• Information Systems Review
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• v.17 no.2
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• pp.101-132
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• 2015

National technical qualifications to enhance an individual's vocational skills, the competitiveness of companies and countries have an important function to improve. Especially 'qualifications' will have a signal function to show objectively measure an individual's ability with the 'Education' The "knowledge necessary for the performance of their duties. Technology will gain knowledge about such assessment or recognition is based on certain criteria and procedures." Learning to qualify are being made through a smart learning a lot. Due to the revolution of the Internet in recent years with the development of information and communication technologies are entering into a knowledge society, the importance of information and knowledge. This contemporary smart learning education system is continuing to rapidly growing in pace with the changing time and space constraints, without teaching and learning is taking place. The purpose of this study is the ARCS motivation theory can determine a representative theory of human motivation factors and basic psychological needs dealing with the human nature of the psychological needs Interactivity and immersive learning, and to validate the empirical causality Affecting the continued use of smart learning through fun. Specifically, attention, relevance, confidence in the ARCS motivation, see their effect on the learning flow through the satisfaction we analyze empirically. Through this national technical qualifications smart learner's learning by supporting the implicit synchronization of students in learning are the degree of continued use. Therefore, to achieve the objectives of national technical qualifications and skills through a smart learning can contribute to the activation of the development and certification of course industry.

• The Journal of Bigdata
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• v.4 no.2
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• pp.115-126
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• 2019

There have been many discussions on export indices in trade exports, but there is no definite trade export index which can be explained by objective indicators. Korea International Trade Association (KITA), Korea Trade-Investment Promotion Agency (KOTRA), etc., but we are currently in the process of thinking about ways to express the capabilities of exporting companies. In this study, we constructed the AI data sets by setting the activity indicators such as the size of the company and the credit score, the number of transaction customers, the number of transactions, the number of items, the transaction volume, and the transaction period as features, Lightgbm. Using the Graph Neural Network as an industrial cluster classification model, the export live index which expresses the exportable capacity among companies, items, and business groups was calculated. This includes the past activity of the company from the current calculating index Objectivity.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.165-171
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• 2018

The most threatening attack that has become a hot topic of recent IT security is APT Attack.. So far, there is no way to respond to APT attacks except by using artificial intelligence techniques. Here, we have implemented a machine learning algorithm for analyzing cyber threat data using machine learning method, using a data set that collects cyber attack cases using Scikit Learn, a big data machine learning framework. The result showed an attack classification accuracy close to 70%. This result can be developed into the algorithm of the security control system in the future.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1195-1210
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• 2023

Coastal debris has emerged as a salient issue due to its adverse effects on coastal aesthetics, ecological systems, and human health. In pursuit of effective countermeasures, the present study delineated the construction of a specialized image dataset for coastal debris detection and embarked on a comparative analysis between two paramount real-time object detection algorithms, YOLOv8 and RT-DETR. Rigorous assessments of robustness under multifarious conditions were instituted, subjecting the models to assorted distortion paradigms. YOLOv8 manifested a detection accuracy with a mean Average Precision (mAP) value ranging from 0.927 to 0.945 and an operational speed between 65 and 135 Frames Per Second (FPS). Conversely, RT-DETR yielded an mAP value bracket of 0.917 to 0.918 with a detection velocity spanning 40 to 53 FPS. While RT-DETR exhibited enhanced robustness against color distortions, YOLOv8 surpassed resilience under other evaluative criteria. The implications derived from this investigation are poised to furnish pivotal directives for algorithmic selection in the practical deployment of marine debris monitoring systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.107-116
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• 2022

Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2^nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members.

• Information Systems Review
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• v.20 no.4
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• pp.25-41
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• 2018

Applying predictive analytics to enterprise processes is an effective way to reduce operation costs and enhance productivity. Accordingly, the ability to predict business processes and performance indicators are regarded as a core capability. Recently, several works have predicted processes using deep learning in the form of recurrent neural networks (RNN). In particular, the approach of predicting the next step of activity using static or dynamic RNN has excellent results. However, few studies have given attention to applying deep learning in the form of dynamic RNN to predictions of process performance indicators. To fill this knowledge gap, the study developed an approach to using process mining and dynamic RNN. By utilizing actual data from a large domestic company, it has applied the suggested approach in estimating timely stocking in purchasing process, which is an important indicator of the process. The analytic methods and results of this study were presented and some implications and limitations are also discussed.

• Digital Contents
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• no.7 s.146
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• pp.66-71
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• 2005

교육(Education)과 오락(Entertainment)의 합성어인 에듀테인먼트(Edutainment)는 학생이 학습을 하거나 기업의 종업원이 특정 능력을 익히는 데 사용되는 교육용 도구를 의미한다. 에듀테인먼트는 교육용 소프트웨어에 게임이나 친근한 인물과 음악, 이야기 등 오락성을 가미해 싫증을 느끼지 않고 즐기면서 교육적 효과를 거두고자 하는 목표를 지향하고 있다. 이러한 재미와 교육이라는 두가지 목표를 달성하기 위해 에듀테인먼트에 포함되는 디지털콘텐츠는 게임이나 e러닝의 콘텐츠와는 다른 차원의스토리텔링 기법이 요구된다. 이번호에서는 에듀테인먼트 콘텐츠에 적합한 퀘스트 스토리텔링(Quest Story telling)과 공간 스토리텔링(Spatial Storytelling)을 소개한다.

• The monthly packaging world
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• s.310
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• pp.71-75
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• 2019

국내 최고의 브랜드와 상품 패키지디자인을 선정해 시상하는 '제24회 베스트브랜드 & 패키지디자인어워즈(The 24th Best Brand & Package Design Awards)' 시상식이 지난해 12월21일 서울사이버대학교에서 개최됐다. (사)한국상품문화디자인학회(회장 김윤배)와 한국경제신문사의 공동주최로 진행된 이날 시상식에서는 교과서부터 스마트러닝까지 교육의 미래를 선도하고자 하는 천재교육의 우등생(해법시리즈)이 베스트브랜드&패키지 종합대상을 수상했다. 베스트브랜드&패키지디자인어워즈는 브랜드와 패키지 디자인에 관한 연구와 학술활동을 지속해온 한국상품문화디자인학회에서 기업들의 상품개발 의욕과 디자인 역량 강화를 위해 1995년부터 수행해온 행사이다. 다음에 '제24회 베스트브랜드&패키지디자인어워즈' 수상작을 살펴보도록 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.105-108
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• 2024

랜섬웨어는 Ransom(몸값)과 Software(소프트웨어)의 합성어로, 데이터를 암호화하여 이를 인질로 금전을 요구하는 악성 프로그램이다. 블랙캣(BlackCat)과 같은 랜섬웨어가 스위스 항공 서비스 기업의 시스템을 마비시키는 공격을 시도하였으며, 이와 같은 랜섬웨어로 인한 피해는 지속적으로 발생하고 있다. 랜섬웨어에 의한 피해 감소 및 방지를 위하여, 다양한 랜섬웨어 탐지방안이 등장하였으며, 최근 행위 기반 침입탐지 시스템에 인공지능 기술을 결합하여 랜섬웨어를 탐지하는 방안이 연구되는 실정이다. 인공지능 기술은 딥러닝 및 하드웨어의 발전으로 데이터를 처리할 수 있는 범위가 넓어지면서, 다양한 분야와 접목하여 랜섬웨어 탐지를 위한 시스템에 적용되고 있지만, 국내는 국외만큼 활발하게 연구되지 않고 연구 개발 단계에 머물러 있다. 따라서 본 논문에서는 랜섬웨어에 감염된 파일에서 나타나는 특징 중 하나인 엔트로피를 데이터 분석에 활용함으로써, 랜섬웨어를 탐지하는 시스템을 제안하고 설계하였다.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.