• 한국반도체및디스플레이장비학회:학술대회논문집
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• 한국반도체및디스플레이장비학회 2002년도 추계학술대회 발표 논문집
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• pp.81-84
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• 2002

The effects of the isoelectronic AI-doping of GaN grown by metal organic chemical vapor deposition were investigated for the first time using scanning electron microscopy (SEM), Hall measurements, photoluminescence (PL), and time-resolved PL. When a certain amount of Al was incorporated into the GaN films, the room temperature photoluminescence intensity of the films was approximately two orders larger than that of the undoped GaN. More importantly, the electron mobility significantly increased from 130 for the undoped sample to $500\textrm{cm}^2/Vs$ for the sample grown at a TMAl flow rate of $10{\mu}mol/min$, while the unintentional background concentration only increased slightly relative to the TMAl flow. The incorporation of Al as an isoelectronic dopant into GaN was easy during MOCVD growth and significantly improved the optical and electrical properties of the film. This was believed to result from a reduction in the dislocation-related non-radiative recombination centers or certain other defects due to the isoelectronic Al-doping.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2006년도 학술대회 및 기술세미나 논문집 디스플레이 광소자
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• pp.49-51
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• 2006

OLEOs was fabricated by PLD method. Wet etching process and plasma treatment of ITO on the glass were performed to extend the lifetime of the OLED and increase its brightness. The NPB, $Alq_3$, Li-Benzoate and AI layers on ITO pattern on the glass were deposited by PLO method, sequentially. When the etched ITO was treated by $O_2$ plasma with RF power of 50W, the best result was obtained. The lifetime of the OLED treated by $O_2$ plasma was extended from 3,770sec to 12,586sec compared to that without the plasma treatment.

• 한국멀티미디어학회논문지
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• 제22권9호
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• 반도체디스플레이기술학회지
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• 제20권2호
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• pp.121-125
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• 2021

Cloud service has the characteristic that it must be always available and that it must be able to respond immediately to user requests. This study suggests a method for constructing a proactive and autonomous quality and performance management system to meet these characteristics of cloud services. To this end, we identify quantitative measurement factors for cloud service quality and performance management, define a structure for applying a time series framework to cloud service application quality and performance management for proactive management, and then use big data and artificial intelligence for autonomous management. The flow of data processing and the configuration and flow of big data and artificial intelligence platforms were defined to combine intelligent technologies. In addition, the effectiveness was confirmed by applying it to the cloud service quality and performance management system through a case study. Using the methodology presented in this study, it is possible to improve the service management system that has been managed artificially and retrospectively through various convergence. However, since it requires the collection, processing, and processing of various types of data, it also has limitations in that data standardization must be prioritized in each technology and industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4105-4121
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• 2021

Recently, malware classification based on Deep Neural Networks (DNN) has gained significant attention due to the rise in popularity of artificial intelligence (AI). DNN-based malware classifiers are a novel solution to combat never-before-seen malware families because this approach is able to classify malwares based on structural characteristics rather than requiring particular signatures like traditional malware classifiers. However, these DNN-based classifiers have been found to lack robustness against malwares that are carefully crafted to evade detection. These specially crafted pieces of malware are referred to as adversarial examples. We consider a clever adversary who has a thorough knowledge of DNN-based malware classifiers and will exploit it to generate a crafty malware to fool DNN-based classifiers. In this paper, we propose a DNN-based malware classifier that becomes resilient to these kinds of attacks by exploiting Generative Adversarial Network (GAN) based data augmentation. The experimental results show that the proposed scheme classifies malware, including AEs, with a false positive rate (FPR) of 3.0% and a balanced accuracy of 70.16%. These are respective 26.1% and 18.5% enhancements when compared to a traditional DNN-based classifier that does not exploit GAN.

• 방송공학회논문지
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• 제27권5호
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• pp.703-717
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• 2022

메타버스는 가상과 현실이 연결된 디지털 사회에서 사회·문화·경제 활동을 하며 다양한 가치를 생산할 수 있는 시뮬레이션 환경을 제공한다. 본 논문에서는 메타버스 서비스와 산업 현황을 분석하고 서비스 플랫폼 구현 기술들을 살펴봄으로써 그 발전 방향을 예측해 본다. 그리고, 지속가능한 메타버스를 구성하기 위한 메타버스 윤리(Metaverse Ethics)와 사용자들의 서비스 참여를 높이기 위한 메타버스 사용자경험(UX)에 대해서도 논의한다. 마지막으로, 기술 경쟁력 확보를 위해 디지털 윤리와 UX가 반영된 메타버스 R&D 추진전략을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• The Journal of Asian Finance, Economics and Business
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• 제9권9호
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• pp.327-337
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• 2022

The purpose of this study is to determine the direction and significance of variables influencing small and medium enterprises (SMEs) decisions regarding accounting policy in Vietnam. Research data was collected through a survey of 296 subjects, including chief accountants, accountants, managers, and lecturers with practical experience in accounting work at enterprises. With the help of specialized software SPSS, determining the impact of factors on the choice of accounting policy of enterprises is done through a multivariate regression model with control tools Cronbach's alpha determination, EFA factor analysis, and Pearson correlation analysis. Research results show that there are seven factors affecting the choice of accounting policy in Vietnamese SMEs; in which, the factors information technology, legal environment, information demand, manager's awareness, and accounting qualification have a positive impact; and two factors are tax pressure, and financial leverage have a negative impact on accounting policy choice. These results are consistent with most of the previously published studies. However, in contrast to many previous studies, our research shows that accounting's psychological factor does not affect the accounting policy choice. This is consistent with the characteristics of SMEs in Vietnam because the role of accountants is not appreciated in the business.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1122-1135
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• 2022

With the development of digital technology and the influence of the global pandemic, the metaverse, a three-dimensional virtual world, is receiving attention in society, economy and overall industry, and the manufacturing industry is also accepting it as a major strategic agenda for digital transformation. Therefore, in this study, the concept of the industry metaverse from the perspective of the manufacturing industry was defined, and the types of the industry metaverse were classified into four types by reflecting the characteristics of the manufacturing industry based on the general metaverse scenario presented in previous studies. These are Virtual behavior simulation, Augmented operation of business objects and Virtual experience simulation, Augmented decision of business subjects. In addition, through case analysis of solutions used in the manufacturing industry, it was confirmed that the central technology of the Industry Metaverse is the digital twin, and that it is being implemented by convergence with major digital technologies such as virtual reality, augmented reality, digital human, and AI. This study will be able to provide guidelines for future research on the metaverse from the perspective of the manufacturing industry and establishment of a digital transformation strategy for the industry.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.104-112
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• 2023

Agriculture plays an industrial and economic role, as well as an environmental and ecological conservation role, group harmony and the inheritance of traditional culture. However, no matter how advanced the industry is, the basic food necessary for human life can only be produced through the photosynthesis of plants with natural resources such as the sun, water, and air. The Food and Agriculture Organization of the United Nations (FAO) predicts that the world's population will increase by another 2 billion people by 2050, and it faces a myriad of complex and diverse factors to consider, including climate change, food security concerns, and global ecosystems and political factors. In particular, in order to solve problems such as increasing productivity and production of agricultural products, improving quality, and saving energy, it is difficult to solve them with traditional farming methods. Recently, with the wind of the 4th industrial revolution, ICT convergence technology and artificial intelligence have been rapidly developing in many fields, but it is also true that the application of new technologies is somewhat delayed due to the unique characteristics of agriculture. However, in recent years, as ICT and artificial intelligence utilization technologies have been developed and applied by many researchers, a revolution is also taking place in agriculture. This paper summarizes the current state of research so far in four categories of agriculture, namely crop cultivation environment management, soil management, pest management, and irrigation management, and smart farm research data that has recently been actively developed around the world.