• 신재생에너지
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• 제20권2호
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• pp.26-34
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• 2024

Floating offshore wind turbines (FOWTs) have been developed to overcome large water depths and leverage the abundant wind resource in deep seas. However, wind-wave misalignment can occur depending on the weather conditions, and most megawatt (MW)-class turbines are horizontal-axis wind turbines subjected to yaw errors. Therefore, the power performance and dynamic response of super-large FOWTs exposed simultaneously to these external conditions must be analyzed. In this study, several scenarios combining wind-wave misalignment and yaw error were considered. The IEA 15 MW reference FOWT (v1.1.2) and OpenFAST (v3.4.1) were used to perform numerical simulations. The results show that the power performance was affected more significantly by the yaw error; therefore, the generator power reduction and variability increased significantly. However, the dynamic response was affected more significantly by the wind-wave misalignment increased; thus, the change in the platform 6-DOF and tower loads (top and base) increased significantly. These results can be facilitate improvements to the power performance and structural integrity of FOWTs during the design process.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.80-85
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• 2024

The concept of single-modal digital art originated in the 20th century and has evolved through three key stages. Over time, digital art has transformed into multi-modal interaction, representing a new era in art forms. Based on multi-modal theory, this paper aims to explore the characteristics of interactive digital art in innovative art forms and its impact on user experience. Through an analysis of practical application of multi-modal interactive digital art, this study summarises the impact of creative models of digital art on the physical and mental aspects of user experience. In creating audio-visual-based art, multi-modal digital art should seamlessly incorporate sensory elements and leverage computer image processing technology. Focusing on user perception, emotional expression, and cultural communication, it strives to establish an immersive environment with user experience at its core. Future research, particularly with emerging technologies like Artificial Intelligence(AR) and Virtual Reality(VR), should not merely prioritize technology but aim for meaningful interaction. Through multi-modal interaction, digital art is poised to continually innovate, offering new possibilities and expanding the realm of interactive digital art.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

• 유통과학연구
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• 제22권6호
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• pp.1-10
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• 2024

Purpose: This study's objective is to examine the impact of firm-specific and macroeconomic factors on the business performance of non-cyclical and cyclical sectors in Indonesian listed firms. The evaluation of business performance holds paramount importance for the achievement and long-term viability of a company. Research Design Data and Methodology: The data for 61 non-cyclicals sector companies and 57 cyclicals sector companies was gathered over a 4-year period from 2018-2021. The model integrates firm size, leverage, and sales growth as firm-specific factors, with real GDP growth and inflation rate as macroeconomic variables. ROA and ROE are indicators of a firm's business performance. The regression models are estimated using the distribution of a dynamic approach with Arellano-Bond Panel Generalized Method of Moments (GMM) estimation. Results: The results of the pooled sample indicate that the historical ROA and ROE have a positive relationship with the business performance of all sectors, including both non-cyclical and cyclical industries. The ROE of non-cyclical enterprises is primarily influenced by firm-specific characteristics and macroeconomic influences. Conclusion: To ensure the successful implementation of the distribution of a dynamic approach towards enhancing corporate business performance, organizations need to take into account a combination of firm-specific factors and macroeconomic factors.

• Nuclear Engineering and Technology
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• 제56권3호
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• pp.855-865
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• 2024

Commercial deployment of nuclear microreactors presents an opportunity for the industry to rethink its approach to manufacturing, siting, operation and maintenance, and fuel cycle management as certain principles used in grid-scale nuclear projects are not applicable to a decentralized microreactor economy. The success of this nascent industry is dependent on its ability to reduce infrastructure, logistical, regulatory and lifecycle costs. A utility-like 'Central Facility' that consolidates the services required and responsibilities borne by vendors into one or a few centralized locations will be necessary to support the deployment of a fleet of microreactors. This paper discusses the requirements for a Central Facility, its implications on the cost structures of owners and suppliers of microreactors, and the impact of the facility for the broader microreactor industry. In addition, this paper discusses the pre-requisites for eligibility as well as the opportunities for a Central Facility host site. While there are many suitable locations for such a capability across the U.S., this paper considers a facility co-located with the Vogtle Nuclear Power Plant and Savannah River Sites to illustrate how a Central Facility can leverage the existing infrastructure and stimulate a local ecosystem.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.1-9
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• 2024

As the Internet of Things (IoT) continues to permeate every facet of modern life, the imperative to secure this vast and dynamic frontier becomes increasingly paramount. This presents a comprehensive exploration of the challenges and opportunities inherent in safeguarding the interconnected web of IoT devices. The research critically examines the limitations of current cybersecurity measures through an extensive review of diverse topics, including IoT network performance, smart grid security, and the escalating cyber threats against critical infrastructures. A meticulous analysis of research findings underscores the need for enhanced infrastructure and ongoing research to fortify the cybersecurity mechanisms surrounding IoT objects. We underline the imperative of relentless research efforts to parry the advancing threats and leverage the promise of nascent technologies. Our findings affirm the pivotal influence of robust cybersecurity measures in crafting a resiliently connected ecosystem. The paper underscores the importance of ongoing research to address evolving threats and harness the potential of emerging technologies, reaffirming the central role of cybersecurity in shaping a secure interconnected world. In conclusion, the study emphasizes the dynamic and ever-evolving nature of cybersecurity on the IoT frontier. It unveils a complex landscape of challenges, ranging from network performance intricacies to the security concerns of critical infrastructures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• Nonlinear Functional Analysis and Applications
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• 제29권3호
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• pp.649-672
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• 2024

This paper explores the significance and implications of fixed point results related to orbital contraction as a novel form of contraction in various fields. Theoretical developments and theorems provide a solid foundation for understanding and utilizing the properties of orbital contraction, showcasing its efficacy through numerous examples and establishing stability and convergence properties. The application of orbital contraction in control systems proves valuable in designing resilient and robust control strategies, ensuring reliable performance even in the presence of disturbances and uncertainties. In the realm of financial modeling, the application of fixed point results offers valuable insights into market dynamics, enabling accurate price predictions and facilitating informed investment decisions. The practical implications of fixed point results related to orbital contraction are substantiated through empirical evidence, numerical simulations, and real-world data analysis. The ability to identify and leverage fixed points grants stability, convergence, and optimal system performance across diverse applications.

• 근관절건강학회지
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• 제31권2호
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• pp.95-106
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• 2024

Purpose: This study investigated how distress and stigma affect the quality of life (QOL) in stroke patients. Methods: A descriptive research design was utilized with 150 stroke patients from three general and three long-term care facilities. Data were collected through an 86-item questionnaire from February 15 to April 10, 2023, using measures of distress, stigma, and QOL. Analysis was conducted using descriptive statistics, independent t-tests, one-way ANOVA, Pearson's correlation coefficients, and hierarchical regression analysis in SPSS/WIN 25.0. Results: The average QOL score was 156.37±38.27 out of 245 points. Factors affecting QOL of stroke patients were distress (β=-.56, p<.001), stigma(β=-.26, p<.001), biplegia (β=-.11, p=.045), and unemployment (β=-.10, p=.045), explaining 68% of the QOL variance. Conclusion: Programs aimed at reducing distress and stigma in stroke patients are essential for enhancing QOL. Effective strategies should address post-stroke physical and mental states, prevent complications, restore health, reduce anxiety, and leverage family and social support to mitigate stigma. Special attention is needed for stroke patients with hemiplegia and those who are unemployed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2316-2332
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• 2024

In this paper, we introduce a web-based system that leverages the capabilities of the ρ(rho)-algorithm, which is a Structure Information Control Net (SICN)-oriented process mining algorithm, with open-source platforms, including Django, Graphviz, and Cytoscape, to facilitate the rediscovery and visualization of business process models. Our approach involves discovering SICN-oriented process models from process instances from the IEEE XESformatted process enactment event logs dataset. This discovering process is facilitated by the ρ-algorithm, and visualization output is transformed into either a JSON or DOT formatted file, catering to the compatibility requirements of Cytoscape or Graphviz, respectively. The proposed system utilizes the robust Django platform, which enables the creation of a userfriendly web interface. This interface offers a clear, concise, modern, and interactive visualization of the rediscovered business processes, fostering an intuitive exploration experience. The experiment conducted on our proposed web-based process discovery system demonstrates its ability and efficiency showing that the system is a valuable tool for discovering business process models from process event logs. Its development not only contributes to the advancement of process mining but also serves as an educational resource. Readers, students, and practitioners interested in process mining can leverage this system as a completely free process miner to gain hands-on experience in rediscovering and visualizing process models from event logs.