• Journal of the Korean Geographical Society
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• v.39 no.1
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• pp.132-152
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• 2004

The critical point in this research is that the research on the phenomenon "teaching geography" should include how various elements consisting of the phenomenon are interrelated with each other in diverse angles, not deal with only teaching methods. This research focused on the relationships of the three components of teaching geography : contents of geography subject matter; learner; and scaffolding. Firstly, the characteristics of contents of geography subject matter were analyzed. Geographical knowledge was classified into four categories based on the way of perception. And then the selected geographic contents for this study were done didactic transposition into materials for geography education. These can be presented in a specific classification system from a context of geography education. Secondly, four categories of learning styles were divided by the way learners perceive and process information : Diverger; Assimilator; Converger; Accommodator. Each was connected with learner′s preferred contents of geography subject matter. The correlation between divergers and typical CulturalㆍHistorical Geography and Environmental Geography was high. So was between assimilators and typical Physical Geography and UrbanㆍEconomic Geography. Learners of Converger style tend to prefer GIS and Cartography. Finally, Regional Development and Regional Environmental Problems were highly correlated with accommodators.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.479-486
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• 2024

This study reviews the recently conducted case studies to explore the innovative integration of Artificial Intelligence (AI) and Machine Learning (ML) in the domain of building facility management and predictive maintenance. It systematically examines recent developments and applications of advanced computational methods, emphasizing their role in enhancing asset management accuracy, energy efficiency, and occupant comfort. The study investigates the implementation of various AI and ML techniques, such as regression methods, Artificial Neural Networks (ANNs), and deep learning models, demonstrating their utility in asset management. It also discusses the synergistic use of ML with domain-specific technologies such as Geographic Building Information Modeling (BIM), Information Systems (GIS), and Digital Twin (DT) technologies. Through a critical analysis of current trends and methodologies, the paper highlights the importance of algorithm selection based on data attributes and operational challenges in deploying sophisticated AI models. The findings underscore the transformative potential of AI and ML in facility management, offering insights into future research directions and the development of more effective, data-driven management strategies.

• Journal of the Korean association of regional geographers
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• v.9 no.3
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• pp.395-409
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• 2003

In this study the meaning and necessity for self-regulation in managing and organizing the national curriculum is researched in order to reflect the reality that the tide of regionalization appears apparently with globalism. Hwacheon is chosen and applied as an example region for selecting and forming a new learning content in geography education and teaching and learning that content. The regional geography should be learned in high schools according to the approach of regional textbooks being made and used now in primary and middle schools, and the contents of textbooks should be properly reorganized in accordance with the students' school ages rather than organized simply with enumerating geographic facts in a row. And the contents should be organized centering on the learners' daily living sphere. In addition, teaching-learning method should be taken into consideration according to the scale of the regions. Consequently, in this study small-scaled area was chosen as a learning content, laying stress on daily lives within the living zone, and therefore field work is considered as a learning method.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.9-19
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• 2004

The selection of comparative standard parcels should be objective and reasonable, which is an important task in the individual land price appraisal procedure. However, the current procedure is mainly done manually by government officials. Therefore, the efficiency and objectiveness of this selection procedure is not guaranteed and questionable. In this study, we first defined the problem by analyzing the current comparative standard land parcel selection method. In addition, we devised a decision tree-based method using a machine learning algorithm that is considered to be efficient and objective compared to the current selection procedure. Finally the proposed method is then applied to the study area for evaluating the appropriateness and accuracy.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.69-78
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• 1999

In this paper, a neural network approach to forecast Korean regional precipitation is presented. We first analyze the characteristics of the conventional models for time series prediction, and then propose a new model and its learning method for the precipitation forecast. The proposed model is a layered network in which the outputs of a layer are buffered within a given period time and then fed fully connected to the upper layer. This study adopted the dual connections between two layers for the model. The network behavior and learning algorithm for the model are also described. The dual connection structure plays the role of the bias of the ordinary Multi-Layer Perceptron(MLP), and reflects the relationships among the features effectively. From these advantageous features, the model provides the learning efficiency in comparison with the FIR network, which is the most popular model for time series prediction. We have applied the model to the monthly and seasonal forecast of precipitation. The precipitation data and SST(Sea Surface Temperature) data for several decades are used as the learning pattern for the neural network predictor. The experimental results have shown the validity of the proposed model.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Journal of the Korean Geographical Society
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• v.31 no.2
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• pp.329-345
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• 1996

The purposes of this study are to review research trends of applied geography field, to retrospect geographical works done by Korean geographers in applied geography, and to prospect the future of applied geography. We are in the period where societal problems such as energy, transportation, pollution, environment, health care, and many others, require careful consideration and need throughout strategies for solution. Most societal problems have some geographical dimensions. Because these problems are geographic in nature, there is an obvious implication that geography as a discipline has something to offer in their solutions. In fact, most geographic problems are best presented and analyzed through the applications of geographic theories, concepts and tools. Applied geography is a branch of general geography. It relies on the scientific methods and uses the principles and methods of pure geography. However applied geography is different in that it analyzes and evaluates real world action and planning and seeks to implement and manipulate environmental and spatial realities. Thus, geographic theories and other social theories that have geographic dimensions are fundamental to applied geography. Applied geography has a short history as theme in Korean geography. During the last two decades. Korea achieved remarkable economic growth. We have also encountered widening regional disparity, housing shortage of larger cities, transportation congestion, environmental pollution and many other problems. Applied geographers have tried to analyze and solve such spatial problems during the last 30 years. The research trend of Korean applied geography can be subdivided into 5 categories: (1) land use analysis and efficient utilization, (2) national physical development and planning. (3) regional development and regional planning, (4) tourism and location-allocation, transportation planning. Still the overconcentration of Seoul metropolitan region and unbalanced regional development are perceived to be the serious spatial problems which may induce more works to solve these problems. In Korea new emphasis has to be given to some professional training and experimental learning, including methodology, field techniques data management, statistical analysis, cartography, GIS, and other tools, as applicable and beneficial to problem solving in real world. The growth of applied geography depends on new insights and purposed solutions of future applied geographers in Korea. Applied geographers will contribute to the creation of future Korean geographies.

• Journal of the Korean association of regional geographers
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• v.1 no.1
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• pp.103-116
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• 1995

The purpose of this paper is to explore a brief review of trends in existing geographical research on music and to analyze music by the 5 themes of geography and to explore a variety of classroom techniques which examine song lyrics for their geographic content. The results of this paper are summarized as followed : Firstly, the trends in geographical research on music can be classified into five areas, the first is on spatial diffusion in music, the second on spatial diffusion in music, the third on regional division in music, the fourth on regional characteristics in music, the fifth on pedagogical tools in the teaching of geography. Secondly, music holds numerous possibilities for regional geographical study. The lyrics of music are littered with geographical term through which song writers impart image of culture, the distinct geographical nature of music lyrics gives rise to many geographical question, also, music lyrics gives place its special character. The results of analyses by the 5 themes of geography indicate that music are useful to learning of regional geography. The application of music to learning regional geography attracts much attentions. In the respect of importance of learning new regional geography, and in the respect of adapting globalization have to be focused on this subject.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.