• 한국IT서비스학회지
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• 제21권1호
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• pp.145-161
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• 2022

The data is a key catalyst for the development of the fourth industry, and has been viewed as an essential element of the new industry, with technology convergence such as artificial intelligence, augmented/virtual reality, self-driving and 5 G. This will determine the price and value of the data as the user uses data in which the data is based on the context of the situation, rather than the data itself of the past supplier-centric data. This study began with, what factors will increase the value of data from a user perspective not a supplier perspective The study was limited to public data and users conducted research on users using data, such as analysis or development based on data. The study was designed to gauge the value of data that was not studied in the user's perspective, and was instrumental in raising the value of data in the jurisdiction of supplying and managing data.

• 전자통신동향분석
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• 제37권1호
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• pp.32-41
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• 2022

With the development of computing technology, the convergence of ICT with existing traditional industries is being attempted. In particular, with the recent advent of 5G, connectivity with numerous AuT (autonomous Things) in the real world as well as simple mobile terminals has increased. As more devices are deployed in the real world, the need for technology for devices to learn and act autonomously to communicate with humans has begun to emerge. This article introduces "Device to the Edge," a new computing paradigm that enables various devices in smart spaces (e.g., factories, metaverse, shipyards, and city centers) to perform ultra-reliable, low-latency and high-speed processing regardless of the limitations of capability and performance. The proposed technology, referred to as EdgeCPS, can link devices to augmented virtual resources of edge servers to support complex artificial intelligence tasks and ultra-proximity services from low-specification/low-resource devices to high-performance devices.

• 전자통신동향분석
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• 제37권4호
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• pp.81-88
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• 2022

In this study, we investigate the trends and prospects of spatial light modulation (SLM) technology that enables full complex modulation as a next-generation SLM. Current SLM technology, which is used as a key element in holography, augmented reality (AR), XR, and realistic displays, has performance limits that modulate only amplitude or phase. Notably, SLM capable of full complex modulation does not produce diffraction noise, unlike DC and twin image, and thus has a high-efficiency performance. In the future, the application field of next-generation SLM, which can be full-complex modulated, is expected to cover a wide range of holography-AR and-XR devices, optical artificial intelligence, and 6G free space optics communications, which will greatly contribute to the development of a super-realistic metaverse platform and service.

• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.159-166
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• 2023

The COI gene is a sequence of approximately 650 bp at the 5' terminal of the mitochondrial Cytochrome c Oxidase subunit I (COI) gene. As an effective DeoxyriboNucleic Acid (DNA) barcode, it is widely used for the taxonomic identification and evolutionary analysis of species. We created a CNN-LSTM hybrid model by combining the gene features partially extracted by the Long Short-Term Memory ( LSTM ) network with the feature maps obtained by the CNN. Compared to K-Means Clustering, Support Vector Machines (SVM), and a single CNN classification model, after training 278 samples in a training set that included 15 genera from two orders, the CNN-LSTM hybrid model achieved 94% accuracy in the test set, which contained 118 samples. We augmented the training set samples and four genera into four orders, and the classification accuracy of the test set reached 100%. This study also proposes calculating the cosine similarity between the training and test sets to initially assess the reliability of the predicted results and discover new species.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.129-131
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• 2022

본 논문에서는 다양한 인공지능을 모듈화하고 모듈들을 서로 결합하여 서비스를 제공할 수 있는 지능형 서비스 로봇에서, 인공지능 모듈들을 라이브러리 간의 의존성을 해소하기 위한 방법 중 하나인 가상 머신의 일종인 도커(Docker)를 활용하여 컨테이너화하여 사용할 때, 인공지능 모듈 내부에서 사용하는 신경망 데이터에 해당하는 지능 모델에 대해 버전 관리를 수행하면서 클라우드 등 외부 서버를 이용하여 증강시킨 지능 모델을 공유하는 기술 개발에 대해 설명한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.16-18
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• 2023

본 연구는 궤적 데이터(trajectory data)를 대상으로 증강 그래프 기반의 그래프 뉴럴 네트워크를 활용하여 다음에 방문한 장소를 추천하는 모델을 제안한다. 제안 모델은 전체 궤적 데이터를 그래프로 표현하여 추출한 글로벌 궤적 플로우의 특성을 다음 방문할 POI 추천에 활용한다. 이때, POI 추천시 자주 발생하는 두 가지 문제를 추가로 해결함으로써 POI 추천의 정확도를 높이는 것을 목표로 한다. 첫 번째 문제는 추천 대상 궤적 데이터의 길이가 짧은 경우에 성능 저하가 발생한다는 것이다. 두 번째 문제는 콜드-스타트 문제이다. 기존 POI 추천 모델은 매우 적은 방문 기록만 가지는 사용자 또는 POI에 대해서는 매우 낮은 예측 성능을 보인다. 본 연구에서는 궤적 그래프에서 일부 엣지를 삭제하여 생성한 증강 그래프 기반의 궤적 플로우 특징 기반 모델을 제안함으로써 짧은 길이의 궤적 데이터 및 콜드-스타트 사용자/POI에 대한 추천 성능을 높인다.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.163-168
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• 2024

Technologies that are considered to be a part of the fourth industrial revolution include holograms, augmented reality, and virtual reality. As technology advances, the industry's scale is growing quickly as well. While the development of technology for direct use is moving slowly, awareness of floating holograms-which are considered realistic content-is growing as the industry's scale and rate of technological advancement continue to accelerate. Specifically, holograms that have been incorporated into museums and exhibition spaces are static forms of content that viewers gaze at inertly. Additionally, their use in educational fields is very passive and has a low rate of utilization. Therefore, in order to improve usability from the viewpoint of viewers of realistic content, such as exhibition halls or museums, we introduce realistic content control technology in this study using a machine learning framework to recognize hands. It is anticipated that using the study's findings, manipulating realistic content independently will enhance comprehension of objects presented as realistic content and boost its applicability in the industrial and educational domains.

• International Journal of Advanced Culture Technology
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• 제12권1호
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• pp.123-127
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• 2024

In the early 20th century, Walter Benjamin analyzed the changes in the value of traditional art forms under the industrial era and the changes in the aesthetic attitude of the masses. A century later, in the contemporary multi-art world, the traditional medium of shelf painting is once again experiencing a similar situation as the last century. Emerging technology display modes such as digital virtual reality and digital immersive experience can achieve digital reproduction of paintings on shelves and reach a certain level of performance, which once again shocks the public's aesthetic perception. This paper attempts to illustrate the outstanding characteristics of the new art form after digital reconstruction by exploring the transformation and sublimation of digital technology to shelf painting. We predict that art research on future reality and augmented reality according to the artificial intelligence era will be conducted in depth in the future.

• ETRI Journal
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• 제46권2호
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.346-356
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• 2022

The jurisprudential legal rules govern the way Muslims react and interact to daily life. This creates a huge stream of questions, that require highly qualified and well-educated individuals, called Muftis. With Muslims representing almost 25% of the planet population, and the scarcity of qualified Muftis, this creates a demand supply problem calling for Automation solutions. This motivates the application of Artificial Intelligence (AI) to solve this problem, which requires a well-designed Question-Answering (QA) system to solve it. In this work, we propose a QA system, based on retrieval augmented generative transformer model for jurisprudential legal question. The main idea in the proposed architecture is the leverage of both state-of-the art transformer models, and the existing knowledge base of legal sources and question-answers. With the sensitivity of the domain in mind, due to its importance in Muslims daily lives, our design balances between exploitation of knowledge bases, and exploration provided by the generative transformer models. We collect a custom data set of 850,000 entries, that includes the question, answer, and category of the question. Our evaluation methodology is based on both quantitative and qualitative methods. We use metrics like BERTScore and METEOR to evaluate the precision and recall of the system. We also provide many qualitative results that show the quality of the generated answers, and how relevant they are to the asked questions.