• Annual Conference of KIPS
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• 2021.11a
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• pp.588-591
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• 2021

4차 산업혁명 시대가 도래하면서 세상이 빠른 속도로 변하고 있다. 특히 데이터·인공지능(AI, Artificial Intelligence)의 활용이 적극적으로 다양한 분야에서 적용되기 시작하고 있다. 하지만 산림수종을 분석하는 업무를 수행하는 과정은 수작업으로 진행하다 보니 오류가 다수 발생하고 있다. 따라서 본 논문에서는 수도권 항공사진을 이용하여 소나무, 낙엽송, 침엽수, 활엽수를 대상으로 자동으로 분석하는 AI 학습용 데이터 약 60,000장을 구축하고, 수종을 구분할 수 있는 AI 모델을 개발하였다. 이를 통해 산림변화탐지 및 산림 분야 주제도 제작 시 수종 분할 이미지를 기초자료로 활용함으로써 업무효율 증대를 기대할 수 있다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.558-561
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• 2021

심리학에서 사람의 심리 상태를 알아보기 위해 사용되는 검사 방법 중, 집-나무-사람 검사(HTP Test)는 피실험자가 그린 집, 나무, 사람을 포함하는 그림을 사용하여 피실험자의 심리를 분석하는 투영 검사법이다. 본 논문에서는 딥러닝 모델을 이용해 HTP Test 에 사용되는 그림을 분석하는 시스템을 제안하며, 성능 평가를 통해 심리학에서의 딥러닝 모델 적용 가능성을 확인한다. 또한 그림 데이터 분석에 적합한 사전 훈련 모델을 개발하기 위해, ImageNet 과 스케치 데이터셋으로 사전 훈련하여 성능을 비교한다. 본 논문에서 제안하는 시스템은 크게 감정 분석을 위한 이미지 객체 추출부, 추출된 객체로 피실험자의 감정을 분류하는 감정 분류부로 구성되어 있다. 객체 추출과 이미지 분류 모두 CNN(Convolution Neural Network) 기반의 딥러닝 모델을 사용하며, 이미지 분류 모델은 서로 다른 데이터셋으로 모델을 사전 훈련한 후, 훈련 데이터셋으로 전이 학습하여 모델의 성능을 비교한다. 그림 심리 분석을 위한 HTP test 스케치 데이터셋은, HTP Test 와 동일하게 피실험자가 3 개 클래스의 집, 나무, 사람의 그림을 그려 자체 수집하였다.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.155-164
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• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1633-1641
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• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.193-206
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• 2008

Medical diagnosis can be considered a classification task which classifies disease types from patient's condition data represented by a set of pre-defined attributes. This study proposes a hybrid genetic algorithm based classification method to develop classifiers for multidimensional pattern classification problems related with medical decision making. The classification problem can be solved by identifying separation boundaries which distinguish the various classes in the data pattern. The proposed method fits a finite number of regional agents to the data pattern by combining genetic algorithms and local adaptive operations. The local adaptive operations of an agent include expansion, avoidance and relocation, one of which is performed according to the agent's fitness value. The classifier system has been tested with well-known medical data sets from the UCI machine learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.332-335
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• 2018

In recent years, artificial intelligence using big data has become a big issue in IT. Various studies are being conducted on services or technologies to effectively handle big data. The educational field, there is big data about students, but it is only a simple process to collect, lookup and store such data. In the future, it makes extensive use of artificial intelligence, machine learning, and statistical analysis to find meaningful rules, patterns, and relationships in the big data of the educational field, and to produce intelligent and useful data for the actual students. Accordingly, this study aims to design a program to predict the career of students using a decision tree algorithm based on the data from the student's classroom observations. Through a career prediction program, it is believed to be helpful to present application paths to students ' counseling and to also provide classroom behavior and direction based on the desired courses.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.10
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• pp.40-45
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• 2009

In this paper, we introduce a mobile-type tiny sensor device that can classify the activities of daily living based on the state-dependent motion analysis using a 3-axial accelerometer in real-time. The device consists of an accelerometer, GPS module, 32bit micro-controller for sensor data processing and activity classification, and a bluetooth module for wireless data communication. The size of device is 50*47*14(mm) and lasts about 10 hours in operation-mode and 160 hours in stand-by mode. Up to now, the device can recognize three user activities ("Upright", "Running", "Walking") based on the decision tree. This tree is constructed by the pre-learning process to activities of subjects. The accuracy rate of recognizing activities is over 90% for various subjects.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.1-8
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• 2009

Detecting entry and exit zones in a view covered by multiple cameras is an essential step to determine the topology of the camera setup, which is critical for achieving and sustaining the accuracy and efficiency of multi-camera surveillance system. In this paper, a graph theoretic clustering method is proposed to detect zones using data points which correspond to entry and exit events of objects in the camera view. The minimum spanning tree (MST) is constructed by associating the data points. Then a set of well-formed clusters is sought by removing inconsistent edges of the MST, based on the concepts of the cluster balance and the cluster density defined in the paper. Experimental results suggest that the proposed method is effective, even for sparsely elongated clusters which could be problematic for expectation-maximization (EM). In addition, comparing to the EM-based approaches, the number of data required to obtain stable outcome is relatively small, hence shorter learning period.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.35-42
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• 2019

Recently, as the Internet and various wearable devices have appeared, Internet technology has contributed to obtaining more convenient information and doing business. However, as the internet is used in various parts, the attack surface points that are exposed to attacks are increasing, Attempts to invade networks aimed at taking unfair advantage, such as cyber terrorism, are also increasing. In this paper, we propose a feature selection method to improve the classification performance of the class to classify the abnormal behavior in the network traffic. The UNSW-NB15 dataset has a rare class imbalance problem with relatively few instances compared to other classes, and an undersampling method is used to eliminate it. We use the SVM, k-NN, and decision tree algorithms and extract a subset of combinations with superior detection accuracy and RMSE through training and verification. The subset has recall values of more than 98% through the wrapper based experiments and the DT_PSO showed the best performance.