• Archives of Craniofacial Surgery
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• v.23 no.2
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• pp.89-92
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• 2022

Post-traumatic pseudoaneurysms of internal maxillary artery are rare, but may be life-threatening. When arterial damage leads to pseudoaneurysm formation, delayed intractable epistaxis can occur. We report our experience with the diagnosis and management of a ruptured internal maxillary arterial pseudoaneurysm that was discovered preoperatively in a patient with a zygomaticomaxillary complex (ZMC) fracture. He presented to the emergency room with epistaxis, which ceased shortly, and sinus hemorrhage was observed with a fracture of the posterior maxillary wall. The patient was scheduled for open reduction and internal fixation (ORIF) of the ZMC fracture. However, immediately before surgery, uncontrolled epistaxis of unknown origin was observed. Angiography indicated a pseudoaneurysm of the posterior superior alveolar artery. Selective endovascular embolization was performed, and hemostasis was achieved. After radiologic intervention, ORIF was successfully implemented without complications. Our case shows that in patients with a posterior maxillary wall fracture, there is a risk of uncontrolled bleeding in the perioperative period that could be caused by pseudoaneurysms, which should be considered even in the absence of typical symptoms.

• Archives of Plastic Surgery
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• v.49 no.2
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• pp.174-183
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• 2022

Management of traumatic skull base fractures and associated complications pose a unique reconstructive challenge. The goals of skull base reconstruction include structural support for the brain and orbit, separation of the central nervous system from the aerodigestive tract, volume to decrease dead space, and restoration of the three-dimensional appearance of the face and cranium with bone and soft tissues. An open bicoronal approach is the most commonly used technique for craniofacial disassembly of the bifrontal region, with evacuation of intracranial hemorrhage and dural repair performed prior to reconstruction. Depending on the defect size and underlying patient and operative factors, reconstruction may involve bony reconstruction using autografts, allografts, or prosthetics in addition to soft tissue reconstruction using vascularized local or distant tissues. The vast majority of traumatic anterior cranial fossa (ACF) injuries resulting in smaller defects of the cranial base itself can be successfully reconstructed using local pedicled pericranial or galeal flaps. Compared with historical nonvascularized ACF reconstructive options, vascularized reconstruction using pericranial and/or galeal flaps has decreased the rate of cerebrospinal fluid (CSF) leak from 25 to 6.5%. We review the existing literature on this uncommon entity and present our case series of n = 6 patients undergoing traumatic reconstruction of the ACF at an urban Level 1 trauma center from 2016 to 2018. There were no postoperative CSF leaks, mucoceles, episodes of meningitis, or deaths during the study follow-up period. In conclusion, use of pericranial, galeal, and free flaps, as indicated, can provide reliable and durable reconstruction of a wide variety of injuries.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.163-173
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• 2023

In this study, topographic change analysis was performed on the Nak-dong River estuary area. The factors affecting the changes in the bathymetry of the Nak-dong River estuary were analyzed using data from the discharge, suspended sediments, and rainfall of the Nak-dong River barrier as analysis data. As a result, erosion and sedimentation are judged to appear repeatedly due to complex effects such as discharge of the estuary barrier of the Nak-dong River and invasion of the open sea waves, and it is judged that there is no one-sided tendency. However, as a result of checking the data in the second half of 2020, it was possible to confirm a large amount of erosion, which is different from the past data. It is clear that this is a result beyond the trend of erosion in the first half and sedimentation in the second half. In the summer of 2020, the rainy season lasted for more than a month and torrential rains occurred, which seems to be due to about three times higher rainfall than other periods, and erosion is believed to have occurred as the discharge increased rapidly compared to the time deposited by river water outflow. In addition, compared to other times, the influence of many typhoons in the summer of 2020 is believed to have affected the topographical change at the mouth of the Nak-dong River.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1346-1351
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• 2009

In this paper we propose a framework for an Object Relational IFC Server (OR-IFC Server). Enormous amounts of information are generated in each project. Today, many BIM systems are developed by various CAD software vendors. Industry Foundation Classes (IFC) developed by International Alliance for Interoperability (IAI) is an open standard data model for exchanging data between the various BIM tools. The IFC provides a foundation for exchanging and sharing of information directly between software applications and define a shared building project model. The IFC model server is a database management system that can keep track of transactions, modifications, and deletions. It plays a role as an information hub for storing and sharing information between various parties involved in construction projects. Users can communicate with each other via the internet and utilize functions implemented in the model server such as partial data import/export, file merge, version control, etc. IFC model servers using relational database systems have been developed. However, they suffered from slow performance and long transaction time due to a complex mapping process between the IFC structure and a relational-database structure because the IFC model schema is defined in the EXPRESS language which is object-favored language. In order to simplify the mapping process, we developed a set of rules to map the IFC model to an object-relational database (ORDB). Once the database has been configured, only those pieces of information that are required for a specific information-exchange scenario are extracted using the pre-defined information delivery manual (IDM). Therefore, file sizes will be reduced when exchanging data, meaning that files can now be effectively exchanged and shared. In this study, the framework of the IFC server using ORDB and IDM and the method to develop it will be examined.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Journal of English Language & Literature
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• v.53 no.2
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• pp.307-333
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• 2007

When W. E. B. Du Bois says that "the problem of the twentieth century is the problem of the color line," such a statement clearly recognizes the significance of the issue of racial identity, a cultural phenomenon called 'passing.' Both Johnson in The Autobiography of an Ex-Colored Man and Larsen in Passing confront this issue. Both novels, using the metaphor of passing, not only trace the racial anxiety and race politics of the time but also expose the unstable landscape of the established social and cultural boundaries of racial identity. Mapping out multiple meanings and various dimensions of passing, this paper argues how Johnson's and Larsen's narratives display the ambivalence of color line while they at the same time complicate, problematize, and destabilize the mainstream racial boundaries and differences. It furthers to delineate how the two writers, with difference, deal with the problem of passing, the significance of racial identity, and black middle class values along with its intraracial differences. Rather than draw a clear definition of and a definitive closure on passing narrative, this paper focuses on its complexities and undecidability, challenging every dimension of its established significations. It also explores the complex dynamic between passing act and individual identity, for passing here is not just a racially signified term but extends its significance to the other factors of identity, such as class and even sexuality. Johnson and Larsen open up a site for a newly emergent, modern racial identity for black middle class in the twentieth century American urban spaces. Both writers, illuminating the subversive and slippery nature of language in their passing narrative, clearly herald new, different forms of Afro-American writings and themes for the different century they face.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.31-38
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• 2023

In this paper, a basic machine learning model learning and utilization education curriculum for non-majors is proposed, and an education method using Orange machine learning model learning and analysis tools is proposed. Orange is an open-source machine learning and data visualization tool that can create machine learning models by learning data using visual widgets without complex programming. Orange is a platform that is widely used by non-major undergraduates to expert groups. In this paper, a basic machine learning model learning and utilization education curriculum and weekly practice contents for one semester are proposed. In addition, in order to demonstrate the reality of practice contents for machine learning model learning and utilization, we used the Orange tool to learn machine learning models from categorical data samples and numerical data samples, and utilized the models. Thus, use cases for predicting the outcome of the population were proposed. Finally, the educational satisfaction of this curriculum is surveyed and analyzed for non-majors.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• Korean Journal of Materials Research
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• v.34 no.4
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• pp.202-207
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• 2024

In this study, we undertook detailed experiments to increase hydrogen production efficiency by optimizing the thickness of titanium dioxide (TiO₂) thin films. TiO₂ films were deposited on p-type silicon (Si) wafers using atomic layer deposition (ALD) technology. The main goal was to identify the optimal thickness of TiO₂ film that would maximize hydrogen production efficiency while maintaining stable operating conditions. The photoelectrochemical (PEC) properties of the TiO₂ films of different thicknesses were evaluated using open circuit potential (OCP) and linear sweep voltammetry (LSV) analysis. These techniques play a pivotal role in evaluating the electrochemical behavior and photoactivity of semiconductor materials in PEC systems. Our results showed photovoltage tended to improve with increasing thickness of TiO₂ deposition. However, this improvement was observed to plateau and eventually decline when the thickness exceeded 1.5 nm, showing a correlation between charge transfer efficiency and tunneling. On the other hand, LSV analysis showed bare Si had the greatest efficiency, and that the deposition of TiO₂ caused a positive change in the formation of photovoltage, but was not optimal. We show that oxide tunneling-capable TiO₂ film thicknesses of 1~2 nm have the potential to improve the efficiency of PEC hydrogen production systems. This study not only reveals the complex relationship between film thickness and PEC performance, but also enabled greater efficiency and set a benchmark for future research aimed at developing sustainable hydrogen production technologies.