• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.35-40
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• 2017

Out-of-limit (OOL) alarm method that is threshold checking of telemetry value is widely used for the satellites fault diagnosis and health monitoring. However, it requires engineering knowledge and effort to define delicate threshold value and has limitations that anomalous behaviors within the defined limits can't be detected. In this paper, we propose a satellite anomalous behavior detection system through fuzzy model that is composed by important statistical feature selected by rough-set theory. Not pre-defined anomaly is detected because only normal state data is used for fuzzy model. Also, anomalous behavior within the threshold limit is detected by using statistic feature that can be collected without engineering knowledge. The proposed system successfully detected non-ordinary state for battery temperature telemetry.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1293-1299
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• 2012

In this paper, a real-time vehicle detection and tracking algorithms is proposed. The vehicle detection could be processed using GMM (Gaussian Mixture Model) algorithm and mathematical morphological processing with HD CCTV camera images. The vehicle tracking based on separated vehicle object was performed using Euclidean distance between detected object. In more detail, background could be estimated using GMM from CCTV input image signal and then object could be separated from difference image of the input image and background image. At the next stage, candidated objects were reformed by using mathematical morphological processing. Finally, vehicle object could be detected using vehicle size informations dependent on distance and vehicle type in tunnel. The vehicle tracking performed using Euclidean distance between the objects in the video frames. Through computer simulation using recoded real video signal in tunnel, it is shown that the proposed system works well.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.521-528
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• 2015

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.

• Plant Journal
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• v.17 no.3
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• pp.37-41
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• 2021

P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.411-418
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• 2022

Incorrectly loaded containers can easily knock down by strong winds. Container collapse accidents can lead to material damage and paralysis of the port system. In this paper, We propose a deep learning-based container loading state and accident risk detection technique. Using Darknet-based YOLO, the container load status identifies in real-time through corner casting on the top and bottom of the container, and the risk of accidents notifies the manager. We present criteria for classifying container alignment states and select efficient learning algorithms based on inference speed, classification accuracy, detection accuracy, and FPS in real embedded devices in the same environment. The study found that YOLOv4 had a weaker inference speed and performance of FPS than YOLOv3, but showed strong performance in classification accuracy and detection accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.392-392
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• 2022

세계적 규모의 팬데믹 감염병의 출현은 전 세계적으로 경제적, 문화적, 사회적 파급효과가 매우 강력하며 전 인류를 위협하고 있다. 최근에 발병한 중증급성 호흡기질환 코로나바이러스 2(Severe Acute Respiratory Syndrome Coronavirus 2, SARS-CoV-2)는 2019년 12월 중국 우한에서 첫 보고 되었고 2022년 현재까지 종식되지 않고 있으며 바이러스의 전파력과 치명률이 높고 무증상 감염상태일 때에도 전염이 가능하여 현재 역학조사의 사후적 대응에 대한 한계가 있어 선제적 대응을 위한 수단이 필수 불가결해지고 있는 실정이다. 하수기반역학(Waste Based Epidemiology, WBE)이란 하수처리장으로 유입되기 전의 하수를 분석하여 하수 집수구역 내 도시민의 생활상을 예측하는 것으로 하수로 배출된 감염자의 분비물 및 배설물 속 바이러스를 하수관로에서 신속하게 검출함으로써 특정지역의 감염성 질환 전파 정도와 유행하는 타입(변이)등을 분석하고 기존 역학조사의 문제점을 극복할 수 있으며 선제적인 대응이 가능하다. 현재 COVID-19의 대유행과 관련하여 WBE를 기반으로 한 다양한 연구가 진행되고 있으며 실제 환자의 발생과 상관관계가 있음이 확인되고 있고 백신 접종과 새롭게 발생한 변이바이러스의 관계 속에서 발생하는 변수를 고려한 모델이 없다는 점을 들어 새로운 감염병 확산 예측 모델에 대한 필요성 또한 커지고 있다. 본 연구에서는 병원에서부터 하수처리장까지의 하수관거와 하수처리장에서의 SARS-CoV-2 검출농도 및 거동을 파악하는 것을 목적으로 하고 있으며 COVID-19의 감염규모 확산에 관한 방법론에서 수학적모델 (Euler Method, RK4 Method, Gillespie Algorithm)과 딥러닝 기반의 Nowcasting model과 Fore casting model을 살펴보고자 한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.392-399
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• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.208-217
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• 2024

There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1998.10a
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• pp.1035-1038
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• 1998

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.281-282
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• 2016

영구자석 동기전동기의 회전자 위치검출센서를 사용하지 않는 센서리스 제어를 위해서는 모터에 인가되는 전압과 전류를 정확하게 알아야 한다. 일반적으로 영구자석동기전동기 모델기반의 회전자 위치검출 관측기에 사용되는 전압은 전류제어기의 출력인 전압지령 값을 사용한다. 그러나 전압지령 값은 인버터에 사용되는 데드타임의 영향으로 인해 실제 모터에 인가되는 전압과 차이를 갖는다. 특히 고속운전 시 과변조 구간에서의 전압지령 값은 실제 모터에 인가되는 전압과 큰 오차를 갖게 된다. 데드타임에 의한 전압 오차는 회전자 위치검출 오차에 미치는 영향이 비교적 작게 나타나지만, 과변조 구간에서의 전압 오차는 회전자 위치검출 오차를 매우 크게 만들어 센서리스 제어를 불가능하게 만들 수 있다. 본 논문에서는 추가적인 전압검출회로를 사용하여 모터에 인가되는 3상 전압을 검출하여 회전자 위치검출 관측기에 적용함으로써 과변조 구간까지 센서리스 제어 범위를 확장하는 방법을 시뮬레이션을 통하여 타당성을 검증하였다.