• 한국항행학회논문지
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• 제25권5호
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• pp.409-414
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• 2021

미세먼지에 대한 심각성이 사회적으로 대두됨에 따라 대중들은 미세먼지 예보에 대한 정보의 높은 신뢰성을 요구하고 있다. 이에 따라 다양한 신경망 알고리즘을 이용하여 미세먼지 예측을 위한 연구가 활발히 진행되고 있다. 본 논문에서는 미세먼지 예측을 위해 다양한 알고리즘으로 연구되고 있는 신경망 알고리즘들 중 대표적인 알고리즘들의 예측 성능 비교를 진행하였다. 신경망 알고리즘 중 DNN(deep neural network), RNN(recurrent neural network), LSTM(long short-term memory)을 이용하였으며, 하이퍼 파라미터 탐색을 이용하여 최적의 예측 모델을 설계하였다. 각 모델의 예측 성능 비교 분석 결과, 실제 값과 예측 값의 변화 추이는 전반적으로 좋은 성능을 보였다. RMSE와 정확도를 기준으로 한 분석에서는 DNN 예측 모델이 다른 예측 모델에 비해 예측 오차에 대한 안정성을 갖는 것을 확인하였다.

• 한국정보통신학회논문지
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• 제25권12호
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• pp.1797-1808
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• 2021

4차 산업혁명을 주도하는 기술 중 가장 핵심적인 기술로 꼽히고 있는 인공지능은 다양한 분야에 접목되면서 우리 사회 전반에 걸쳐 패러다임의 전환을 가져오고 있다. 바이오 분야 역시 예외는 아니어서 컴퓨터, 전기·전자, 기계 등 타 학문과 융합되면서 방대한 데이터 기반의 AI 기술을 도입하고 있다. 신약개발에서 AI 기술 도입은 신약개발의 효율성을 개선하고 효능 및 품질 향상을 가져올 수 있다. 신약개발은 다학제 분야가 접목된 융합 분야이고 개발 과정 단계별로 결과의 불확실성이 존재하고 있어 실용적 수준의 신약 개발을 위해서는 화학, 생물학, 독성학, 약동학 등 전문지식의 융합을 기반으로 하는 AI 기술 개발이 필요하다. 신약개발은 크게 주어진 질병에 대한 타겟 물질 발굴 및 검증, 히트 및 선도물질 발굴, 도출된 화합물에 대한 합성 가능성 및 효능 등에 대한 평가(Scoring)를 거쳐 최적의 신약 후보 물질을 발굴하고 마지막으로 전임상과 임상 과정의 단계를 거친다. 이때 AI 기술은 모든 단계에서 적용될 수 있고 단계마다 특화되어 적용될 수 있다. 본 논문에서는 신약개발을 위해 적용되고 있는 AI 기술 현황과 현재 기술의 한계를 살펴보고 향후 신약개발에서 AI 기술의 발전 방향을 고찰해 보고자 한다.

• 스마트미디어저널
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• 제13권1호
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• pp.24-35
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• 2024

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

• ETRI Journal
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• 제41권6호
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• pp.760-770
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• 2019

Based on the growing demand for neural network technologies, various neural network inference engines are being developed. However, each inference engine has its own neural network storage format. There is a growing demand for standardization to solve this problem. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. The proposed converter includes a lexical, syntax, and parser. This NNEF parser converts neural network information into a parsing tree and quantizes data. To validate the proposed system, we verified that MNIST is immediately executed by importing AlexNet's neural network and learned data. Therefore, this study contributes an efficient design technique for a converter that can execute a neural network and learned data in various frameworks regardless of the storage format of each framework.

• Korean Journal of Radiology
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• 제25권8호
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• pp.687-690
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• 2024

• 대한치과교정학회지
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• 제52권2호
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• 농업과학연구
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• 제49권3호
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• pp.619-627
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• 2022

In Korea, apples and pears are among the most important agricultural products to farmers who seek to earn money as income. Generally, farmers make decisions at various stages to maximize their income but they do not always know exactly which option will be the best one. Many previous studies were conducted to solve this problem by predicting farmers' income structure, but researchers are still exploring better approaches. Currently, machine learning technology is gaining attention as one of the new approaches for farmers' income prediction. The machine learning technique is a methodology using an algorithm that can learn independently through data. As the level of computer science develops, the performance of machine learning techniques is also improving. The purpose of this study is to predict the income structure of apples and pears using the automatic machine learning solution H2O.AI and to present some implications for apple and pear farmers. The automatic machine learning solution H2O.AI can save time and effort compared to the conventional machine learning techniques such as scikit-learn, because it works automatically to find the best solution. As a result of this research, the following findings are obtained. First, apple farmers should increase their gross income to maximize their income, instead of reducing the cost of growing apples. In particular, apple farmers mainly have to increase production in order to obtain more gross income. As a second-best option, apple farmers should decrease labor and other costs. Second, pear farmers also should increase their gross income to maximize their income but they have to increase the price of pears rather than increasing the production of pears. As a second-best option, pear farmers can decrease labor and other costs.

• 한국인터넷방송통신학회논문지
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• 제17권5호
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• pp.119-124
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• 2017

1997년 IBM의 딥블루가 세계 체스 챔피언인 카스파로프를 이기고, 최근 구글의 알파고가 중국의 커제에게 완승을 거두면서 딥러닝에 대한 관심이 급증하였다. 본 논문은 딥러닝에 기반을 둔 인고지능 체스엔진인 딥퍼플(DeepPurple) 개발에 대해 기술한다. 딥퍼플 체스엔진은 크게 몬테카를로 트리탐색과 컨볼루션 신경망으로 구현된 정책망 및 가치망으로 구성되어 있다. 딥러닝을 통해 구축된 정책망을 통해 다음 수를 예측하고, 가치망을 통해 주어진 상황에서의 판세를 계산한 후, 몬테카를로 트리탐색을 통해 가장 유리한 수를 선택하는 것이 기본 원리이다. 학습 결과, 정책망의 경우 정확도 43%, 손실함수 비용 1,9로 나타났으며, 가치망의 경우 정확도 50%, 손실함수 비용 1점대에서 진동하는 것으로 나타났다.

• 한국재난정보학회 논문집
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• 제18권2호
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• pp.364-373
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• 2022

연구목적: 본 논문은 지하공동구의 초기 화재 감지를 위해 CCTV를 활용한 AI 연기 객체 감지 모델을 개발하는데 목적이 있다. 연구방법:비정형성이 높은 연기 객체의 감지 성능을 제고하기 위해 화재 감지에 특화된 딥러닝 객체 감지 모델을 지하공동구 연기 감지에 특화되도록 학습시켰고, 학습데이터셋의 정제 및 학습 중 Gradient explosion 완화 등 감지 성능 개선을 위한 방법들을 적용해 모델 결과를 비교하였다. 연구결과: 결과는 제안된 방법을 통해 모델 성능을 향상시켰고 mAP 등의 지표를 평가를 통해 개발 모델이 우수한 성능을 보유하고 있음을 보여준다. 최종 모델은 지하공동구 환경의 연기에 대해 미탐이 낮은 반면 오탐이 다수 발견되는 성능을 보였다. 결론: 본 논문의 모델은 지하공동구 관리시스템과 연계를 통해 보완함으로써 지하공동구의 연기 객체 감지에 활용할 수 있을 것으로 판단된다.