• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2019년도 추계 학술논문 발표대회
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• pp.60-61
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• 2019

Concrete carbonation is one of the factors that reduce the durability of concrete. In modern times, due to industrialization, the carbon dioxide concentration in the atmosphere is increasing, and the impact of carbonation is increasing. So, it is important to understand the carbonation resistance according to the concrete compounding to secure the concrete durability life. In this study, we want to predict the concrete carbonation velocity coefficient, which is an indicator of the carbonation resistance of concrete, through the deep learning algorithm, and to find the activation function suitable for the prediction of carbonation rate coefficient as a process to determine the learning accuracy through the deep learning algorithm. In the scope of this study, using the ReLU function showed better accuracy than using other activation functions.

• 한국지반신소재학회논문집
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• 제15권2호
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• pp.1-12
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• 2016

본 연구에서는 기존의 심층혼합처리공법용 안정재의 문제점을 해결하기 위해 탈황분진을 이용하여 개발한 친환경 지반안정재(CMD-SOIL)의 적용성을 평가하기 위해 실내배합시험 및 현장시험시공을 실시하였다. 실내배합시험 결과 함수비, 투입비 및 W/B 변화에 따른 CMD-SOIL의 일축압축강도가 기존의 고로슬래그 시멘트와 비교하여 최대 1.136배 큰 것으로 나타났고, 패각이 함유된 흙 재료에서는 최대 1.222배, 부상토가 혼합된 시료에서는 최대 1.363배 큰 것으로 나타났다. 또한 현장시험시공 결과, 실내배합강도와 현장강도의 비(${\lambda}$)가 0.77로 나타나 기존의 연구결과(${\lambda}=2/3$)와 유사한 경향을 보이고 있어 기존의 안정재와 비교하여 동등 이상의 성능을 발휘할 수 있을 것으로 판단된다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2004년도 춘계학술대회 논문집
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• pp.247-253
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• 2004

As a large scale port development in coastal waters proceeds step by step and populations in the vicinity of port are getting increased, the issue on "how to dispose the treated municipal water and wastewater in harbor" brings peoples′ concern. The submarine outfall system discharges the primary or secondary treated effluent at the coastline or in deep water, or between these two. The effluent, which has a density similar to that of fresh water, rises to the sea surface forming plume or jet, together with entraining the surrounding sea water and becomes very dilute. We intended in this paper to investigate the impact on dilution of effluent and the behavior of flume under the conditions of the seasonal and spatial temperature variations, which have not been noticeable in designing effective marine outfall system. To predict and analyze the behaviour and dilution characteristics of plume not just with the effluent temperature, but also with the seasonal variation of temperature of surround water and tidal changes, CORMIX(Cornell Mixing Zone Expert System)-GI have been applied. The results should be used with caution in evaluation the mixing zone characteristics of discharged water. We hope to help for the effective operation of outfall system, probable outfall design, protection of water quality, and warm water discharges from a power plant, etc.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.506-515
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• 2023

This study is to find a method to learn engine sound after the start-up of a diesel generator installed in nuclear power plant with an unsupervised deep learning algorithm (CNN autoencoder) and a new method to predict the failure of a diesel generator using it. In order to learn the sound of a diesel generator with a deep learning algorithm, sound data recorded before and after the start-up of two diesel generators was used. The sound data of 20 min and 2 h were cut into 7 s, and the split sound was converted into a spectrogram image. 1200 and 7200 spectrogram images were created from sound data of 20 min and 2 h, respectively. Using two different deep learning algorithms (CNN autoencoder and binary classification), it was investigated whether the diesel generator post-start sounds were learned as normal. It was possible to accurately determine the post-start sounds as normal and the pre-start sounds as abnormal. It was also confirmed that the deep learning algorithm could detect the virtual abnormal sounds created by mixing the unusual sounds with the post-start sounds. This study showed that the unsupervised anomaly detection algorithm has a good accuracy increased about 3% with comparing to the binary classification algorithm.

• 한국음향학회지
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• 제37권6호
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• pp.475-482
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• 2018

본 논문에서는 DNN(Deep Neural Network) 기반 오디오 표식을 위한 데이터 증강 방법을 연구한다. 본 시스템에서는 오디오 신호를 멜-스펙트로그램으로 변환하여 오디오 표식을 위한 심층신경망의 입력으로 사용한다. 적은 수의 훈련 데이터를 사용하는 경우 발생하는 문제를 해결하기 위해, 타임 스트레칭, 피치 변화, 동적 영역 압축, 블록 혼합 등의 방법을 사용하여 훈련 데이터를 증강시켰다. 사용된 데이터 증강 기법의 최적 파라미터와 최적 조합을 오디오 표식 시뮬레이션을 통해 확인하였다.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.260-267
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• 2021

In this paper, we intend to use a new mixed model of YoloV5 and DeepSort. For fire detection, we want to increase the accuracy by automatically extracting the characteristics of the flame in the image from the training data and using it. In addition, the high false alarm rate, which is a problem of fire detection, is to be solved by using this new mixed model. To confirm the results of this paper, we tested indoors and outdoors, respectively. Looking at the indoor test results, the accuracy of YoloV5 was 75% at 253Frame and 77% at 527Frame, and the YoloV5+DeepSort model showed the same accuracy at 75% at 253 frames and 77% at 527 frames. However, it was confirmed that the smoke and fire detection errors that appeared in YoloV5 disappeared. In addition, as a result of outdoor testing, the YoloV5 model had an accuracy of 75% in detecting fire, but an error in detecting a human face as smoke appeared. However, as a result of applying the YoloV5+DeepSort model, it appeared the same as YoloV5 with an accuracy of 75%, but it was confirmed that the false positive phenomenon disappeared.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.320-324
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• 2023

As urbanization accelerates and facilities that use energy increase, human life and property damage due to fire is increasing. Therefore, a fire monitoring system capable of quickly detecting a fire is required to reduce economic loss and human damage caused by a fire. In this study, we aim to develop an improved artificial intelligence model that can increase the accuracy of low fire alarms by mixing DeepSORT, which has strengths in object tracking, with the YOLOv5s model. In order to develop a fire detection model that is faster and more accurate than the existing artificial intelligence model, DeepSORT, a technology that complements and extends SORT as one of the most widely used frameworks for object tracking and YOLOv5s model, was selected and a mixed model was used and compared with the YOLOv5s model. As the final research result of this paper, the accuracy of YOLOv5s model was 96.3% and the number of frames per second was 30, and the YOLOv5s_DeepSORT mixed model was 0.9% higher in accuracy than YOLOv5s with an accuracy of 97.2% and number of frames per second: 30.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2004년도 학술대회지
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• pp.178-182
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• 2004

To establish the design criteria for construction of the rubble mound on improved ground, two kinds of analyses for the soil deformation behavior and the slope stability were performed on various cases for rubble mounds, soft grounds and back fills with application of the finite element method and the Bishop simplified method. The horizontal displacements and settlements at the crest of rubble mounds were analyzed as a function of the safety factor of embankments. The analyzed result shows that the soil movement increases considerably when the safety factor of rubble mounds is lower than 1.3.

• 한국해양공학회지
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• 제15권1호
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• pp.73-78
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• 2001

Soundness evaluation of a structure being constructed under the sea is usually difficult. In this study, a cross-hole sonic logging(CSL) which have been used for non-destructive test of concrete piles is adopted for the integrity test and monitoring of DCM(deep cement mixing) treated soils. Chemical and physical characteristics of raw ground materials are analysed to delineate ground environmental effects on the strength of DCM treated soils. In order to convert cross-hole sonic logging data into compressive strength, correlations between compressive strengths and wave velocities of core samples have been obtained. It is found that there is little effect of ground environment on the strength of the DCM treated soils, and the density distribution of core samples and cross-hole logging data show that a defective zone may exist in the DCM treated soils. With the time lapse, however, the defective zone has been cured and consequently, compressive strength of the DCM treated soils increases and satisfies the design parameter. From this study it can be concluded that the cross-hole sonic logging can be used for the integrity test as well as monitoring the curing stage of the structures, successfully.

• 한국지반신소재학회논문집
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• 제20권4호
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• pp.19-32
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• 2021

본 연구에서는 심층혼합처리공법에 관하여 저자들에 의해 수행된 국내/외 설계, 시공 및 품질관리에 대한 평가와 고찰을 수행하였고, 추후 DCM 공법의 발전을 위한 개선 사항들을 제시하였다. 본 연구 결과, 실내배합실험 시 강도에 대한 단면적 보정이 필요하고, 외삽법 적용 시 실제와 다른 결과가 도출될 수 있으므로 주의가 요구됨을 알 수 있었다. 설계 시 개량율과 개량형식 등을 모두 고려하여 적용 가능한 설계법을 선정해야 하며, 안전율이 적용된 허용압축강도는 안정성 검토를 위한 기준치를 의미하는 것이지 설계 지반정수가 아님을 확인하였다. 응력분담비의 경우 관행적인 값을 적용하기 보다는 설계 조건을 반영한 실험 및 이론적 응력분담비를 산정해야 경제적인 설계를 수행할 수 있었다. 시공 시 선천공이 예상되는 경우 증가된 함수비가 원지반 대비 크지 않은 경우 함수비 증가를 고려하지 못한 결과를 사용하여도 큰 문제가 발생하지 않는 경우도 있었다. 또한, 개량길이 대비 선단처리 길이의 비율이 높은 경우 시공 시 단위길이당 설계 시멘트량이 적게 주입될 수 있음을 확인하였다. 품질관리 시 개량체 코어링은 1축 개량체는 D/4~2D/4, 다축 개량체는 D/4 지점이 최적인 것으로 평가되었다. 품질관리를 위한 항목으로 개량체의 일축압축강도와 더불어 TCR을 고려하는 기준이 국내 실정에 더 적합할 것으로 판단된다.