• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• 한국과학교육학회지
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• 제17권1호
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• pp.75-83
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• 1997

The purpose of this study was to determine the strategies that middle school students used in solving problems concerning density and solubility. These were compared in the aspects of problem contexts for 42 students of varying logical reasoning ability, spatial ability, and learning approach. A coding scheme used consists of five categories: reading & organization, production, errors, evaluation, and strategy. Students' protocols were analyzed after intercoder agreement had been established to be .95. The results were as follows: 1. Students had more difficulties in reading and organizing the problems in everyday contexts than in scientific contexts. Students at the concrete-operational stage and / or surface approach were more likely to have difficulties in reading and organizing the problems than those at the formal-operational stage and / or deep approach. 2. Students tended to split up the solubility problems into sub-problems and to solve the density problem in everyday contexts in random manner. These were significantly correlated with the test scores concerning logical reasoning ability, spatial ability, and learning approach at the .1 level of significance. 3. Major errors in solving the density problems were to disregard the given information or generated and to use inappropriate information. Many errors in solving the solubility problems were found to be executive errors. The strategy to use the information given appropriately was positively related to students' logical reasoning ability, spatial ability, and learning approach. 4. More evaluation strategies were found in everyday contexts. Their strategies to grasp the meaning of answers and to check the math were significantly related to students' logical reasoning ability. 5. Students used the random trial-and-error strategy more than the systematic strategy and the systematic trial-and-error strategy, especially in everyday contexts. The strategies used by the students were significantly related to students' logical reasoning ability, spatial ability, and learning approach.

• 한국차세대컴퓨팅학회논문지
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• 제14권6호
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• pp.87-97
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• 2018

강우량은 매우 중요한 기상 정보이다. 일반적으로, 도로 수준과 같은 높은 공간 해상도의 강우량이 더 높은 가치를 가진다. 하지만, 도로 수준의 강우량을 측정하기 위해 충분한 수의 기상 관측 장비를 설치하는 것은 비용 관점에서 비효율적이다. 본 논문에서는 도로의 감시 카메라 영상으로부터 강우량을 인식하기 위해 심층 신경망을 활용하는 방법에 대해 제시한다. 해당 목표를 달성하기 위해, 본 논문에서는 교내 두 지역의 감시 카메라 영상과 강우량 데이터를 수집했으며, 새로운 심층 신경망 구조인 Temporal and Spatial Segment Networks(TSSN)를 제안한다. 본 논문에서 제시한 심층 신경망으로 강우량 인식을 수행한 결과, 프레임 RGB와 두 연속 프레임 RGB 차이를 입력으로 사용했을 때, 높은 성능으로 강우량 인식을 수행할 수 있었다. 또한, 기존의 심층 신경망 모델과 비교했을 때, 본 논문에서 제안하는 TSSN이 가장 높은 성능을 기록함을 확인할 수 있었다.

• 대한원격탐사학회지
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• 제39권4호
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• pp.459-466
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• 2023

In the last decade, artificial intelligence's dramatic advancement with the development of various deep learning techniques has significantly contributed to remote sensing fields and satellite image applications. Among many prominent areas, super-resolution research has seen substantial growth with the release of several benchmark datasets and the rise of generative adversarial network-based studies. However, most previously published remote sensing benchmark datasets represent spatial resolution within approximately 10 meters, imposing limitations when directly applying for super-resolution of small objects with cm unit spatial resolution. Furthermore, if the dataset lacks a global spatial distribution and is specialized in particular land covers, the consequent lack of feature diversity can directly impact the quantitative performance and prevent the formation of robust foundation models. To overcome these issues, this paper proposes a method to generate benchmark datasets by simulating the modulation transfer functions of the sensor. The proposed approach leverages the simulation method with a solid theoretical foundation, notably recognized in image fusion. Additionally, the generated benchmark dataset is applied to state-of-the-art super-resolution base models for quantitative and visual analysis and discusses the shortcomings of the existing datasets. Through these efforts, we anticipate that the proposed benchmark dataset will facilitate various super-resolution research shortly in Korea.

• 한국지역지리학회지
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• 제14권3호
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• pp.269-278
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• 2008

본 연구는 기존 초등학교 지리교과의 교수 학습에 대한 대안적 방안으로써의 GIS를 활용한 수업의 효과를 분석하였다. 연구방법은 2006년 3월 6일부터 7월 30일에 걸쳐 경북 안동시 Y초등학교 3학년을 대상으로 GIS를 활용한 실험집단과 종래의 수업방식을 채택한 비교집단으로 구분하여, '우리고장의 모습'단원에 대한 실험수업을 한 이후에 공간인지력의 차이를 집단 간 그리고 실험수업 전후를 비교하는 방식을 택하였다. 우리나라 초등학생의 공간인지 발달단계와 교과서에 제시된 자료가 상충되어 학생들의 학습 의욕이 저하될 뿐만 아니라 교수 목표도 달성하기 어려운 실정이다. 이에 대한 대안적 교수 학습 방안의 수단으로 GIS활용의 중요성이 강조되고 왔다. 이러한 주장을 검증한 초등교육을 대상으로 한 경험적 연구는 전무한 실정이다. GIS를 활용한 실험수업 이전의 초등학교 3학년 공간인지능력은 매우 낮았다. 이는 초등학교 $1{\sim}2$학년 교과과정에서는 학습매체로 그림지도를 주로 활용하기 때문이다. 따라서 지도학습에 있어서 과도기적 단계로 진입하는 3학년의 지리학습은 장래 공간인지능력의 발달에 있어서 매우 중요하다. 그러나 공간인지능력의 발달에 결정적인 영향을 미치는 현장학습은 제대로 실시되지 못하고 있는 것이 현실이다. 이에 GIS 활용수업 등 대안적 학습방안의 개발이 절실하다. 지표물, 방위 그리고 거리를 지표로 한 공간인지능력을 대상으로 GIS를 활용한 실험수업의 효과를 분석한 결과 기존의 지리학습의 대안적 방안으로 GIS를 활용할 경우 공간 인지능력의 제고에 있어서 그 효과가 큰 것으로 밝혀졌다. 뿐만 아니라 GIS활용 수업은 개인 혹은 집단 간 공간인지능력 격차를 완화함에 있어서도 보다 효율적이라는 것을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제21권3호
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• pp.97-104
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• 2016

In this paper, we propose spatial information based simulator for user experience optimization and minimize real space complexity. We focus on developing simulator how to design virtual space model and to implement virtual character using real space data. Especially, we use expanded events-driven inference model for SVM based on machine learning. Our simulator is capable of feature selection by k-fold cross validation method for optimization of data learning. This strategy efficiently throughput of executing inference of user behavior feature by virtual space model. Thus, we aim to develop the user experience optimization system for people to facilitate mapping as the first step toward to daily life data inference. Methodologically, we focus on user behavior and space modeling for implement virtual space.

• BMB Reports
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• 제41권3호
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• pp.242-247
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• 2008

MSS, a comprising mixture of maesil (Prunus mume Sieb. et Zucc) concentrate, disodium succinate and Span80 (3.6 : 4.6 : 1 ratio) showed a significant improvement of memory when daily administered (460 mg/kg day, p.o.) into the normal rats for 3 weeks. During the spatial learning of 4 days in Morris water maze test, both working memory and short-term working memory index were significantly increased when compared to untreated controls. We investigated a molecular signal transduction mechanism of MSS on the behaviors of spatial learning and memory. MSS treatment increased hippocampal mRNA levels of NR2B and TrkB without changes of NR1, NR2A, ERK1, ERK2 and CREB. However, the protein levels of pERK/ERK and pCREB/CREB were all significantly increased to $1.5{\pm}0.17$ times. These results suggest that the improving effect of spatial memory for MSS is linked to MAPK/ERK signaling pathway that ends up in the phosphorylation of CREB through TrkB and/or NR2B of NMDA receptor.

• Natural Product Sciences
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• 제22권4호
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• pp.246-251
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• 2016

This study investigated the effects of (-)-sesamin on memory deficits in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned mouse model of Parkinson's disease (PD). MPTP lesion (30 mg/kg/day, 5 days) in mice showed memory deficits including habit learning memory and spatial memory. However, treatment with (-)-sesamin (25 and 50 mg/kg) for 21 days ameliorated memory deficits in MPTP-lesioned mouse model of PD: (-)-sesamin at both doses improved decreases in the retention latency time of the passive avoidance test and the levels of dopamine, norepinephrine, 3,4-dihydroxyphenylacetic acid, and homovanillic acid, improved the decreased transfer latency time of the elevated plus-maze test, reduced the increased expression of N-methyl-D-aspartate (NMDA) receptor, and increased the reduced phosphorylation of extracellular signal-regulated kinase (ERK1/2) and cyclic AMP-response element binding protein (CREB). These results suggest that (-)-sesamin has protective effects on both habit learning memory and spatial memory deficits via the dopaminergic neurons and NMDA receptor-ERK1/2-CREB system in MPTP-lesioned mouse model of PD, respectively. Therefore, (-)-sesamin may serve as an adjuvant phytonutrient for memory deficits in PD patients.

• Animal cells and systems
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• 제7권4호
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• pp.303-307
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• 2003

The aim of this study is to investigate whether alcohol alters learning and memory processes pertaining to emotional and spatial factors using the active avoidance and T-maze task in zebrafish. In the active avoidance task, zebrafish were trained to escape from one compartment to another to avoid electric shocks (unconditioned stimulus) following a conditioned light signal. Acquisition of active avoidance task appeared to be normal in zebrafish that were treated with 1% alcohol for 30 min for 17 days until the end of the behavioral test, and retention ability of learned behavior, tested 2 days later, was the same as control group. In the T-maze task, the time to find a reservoir was compared. While the latency was similar during the 1 st training session between control and alcohol-treated zebrafish, it was significantly longer in alcohol-treated zebrafish during retention test 24 h later. Furthermore, when alcohol was treated 30 min after 2nd session without prior treatment, zebrafish demonstrated similar retention ability compared to control. These results suggest that chronic alcohol treatment alters spatial learning of zebrafish, but not emotional learning.