• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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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.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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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.

• Journal of The Korean Association For Science Education
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• v.17 no.1
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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.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.87-97
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we proposed to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collected two new video datasets, and proposed a new deep learning architecture named Temporal and Spatial Segment Networks (TSSN) for rainfall depth recognition. Under TSSN, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. Also, the proposed TSSN architecture outperforms other architectures implemented in this paper.

• Korean Journal of Remote Sensing
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• v.39 no.4
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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.

• Journal of the Korean association of regional geographers
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• v.14 no.3
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• pp.269-278
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• 2008

This research analyzed the effect of lessons with the GIS application as an alternative scheme of teaching and learning of geography in elementary school. Two classes in the third grade at Y elementary school in Andong were selected to conduct lessons on 'The Landscape of My Hometown' from March 6 through June 30, 2006. In the experimental class, the lessons were conducted with the GIS application; while, in a comparative class, the lessons were carried with usual teaching and learning method. To find out the effect of lessons with the GIS application, differences of spatial cognition of students were figured out between groups, and before and after lessons. The difference between the spatial concept development stages and materials on the textbook discouraged students to pursue their learning as well as made them hard to achieve the goals of lessons. The GIS application had been suggested as an alternative teaching and learning method to overcome the difference; however, it has been hard to find any empirical research to verify the effect of the lessons with GIS application in elementary school. The ability of spatial cognition of the third graders at an elementary school was very low as the result of that curricula in the first and second grades dealt with sketch maps as teaching and learning media. The map learning of third grader on the transitional stage would play the critical role to develop the spatial cognition ability in the future. The field study contributing to developing spatial cognition ability would not be conducted at school. It was required to have the alternative learning schemes such as lessons with GIS application. The lessons with GIS application verified effect of GIS application as the alternative method. The GIS application helped students to recognize landmarks, directions and distance effectively as well as reduced the spatial cognition difference among individuals and/or groups.

• BMB Reports
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• v.41 no.3
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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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• v.22 no.4
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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.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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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.

• Animal cells and systems
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• v.7 no.4
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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.