• Steel and Composite Structures
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• v.47 no.6
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• pp.743-757
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• 2023

In this paper, the dynamic response of a simply-supported composite sandwich plate with a viscoelastic core based on the Golla-Hughes-McTavish (GHM) viscoelastic model is investigated analytically. The formulation is developed using the three-layered sandwich panel theory. Hamilton's principle has been employed to derive the equations of motion. Since classical models, like kelvin-voigt and Maxwell models, cannot express a comprehensive description of the dynamic behavior of viscoelastic material, the GHM method is used to model the viscoelastic core of the plate in this research. The main advantage of the GHM model in comparison with classical models is the consideration of the frequency-dependent characteristic of viscoelastic material. Identification of the material parameters of GHM mini-oscillator terms is an essential procedure in applying the GHM model. In this study, the focus of viscoelastic modeling is on the development of GHM parameters identification. For this purpose, a new method is proposed to find these constants which express frequency-dependent behavior characterization of viscoelastic material. Natural frequencies and loss factors of the sandwich panel based on ESL and three-layered theories in different geometrics are described at 30℃ and 90℃; also, the comparisons show that obtained natural frequencies are grossly overestimated by ESL theory. The argumentations of differences in natural frequencies are also illustrated in detail. The obtained results show that the GHM model presents a more accurate description of the plate's dynamic response by considering the frequency dependency behavior of the viscoelastic core.

• Journal of the Korean Home Economics Association
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• v.31 no.3
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• pp.89-100
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• 1993

Recently, comprehensive financial service system based on individual, households' economic security and financial independence has emerged as a professional service system in America, while it has not been studied in our country. In order to develop conceptual model of Financial Service System, this paper reviewed ; 1) the concept of financial problem divided into tow dimension, such as financial resource and financial demand, 2) theories of financial service system, further this work also included the identification of relations between financial problem and financial service system.

• Smart Structures and Systems
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• v.20 no.2
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• pp.247-261
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• 2017

In the field of dynamic measurement and structural damage identification, it is generally known that modal frequencies may be measured with higher accuracy than mode shapes. However, the number of natural frequencies within a measurable range is limited. Accessing additional forms of modal frequencies is thus desirable. The present study is concerned about the extraction of artificial boundary condition (ABC) frequencies from modal testing. The ABC frequencies correspond to the natural frequencies of the structure with a perturbed boundary condition, but they can be extracted from processing the frequency response functions (FRF) measured in a specific configuration from the structure in its existing state without the need of actually altering the physical support condition. This paper presents a comprehensive experimental investigation into the measurability of the ABC frequencies from physical experiments. It covers the testing procedure through modal testing, the data processing and data analysis requirements, and the FRF matrix operations leading to the extraction of the ABC frequencies. Specific sources of measurement errors and their effects on the accuracy of the extracted ABC frequencies are scrutinised. The extracted ABC frequencies are subsequently applied in the damage identification in beams by means of finite element model updating. Results demonstrate that it is possible to extract the first few ABC frequencies from the modal testing for a variety of artificial boundary conditions incorporating one or two virtual pin supports, and the inclusion of ABC frequencies enables the identification of structural damages without the need to involve the mode shape information.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.3
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• pp.499-515
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• 2020

This study develops and empirically tests a comprehensive model of consumers' brand page experiences that illuminates dynamics among perceived values (practical value, stimulation, enjoyment, and social identification), brand page engagement dimensions (cognitive engagement, affective engagement, and behavioral engagement) and brand loyalty. An online survey was employed for data collection. We collected 358 usable responses for data analysis. Structural equation modeling results show that three dimensions of perceived values (practical value, perceived enjoyment, and social identification) positively affect brand engagement dimensions, while perceived stimulation affects affective engagement only. As for the roles of brand page engagement, affective and behavioral engagement positively influence brand loyalty. This study demonstrates how consumers' perceived values of brand page experience influence each dimension of brand page engagement and how each dimension has a different impact on brand loyalty. The results of this study provide substantive contributions to the consumers' brand page experience and engagement literature and brand page management on social media for developing brand loyalty.

• Journal of Ocean Engineering and Technology
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• v.20 no.2 s.69
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• pp.1-7
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• 2006

This paper, presents a comparison between numerical analysis and field test on a real offshore platform in Bohai Bay, China. This platform is a steel jacket offshore platform with vertical piles. The field testing under wave-induced force and wind force etc. was conducted, in order to obtain the dynamic parameters of the structure, including the frequencies of the jacket platform, as well as the corresponding damping ratios and mode shapes. The natural excitation technology (NexT) combined with eigensystem realization algorithm (ERA) and the peak picking (PP) method in frequency domain are carried out for modal parameter indentification under operational conditions. The three-dimeansional finite element model (FEM) is constructed by ANSYS and analytical modal analysis is performed to generate modal parameters. The analytical results were compared with experimental results. A good agreement was achieved between the finite element and analysis and field test results. It is further demonstrated that the numerical and experimental modal analysis provide a comprehensive study on the dynamic properties of the jacket platform. According to the analysis results, the modal parameters identification under ambient excitation can calibrate finite element model of the jacket platform structures, or can be used for the structural health monitoring system.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.671-672
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• 2015

Due to harsh conditions of disaster areas, understanding of current feature of collapsed buildings, terrain, and other infrastructures is critical issue for disaster managers. However, because of difficulties in acquiring the geographical information of the disaster site such as large disaster site and limited capability of rescue workers, comprehensive site investigation of current location of survivors buried under the remains of the building is not an easy task for disaster managers. To overcome these circumstances of disaster site, this study makes use of an unmanned aerial vehicle, commonly known as a drone to effectively acquire current image data from the large disaster areas. The framework of 3D model reconstruction of disaster site using aerial imagery acquired by drones was also presented. The proposed methodology is expected to assist rescue workers and disaster managers in achieving a rapid and accurate identification of survivors under the collapsed building.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Tribology and Lubricants
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• v.39 no.3
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• pp.118-122
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• 2023

In our study, we develop a finite element model based on Archard's wear law to predict the cumulative wear and the evolution of the tool profile in friction stir welding (FSW) applications. Our model considers the rotational and translational behaviors of the tool, providing a comprehensive description of the wear process. We validate the accuracy of our model by comparing it against experimental results, examining both the predicted cumulative wear and the resulting changes to the tool profile caused by wear. We perform a detailed comparison between the predictions of the model and experimental data by manipulating non-dimensional coefficients comprising model parameters, such as element sizes and time increments. This comparison facilitates the identification of a specific non-dimensional coefficient condition that best replicates the experimentally observed cumulative wear. We also directly compare the worn tool profiles predicted by the model using this specific non-dimensional coefficient condition with the profiles obtained from wear experiments. Through this process, we identify the model settings that yield a tool wear profile closely aligning with the experimental results. Our research demonstrates that carefully selecting non-dimensional coefficients can significantly enhance the predictive accuracy of finite element models for tool wear in FSW processes. The results from our study hold potential implications for enhancing tool longevity and welding quality in industrial applications.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• The Korean Journal of Pain
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• v.36 no.2
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• pp.149-162
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• 2023

Chronic postsurgical pain (CPSP) is a multifactorial condition that affects a significant proportion of patients undergoing surgery. The prevention and management of CPSP require the identification of preoperative risk factors to screen high-risk patients and establish appropriate perioperative pain management plans to prevent its development. Active postoperative pain management should be provided to prevent CPSP in patients with severe pain following surgery. These tasks have become important for perioperative team members in the management of CPSP. This review article provides a comprehensive overview of the latest research on the role of perioperative team members in preventing and managing CPSP. Additionally, it highlights practical strategies that can be employed in clinical practice, covering the definition and risk factors for CPSP, including preoperative, intraoperative, and postoperative factors, as well as a risk prediction model. The article also explores various treatments for CPSP, as well as preventive measures, including preemptive analgesia, regional anesthesia, pharmacological interventions, psychoeducational support, and surgical technique modification. This article emphasizes the importance of a comprehensive perioperative pain management plan that includes multidisciplinary interventions, using the transitional pain service as an example. By adopting a multidisciplinary and collaborative approach, perioperative team members can improve patient outcomes, enhance patient satisfaction, and reduce healthcare costs. However, further research is necessary to establish targeted interventions to effectively prevent and manage CPSP.