The validation result indicates that the thermal prediction is more accurate and efficient when the developed semi-empirical oil–air two-phase flow model is employed to assess the thermal change of bearings. Clearly, we provide a more proper model for the thermal assessment of bearing and even spindle heating.

To the best of the authors’ knowledge, this paper introduced the oil–air separation and gray-box model for the first time to describe the heat exchange inside bearing cavities and accordingly presents an efficient semi-empirical oil–air two-phase flow model to evaluate the bearing temperature variation by using thermal network method.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2023-0180/

The paper aims to contribute conceptually to the conversation about organizational models and to the future development of an organizational diagnostic method, based on the human being seen as an allopoietic information processing gray box.

Methodologically, the approach is qualitative and interpretative, using the concepts of autopoiesis and allopoiesis of H. Maturana and F. Varela, the ideas of cybernetic machine, black box and functional homomorphism of W.R. Ashby, moving from the human being to the organizations producing goods and/or services.

Observing the human being as an allopoietic gray box allowed us to confirm the human being as an information-producing entity and the nervous system as its productive component. The functions distinguished were to emote, feel, perceive, think, memorize, decide, communicate, regulate, control, coordinate and move. Similarly, the proposed organizational model is composed of the same functions in which emoting is homologated with distributed leadership for the achievement of the organizational climate and to move with production. Notwithstanding the circularity of affectation between the functional components, the climate is the basis of organizational operation and consequently, the decisional closure distributed between owners and employees.

This is a theoretical proposal that needs to be discussed, and although there are precedents that could help in this regard, it is essential to enrich the model and derive thereof specific tools that can be applied.

A general model is provided from which methods of organizational design, diagnosis and treatment could be derived.

The proposed model is expected to be a contribution to organizational research discussion.

It is considered that the work has a certain degree of originality when proposing a functional organizational model of a general nature, based on the emotionality of the people that constitute it.

Data-driven market segmentation is heavily used by academic tourism and hospitality researchers to create knowledge and by data analysts in tourism industry to generate market insights. The stability of market segmentation solutions across repeated calculations is a key quality indicator of a segmentation solution. Yet, stability is typically ignored, risking that the segmentation solution arrived at is random. This study aims to offer an overview of market segmentation analysis and propose a new procedure to increase the stability of market segmentation solutions derived from binary data.

The authors propose a new method – based on two independently proposed algorithms – to increase the stability of market segmentation solutions. They demonstrate the superior performance of the new method using empirical data.

The proposed approach uses k-means as base algorithm and combines the variable selection method proposed by Brusco (2004) with the global stability analysis introduced by Dolnicar and Leisch (2010). This new approach increases the stability of segmentation solutions by simultaneously selecting variables and numbers of segments.

The new approach can be adopted immediately by academic researchers and industry data analysts alike to improve the quality of market segmentation solutions derived from empirical tourist data. Higher quality market segmentation solutions translate into competitive advantage and increased business or destination performance.

The proposed approach is newly developed in this study. It helps industry data analysts and academic researchers to reduce the risk of deriving random segmentation solutions by analyzing the data in a systematic way, then selecting the most stable solution using the segmentation variables contributing to this most stable solution only.