• Journal of agriculture & life science
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• v.45 no.4
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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 ($IVI{\geq}18{\sim}50%$).

• Journal of agriculture & life science
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• v.45 no.2
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• pp.51-59
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• 2011

This study was conducted to analysis the growth characteristics and the diameter at breast height (10 cm) reached ages in natural stand and platation for the optimal planting density and planting timing of Acer mono for. rubripes. There was high correlation between the DBH and crown diameter (E-W: r=0.82, S-N: r=0.76) in natural stand, and between the DBH and crown diameter (E-W: r=0.76, S-N: r=0.90) in plantation. In natural stand, average reached age on DBH 10 cm was $21.1{\pm}7.0$, and was $9.2{\pm}1.3$ in plantation. Therefore, the collectable timing of sap in artificial planting short approximately 2.3 times over the natural regeneration.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.89-94
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• 2011

In this study, the amount of seedlings and seed dispersal of evergreen broad-leaved trees in forest community adjacent to Camellia japonica forest were studied to provide basic information for effective management of evergreen broad-leaved forest. Evergreen broad-leaved trees including C. japonica, Neolitsea sericea, Machilus thunbergii, Ligustrum japonicum, Cinnamomum japonicum, Litsea japonica, Pittosporum tobira showed high density and ratio of seedlings in community adjacent to C. japonica forest. Although individual densities of N. sericea, M. thunbergii, L. japonicum were low, their seedlings were distributed up to Pinus thunbergii and coniferous broad-leaved forest at a distance of 100m and 200m from C. japonica forest. Distribution of DBH class of C. japonica suggested a continuous spread from higher frequency of young individuals, N. sericea, M. thunbergii and L. japonicum did not showed an obvious trend. Seed of C. japonica mainly dispersed within 50m from mother tree.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.29-36
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• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.447-456
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• 2020

The study area is a slope in Dogye-eup, Samcheok-si, Gangwon-do. The cutting method was applied to this slope for stabilization in 2009 due to the influence of the waste-rock dump located at the top of slope. Recently, soil cracks and creep have occurred on this slope, and the drainage channel was damaged. Therefore, it was analyzed the topography change through photogrammetry using a UAV. Orthophotos were taken in April and October 2019 respectively. From the Orthophots, Digital Surface Model (DSM) was extracted. Time series analysis was performed by comparing each DSM. The topography of October was pushed forward while maintaining the topography of April. Through these features, it is judged that the soil creep is occurring in this study area.

• Journal of Korea Multimedia Society
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• v.23 no.12
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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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• v.52 no.3
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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.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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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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• v.53 no.1
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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.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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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.