• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.45-46
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• 2023

In order to improve and promote immersive learning experiences for English as a Second Language (ESL) students, the deployment of a YOLOv5 model for object identification in videos is proposed. The procedure includes collecting annotated datasets, preparing the data, and then fine-tuning a model using the YOLOv5 framework. The study's major objective is to integrate a well-trained model into ESL instruction in order to analyze the effectiveness of AI application in the field.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1267-1276
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• 2024

The emerging technologies at the industrial level have deployed rapidly within the energy transition process innovations. The nuclear industry incorporates several technologies like Artificial Intelligence (AI), Machine Learning (ML), Digital Twins, High-Performance-Computing (HPC) and Quantum Computing (QC), among others. Factors identifications are explained to set up a regulatory framework in the digitalization era, providing new capabilities paths for nuclear technologies in the forthcoming years. The Analytical Network Process (ANP) integrates the quantitative-qualitative decision-making analysis to assess the implementation of different aspects in the digital transformation for the New-Energy Transition Era (NETE) with a Nuclear Power Infrastructure Development (NPID).

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.229-230
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• 2024

최근 드론으로 극한 환경에서 범죄 수배자 및 실종자를 탐색하는 시도가 활발하다. 이때 생체 인증 기술인 얼굴 인증 기술을 사용하면 탐색 효율이 높아지지만, 암호화되지 않은 인증 프로토콜 적용 시 생체 정보 유출의 위험이 있다. 본 논문에서는 드론이 수집한 얼굴 이미지 템플릿을 암호화하여 안전하게 인증할 수 있는 효율적인 생체 인증 프레임워크인 DF-PPHDM(Privacy-Preserving Hamming Distance biometric Matching for Drone-collected Facial images)을 제안한다. 수집된 얼굴 이미지는 암호문 형태로 서버에 전달되며 서버는 기존 등록된 암호화된 템플릿과의 Hamming distance 분석을 통해 검증한다. 제안한 DF-PPHDM을 RaspberryPI 4B 환경에서 직접 실험하여 분석한 결과, 한정된 리소스를 소유한 드론에서 효율적인 구현이 가능하며, 인증 단계에서 7.83~155.03 ㎲ (microseconds)가 소요된다는 것을 입증하였다. 더불어 서버는 드론이 전송한 암호문으로부터 생체 정보를 복구할 수 없으므로 프라이버시 침해 문제를 예방할 수 있다. 향후 DF-PPHDM에 AI(Artificial Intelligence)를 결합하여 자동화 기능을 추가하고 코드 최적화를 통해 성능을 향상시킬 예정이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1364-1370
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• 2006

Recently, Adopting Accounting Information Systems(AIS) has spread rapidly for efficient and rational making decision in the business organization. There are many types of AIS. These are from simple package to integrated packages which are including HR, Product, Sales and Distribute. In case of big enterprises, ERP systems have been implemented and attention is now being directed as to AIS module. AIS module is not easy to change its form, therefore this module need to be considered enough when it comes to the corporations. However there we few standard fer this module as a successful information systems. This study analyze critical factors of certain companies when the companies were implementing AIS and based on this analysis, this study suggest a framework for successful implementation of AIS Using Case Study. 42 AIS adopted companies are surveyed and their factors' correlations are analyzed by mean analysis and factor analysis in this study. As a result of this study, when a company adopt AIS, criteria or particularities for the adoption are more important than environment of the company. Thus, it is significant to empirically prove previous studies' factors relation and importance relations for successful AIS implementation through empirical method in this study.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.309-316
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• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.49-57
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• 2022

Currently, the problem of food shortage is emerging in our society due to climate problems and an increase population in the world. As a solution to this problem, we propose a multi-remote control smart farm that combines artificial intelligence (AI) and information and communication technology (ICT) technologies. The proposed smart farm integrates ICT technology to remotely control and manage crops without restrictions in space and time, and to multi-control the growing environment of crops. In addition, using Arduino and deep-learning technology, a smart farm capable of multiple control through a smart-phone application (APP) was proposed, and Ai technology with various data securing and diagnosis functions while observing crop growth in real-time was included. Various sensors in the smart farm are controlled by using the Arduino, and the data values of the sensors are stored in the built database, so that the user can check the stored data with the APP. For multiple control for multiple crops, each LED, COOLING FAN, and WATER PUMP for two or more growing environments were applied so that the user could control it conveniently. And by implementing an APP that diagnoses the growth stage through the Tensor-Flow framework using deep-learning technology, we developed an application that helps users to easily diagnose the growth status of the current crop.

• Convergence Security Journal
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• v.19 no.5
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• pp.63-73
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• 2019

In the 4th Industrial Revolution, information is collected and analyzed from people and objects through the rapid development of ICT. It is possible to create value. However, there are many legal and institutional restrictions on the collection of information aimed at people.Therefore, in-depth research on the protection and use of personal information in the rapidly changing cyber security environment is needed. The purpose of this study is to protect and utilize personal information for the operation of AI (Artificial Intelligence) and big data during the 4th Industrial Revolution. It is to seek a paradigm shift. The organization of the research for this is: Chapter 1 examines the meaning of personal information during the 4th Industrial Revolution, Chapter 2 presents the framework for the review and analysis of prior research. In Chapter 3, after analyzing policies for the protection and utilization of personal information in major countries, Chapter 4 looks at the paradigm shift in personal information protection during the 4th Industrial Revolution and how to respond. Chapter 5 made some policy suggestions for the protection and utilization of personal information.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.169-169
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• 2003

In Korea, there is no CRA studies and has not well known CRA and not well established their methodologies. Therefore, objectives of this study is to establish the framework of CRA consisting of health risk, economic risk and perceived risk and the detail methodologies of three main component of estimating and comparing those risks for on the three environmental problems of air pollution, indoor air pollution and drinking water contamination which being subjective to the eight sub-problems of hazardous ai. pollutants (HAPs), regulated pollutants (representative as PM10) and Dioxins (PCDDS/ PCDFs) in air pollution, and indoor ai. pollutants (IAPs) and Radon in indoor air pollution, and drinking water pollutants (DWPs), disinfection-by- products(DBPs) and radionuclides in drinking water contamination in Seoul, Korea. And then, their problems set priorities by individual and integrated risk. As a results, ranking of health risk were the following order of indoor air pollution, air pollution and then drinking water contamination, in three environmental problems and of radon, PM10, IAPs, HAPs, DWPs, Dioxins, DBPs, and then radionuclides in eight sub-problems. And that of economic risk were the same order. In the contrary, ranking of perceived risk were the following order of air pollution, drinking water contamination, and then indoor air pollution, and of HAPs, Dioxins, radionuclides, PM10, DWPs, IAPs, Radon and then DBPs.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1187-1208
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• 2011

This research aims to propose a systematic approach to identify emerging technology-product portfolio for small and medium enterprises (SMEs). Firstly, operational definition of emerging technology for SMEs is presented. Secondly, research framework is suggested and case study to show usefulness of the newly proposed framwork is analyzed. In detail, reference patent set which represent company's capabilities and business area are constructed. The research constructs patent data set for bibliometric analysis using reference patent set and citing patents to 2nd level. Clustering (expert judgement) and keyword based bibliometric approach are used. Then, cluster activity index (AI) and relevance index (RI) comparing with reference patent set are estimated. With emerging technology-product portfolio using AI and RI, a firm can identify emerging technology-product area and monitoring area.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.155-161
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• 2021

Gesture recognition analytics through a camera in real time have been widely studied in recent years. Since a small number of features from human joints are extracted, low accuracy of classifying models is get in conventional gesture recognition studies. In this paper, CBAM (Convolutional Block Attention Module) with high accuracy for classifying images is proposed as a classification model and algorithm calculating the angle of joints depending on actions is presented to solve the issues. Employing five exercise gestures images from the fitness posture images provided by AI Hub, the images are applied to the classification model. Important 8-joint angles information for classifying the exercise gestures is extracted from the images by using MediaPipe, a graph-based framework provided by Google. Setting the features as input of the classification model, the classification model is learned. From the simulation results, it is confirmed that the exercise gestures are classified with high accuracy in the proposed model.