• Journal of Chest Surgery
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• v.55 no.2
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• pp.101-107
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• 2022

Background: Spread through air spaces (STAS) has recently emerged as a prognostic factor in lung adenocarcinoma, but little is known about the association of STAS and its grade with recurrence in neuroendocrine tumors (NETs). This study investigated the prognostic effect of STAS grade in NETs after curative resection. Methods: Seventy-seven patients were retrospectively reviewed, including 9 with typical carcinoid (TC), 6 with atypical carcinoid (AC), 26 with large cell neuroendocrine carcinoma (LCNEC), and 36 with small cell carcinoma (SCC). STAS was defined as the presence of floating tumor cells within air spaces in the lung parenchyma beyond the edge of the main tumor. STAS was classified as grade 1 or 2 depending on whether it was found within or beyond one ×10 objective lens field away from the main tumor margin, respectively. Results: Fifty-four patients (70%) had STAS, including 22% with TC, 50% with AC, 69% with LCNEC, and 86% with SCC. Patients with STAS had more nodal metastasis, lymphatic and vascular invasion, tumor necrosis, and tumor subtypes other than TC. Among STAS cases, grade 2 STAS was present in 33% of AC, 78% of LCNEC, and 87% of SCC. The 5-year recurrence-free survival (RFS) rate was 81%, 63%, and 35% in patients with no STAS, grade 1, and grade 2 STAS, respectively. Multivariate analysis found that grade 2 STAS was an independent negative prognostic factor for RFS. Conclusion: Although STAS itself was not associated with a poor prognosis, grade 2 STAS was an independent negative prognostic factor for RFS.

• Journal of the Korean Society of Visualization
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• v.20 no.3
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• pp.94-101
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• 2022

The evaluation of the drag and lift as the aerodynamic performance of airfoils is essential. In addition, the analysis of the velocity and pressure fields is needed to support the physical mechanism of the force coefficients of the airfoil. Thus, the present study aims at establishing two different deep learning models to predict force coefficients and flow fields of the airfoil. One is the convolutional neural network (CNN) model to predict drag and lift coefficients of airfoil. Another is the Encoder-Decoder (ED) model to predict pressure distribution and velocity vector field. The images of airfoil section are applied as the input data of both models. Thus, the computational fluid dynamics (CFD) is adopted to form the dataset to training and test of both CNN models. The models are established by the convergence performance for the various hyperparameters. The prediction capability of the established CNN model and ED model is evaluated for the various NACA sections by comparing the true results obtained by the CFD, resulting in the high accurate prediction. It is noted that the predicted results near the leading edge, where the velocity has sharp gradient, reveal relatively lower accuracies. Therefore, the more and high resolved dataset are required to improve the highly nonlinear flow fields.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• KNOM Review
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• v.24 no.1
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• pp.20-28
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• 2021

Extensive use of social media applications and mobile devices continues to increase data traffic. Social media applications generate an endless and massive amount of multimedia traffic, specifically video traffic. Many social media platforms such as YouTube, Daily Motion, and Netflix generate endless video traffic. On these platforms, only a few popular videos are requested many times as compared to other videos. These popular videos should be cached in the user vicinity to meet continuous user demands. MEC has emerged as an essential paradigm for handling consistent user demand and caching videos in user proximity. The problem is to understand how user demand pattern varies with time. This paper analyzes three publicly available datasets, MovieLens 20M, MovieLens 100K, and The Movies Dataset, to find the user request pattern over time. We find hourly, daily, monthly, and yearly trends of all the datasets. Our resulted pattern could be used in other research while generating and analyzing the user request pattern in MEC-based video caching scenarios.

• KNOM Review
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• v.23 no.1
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• pp.1-9
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• 2020

With the rapid development of science and technology in recent years, the network environment are growing, and a huge amount of traffic is generated. In particular, the development of 5G networks and edge computing will accelerate this phenomenon. However, according to these trends, network malicious behaviors and traffic overloads are also frequently occurring. To solve these problems, network administrators need to build a network management system to implement a high-speed network and should know exactly about the connection topology of network devices through the network management system. However, the existing network topology discovery method is inefficient because it is passively managed by an administrator and it is a time consuming task. Therefore, we proposes a method of network topology discovery according to the amount of in and out network traffic. The proposed method is applied to a real network to verify the validity of this paper.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.205-210
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• 2022

With the rapid development of the social economy and the continuous improvement of living standards, a favorable environment has been provided for the rapid development of the tourism industry. The highly developed Internet technology has provided people with more convenient online travel. With the rapid development of online tourism, tourism-related Internet businesses are also developing rapidly. This study first introduces the research background and significance of the study, and suggests the necessity of Ctrip's business model optimization study based on the development of social economy such as online tourism development hotspots and business model research issues. Ctrip explains the current status of research inside and outside the online travel industry and business model, in-depth introduction and analysis of related concepts and theories such as online travel and OTA business models, and expands them based on expert research. Several aspects such as insufficient Ctrip's existing business model, high current operating costs, major factors affecting suppliers, and slow development of new business were suggested, and alternatives were suggested to solve these problems. Ctrip maintained a sustainable competitive edge, including Ctrip's business model optimization strategy for value creation and innovation and Internet business model optimization strategy for customer value chains.

• Smart Media Journal
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• v.12 no.2
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• pp.42-55
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• 2023

The combination of blockchain (BC), artificial Intelligence (AI), quantum computing (QC), and the Internet of Things (IoT) can potentially transform various industries and domains, including healthcare, logistics, and finance. In this paper, we look at the trends and developments in integrating these emerging technologies and the potential benefits and challenges that come with them. We present a conceptual framework for integrating BC, AI, QC, and IoT and discuss the framework's key characteristics and challenges. We also look at the most recent cutting-edge research and developments in integrating these technologies, as well as the key challenges and opportunities that come with them. Our analysis highlights the potential benefits of integrating the technologies and looks to increased security, privacy, and efficiency to provide insights into the future of these technologies.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2001.06a
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• pp.3-3
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• 2001

Recently TiN, TiAlN, CrN hardcoatings have adapted many industrial application such as die, mold and cutting tools because of good wear resistant and thermal stability. However, in terms of high speed process, general hard coatings have been limited by oxidation and thermal hardness drop. Especially in the case of PCB drill, high speed cutting and without lubricant process condition have not adapted these coatings until now. Therefore more recently, superhard nanocomposite coating which have superhard and good thermal stability have developed. In previous works, WC-TiAlN new nanocomposite film was investigated by cathodic arc ion plating system. Control of AI concentration, WC-TiAlN multi layer composite coating with controlled microstructure was carried out and provides additional enhancement of mechanical properties as well as oxidation resistance at elevated temperature. It is noted that microhardness ofWC-TiA1N multi layer composite coating increased up to 50 Gpa and got thermal stability about $900^{\circ}C$. In this study WC-TiAlN nanocomposite coating was deposited on PCB drill for enhancement of life time. The parameter was A1 concentration and plasma cleaning time for edge sharpness maintaining. The characteristic of WC-TiAlN film formation and wear behaviors are discussed with data from AlES, XRD, EDS and SEM analysis. Through field test, enhancement of life time for PCB drill was measured.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.266-283
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• 2022

With the rapid development of autonomous vehicle technology, unexpected accidents are occurring. Therefore, it is necessary to minimize user accident damage through the development of autonomous traffic safety education. Since edge cases, accident type, and risk factor analysis are important for realistic education, overseas case studies and demonstrations were carried out, and based on this, two curriculum for service providers and general users were developed. The service provider curriculum consisted of OEDR, sudden stop, cut-in, take-over, defensive driving, system malfunction, policy and information security education, and the general user curriculum consisted of attention duty, take-over, operating design domain, accidents type, laws, functions, information security education.

• Journal of Environmental Science International
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• v.31 no.12
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• pp.1079-1087
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• 2022

This study investigated the effect of volcanic materials that erupted from the Nishinoshima volcano, Japan, 1,300 km southeast of the Busan area at the end of July 2020, on the fine particle concentration in the Busan area. Backward trajectory analysis from the HYSPLIT model showed that the air parcel from the Nishinoshima volcano turned clockwise along the edge of the North Pacific high pressure and reached the Busan area. From August 4 to August 5, 2020, the concentration of PM₁₀ and PM_2.5 in Busan started to increase rapidly from 1000 LST on August 4, and showed a high concentration for approximately 13 hours until 2400 LST. The PM_2.5/PM₁₀ ratio showed a relatively high value of 0.7 or more, and the SO₂ concentration also showed a high value at the time when the PM₁₀ and PM_2.5 concentrations were relatively high. The SO₄^2- concentration in PM_2.5 in Busan showed a similar trend to the change in PM₁₀ and PM_2.5 concentrations. It rose sharply from 1300 LST on August 4, at the time where it was expected to have been affected by the Nishinoshima Volcano. This study has shown that the occurrence of high concentration fine particle in Busan in summer has the potential to affect Korea not only due to anthropogenic factors but also from natural causes such as volcanic eruptions in Japan.