• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.761-766
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• 2023

In this paper, TI was measured to classify the presence or absence of mild left ventricular diastolic dysfunction. Of the total 306 data, 206 were used as training data and 100 were used as test data, and the machine learning models used for classification used SVM and KNN. As a result, it was confirmed that SVM showed relatively higher accuracy than KNN and was more useful in diagnosing the presence of left ventricular diastolic dysfunction. In future research, it is expected that classification performance can be further improved by adding various indicators that evaluate not only TI but also cardiac function and securing more data. Furthermore, it is expected to be used as basic data to predict and classify other diseases and solve the problem of insufficient medical manpower compared to the increasing number of tests.

• Smart Media Journal
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• v.12 no.9
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• pp.159-170
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• 2023

It takes a lot of time and manpower to search for the missing. As part of the solution, a missing person search AI system was implemented using a YOLO-based model. In order to train object detection models, the model was learned by collecting recognition images (road fixation) of drone mobile objects from AI-Hub. Additional mountainous terrain datasets were also collected to evaluate performance in training datasets and other environments. In order to optimize the missing person search AI system, performance evaluation based on model size and hyperparameters and additional performance evaluation for concerns about overfitting were conducted. As a result of performance evaluation, it was confirmed that the YOLOv5-L model showed excellent performance, and the performance of the model was further improved by applying data augmentation techniques. Since then, the web service has been applied with the YOLOv5-L model that applies data augmentation techniques to increase the efficiency of searching for missing people.

• Information Systems Review
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• v.22 no.1
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• pp.113-124
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• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.209-216
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• 2024

Land shrimp farms can control the growth environment more stably than coastal ones, making them advantageous for high-quality, large-scale production. In order to maintain an optimal shrimp growth environment, various factors such as water circulation, maintaining appropriate water temperature, oxygen supply, and feed supply must be managed. In particular, failure to properly manage water quality can lead to the death of shrimp, making it difficult to have people stationed at the farm 24 hours a day to continuously manage them. In this paper, to solve this problem, we design an integrated monitoring system for land farms that can be operated with minimal manpower. The proposed design plan uses IoT technology to collect real-time images of land farms, pump status, water quality data, and energy usage and transmit them to the server. Through web interfaces and smartphone apps, administrators can check the status of the farm stored on the server anytime, anywhere in real time and take necessary measures. Therefore, it is possible to significantly reduce field work hours without the need for managers to reside in the farm.

• Journal of Environmental Policy
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• v.5 no.3
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• pp.25-56
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• 2006

We reviewed and compared nationwide surveys conducted in advanced countries (i.e., Japan, Germany, USA, and UK) with those in Korea, including the third Nationwide Survey of Natural Environments, which began in 2006 and will proceed until 2010. Based on this comparative analysis, we suggest alternatives to the nationwide survey of natural environments that are suited to Korea. Given the focus on species abundances and distributions in previous nationwide surveys in Korea, surveys of habitats, ecosystems, and ecoregions are required to more effectively protect biological resources in Korea. Furthermore, their spatial distributions should be mapped using periodical satellite images and aerial photographs. In particular, satellite images can be used to survey species, habitats, and ecosystems. Natural resources monitoring and management specialists are needed to collect various data and improve survey results. The participation of community volunteers is also important to develop an awareness in local residents of natural environment conservation. Independent survey institute (i.e., a 'National Ecosystem Institute') should be established to develop a database and survey scheme for species, habitats, and ecosystems throughout Korea. Moreover, the survey institute could develop natural environmental policy through the data analysis to meet the objectives of the Convention on Biological Diversity. The establishment of a survey institute will allow the completion of a natural environment survey that considers various factors, including physical habitat conditions. This will allow us to detect subtle changes in species abundance and spatial distributions and provide accurate and timely information on natural environments.

• Journal of Korean Society of Forest Science
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• v.106 no.1
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• pp.100-109
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• 2017

This study was conducted for preliminary survey and management support for Pine Wood Nematode (PWN) suppression. We took areal photographs of 6 areas for a total of 2,284 ha during 2 weeks period from 15/02/2016, and produced 6 ortho-images with a high resolution of 12 cm GSD (Ground Sample Distance). Initially we classified 423 trees suspected for PWN infection based on the ortho-images. However, low accuracy was observed due to the problems of seasonal characteristics of aerial photographing and variation of forest stands. Therefore, we narrowed down 231 trees out of the 423 trees based on the initial classification, snap photos, and flight information; produced thematic maps; conducted field survey using GNSS; and detected 23 trees for PWN infection that was confirmed by ground sampling and laboratory analysis. The infected trees consisted of 14 broad-leaf trees, 5 pine trees (2 Pinus rigida), and 4 other conifers, showing PWN infection occurred regardless of tree species. It took 6 days for 2.3 men from to start taking areal photos using UAV (Unmanned Aerial Vehicle) to finish detecting PNW (Pine Wood Nematode) infected tress for over 2,200 ha, indicating relatively high efficacy.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1837-1846
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• 2022

The annual increase in chemical accidents is causing damage to life and the environment due to the spread and residual of substances. Environmental damage investigation is more difficult to determine the geographical scope and timing than human damage investigation. Considering the reality that there is a lack of professional investigation personnel, it is urgent to develop an efficient quantitative evaluation method. In order to improve this situation, this paper conducted a chemical accidents investigation using unmanned aerial vehicles(UAV) equipped with various sensors. The damaged area was calculated by Ortho-image and strength of agreement was calculated using the normalized difference vegetation index image. As a result, the Cohen's Kappa coefficient was 0.649 (threshold 0.7). However, there is a limitation in that analysis has been performed based on the pixel of the normalized difference vegetation index. Therefore, there is a need for a chemical accident investigation plan that overcomes the limitations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.117-126
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• 2022

Currently, the number of bridge structures in Korea is continuously increasing and enlarged, and the number of old bridges that have been in service for more than 30 years is also steadily increasing. Bridge aging is being treated as a serious social problem not only in Korea but also around the world, and the existing manpower-centered inspection method is revealing its limitations. Recently, various bridge damage detection studies using deep learning-based image processing algorithms have been conducted, but due to the limitations of the bridge damage data set, most of the bridge damage detection studies are mainly limited to one type of crack, which is also based on a close set classification model. As a detection method, when applied to an actual bridge image, a serious misrecognition problem may occur due to input images of an unknown class such as a background or other objects. In this study, five types of bridge damage including crack were defined and a data set was built, trained as a deep learning model, and an open set recognition-based bridge multiple damage recognition model applied with OpenMax algorithm was constructed. And after performing classification and recognition performance evaluation on the open set including untrained images, the results were analyzed.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1721-1730
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• 2023

As awareness of the problem of global warming emerges around the world, the role of carbon sinks in settlement is increasingly emphasized to achieve carbon neutrality in urban areas. In order to manage carbon sinks in settlement, it is necessary to identify the current status of carbon sinks. Identifying the status of carbon sinks requires a lot of manpower and time and a corresponding budget. Therefore, in this study, a map predicting the location of trees was created using already established tree location information and Sentinel-2 satellite images targeting Seoul. To this end, after constructing a tree presence/absence dataset, structured data was generated using 16 types of vegetation indices information constructed from satellite images. After learning this by applying the Extreme Gradient Boosting (XGBoost) model, a tree prediction map was created. Afterward, the correlation between independent and dependent variables was investigated in model learning using the Shapely value of Shapley Additive exPlanations(SHAP). A comparative analysis was performed between maps produced for local parts of Seoul and sub-categorized land cover maps. In the case of the tree prediction model produced in this study, it was confirmed that even hard-to-detect street trees around the main street were predicted as trees.