• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.246-246
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• 2021

본 연구는 강우 발생시 유량을 추정하는 것에 목적이 있다. 이를 위해 본 연구는 선행연구의 모형 개발방법론에서 벗어나 딥러닝 알고리즘 중 하나인 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 유량을 추정하였다. 합성곱 신경망은 일반적으로 분류 문제 (classification)을 해결하기 위한 목적으로 개발되었기 때문에 불특정 연속변수인 유량을 모의하기에는 적합하지 않다. 이를 위해 본 연구에서는 합성곱 신경망의 완전 연결층 (Fully connected layer)를 개선하여 연속변수를 모의할 수 있도록 개선하였다. 대부분 합성곱 신경망은 RGB (red, green, blue) 사진 (photograph)을 이용하여 해당 사진이 나타내는 것을 예측하는 목적으로 사용하지만, 본 연구의 경우 일반 RGB 사진을 이용하여 유출량을 예측하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이를 위해 본 연구에서는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는 수문학적 이미지는 입력자료로 활용했다. 합성곱 신경망의 구조는 Convolution Layer와 Pulling Layer가 5회 반복하는 구조로 설정하고, 이후 Flatten Layer, 2개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 다시 1개의 Dense Layer가 이어지는 구조로 설계하였다. 마지막 Dense Layer의 활성화 함수는 분류모형에 이용되는 softmax 또는 sigmoid 함수를 대신하여 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 이와 함께 각 층의 활성화 함수는 정규화 선형함수 (ReLu)를 이용하였으며, 모형의 학습 평가 및 검정을 판단하기 위해 MSE 및 MAE를 사용했다. 또한, 모형평가는 NSE와 RMSE를 이용하였다. 그 결과, 모형의 학습 평가에 대한 MSE는 11.629.8 m3/s에서 118.6 m3/s로, MAE는 25.4 m3/s에서 4.7 m3/s로 감소하였으며, 모형의 검정에 대한 MSE는 1,997.9 m3/s에서 527.9 m3/s로, MAE는 21.5 m3/s에서 9.4 m3/s로 감소한 것으로 나타났다. 또한, 모형평가를 위한 NSE는 0.7, RMSE는 27.0 m3/s로 나타나, 본 연구의 모형은 양호(moderate)한 것으로 판단하였다. 이에, 본 연구를 통해 제시된 방법론에 기반을 두어 CNN 모형 구조의 확장과 수문학적 이미지의 개선 또는 새로운 이미지 개발 등을 추진할 경우 모형의 예측 성능이 향상될 수 있는 여지가 있으며, 원격탐사 분야나, 위성 영상을 이용한 전 지구적 또는 광역 단위의 실시간 유량 모의 분야 등으로의 응용이 가능할 것으로 기대된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.729-735
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• 2008

For well-constructed concrete, its service life is a long period and it has an enough durability performance. For cracked concrete, however, it is clear that cracks should be a preferential channel for the penetration of aggressive substance such as chloride ions accoding to author's previous researches. Even though crack width can be reduced due to the high reinforcement ratio, the question is to which extend these cracks may jeopardize the durability of cracked concrete. If the size of crack is small, surface treatment system can be considered as one of the best options to extend the service life of concrete structures exposed to marine environment simply in terms of cost effectiveness versus durability performance. Thus, it should be decided to undertake an experimental study to deal with the effect of different types of surface treatment system, which are expected to seal the concrete and the cracks to chloride-induced corrosion in particular. In this study, it is examined the effect of surfaced treated systems such as penetrant, coating, and their combination on chloride penetration through microcracks. Experimental results showed that penetrant can't cure cracks. However, coating and combined treatment can prohibit chloride penetration through cracks upto 0.06 mm, 0.08 mm, respectively.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.85-92
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• 2023

Recently, interest in artificial intelligence has increased due to the development of artificial intelligence fields such as ChatGPT and self-driving cars. However, there are still many unknown elements in training process of artificial intelligence, so that optimizing the model requires more time and effort than it needs. Therefore, there is a need for a tool or methodology that can analyze the weight changes during the training process of artificial intelligence and help out understatnding those changes. In this research, I propose a visualization system which helps people to understand the accumulated weight changes. The system calculates the weights for each training period to accumulates weight changes and stores accumulated weight changes to plot them in 3D space. This research will allow us to explore different aspect of artificial intelligence learning process, such as understanding how the model get trained and providing us an indicator on which hyperparameters should be changed for better performance. These attempts are expected to explore better in artificial intelligence learning process that is still considered as unknown and contribute to the development and application of artificial intelligence models.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.217-228
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• 2023

Video instance segmentation is an intelligent visual task with high complexity because it not only requires object instance segmentation for each image frame constituting a video, but also requires accurate tracking of instances throughout the frame sequence of the video. In special, human instance segmentation in drama videos has an unique characteristic that requires accurate tracking of several main characters interacting in various places and times. Also, it is also characterized by a kind of the class imbalance problem because there is a significant difference between the frequency of main characters and that of supporting or auxiliary characters in drama videos. In this paper, we introduce a new human instance datatset called MHIS, which is built upon drama videos, Miseang, and then propose a novel video data augmentation method, CDVA, in order to overcome the data imbalance problem between character classes. Different from the previous video data augmentation methods, the proposed CDVA generates more realistic augmented videos by deciding the optimal location within the background clip for a target human instance to be inserted with taking rich spatio-temporal context embedded in videos into account. Therefore, the proposed augmentation method, CDVA, can improve the performance of a deep neural network model for video instance segmentation. Conducting both quantitative and qualitative experiments using the MHIS dataset, we prove the usefulness and effectiveness of the proposed video data augmentation method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.

• Membrane Journal
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• v.33 no.1
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• pp.23-33
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• 2023

In this study, a mixed matrix membrane was prepared by varying the contents of PEI-GO@ZIF-8 synthesized in PEBAX 2533, and the permeation characteristics of N₂ and CO₂ were studied. The N₂ permeability of the PEBAX/PEIGO@ZIF-8 mixed matrix membrane decreased as the PEI-GO@ZIF-8 content increased, and the CO₂ permeability showed different trends depending on the PEI-GO@ZIF-8 content. The CO₂ permeability increased in pure PEBAX membrane up to PEBAX/PEI-GO@ZIF-8 0.1 wt%, but decreased at the subsequent content. The PEI-GO@ZIF-8 0.1 wt% mixed matrix membrane had a CO₂ permeability of 221.9 Barrer and a CO₂/N₂ selectivity of 60.0, showing the highest permeation properties with improved CO₂ permeability and CO₂/N₂ selectivity among the prepared mixed matrix membrane and we obtained a result that reached the Robeson upper-bound. This is due to the -COOH, -O-, and -OH functional groups of GO and the amine group bonded to PEI, which interact friendly with CO₂, and the effect of ZIF-8, which causes gate-opening for CO₂ while the fillers are evenly dispersed in PEBAX.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4A
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• pp.417-429
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• 2008

This paper presents an experimental result to evaluate the redundancy in continuous span two plate-girder bridges which are generally classified as a non-redundant load path structure. The experiments were performed when one of the two girders is seriously cracked. To estimate the effects of bottom lateral bracing on the redundancy, the experiment variable was considered as the bottom lateral bracing, and two 1/5-scaled bridge specimens with and without lateral bracing system were fabricated. The ultimate loading tests were conducted on the damaged specimens with an induced crack at a girder in the side span. The test results showed that the load carrying capacity of damaged specimen with bracing was about 1.2 times higher than that without bracing. To evaluate the redundancy in each specimen, numerical analysis was performed to calibrate the difference of dead load between the actual bridge and the test specimens. When the dead load calibration is considered, the results showed that a continuous span two-girder bridges have a reasonable redundancy even without lateral bracing. Especially, the level of redundancy is increased by about 1.8 times when the lateral bracing is provided.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.45-52
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• 2009

In conventional structural design, deterministic optimization which satisfies codified constraints is performed to ensure safety and maximize economical efficiency. However, uncertainties are inevitable due to the stochastic nature of structural materials and applied loads. Thus, deterministic optimization without considering these uncertainties could lead to unreliable design. Recently, there has been much research in reliability-based design optimization (RBDO) taking into consideration both the reliability and optimization. RBDO involves the evaluation of probabilistic constraint that can be estimated using the RIA (Reliability Index Approach) and the PMA(Performance Measure Approach). It is generally known that PMA is more stable and efficient than RIA. Despite the significant advancement in PMA, RBDO still requires large computation time for large-scale applications. In this paper, A new reliability-based design optimization (RBDO) method is presented to achieve the more stable and efficient algorithm. The idea of the new method is to integrate a response surface method (RSM) with PMA. For the approximation of a limit state equation, the moving least squares (MLS) method is used. Through a mathematical example and ten-bar truss problem, the proposed method shows better convergence and efficiency than other approaches.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2A
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• pp.93-102
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• 2010

The numerical analysis and safety assessment by construction stages were considered the essential examination particular in order to solving the unstability of long-span bridges in the middle a construction. When estimating structural response characteristics by the construction stage analysis of long-span bridges, the influence of the near-field ground motion (NFGM) would be evaluated as a critical factor for the seismic design because it indicates clearly different aspects from the existing input earthquake motion data. Therefore, this study re-examined the response aspect of long-span bridges considering NFGM characteristics based on the response spectrum result, and advanced the presented numerical analysis program by the related research for conducting the construction stage analysis and reliability assessment of long-span bridges efficiently. The excellency of various construction schemes was assessed using the time history analysis result of critical member considering NFGM characteristics. For evaluating quantitative safety level, the reliability analysis was conducted considering the influence of external uncertainties included in random variables, and presented the safety index and failure probability of the critical construction stage by NFGM characteristics. In addition, the reliability result was examined the influence of internal uncertainties using monte carlo simulation (MCS), and assessed the distribution aspect of the essential analysis result. It is expected that this study will provide the basic information for the construction safety improvement when performing seismic design of long-span bridges considering NFGM characteristics.