• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.134-138
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• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.205-211
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• 2022

Performing the rituals of Hajj and Umrah is an obligation of Allah Almighty to all Muslims from all over the world. Millions of Muslims visit the holy mosques in Makkah every year to perform Hajj and Umrah. One of the most important pillars in Performing Hajj/Umrah is Tawaf and Sa'ay. Tawaf finished by seven rounds around the holy house (Al-Kabaa) and Sa'ay is also seven runs between As-Safa and Al-Marwa. Counting/knowing the number of runs during Tawaf/Sa'ay is one of the difficulties that many pilgrims face. The pilgrim's confusing for counting (Tawaf/Sa'ay) rounds finished at a specific time leads pilgrims to stay more time in Mataff bowl or Masa'a run causing stampedes and more crowded as well as losing the desired time for prayers to get closer to Almighty Allah in this holy place. These issues can be solved using effective crowd management systems for Tawaf/Sa'ay pillars, which is the topic of this research paper. While smart devices and their applications are gaining popularity in helping pilgrims for performing Hajj/Umrah activities efficiently, little has been dedicated for solving these issues. We present an autonomous Mobile-based framework for guiding pilgrims during Tawaf/Sa'ay pillars with the aid of GPS for points tracking and rounds counting. This framework is specially designed to prevent and manage stampedes during Tawaf/Sa'ay pillars, by helping pilgrims automatically counting the rounds during Tawaf/Sa'ay with supported Supplications (in written/audio form with different languages) from the Quran and Sunna'a.

• Smart Structures and Systems
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• 제31권4호
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• 한국산업정보학회논문지
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• 제15권5호
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• pp.125-135
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• 2010

최근 무선통신의 발달로 인하여 PDA나 스마트폰 등 R 바일 기기를 활용한 원격지 업무처리가 보편화되고 있다. 특히, 웹 서비스와 XML 기술을 사용하게 되면 다양한 기종의 모바일 기기를 대상으로 서비스를 제공할 수 있으며 SW 재사용성과 확장성 및 통합성이 높아지게 된다. 본 논문에서는 웹서비스와 XML 기법을 사용하는 SOA(Service Oriented Architecture) 방식으로 지반정보시스템을 개발하는 방법을 제안하고, 프로토타입 시스템을 개발하여 평가한다. 웹서비스 기법을 사용하는 경우 서버 주소만 알면 WSDL을 통해 Local method를 사용하는 것과 동일하게 서비스를 사용할 수 있으므로 확장성과 통합성이 뛰어나다. 웹서비스 방식은 클라이언트와 서버에서 서로 다른 프로그래밍 언어를 사용하여 SW를 개발한 이질적인 분산 시스템들을 통합하는 경우에도 장점을 가진다. 제안된 시스템에서도 서버는 Java를 사용하고, Mobile Client는 Visual Basic.Net으로 개발한 SW를 서로 통합하여 서비스를 제공한다.

• 문화기술의 융합
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• 제9권1호
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• pp.655-661
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• 2023

4차 산업혁명의 중요한 요소인 빅데이터는 공공기관과 지자체에서 공공데이터를 적극 개방하고 있다. 공공데이터 포털에서 모두가 편리하게 데이터를 검색하고 연관 데이터를 확인 할 수 있지만, ICT관련 분야에 있는 사람들만 공공데이터를 활용하고 있는 실정이다. 공공기관이 보유하고 있는 데이터를 시민에게 개방은 하고 있지만, 누구나 쉽게 공공데이터를 활용하여 응용 프로그램을 개발하기에는 어려운 상황이다. 본 논문은 공공데이터 포털에서 오픈API 형식으로 제공되는 데이터가 XML과 JSON 형식이다. 우리는 본 연구에서 XML형식의 공공데이터를 GUI 인터페이스에 연동하여 손쉽게 프로그램 개발이 가능한 부분으로 모듈화 하는 방법이다. 필요한 공공데이터를 기반으로 모바일 프로그램을 쉽게 개발하는 방안을 제시하여, 공공데이터 활용을 활성화 하는 방안을 제안한다.

• 한국비블리아학회지
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• 제33권1호
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• pp.139-166
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• 2022

본 연구는 5G 신기술에 가장 민감한 세대의 윤리 행태를 권장하는 일환으로 미래 정보전문가를 희망하는 주체를 위한 윤리 리터러시(Ethics-Literacy) 교육과정모형 개발을 목적으로 한다. 연구 범위의 핵심 주제인 5G 특성, 리터러시, 윤리 쟁점, 6C 역량기반 교육, 이용자 경험 등을 주제어로 최근 5년 이내(2022-2017) 출간된 국내외 학술 연구자료를 조사하고 내용분석으로 최종 86편을 연구대상으로 선정하여 문헌 연구가 진행되었다. 분석 결과가 제시하는 것은 첫째, 기존의 리터러시는 5G 환경에 대응된 네 영역으로 구분될 수 있고 둘째, 분석된 윤리 쟁점은 모든 리터러시 영역에서 보이는 공통 쟁점과 각 리터러시 영역별 특수 쟁점으로 비교 구분되었다. 분석된 결과와 4차 산업혁명 교육방식으로 제시된 6C 역량기반 교육을 바탕으로 대학 차원의 5G 정보환경 정보전문가를 위한 윤리 리터러시 교육과정모형을 개발하여 제시하였다.

• International journal of advanced smart convergence
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• 제11권1호
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• pp.111-116
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• 2022

After COVID-19, the number of people with sleep disorders around the world is increasing. In particular, in the flow of the 4th industrial revolution, the differentiation of types and characteristics of the sleep industry is accelerating. Therefore, in this study, the characteristics of each type of sleep-related industry were reclassified from an industrial point of view, and based on this, an attempt was made to review the classification system that can help companies develop sleep products and improve related national systems. Based on the 10th standard industry classification, we compared input cost, value, and usability and analyzed common characteristics, treatments, and preventive effects based on this. A comprehensive taxonomy using matrix analysis was reviewed. As a result, in terms of cost (A), the most common sleeping products are general mattresses and general bedding. It is an IOT device (auxiliary device), and the value aspect (B, B/D) included sleep cafe, bedding rental and management service, and sleep consulting. In terms of utility (A/B), a total of 6 product groups including sleep aids (health functional foods) belong to this category, and in terms of treatment (A/C), a total of 3 product groups including sleep clinics (medical services) belong to this category. As for the product group (A/D) with both properties, it was found that non-insurance sleep treatment medical devices, sleep-related over-the-counter drugs, and some sleep monitoring applications belong to this category. Ultimately, it was found that the sleep industry classification enables the most active product development and composition according to the relative relationship between cost and utility, and treatment and utility. appeared to be necessary.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.273-283
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• 2022

Banking and finance is a broad term that incorporates a variety of smaller, more specialized subjects such as corporate finance, tax finance, and insurance finance. A virtual assistant that assists users in searching for information about banking and finance terms might be an extremely beneficial tool for users. In this study, we explored the process of searching for information, seeking opportunities, and developing a virtual assistant in the first stages of starting learning and understanding Vietnamese to increase effectiveness and save time, which is also an innovative business practice in Use-case Vietnam. We built the FIBA2020 dataset and proposed a pipeline that used Natural Language Processing (NLP) inclusive of Natural Language Understanding (NLU) algorithms to build chatbot applications. The open-source framework RASA is used to implement the system in our study. We aim to improve our model performance by replacing parts of RASA's default tokenizers with Vietnamese tokenizers and experimenting with various language models. The best accuracy we achieved is 86.48% and 70.04% in the ideal condition and worst condition, respectively. Finally, we put our findings into practice by creating an Android virtual assistant application using the model trained using Whitespace tokenizer and the pre-trained language m-BERT.

• 품질경영학회지
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• 제50권1호
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• pp.125-138
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• 2022

Purpose: Failure Mode and Effect Analysis (FMEA) is a widely utilized technique to measure product reliability by identifying potential failure modes. Even though FMEA techniques have been studied, the form of Risk Priority Number (RPN) used to evaluate risk priority in FMEA is still questionable because of its shortcomings. In this study, we suggest common RPN(cRPN) to resolve shortcomings of the traditional RPN and show the extensibility of cRPN. Methods: We suggest cRPN which is based on Cobb-Douglas production function, and represent the various application on weighting risk factors, weighted RPN in a mathematical way, and show the possibility of statistical approach. We also conduct numerical study to examine the difference of the traditional RPN and cRPN as well as the potential application from the analysis on marginal effects of each risk factor. Results: cRPN successfully integrates previously suggested approaches especially on the relative importance of risk factors and weighting RPN. Moreover, we analyze the effect of corrective actions in terms of econometric analysis using cRPN. Since cRPN is rely on the reliable mathematical model, there would be numerous applications using cRPN such as smart factory based on A.I. techniques. Conclusion: We propose a reliable mathematical model of RPN based on Cobb-Douglas production function. Our suggested model, cRPN, resolves various shortcomings such as consideration of the relative importance, the effect of combinations among risk factors. In addition, by adopting a reliable mathematical model, quantitative approaches are expected to be applied using cRPN. We find that cRPN can be utilized to the field of industry because it is able to be applied without modifying the entire systems or the conventional actions.

• 산업과 과학
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• 제2권2호
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• pp.1-8
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• 2023

본 연구에서는 블록체인 기술의 지속 가능성을 달성하기 위해 소매 공급망을 개선할 수 있는 방법과 공급망 관리에 대한 잠재적인 블록체인 및 스마트 계약 애플리케이션에 대한 분석 결과를 제시하였다. 또한 블록체인 기술이 어떻게 다양한 소매점 운영에서 고객과 상인 모두에게 많은 이익을 줄 수 있을지 그 방법을 제안하였다. UTUAT 모델의 수정된 버전을 활용하여 소매점에서 공급망 관리를 위한 블록체인 사용의 실행 가능성을 추가적으로 검증하였다. 그리고 소매 산업의 공급망 관리에서 블록 네트워크 사용에 대한 행동 의도와 수용 사이에 통계적으로 중요하고 긍정적인 연관성이 있음이 확인되었다. 블록체인 기술을 채택하려는 행동 의도(BI)는 성능 기대치, 효과 기대치, 주관적 기준 및 활성화 변수에 의해 크게 영향을 받고 있고 성능 및 노력 기대치는 공급망 관리에서 블록체인을 채택하려는 행동 의도에 상당한 영향을 미치는 것으로 나타났다.