• Investigative Magnetic Resonance Imaging
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• 제25권2호
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• pp.81-92
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• 2021

The role of neuroimaging in patients with acute ischemic stroke has been gradually increasing. The ultimate goal of stroke imaging is to make a streamlined imaging workflow for safe and efficient treatment based on optimized patient selection. In the era of multimodal comprehensive imaging in strokes, imaging based on computed tomography (CT) has been preferred for use in acute ischemic stroke, because, despite the unique strengths of magnetic resonance imaging (MRI), MRI has a longer scan duration than does CT-based imaging. However, recent improvements, such as multicoil technology and novel MRI acceleration techniques, including parallel imaging, simultaneous multi-section imaging, and compressed sensing, highlight the potential of comprehensive MR-based imaging for strokes. In this review, we discuss the role of stroke imaging in acute ischemic stroke management, as well as the strengths and limitations of MR-based imaging. Given these concepts, we review the current MR acceleration techniques that could be applied to stroke imaging and provide an overview of the previous research on each essential sequence: diffusion-weighted imaging, gradient-echo, fluid-attenuated inversion recovery, contrast-enhanced MR angiography, and MR perfusion imaging.

• Advances in Computational Design
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• 제6권1호
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• pp.15-30
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• 2021

With improvement in innovative manufacturing technologies, it became possible to fabricate any complex shaped structural design for practical applications. This allows for the fabrication of curvilinearly stiffened pressure vessels and pipes. Compared to straight stiffeners, curvilinear stiffeners have shown to have better structural performance and weight savings under certain loading conditions. In this paper, an optimization framework for designing curvilinearly stiffened composite pressure vessels and pipes is presented. NURBS are utilized to define curvilinear stiffeners over the surface of the pipe. An integrated tool using Python, Rhinoceros 3D, MSC.PATRAN and MSC.NASTRAN is implemented for performing the optimization. Rhinoceros 3D is used for creating the geometry, which later is exported to MSC.PATRAN for finite element model generation. Finally, MSC.NASTRAN is used for structural analysis. A Bi-Level Programming (BLP) optimization technique, consisting of Particle Swarm Optimization (PSO) and Gradient-Based Optimization (GBO), is used to find optimal locations of stiffeners, geometric dimensions for stiffener cross-sections and layer thickness for the composite skin. A cylindrical pipe stiffened by orthogonal and curvilinear stiffeners under torsional and bending load cases is studied. It is seen that curvilinear stiffeners can lead to a potential 10.8% weight saving in the structure as compared to the case of using straight stiffeners.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.2960-2973
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• 2022

Since Fukushima nuclear power plant (NPP) accident in 2011, the importance of research on various severe accident phenomena has been emphasized. Particularly, detailed analysis of combustion risk is necessary following the containment damage caused by combustion in the Fukushima accident. Many studies have been conducted to evaluate the risk of local hydrogen concentration increases and flame propagation using computational code. In particular, the potential for combustion by local hydrogen concentration in specific areas within the containment has been emphasized. In this study, the process of flame propagation generated inside a reactor core to containment during a loss of coolant accident (LOCA) was analyzed using MELCOR 2.1 code. Later in the LOCA scenario, it was expected that hydrogen combustion occurred inside the reactor core owing to oxygen inflow through the cold leg break area. The main driving force of the oxygen intrusion is the elevated containment pressure due to the molten corium-concrete interaction. The thermal and mechanical loads caused by the flame threaten the integrity of the containment. Additionally, the containment spray system effectiveness in this situation was evaluated because changes in pressure gradient and concentrations of flammable gases greatly affect the overall behavior of ignition and subsequent containment integrity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

• 아태비즈니스연구
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• 제13권2호
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• pp.107-122
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• 2022

Purpose - This study aims to develop machine learning models to predict administrative issue designation in KOSDAQ Market using financial data. Design/methodology/approach - Employing four classification techniques including logistic regression, support vector machine, random forest, and gradient boosting to a matched sample of five hundred and thirty-six firms over an eight-year period, the authors develop prediction models and explore the practicality of the models. Findings - The resulting four binary selection models reveal overall satisfactory classification performance in terms of various measures including AUC (area under the receiver operating characteristic curve), accuracy, F1-score, and top quartile lift, while the ensemble models (random forest and gradienct boosting) outperform the others in terms of most measures. Research implications or Originality - Although the assessment of administrative issue potential of firms is critical information to investors and financial institutions, detailed empirical investigation has lagged behind. The current research fills this gap in the literature by proposing parsimonious prediction models based on a few financial variables and validating the applicability of the models.

• Steel and Composite Structures
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• 제45권5호
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• pp.641-652
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• 2022

Fluid-conveying tubes are widely used to transport oil and natural gas in industries. As an advanced composite material, functionally graded carbon nanotube-reinforced composites (FG-CNTRC) have great potential to empower the industry. However, nonlinear free vibration of the FG-CNTRC fluid-conveying pipe has not been attempted in thermal environment. In this paper, the nonlinear free vibration characteristic of functionally graded nanocomposite fluid-conveying pipe reinforced by single-walled carbon nanotubes (SWNTs) in thermal environment is investigated. The SWCNTs gradient distributed in the thickness direction of the pipe forms different reinforcement patterns. The material properties of the FG-CNTRC are estimated by rule of mixture. A higher-order shear deformation theory and Hamilton's variational principle are employed to derive the motion equations incorporating the thermal and fluid effects. A two-step perturbation method is implemented to obtain the closed-form asymptotic solutions for these nonlinear partial differential equations. The nonlinear frequencies under several reinforcement patterns are presented and discussed. We conduct a series of studies aimed at revealing the effects of the flow velocity, the environment temperature, the inner-outer diameter ratio, and the carbon nanotube volume fraction on the nature frequency.

• 한국정보시스템학회지:정보시스템연구
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• 제32권4호
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• pp.229-245
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• 2023

Purpose This study aims to understand the inclinations of young job seekers towards "hidden champions" - small but competitive companies that are emerging as potential solutions to the growing disparity between youth-targeted job vacancies and job seekers. We utilize machine learning techniques to discern the appeal of these hidden champions. Design/methodology/approach We examined the characteristics of small and medium-sized enterprises using data sourced from the Ministry of Employment and Labor and Youth Worknet. By comparing the efficacy of five machine learning classification models (i.e., Logistic Regression, Random Forest Classifier, Gradient Boosting Classifier, LGBM Classifier, and XGB Classifier), we discovered that the predictive model utilizing the LGBM Classifier yielded the most consistent performance. Findings Our analysis of the relative significance of preference determinants revealed that industry type, geographical location, and employee count are pivotal factors influencing preference. Drawing from these insights, we propose targeted strategic interventions for policymakers, hidden champions, and young job seekers.

• Communications for Statistical Applications and Methods
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• 제30권5호
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• pp.485-499
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• 2023

This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Results showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.233-262
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• 2024

Due to the rapid evolution of vehicular ad hoc networks (VANETs), effective communication and security are now essential components in providing secure and reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, due to their dynamic nature and potential threats, VANETs need to have strong security mechanisms. This paper presents a novel approach to improve VANET security by combining the Vehicular Delay-Tolerant Network (VDTN) protocol with the Deep Reinforcement Learning (DRL) technique known as the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A store-carry-forward method is used by the VDTN protocol to resolve the problems caused by inconsistent connectivity and disturbances in VANETs. The TD3 algorithm is employed for capturing and detecting Worm Hole Attack (WHA) behaviors in VANETs, thereby enhancing security measures. By combining these components, it is possible to create trustworthy and effective communication channels as well as successfully detect and stop rushing attacks inside the VANET. Extensive evaluations and simulations demonstrate the effectiveness of the proposed approach, enhancing both security and communication efficiency.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.