• Smart Media Journal
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• v.12 no.11
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• pp.125-133
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• 2023

Traditional attire has assumed a pivotal role within the contemporary fashion industry. The objective of this study is to construct a computer vision model tailored to the recognition of traditional costumes originating from five distinct countries, namely India, Korea, Japan, Tanzania, and Vietnam. Leveraging a dataset comprising 1,608 images, we proceeded to train the cutting-edge computer vision model YOLOv8. The model yielded an impressive overall mean average precision (MAP) of 96%. Notably, the Indian sari exhibited a remarkable MAP of 99%, the Tanzanian kitenge 98%, the Japanese kimono 92%, the Korean hanbok 89%, and the Vietnamese ao dai 83%. Furthermore, the model demonstrated a commendable overall box precision score of 94.7% and a recall rate of 84.3%. Within the realm of the fashion industry, this model possesses considerable utility for trend projection and the facilitation of personalized recommendation systems.

• Korean Journal of Optics and Photonics
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• v.7 no.3
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• pp.251-259
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• 1996

Two-Photon Fluorescence (TPF) experiment measures temporal width of an amplified short laser pulse which has passed through a four-pass Nd: glass amplifier, after selecting a single pulse from pulse train Q-switched and mode-locked(QSML) in Nd:YLF master oscillator. Determination of pulsewidth and pulseshape was also made with detection of autocorrelation trace of CW mode-locked pulse train by using noncollinear type I Second Harmonic Generation (SHG) method. The observed TPF track showed various patterns, depending on pulse-selecting position in QSML pulse train. That is, autocorrelation of a pulse extracted at front of the train displayed smooth pulse shape, while one from the trailing part of the train created many sharp spikes and substructure in the pulse. By TPF method, pulsewidth was measured to be 44.4 ps with contrast ratio of 2.86 which enabled us to find out energy fraction of a pulse to total energy, (sum of pulse and background); we obtain the value of 0.62. Pulsewidth of 46.6ps was also acquired in another SHG experiment with the help of only mode-locked pulse train. On the other hand, we confirmed that shape of the pulse is close to $sech^2$ one as a result of fitting the SHG autocorrelation signal with various functions. With simulation using this $sech^2$ type of pulse, pulsewidth reduction of the beam, having passed through four-pass amplifier, was also verified.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.137-137
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• 2023

해양쓰레기는 해안의 심미적 가치 저하뿐만 아니라 생태계 파괴, 유령 어업에 따른 수산업 피해 등의 사회적·환경적 문제를 발생시키며, 그중 70% 이상은 육상 기인으로 플라스틱 및 기타 쓰레기가 주를 이루는 해외와 달리 국내의 경우 다량의 초목류를 포함하고 있다. 다양한 부유쓰레기에 대한 기존의 해양쓰레기량 추정의 한계와 하천·하구 쓰레기 수거의 효율화를 위해 해양으로 유입되는 부유쓰레기 방지를 위한 실효성 있는 대책 수립이 필요한 실정이다. 본 연구는 해양 유입 전 하천의 차단시설에 차집된 부유쓰레기의 수거 효율화 및 지속가능한 해양쓰레기 데이터 구축을 위해 AI기반의 기술을 통해 부유쓰레기 성상 분석 기법(Object Detection)과 차집량 분석 기법(Semantic Segmentation)을 활용하였다. 실제와 유사한 데이터 수집을 위해 다양한 하천 환경(정수조, 소하천, 급경사수로)에 대해 탁도(녹조, 유사), 광량, 쓰레기형상, 초목류 함량, 날씨(소하천), 유속(급경사수로) 등의 실험조건에 대하여 해양쓰레기 분류 기준 및 통계를 바탕으로 부유쓰레기 종류 선정하여 학습을 위한 데이터를 수집하였다. 학습 목적에 따라 구분하여 라벨링(Bounding box, Polygon)을 수행하고, 각 분석 기법별 전이학습을 통해 Phase 1(정수조), Phase 2(소하천), Phase 3(급경사수로) 순서로 모델을 고도화하였다. 성상 분석을 위해 YOLO v4를 활용하여 Train, Test DataSet(9:1)을 구성하고 학습 및 평가는 Iteration마다의 mAP, loss 값을 통해 비교하였으며, 학습 Phase에 따라 모델 고도화로 Test Set의 mAP 값이 성상별로 높아짐을 확인하였으며, 차집량 분석을 위해 Unet을 활용하여 Train, Test, Validation DataSet(8.5:1:0.5)을 구성하고 epoch별 IoU(intersection over Union), F1-score, loss 값을 비교하여 정성적, 정량적 평가 모두 Phase 3에서 가장 높은 성능을 확인하였다. 향후 하천 환경에서의 다양한 영양인자별 분석을 통해 주요 영향인자 도출 및 Hyper Parameter 최적화를 통한 모델 고도화로 인해 활용성이 높아질 것으로 판단된다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.37-46
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• 2009

The movement of the MR loaded on the train is confined to the bidirectional movement along the rail. Therefore, the AR connected to the MR can use the address information of the neighboring ARs and configure CoA in advance before performing L2 and L3 handoff. The MR can acquire new CoA immediately from the present AR after the movement detection procedure. The performance analysis shows that the proposed scheme can provide CoA to the MR about 1.8(s) at minimum and 4.98(s) at maximum faster than the Stateless scheme because the proposed scheme does not carry out any additional CoA and DAD procedure unlike the Stateless scheme.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.59-66
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• 2018

The performance of a network intrusion detection system using the machine learning method depends heavily on the composition and the size of the feature set. The detection accuracy, such as the detection rate or the false positive rate, of the system relies on the feature composition. And the time it takes to train and detect depends on the size of the feature set. Therefore, in order to enable the system to detect intrusions in real-time, the feature set to beused should have a small size as well as an appropriate composition. In this paper, we show that the size of the feature set can be further reduced without decreasing the detection rate through using Pearson correlation coefficient between features along with the multi-objective genetic algorithm which was used to shorten the size of the feature set in previous work. For the evaluation of the proposed method, the experiments to classify 10 kinds of attacks and benign traffic are performed against NSL_KDD data set.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.161-168
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• 2021

Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.367-374
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• 2006

This research revealed the operation problems of the current crossing control systems through inspecting and testing the obstacle detection systems and gate control systems for the crossings. To resolve the problems of the crossing control systems, this research developed new algorithms of four-quadrant gate system and obstacle detection systems combing the functions of rasar sensors and magnetic sensors and tested the reliability of the systems. Currently, the obstacle detection systems and gate control systems controls approaching and departing traffic by simply detecting vehicles and obstacles but do not consider traffic movements at the crossings. In addition, they do not make signal cooperation for gate controls. As a result, such inefficient crossing controls result in unsafe gate controls for drivers. Therefore, the newly developed crossing control systems through this study will provide more effective crossing control services with more strengthen information cooperation within control systems. Besides they will help to reduce train crashes at the crossings by gate control systems considering various driving behaviors.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.17-25
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• 2023

In this paper, we propose a lightweight deep learning architecture tailored for efficient text detection in fashion design sketch images. Given the increasing prominence of Digital Transformation in the fashion industry, there is a growing emphasis on harnessing digital tools for creating fashion design sketches. As digitization becomes more pervasive in the fashion design process, the initial stages of text detection and recognition take on pivotal roles. In this study, a lightweight network was designed by building upon existing text detection deep learning models, taking into consideration the unique characteristics of apparel design drawings. Additionally, a separately collected dataset of apparel design drawings was added to train the deep learning model. Experimental results underscore the superior performance of our proposed deep learning model, outperforming existing text detection models by approximately 20% when applied to fashion design sketch images. As a result, this paper is expected to contribute to the Digital Transformation in the field of clothing design by means of research on optimizing deep learning models and detecting specialized text information.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.8
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• pp.553-558
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• 2015

This paper aims at making a dedicated vibration monitoring and diagnosis framework for 3MW WTG(wind turbine generator). Within the scope of the research, vibration data of WTG drive train are used and WTG operating conditions are involved for dividing the vibration data class which included transient and steady state vibration signals. We separate two health detections which are CHD(continuous health detection) and EHD(event health detection). CHD has function of early detection and continuous monitoring. EHD makes the use of finding vibration values of fault components effectively by spectrum matrix subsystem. We proposed framework and showed application for 3MW WTG in a practical point of view.