• Smart Structures and Systems
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• v.31 no.5
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• pp.501-516
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• 2023

Dynamic irregularity and acceleration of bridges subjected to high-speed trains provide crucial information for comprehensive evaluation of the health state of under-track structures. This paper proposes a novel approach for real-time estimation of vertical track dynamic irregularity and bridge acceleration using deep generative adversarial network (GAN) and vibration data from in-service train. The vehicle-body and bogie acceleration responses are correlated with the two target variables by modeling train-bridge interaction (TBI) through least squares generative adversarial network (LSGAN). To realize supervised learning required in the present task, the conventional LSGAN is modified by implementing new loss function and linear activation function. The proposed approach can offer pointwise and accurate estimates of track dynamic irregularity and bridge acceleration, allowing frequent inspection of high-speed railway (HSR) bridges in an economical way. Thanks to its applicability in scenarios of high noise level and critical resonance condition, the proposed approach has a promising prospect in engineering applications.

• Earthquakes and Structures
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• v.23 no.4
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• pp.385-401
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• 2022

Uncertainty quantification is the most important challenge in seismic fragility assessment of structures. The precision increment of the quantification method leads to reliable results but at the same time increases the computational costs and the latter will be so undesirable in cases such as reliability-based design optimization which includes numerous probabilistic seismic analyses. Accordingly, the authors' effort has been put on the development and validation of an approach that has reduced computational cost in seismic fragility assessment. In this regard, it is necessary to apply the appropriate methods for consideration of two categories of uncertainties consisting of uncertainties related to the ground motions and structural characteristics, separately. Also, cable-stayed bridges have been specifically selected because as a result of their complexity and the according time-consuming seismic analyses, reducing the computations corresponding to their fragility analyses is worthy of studying. To achieve this, the fragility assessment of three case studies is performed based on existing and proposed approaches, and a comparative study on the efficiency in the estimation of seismic responses. For this purpose, statistical validation is conducted on the seismic demand and fragility resulting from the mentioned approaches, and through a comprehensive interpretation, sufficient arguments for the acceptable errors of the proposed approach are presented. Finally, this study concludes that the combination of the Capacity Spectrum Method (CSM) and Uniform Design Sampling (UDS) in advanced proposed forms can provide adequate accuracy in seismic fragility estimation at a significantly reduced computational cost.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.1-18
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• 2024

The present study conducts a thorough analysis of thermal vibrations in functionally graded porous nanocomposite beams within a thermal setting. Investigating the temperature-dependent material properties of these beams, which continuously vary across their thickness in accordance with a power-law function, a finite element approach is developed. This approach utilizes a nonlocal strain gradient theory and accounts for a linear temperature rise. The analysis employs four different patterns of porosity distribution to characterize the functionally graded porous materials. A novel two-variable shear deformation beam nonlocal strain gradient theory, based on trigonometric functions, is introduced to examine the combined effects of nonlocal stress and strain gradient on these beams. The derived governing equations are solved through a 3-nodes beam element. A comprehensive parametric study delves into the influence of structural parameters, such as thicknessratio, beam length, nonlocal scale parameter, and strain gradient parameter. Furthermore, the study explores the impact of thermal effects, porosity distribution forms, and material distribution profiles on the free vibration of temperature-dependent FG nanobeams. The results reveal the substantial influence of these effects on the vibration behavior of functionally graded nanobeams under thermal conditions. This research presents a finite element approach to examine the thermo-mechanical behavior of nonlocal temperature-dependent FG nanobeams, filling the gap where analytical results are unavailable.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.1-9
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• 2024

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.290-297
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• 2024

We investigate the efficacy of ensemble learning methods, specifically the soft voting technique, for enhancing heart disease prediction accuracy. Our study uniquely combines Logistic Regression, SVM with RBF Kernel, and Random Forest models in a soft voting ensemble to improve predictive performance. We demonstrate that this approach outperforms individual models in diagnosing heart disease. Our research contributes to the field by applying a well-curated dataset with normalization and optimization techniques, conducting a comprehensive comparative analysis of different machine learning models, and showcasing the superior performance of the soft voting ensemble in medical diagnosis. This multifaceted approach allows us to provide a thorough evaluation of the soft voting ensemble's effectiveness in the context of heart disease prediction. We evaluate our models based on accuracy, precision, recall, F1 score, and Area Under the ROC Curve (AUC). Our results indicate that the soft voting ensemble technique achieves higher accuracy and robustness in heart disease prediction compared to individual classifiers. This study advances the application of machine learning in medical diagnostics, offering a novel approach to improve heart disease prediction. Our findings have significant implications for early detection and management of heart disease, potentially contributing to better patient outcomes and more efficient healthcare resource allocation.

• The Journal of Industrial Distribution & Business
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• v.15 no.8
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• pp.29-35
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• 2024

Purpose: This study delves into research on the core marketing plans for Modern Art Gallery. It begins with a review of existing literature to purposely get other researchers' contributions and views on the topic. Importantly and pertinent to this research, the review would enable the researcher to identify the existing gaps and how to address them to achieve the research objectives. Data and methodology: The researcher systematically reviewed the sources that met the inclusion criteria. The objective was to get the correct insights about other scholars' contributions and perspectives. The comprehensive outline of all the methods and methodologies is to promote the replicability of this study by other scholars or researchers. Results: The following four solutions are the critical findings vis-a-vis the marketing plan of Modern Art Gallery, its application, and its benefits to it. (1) Marketing Penetration Approach, (2) Product Innovation/ Development Approach, (3)Market Betterment and Development Strategy, and (4) Diversifying Approach. Conclusions: The findings give a direction in the future of the practitioners and stakeholders in the field. It is sufficient to state that this research's findings are helpful and valuable to Modern Art Gallery and other pertinent stakeholders such as learners, cultural departments, and researchers.

• International Journal of Railway
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• v.2 no.2
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• pp.60-69
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• 2009

In recent years, technology developments in different countries, especially in newly industrialized countries, are extremely indebted to appropriate technology transfer by these countries. Nevertheless the technology transfer process in the present situation is complex, and its success is related to the coordination rate with the political, economic, social, and environmental objectives of countries. Today debates related to the transfer of the technical know how accompanied by equipment hardware has found remarkable importance such that countries seek increasing comprehensive capabilities in the field of transferred technology for which Preventive Maintenance (PM) is one of the aspects. This research with the purpose to determine the technological capability level and to study the role of PM in the effective & appropriate technology transfer in subway industry is carried out for presenting a suitable model for the technology transfer in this industry with an attitude towards the effects of principal PM factors. For this purpose, after the study of different and relevant models existing in the field of suitable methods for technology transfer, some equipment PM theories and models were selected as the base for the compilation of the questionnaire. With the help of questionnaire, main PM factors that are effective in the field of technology transfer were extracted, and finally, their effects on technology transfer were analyzed, identified and a comprehensive model suggested in this connection.

• International Journal of CAD/CAM
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• v.5 no.1
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• pp.99-109
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• 2005

This paper introduces an approach to an ontology-based multi-level knowledge framework for a knowledge management system for discrete-product development. Participants in a product life cycle want to share comprehensive product knowledge without any ambiguity and heterogeneity. However, previous knowledge management approaches are limited in providing those aspects: therefore, we suggest an ontology-based multi-level knowledge framework (OBMKF). The bottom level, the axiom, specifies the semantics of concepts and relations of knowledge so ambiguity can be alleviated. The middle level is a product development knowledge map; it defines the concepts and the relations of the product domain knowledge and guides the engineer to process their engineering decisions. The middle level is then classified further into more detailed levels, such as generic product level, specific product level, product version level, and manufactured item level, according to the various viewpoints. The top level is specialized knowledge for a specific domain that gives the solution of a specific task or problem. It is classified into three knowledge types: expert knowledge, engineering function knowledge, and data-analysis-based knowledge. This proposed framework is based on ontology to accommodate a comprehensive range of knowledge and is represented with first-order logic to maintain a uniform representation.

• Clinical and Experimental Reproductive Medicine
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• v.44 no.4
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• pp.171-174
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• 2017

While many fertility preservation (FP) options now exist for reproductive-aged cancer patients, access to these services continues to be limited. A comprehensive FP program should be organized to serve oncofertility patients effectively. Also, much effort is needed from various individuals-patients, specialists from various fields, and consultants-to facilitate FP in a timely manner. Various challenges still exist in improving access to FP programs. To improve access to FP treatment, it is important to educate oncologists and patients via electronic tools and to actively navigate patients through the system. Reproductive endocrinology practices that receive oncofertility referrals must be equipped to provide a full range of options on short notice. A multidisciplinary team approach is required, involving physicians, nurses, mental health professionals, office staff, and laboratory personnel. The bottom line of FP patient care is to understand the true nature of each patient's specific situation and to develop a patient flow system that will help build a successful FP program. Expanding the patient flow system to all comprehensive cancer centers will ensure that all patients are provided with adequate information regarding their fertility, regardless of geography.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.3 s.180
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• pp.22-31
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• 2006

This paper introduces an approach to an ontology-based knowledge framework for product life cycle management (PLM). Participants in a product life cycle want to share comprehensive product knowledge without any ambiguity and heterogeneity. However, previous knowledge management approaches are limited in providing those aspects. Therefore, we suggest an ontology-based knowledge framework including knowledge maps, axioms and specific knowledge far domain. The bottom level, the axiom, specifies the semantics of concepts and relations of knowledge so that ambiguity of the semantics can be alleviated. The middle level is a product development knowledge map; it defines the concepts and the relations of the product domain common knowledge and guides engineers to process their engineering decisions. The middle level is then classified further into more detailed levels, such as generic product level, specific product level, product version level, and product item level for PLM. The top level is specialized knowledge fer a specific domain that gives the solution of a specific task or problem. It is classified into three knowledge types: expert knowledge, engineering function knowledge, and data-analysis-based knowledge. This proposed framework is based on ontology to accommodate a comprehensive range of unambiguous knowledge for PLM and is represented with first-order logic to maintain a uniform representation.