• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제3권2호
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.262-264
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• 2023

Internet of Things (IoT) networks currently employ an increased number of users and applications, raising their susceptibility to cyberattacks and data breaches, and endangering our security and privacy. Intrusion detection, which includes monitoring and analyzing incoming and outgoing traffic to detect and prohibit the hostile activity, is critical to ensure cybersecurity. Conventional intrusion detection systems (IDS) are centralized, making them susceptible to cyberattacks and other relevant privacy issues because all the data is gathered and processed inside a single entity. This research aims to create a blockchain-based architecture to support federated learning and improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권11호
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• pp.479-488
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• 2022

최근 기계학습 기술이 빠르게 발전함에 따라 지능형 시스템을 구성하는 여러 기술 중에서 인지, 추론 및 판단, 행위와 같은 분야에서 기계학습을 활용한 연구가 활발히 이루어지고 있다. 이러한 기계학습을 활용하기 위해서는 학습을 위한 데이터의 구축이 필수적이다. 하지만 데이터가 생성되는 환경에 따라 생성되는 데이터의 종류가 다양하고, 기계학습에 활용할 학습모델에 따라 요구되는 데이터의 종류와 양식이 다르다. 이로 인해 새로운 환경에서 기존의 데이터 수집 방법을 재사용하지 못하고 매번 특화된 데이터 수집 모듈을 개발해야 한다는 문제가 있다. 본 논문에서는 위와 같은 문제를 해결하기 위해 명세 기반 인공지능 데이터 수집 방법을 제안하여 데이터 수집 환경에 따른 데이터 수집 방법의 재사용성을 확보하고, 데이터 수집 기능 구현을 자동화할 수 있는 방법을 제시하고자 한다.

• Journal of Digestive Cancer Research
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• 제11권3호
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• pp.130-140
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• 2023

Gastric cancer (GC) is one of the most common malignant tumors worldwide, with a 5-year survival rate of < 40%. The diagnosis and treatment decisions of GC rely on human experts' judgments on medical images; therefore, the accuracy can be hindered by image condition, objective criterion, limited experience, and interobserver discrepancy. In recent years, several applications of artificial intelligence (AI) have emerged in the GC field based on improvement of computational power and deep learning algorithms. AI can support various clinical practices in endoscopic examination, pathologic confirmation, radiologic staging, and prognosis prediction. This review has systematically summarized the current status of AI applications after a comprehensive literature search. Although the current approaches are challenged by data scarcity and poor interpretability, future directions of this field are likely to overcome the risk and enhance their accuracy and applicability in clinical practice.

• Korean Journal of Radiology
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• 제23권12호
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• pp.1119-1125
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• 2022

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.151-153
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• 2023

Federated learning (FL)-based network intrusion detection techniques have enormous potential for securing the Industrial Internet of Things (IIoT) cybersecurity. The openness and connection of systems in smart industrial facilities can be targeted and manipulated by malicious actors, which emphasizes the significance of cybersecurity. The conventional centralized technique's drawbacks, including excessive latency, a congested network, and privacy leaks, are all addressed by the FL method. In addition, the rich data enables the training of models while combining private data from numerous participants. This research aims to create an FL-based architecture to improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• Nuclear Engineering and Technology
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• 제55권11호
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• pp.4282-4286
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• 2023

Nuclear energy is estimated by the machine learning method as the mathematical quantifications where neural networking is the major algorithm of the data propagations from input to output. As the aspect of nuclear energy, the other energy sources of the traditional carbon emission-characterized oil and coal are compared. The artificial intelligence (AI) oriented algorithm like the intelligence of a robot is applied to the modeling in which the mimicking of biological neurons is utilized in the mathematical calculations. There are graphs for nuclear priority weighted by climate factor and for carbon dioxide mitigation weighted by climate factor in which the carbon dioxide quantities are divided by the weighting that produces some results. Nuclear Priority and CO2 Mitigation values give the dimensionless values that are the comparative quantities with the normalization in 2010. The values are 1.0 in 2010 of the graphs which are changed to 24.318 and 0.0657 in 2040, respectively. So, the carbon dioxide emissions could be reduced in this study.

• 한국기계가공학회지
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• 제18권9호
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• pp.52-57
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• 2019

The key technology of the fourth industrial revolution is artificial intelligence and machine learning. In this study, FMEA was performed on fuel pumps used as key items in most systems to identify major failure components, and artificial neural networks were built using big data. The main failure mode of the fuel pump identified by the test was coil damage due to overheating. Based on the artificial neural network built, machine learning was conducted to predict the failure and the mean error rate was 4.9% when the number of hidden nodes in the artificial neural network was three and the temperature increased to $140^{\circ}C$ rapidly.

• 디지털융복합연구
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• 제16권5호
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• pp.221-229
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• 2018

기계 학습 및 인공지능은 제 4차 산업혁명의 핵심 기술이다. 하지만 프로그래밍 능력을 요구하는 기계 학습 플랫폼의 특성 상 일반 사용자들의 접근이 힘들기 때문에 인공지능이나 기계학습의 대중화는 제한을 받고 있다. 본 연구에서는 그래픽 사용자 인터페이스(Graphic User Interface, GUI)를 도입하여 이러한 한계를 극복하고 인공지능 활용에 대한 일반인의 접근성을 향상시키고자 하였다. 기본 기계 학습 플랫폼으로는 Tensorflow를 채택하였고 GUI는 마이크로 소프트 사의 .Net 환경을 활용하여 작성하였다. 새로운 사용자 인터페이스를 이용하면 일반 사용자도 파이썬 프로그래밍에 대한 부담없이 직관적으로 데이터를 관리하고, 알고리즘을 적용하고, 기계 학습을 실행할 수 있다. 우리는 이 개발이 다양한 분야에서의 인공지능 개발에 기초가 되는 자료로 활용되었으면 한다.

• 마이크로전자및패키징학회지
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• 제29권3호
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• pp.1-12
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• 2022

인공지능 기술의 급속한 발전으로 기존 센서에 인간의 지능과 유사한 기능을 부여하기 위한 연구가 큰 주목을 받고 있다. 기존에는 주로 센서로써의 기초 성능지표, 예를 들어 감도 및 속도 등을 향상시키기 위한 연구가 주로 진행되었지만, 최근에는 분류나 예측 등의 인공지능을 센서에 결합하기 위한 시도가 확대되고 있다. 이를 바탕으로 최근 질병 감지 센서, 모션 감지 센서 및 가스 센서 등 거의 센서 전 분야에서 지능형 센서에 대한 연구 결과가 활발히 보고되고 있다. 본 논문에서는 인공지능의 기본적인 개념, 종류 및 메커니즘과 더불어, 최근 보고된 지능형 센서에의 적용 사례에 대해 알아보고자 한다.