• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2009년도 춘계 종합학술대회 논문집
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• pp.521-525
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• 2009

본 논문에서는 화재의 화재를 검출 하기 위한 시스템 및 화재 패턴을 인식하는 소프트웨어적 학습 알고리즘을 소개한다. 화재의 패턴 이라 함은 일반적으로 인식하는 불에 대한 주기적이고 일관적인 패턴을 나타내며 이를 정의 하여 소프트웨어적으로 처리 하고자 함이다. 또한 이에 대하여 학습은 일정 패턴에서 일부 벗어나더라도 이를 불로서 인식하기 위한 알고리즘으로 화재에 대한 정의 알고리즘을 통하여 스스로 패턴을 기억하고 스스로 화재를 인식 할 수 있도록 하는 시스템이다. 본 논문에서 제시하는 화재 검출용 학습 알고리즘은 기존 알고리즘 보다 정확하고 신속히 검출 능력을 키우기 위한 방법이며 정확한 위치 탐지와 초기단계에서 검출이 가능하도록 설계하였다. 또한 우리는 실험 결과를 통해 성능 향상을 위한 방법을 도출하였으며 이를 적용하여 조기 경보시스템으로서의 타당성을 보여 주었다.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Journal of Gastric Cancer
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• 제23권3호
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• pp.375-387
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• 2023

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

• 한국양봉학회지
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• 제32권3호
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• pp.191-198
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• 2017

말벌류는 국내 양봉 산업에 가장 큰 피해를 입히는 생물군이다. 다양한 트랩 개발 및 포획 방법이 제시되고 있으나 아직까지 뚜렷한 해결책을 제시하지 못하고 있다. 본 연구팀은 장수말벌 특이 날갯짓 파동 프로파일 분석을 통하여, 장수말벌이 양봉장으로 공격해 들어올 때 이를 자동으로 탐지하고 그 정보를 사용자에게 전달할 수 있는 시스템을 개발하고 실험실 현장에서 검증하였다. 장수말벌 파동 프로파일은 명확하게 설정되었으며, 앉아서 날갯짓을 할 때와 실재 비행할 때의 파동 프로파일은 명확히 구분되었다. 이정보를 활용하여, 시스템을 구성하고 그 시스템은 장수말벌을 탐지하면 바로 트위터를 통하여 사용자에게 말벌 경보를 전송하는 방식으로 설계하였다. 시스템의 검증은 야외 온실 내 케이지에서 수행하였다. 수행결과 시스템은 성공적으로 말벌의 비행활동을 탐지하여 그 결과를 사용자에게 전달하였다. 비록 이 시스템은 실내 제한된 조건에서 검증되었지만, 향후 시스템의 개선 및 정확도가 향상된다면 양봉산업 현장에서 활용 가능성이 높다.

• 한국인공지능학회지
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• 제12권1호
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• pp.25-29
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• 2024

In this study, research was conducted to predict the probability of cervical cancer occurrence associated with the use of hormonal contraceptives. Cervical cancer is influenced by various environmental factors; however, the human papillomavirus (HPV) is detected in 99% of cases, making it the primary attributed cause. Additionally, although cervical cancer ranks 10th in overall female cancer incidence, it is nearly 100% preventable among known cancers. Early-stage cervical cancer typically presents no symptoms but can be detected early through regular screening. Therefore, routine tests, including cytology, should be conducted annually, as early detection significantly improves the chances of successful treatment. Thus, we employed artificial intelligence technology to forecast the likelihood of developing cervical cancer. We utilized the logistic regression algorithm, a predictive model, through Microsoft Azure. The classification model yielded an accuracy of 80.8%, a precision of 80.2%, a recall rate of 99.0%, and an F1 score of 88.6%. These results indicate that the use of hormonal contraceptives is associated with an increased risk of cervical cancer. Further development of the artificial intelligence program, as studied here, holds promise for reducing mortality rates attributable to cervical cancer.

• Journal of Biosystems Engineering
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• 제36권6호
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• pp.484-490
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• 2011

Defects exist underneath the fruit skin are not easily discernable by using conventional color imaging technique in the visible wavelength ranges. Development of sensitive detection methods for the defects is necessary to ensure accurate quality sorting of fruits. Hyperspectral imaging techniques, which combine the features of image and spectroscopy to acquire spatial and spectral information simultaneously, have demonstrated good potentials for identifying and detecting anomalies on biological substances. In this study, a high spatial resolution hyperspectral reflectance technique was presented as a tool for detecting bruises on apple. The two-band ratio (494 nm / 952 nm) and simple threshold methods were applied to investigate the feasibility of discriminating the bruises from sound tissue of apple. The pixel wise accuracy of the discrimination was 74%. The resultant images processed with selected wavebands and morphologic algorithm distinctively showed the early stages of bruises on apple which were not discernable by naked eyes as well as a conventional color camera. Results demonstrated good potential of the hyperspectral reflectance imaging for detection of bruises on apple.

• Journal of International Society for Simulation Surgery
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• 제2권1호
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• pp.33-39
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• 2015

Purpose The head of infants under 24 months old who has Craniosynostosis grows extraordinarily that makes head shape unusual. To diagnose the Craniosynostosis, surgeon has to inspect computed tomography(CT) images of the patient in person. It's very time consuming process. Moreover, without a surgeon, it's difficult to diagnose the Craniosynostosis. Therefore, we developed technique which detects Craniosynostosis automatically from the CT volume. Materials and Methods At first, rotation correction is performed to the 3D CT volume for detection of the Craniosynostosis. Then, cranial area is extracted using the iterative thresholding method we proposed. Lastly, we diagnose Craniosynostosis by analyzing centroid relationships of clusters of cranial bone which was divided by cranial suture. Results Using this automatical cranial detection technique, we can diagnose Craniosynostosis correctly. The proposed method resulted in 100% sensitivity and 90% specificity. The method perfectly diagnosed abnormal patients. Conclusion By plugging-in the software on CT machine, it will be able to warn the possibility of Craniosynostosis. It is expected that early treatment of Craniosynostosis would be possible with our proposed algorithm.

• ETRI Journal
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• 제46권4호
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• pp.671-682
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• 2024

Violence can be committed anywhere, even in crowded places. It is hence necessary to monitor human activities for public safety. Surveillance cameras can monitor surrounding activities but require human assistance to continuously monitor every incident. Automatic violence detection is needed for early warning and fast response. However, such automation is still challenging because of low video resolution and blind spots. This paper uses ResNet50v2 and the gated recurrent unit (GRU) algorithm to detect violence in the Movies, Hockey, and Crowd video datasets. Spatial features were extracted from each frame sequence of the video using a pretrained model from ResNet50V2, which was then classified using the optimal trained model on the GRU architecture. The experimental results were then compared with wavelet feature extraction methods and classification models, such as the convolutional neural network and long short-term memory. The results show that the proposed combination of ResNet50V2 and GRU is robust and delivers the best performance in terms of accuracy, recall, precision, and F1-score. The use of ResNet50V2 for feature extraction can improve model performance.

• 전자공학회논문지SC
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• 제45권3호
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• pp.68-79
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• 2008

대동맥 증강지수는 심실의 부하뿐만 아니라 대동맥의 탄력성을 직접적으로 나타낼 수 있는 장점 때문에 동맥의 경직도를 평가하는 지표로 주목받고 있다. 하지만, 정확한 대동맥 증강지수를 계산하기 위해서는 직접 카테터를 피험자에 삽입하여 측정해야 하기 때문에 임상에 적용하기에는 한계가 존재한다. 이러한 문제점 때문에 전달함수를 이용하여 요골 동맥 맥파로부터 대동맥 맥파를 간접적으로 추정하는 방법이 이용되고 있다. 본 논문에서는 전달함수를 구하기 위하여 Millar 카테터를 이용한대동맥 맥파와 토노메트릭 방식의 압력센서를 이용하여 요골동맥 맥파를 측정하였다. 또한, 기존의 증강점 검출 알고리즘 대신단계적으로 미분 차수를 증가시키면서 증강점을 검출하는 새로운 알고리즘을 제안하였다. 10차 ARX 모델을 이용하여 전달함수를 구현하였으며, 잔차 분석을 통하여 모델을 검증하였다. 증강점 검출 알고리즘 검증을 위하여 네 가지 종류의 합성파를 만들어 제안된 알고리즘이 기존 알고리즘 보다 더 정확한 결과를 나타내는 것을 확인할 수 있었다. 본 연구는 쉽게 측정할 수 있는 요골동맥 맥파를 이용하여 대동맥의 경직도를 평가할 수 있는 방법을 제시하였으며 이를 통하여 다양한 심혈관 질환의 조기 진단에 기여할 수 있을 것이다.

• 한국전자통신학회논문지
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• 제14권5호
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• pp.959-968
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• 2019

농업 분야에서 여러 가지 센서들과 임베디드 시스템을 활용하여 한 무선 센서 네트워크 기술이 적용되고 있는 추세이다. 특히, 센서 네트워크를 활용하여 작물의 질병을 조기에 진단할 수 있는 많은 연구가 진행되고 있다. 기존 병충해 진단 연구들은 실제 농가에 적용하기 어려운 부분이 존재한다. 본 논문은 이를 개선하고자 하였으며, 화상카메라를 통해 받아온 작물의 잎사귀 이미지를 분석하여 병충해를 초기에 감지 가능한 알고리즘을 제안한다. 실제 시설원예 및 노지 환경 농가의 캡쳐한 이미지 내에서 감염 의심 영역을 개선된 K 평균 클러스터링 기법을 통해 분류하였다. 그 후 엣지 검출, 엣지 추적 기법을 활용하여 해당 영역의 잎사귀 내부 존재 여부를 확인하였다. 인근 농가에서 촬영한 토마토 잎사귀 이미지를 이용하여 성능 평가를 수행하였다. 기존 논문의 방법 보다 제안 알고리즘의 감영 영역 분류 능력이 우수한 것으로 나타났다.