• 여성건강간호학회지
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• 제26권1호
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• 스마트미디어저널
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• 제11권2호
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.45-52
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• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• 한국가스학회지
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• 제27권2호
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• pp.49-56
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• 2023

4차 산업혁명으로 인공지능(AI, Artificial Intelligence) 관련 기술이 고도로 성장함에 따라 여러 분야에서 AI를 접목하는 사례가 증가하고 있다. 주요 원인은 정보통신기술이 발달됨에 따라 기하급수적으로 증가하는 데이터를 사람이 직접 처리·분석하는데 현실적인 한계가 있고, 새로운 기술을 적용하여 휴먼 에러에 대한 리스크도 감소시킬 수 있기 때문이다. 이번 연구에서는 '원격 전위 측정용터미널(T/B, Test Box)'로부터 수신된 데이터와 해당시점의 '원격 정류기' 출력을 수집 후, AI가 학습하도록 하였다. AI의 학습 데이터는 최초 수집된 데이터의 회기분석을 통한 데이터 전처리로 확보하였고, 학습모델은 심층 강화학습(DRL, Deep Reinforce-ment Learning) 알고리즘 중(中) Value기반의 Q-Learning모델이 적용하였다. 데이터 학습이 완료된 AI는 실제 도시가스 공급지역에 투입하여, 수신된 원격T/B 데이터를 기반으로 AI가 적절하게 대응하는지 검증하고, 이를 통해 향후 AI가 전기방식 관리에 적합한 수단으로 활용될 수 있는지 검증하고자 한다.

• 대한원격탐사학회지
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• 제38권6_1호
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• pp.1091-1100
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• 2022

딥러닝 기술의 진보와 함께 다양한 국내외 고해상도 원격탐사 영상의 활용이 가능함에 따라 딥러닝 기술과 원격탐사 빅데이터를 활용하여 도심 지역 건물 검출과 변화탐지에 활용하고자 하는 관심이 크게 증가하고 있다. 본 논문에서는 고해상도 원격탐사 영상의 의미론적 건물 분할을 위해서 건물 분할에 우수한 성능을 보이는 DeepResUNet 모델을 기본 구조로 하고 잔차 학습 단위를 개선하고 Convolutional Block Attention Module(CBAM)을 결합한 새로운 건물 분할 모델인 CBAM-DRUNet을 제안한다. 제안한 건물 분할 모델은 WHU 데이터셋과 INRIA 데이터셋을 이용한 성능 평가에서 UNet을 비롯하여 ResUNet, DeepResUNet 대비 F1 score, 정확도, 재현율 측면에서 모두 우수한 성능을 보였다.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.262-265
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• 2018

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• 대한원격탐사학회지
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• 제38권6_2호
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• pp.1581-1587
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• 2022

이제는 딥러닝 없는 원격탐사 데이터 처리는 상상하기도 어려운 시대가 되었다. 원격탐사의 활용기술 개발을 위해서는 먼저 인공지능(artificial intelligence, AI)을 위한 데이터를 설계 및 구축하고, AI모델을 학습시키는 과정을 거친다. AI모델은 빠르게 발전하여 모델 정확도가 나날이 높아지고 있지만, 모델을 훈련시키는 사람에 따라 정확도의 편차가 발생하고 있다. 결국 AI모델을 훈련시킬 수 있는 숙련도 높은 전문가가 더욱 더 필요한 시대가 되어가고 있다. 특히, 딥러닝기술은 원격탐사활용에 있어 자동화라는 키워드를 제공하고 있다. 예전에는 60% 이하의 정확도만 있었던 기술도 이제는 90%를 넘어 100%의 시대로 가고 있다. 이 특별호에서는 딥러닝기술이 원격탐사에 어떻게 활용되고 있는지에 관한 13편의 논문을 소개한다.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.1-8
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• 2024

실시간 처리 및 프라이버시 강화를 위해 인공지능 모델을 엣지에서 동작시킬 수 있는 온디바이스 AI 기술이 각광받고 있다. 지능형 사물인터넷 기술이 다양한 산업에 적용되면서 온디바이스 AI 기술을 활용한 서비스가 크게 증가하고 있다. 그러나 일반적인 딥러닝 모델은 추론 및 학습을 위해 많은 연산 자원을 요구하고 있다. 따라서 엣지에 적용되는 경량 기기에서 딥러닝 모델을 동작시키기 위해 양자화나 가지치기와 같은 다양한 경량화 기법들이 적용되어야 한다. 본 논문에서는 다양한 경량화 기법 중 가지치기 기술을 중심으로 엣지 컴퓨팅 기기에서 딥러닝 모델을 경량화하여 적용할 수 있는 방안을 분석한다. 특히, 동적 및 정적 가지치기 기법을 적용하여 경량화된 비전 모델의 추론 속도, 정확도 그리고 메모리 사용량을 시험한다. 논문에서 분석된 내용은 실시간 특성이 중요한 지능형 영상 관제 시스템이나 자율 이동체의 영상 보안 시스템에 적용될 수 있다. 또한 사물인터넷 기술이 적용되는 다양한 서비스와 산업에 더욱 효과적으로 활용될 수 있을 것으로 기대된다.