• 농업생명과학연구
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• 제45권4호
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• pp.47-58
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• 2011

The forest of Korea had been severely degraded since early 1900s until 1950s. Korean Government has successfully accomplished the reforestation works since 1960s. However, some plantations showed poor survival and growth caused by ignoring site characteristics in selecting plantation species and lack of tending works such as thinning. The natural regeneration of indigenous species, such as Quercus species and Pinus densiflora Siebold & Zucc., were examined in the plantations of Pinus koraiensis Siebold & Zucc. and Larix kaempferi Fortune ex Gordon. Quercus species regenerated mainly by sprouting while P. densiflora regenerated naturally from a few mother trees that remained in the plantations. P. koraiensis showed poor survival ($IVI{\leq}25%$) and suppressed growth (height ${\leq}3m$ and $DBH{\leq}3cm$ at 20 year-old) by Quercus species or P. densiflora in the plantation areas, however had high survival ($IVI{\geq}70%$) and growth (8 m height and 14.1 cm DBH at 20 year-old) in areas where silvicultural practices were conducted. L. kaempferi showed good survival ($IVI{\geq}40%$) and growth (17.2 m height and 16.3 cm DBH at 30 year-old) mostly in valley areas, while it was nearly dead ($IVI{\leq}10%$) in ridge or ridge-slope areas and was replaced by indigenous species such as Quercus species ($IVI{\geq}25{\sim}55%$) or P. densiflora ($IVI{\geq}18{\sim}50%$).

• 농업생명과학연구
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• 제45권2호
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• pp.51-59
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• 2011

붉은고로쇠나무의 적정 식재밀도와 대체조림수로서의 적정 조림시기를 위한 기초 자료를 제공하기 위해 천연림과 조림지에서 생장인자분석과 흉고직경 10 cm 도달연수를 측정하였다. 천연림에서 흉고직경은 수관폭과 높은 상관관계(E-W: r=0.82, S-N: r=0.76)를 나타내었고 조림지의 흉고직경과 수관폭도 높은 상관관계 (E-W: r=0.76, S-N: r=0.90)를 나타내었다. 천연림에서 흉고직경 10cm에 도달하는 평균연수는 $21.1{\pm}7.0$년, 조림지에서는 $9.2{\pm}1.3$년으로 측정되어 최초 수액채취가 가능한 시기는 인공조림시 천연갱신을 통해 형성된 자생지 보다 약 2.3배 단축되는 것으로 나타났다.

• 농업생명과학연구
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• 제45권6호
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• pp.89-94
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• 2011

본 연구는 상록활엽수림의 합리적인 관리를 위한 기초정보를 제공하기 위하여 동백나무림 주변 산림군집에 대한 상록활엽수종의 치수발생량과 종자산포량을 분석하였다. 치수밀도와 비율의 경우, 동백나무림과 동백나무림 가장자리, 동백나무림내 해송아래, 편백림에서 발생하는 전체치수의 약 90%이상이 동백나무를 포함한 참식나무와 후박나무, 광나무, 생달나무, 까마귀쪽나무, 돈나무 등의 상록활엽수류가 차지하고 있었으며, 특히 참식나무, 후박나무, 광나무는 동백나무림에서 200 m 정도 떨어진 해송림과 낙엽활엽수림에까지 밀도는 낮지만 치수발생이 이루어지고 있어 상록활엽수림으로 천이가 시작되고 있었다. 흉고 직경급 분포에서 동백나무는 역J자형의 분포를 나타내고 있었고 후박나무, 참식나무, 광나무는 뚜렷한 경향을 나타내지 않았다. 동백나무 종자는 동백나무림 주변 50 m 이내에 주로 산포되고 있는 것으로 조사되었다.

• 한국컴퓨터정보학회논문지
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• 제26권7호
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• pp.29-36
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• 2021

본 논문에서는 딥러닝 모델을 이용하여 모바일 기기의 심전도 신호 측정 데이터를 분류한다. 비정상 심장박동을 높은 정확도로 분류하기 위해 딥러닝 모델의 구성 요소 세 가지를 선정하고 요소의 조건 변화에 따른 분류 정확도를 비교한다. 심전도 신호 데이터의 특징을 스스로 추출할 수 있는 CNN 모델을 적용하고 모델을 구성하는 모델의 깊이, 최적화 방법, 활성화 함수의 조건을 변경하여 총 48개의 조합의 성능을 비교한다. 가장 높은 정확도를 보이는 조건의 조합을 도출한 결과 컨볼루션 레이어 19개, 최적화 방법 SGD, 활성화 함수 Mish를 적용하였을 때 정확도 97.88%로 모든 조합 중 가장 높은 분류 정확도를 얻었다. 이 실험에서 CNN을 활용한 1-채널 심전도 신호의 특징 추출과 비정상 박동 검출의 적합성을 확인하였다.

• 지질공학
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• 제30권4호
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• pp.447-456
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• 2020

연구지역인 강원도 삼척시 도계읍의 사면은 지형변화가 발생하는 지역으로 2009년 사면 절취공사를 수행하여 사면경사완화공법이 적용되었다. 하지만 사면의 상부에 위치한 폐석적치장의 영향으로 2개월 뒤 절취사면의 상부를 확장하여 안정성을 도모하였다. 최근 사면 하부에 위치한 옹벽의 배부름 현상이 발견되고 절취사면의 균열이 나타나 지형변화가 다시 발생하고 있는 것으로 보이고 있다. 이러한 문제를 파악하기 위하여 본 연구에서는 UAV를 이용하여 사진측량을 수행해 지형변화가 발생하는지 확인하고자 하였다. 2019년 4월과 10월 각각 정사영상을 추출하고 Digital Surface Model(DSM)를 추출하여 고해상도의 지형변화를 비교 분석하였다. 10월의 지형은 4월의 지형형상을 그대로 유지한채 앞으로 밀려 나간 모습을 보여 이러한 지형변화는 땅밀림인 것으로 분석하였다.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1464-1475
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• 2020

Due to the spread of COVID-19, the online lecture has become more prevalent. However, it was found that a lot of students and professors are experiencing lack of communication. This study is therefore designed to improve interactive communication between professors and students in real-time online lectures. To do so, we explore deep learning approaches for automatic recognition of students' facial expressions and classification of their understanding into 3 classes (Understand / Neutral / Not Understand). We use 'BlazeFace' model for face detection and 'ResNet-GRU' model for facial expression recognition (FER). We name this entire process 'Degree of Understanding (DoU)' algorithm. DoU algorithm can analyze a multitude of students collectively and present the result in visualized statistics. To our knowledge, this study has great significance in that this is the first study offers the statistics of understanding in lectures using FER. As a result, the algorithm achieved rapid speed of 0.098sec/frame with high accuracy of 94.3% in CPU environment, demonstrating the potential to be applied to real-time online lectures. DoU Algorithm can be extended to various fields where facial expressions play important roles in communications such as interactions with hearing impaired people.

• Imaging Science in Dentistry
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• 제52권3호
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• 대한의용생체공학회:의공학회지
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• 제44권3호
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• pp.182-189
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• 2023

CT is a medical device that acquires medical images based on Attenuation coefficient of human organs related to X-rays. In addition, using this theory, it can acquire sagittal and coronal planes and 3D images of the human body. Then, CT is essential device for universal diagnostic test. But Exposure of CT scan is so high that it is regulated and managed with special medical equipment. As the special medical equipment, CT must implement quality control. In detail of quality control, Spatial resolution of existing phantom imaging tests, Contrast resolution and clinical image evaluation are qualitative tests. These tests are not objective, so the reliability of the CT undermine trust. Therefore, by applying an artificial intelligence classification model, we wanted to confirm the possibility of quantitative evaluation of the qualitative evaluation part of the phantom test. We used intelligence classification models (VGG19, DenseNet201, EfficientNet B2, inception_resnet_v2, ResNet50V2, and Xception). And the fine-tuning process used for learning was additionally performed. As a result, in all classification models, the accuracy of spatial resolution was 0.9562 or higher, the precision was 0.9535, the recall was 1, the loss value was 0.1774, and the learning time was from a maximum of 14 minutes to a minimum of 8 minutes and 10 seconds. Through the experimental results, it was concluded that the artificial intelligence model can be applied to CT implements quality control in spatial resolution and contrast resolution.

• Journal of Periodontal and Implant Science
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• 제53권1호
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• pp.38-53
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• 2023

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.