• Journal of Information Technology Services
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• v.15 no.1
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• pp.245-253
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• 2016

Background in a novel is most important elements with characters and events, and means time, place and situation that characters appeared. Among the background, spatial background can help conveys topic of a novel. So, it may be helpful for choosing a novel that readers want to read. In this paper, we are targeting Korean historical novels. In case of English text, It can be recognize spatial background easily because it use upper and lower case and words used with the spatial information such as Bank, University and City. But, in case Korean text, it is difficult to recognize that spatial background because there is few information about usage of letter. In the previous studies, they use machine learning or dictionaries and rules to recognize about spatial information in text such as news and text messages. In this paper, we build a nation dictionaries that refer to information such as 'Korean history' and 'Google maps.' We Also propose a method for recognizing spatial background based on patterns of postposition in Korean sentences comparing to previous works. We are grasp using of postposition with spatial background because Korean characteristics. And we propose a method based on result of morpheme analyze and frequency in a novel text for raising accuracy about recognizing spatial background. The recognized spatial background can help readers to grasp the atmosphere of a novel and to understand the events and atmosphere through recognition of the spatial background of the scene that characters appeared.

• Korean Journal of Cognitive Science
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• v.10 no.4
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• pp.41-50
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• 1999

It is widely known that the hippocampus plays an important role in spatial memory. Recent studies have suggested that the posterior parietal cortex (PPC) is involved in spatial memory. However it is unclear whether the PPC is involved in w working memory or reference memory of spatial learning. The purpose of the present study was to determine contribution of the hippocampus and the PPC to spatial working memory and acquisition of reference memory. Using an eight-arm radial maze in which e each arm was baited. working memory was tested by measuring rat's ability to remember arms they had visited. Reference memory was tested by measuring rat's ability to avoid visiting four consistently unbaited arms. Effects of hippocampal or PPC lesion on working memory or acquisition of reference memory in radial-arm maze learning were investigated Working memory was impaired by hippocampal lesion whereas not affected by PPC lesion. Acquisition of reference memory was impaired by lesion in either site. The results suggest that the hippocampus plays an important role in the spatial working memory while both the hippocampus and the PPC contribute to the acquisition of spatial reference memory.

• Journal of the Korean Geographical Society
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• v.36 no.2
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• pp.161-176
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• 2001

This study is to find a theoretical basis for the effective teaching-learning of the geographical concept through comparing two cognitive pshychological perspectives: Piaget's cognitive development stage theory and Vygotsky's theory with higher mectal function and zone of proximal development(ZPD). Piaget't theory of cognitive development stage has been empirically proved in the spatial concept development and provided a basis for geographical educational psychology. In spite of this contribution, it has its own limitation in that students cannot learn cocepts beyond their cognitive development stage. On the other hand, Vygotsky supposed that concept development has been done by teaching-learning. This study suggests that Vygotsky's theory gives more comprehensive thoretical basis for its effective teaching-learning about the geographical concept development.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.54-61
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• 2009

This paper presents a new electromyogram (EMG) pattern recognition method based on the Hierarchical Temporal Memory (HTM) algorithm which is originally devised for image pattern recognition. In the modified HTM algorithm, a simplified two-level structure with spatial pooler, temporal pooler, and supervised mapper is proposed for efficient learning and classification of the EMG signals. To enhance the recognition performance, the category information is utilized not only in the supervised mapper but also in the temporal pooler. The experimental results show that the ten kinds of hand motion are successfully recognized.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.99-110
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• 2021

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on 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 analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• School Mathematics
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• v.3 no.2
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• pp.355-372
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• 2001

The primary school mathematics emphasizes some activities such as visualizing figures, drawing figures and comparing figures from various angles. These activities could be undertaken throughout examination, experiments and exploration of the substantial materials. They could also be undertaken by using the objects found in a daily life informally. The 7th curriculum of mathematics reflects this trend and includes the systematized activities in teaching spatial sense in geometry. However, it still requires more researches on the teaching methodology of spatial sense and the conceptual analysis of spatial sense. In this study, the concept of spatial sense is analyzed and Mackim's 3-levels teaching methodology and Bruner's EIS theory and suggestions are reviewed as a possible teaching methodology of spatial tasks. As a conclusion, this study suggests a teaching-learning methodology of spatial tasks at the levels of 2-GA and 3-Ga of the 7th curriculum of mathematics.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.243-250
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• 2020

Systematic farming can be planned and managed if long-term agricultural weather information of the plantation is available. Because the greatest risk factor for crop cultivation is the weather. In this study, a method for long-term predicting of agricultural weather using the GloSea5 and machine learning is presented for the cultivation of Chinese cabbage. The GloSea5 is a long-term weather forecast that is available up to 240 days. The deep neural networks and the spatial randomforest were considered as the method of machine learning. The longterm prediction performance of the deep neural networks was slightly better than the spatial randomforest in the sense of root mean squared error and mean absolute error. However, the spatial randomforest has the advantage of predicting temperatures with a global model, which reduces the computation time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.83-99
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• 2021

With the advent of the fourth industrial revolution, studies on driving management and driving strategies of autonomous vehicles are emerging. While obtaining microscopic traffic data on vehicles is essential for such research, we also see that conventional traffic data collection methods cannot collect the driving behavior of individual vehicles. In this study, UAV videos were used to collect traffic data from the viewpoint of the aerial base that is microscopic. To overcome the limitations of the related research in the literature, the micro-traffic data were estimated using the multiple object tracking of deep learning and an image registration technique. As a result, the speed obtained error rates of MAE 3.49 km/h, RMSE 4.43 km/h, and MAPE 5.18 km/h, and the traffic obtained a precision of 98.07% and a recall of 97.86%.

• Knowledge Management Research
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• v.11 no.1
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• pp.115-128
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• 2010

The dramatic increase in e-Learning enrollments in higher education is likely to continue. These e-Learning environments have made learning much more convenient by stretching the spatial and temporal barriers. Their effectiveness, however, remains to be examined. In this research, the author explore the importance of personalization, interactivity and the important role of contents organizing in online education environment. Furthermore, the authors divide e-learning outcome into psychomotor, cognitive, and affective outcome. Indeed, e-Learning for psychomotor outcome has been viewed as impossible. The authors discuss the implications of the findings for theory and practice.