• Korean Institute of Interior Design Journal
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• no.39
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• pp.108-115
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• 2003

The first object of this study is to clarify the newly demanded service to the visitor in a museum. And the second is to consider the Spatial Configuration on the layout of exhibition and shape of exhibition space from the view points of behavior of appreciation and other methodology, The results of this study are as follows : 1) Museums are required to offer sufficient service to the visitor for the purposes of interest, learning and sight-seeing 2) A strategic space where we can penetrate the whole room is required at the entrance of the room for the easy choice of exhibits. 3) The entrance of the room is suggested to be the most controled space.

• Journal of Technology Innovation
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• v.12 no.3
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• pp.179-203
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• 2004

This paper examines the intra-and inter-regional linkages of public research institutes (PRIs) with various innovation actors in Korea. In spite of the general consensus on the role of PRIs as innovation facilitators as well as creators, the attributes of regional spillover effect of knowledge created by PRIs have not been understood completely. Some argue that PRIs play pivotal role in encouraging intra-regional innovation networks through collective learning process. Others argue that PRIs are not necessarily related to intra-regional knowledge transfer, but play more important role in establishing national and international innovation linkages. This study attempts to figure out the current status of innovation linkages of Korean PRIs and to prove how the internal and external attributes of PRIs influence the development of spatial innovation linkages. Furthermore, it also tries to draw policy implication from empirical analysis results.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.302-306
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• 2021

Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.653-669
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• 2024

Dynamic scene deblurring is a complex computer vision problem owing to its difficulty to model mathematically. In this paper, we present a novel approach for image deblurring with the help of the sharp reference image, which utilizes the reference image for high-quality and high-frequency detail results. To better utilize the clear reference image, we develop an encoder-decoder network and two novel modules are designed to guide the network for better image restoration. The proposed Reference Extraction and Aggregation Module can effectively establish the correspondence between blurry image and reference image and explore the most relevant features for better blur removal and the proposed Spatial Feature Fusion Module enables the encoder to perceive blur information at different spatial scales. In the final, the multi-scale feature maps from the encoder and cascaded Reference Extraction and Aggregation Modules are integrated into the decoder for a global fusion and representation. Extensive quantitative and qualitative experimental results from the different benchmarks show the effectiveness of our proposed method.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.1-10
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• 2021

In the past, designers relied primarily on past experience and reference to industry standard thresholds to design spaces. Such design often results in spaces that do not meet the needs of users. The purpose of this paper is to investigate the process and way of generating design parameters by constructing a BP neural network algorithm for spatial design. From the perspective. This paper adopts an experimental research method to take a kindergarten with a large number of complex needs in space as the object of study, and through the BP neural network algorithm in machine learning, the correlation between environmental behavior parameters and spatial design parameters is imprinted. The way of generating spatial design parameters is studied. In the future, the corresponding spatial design parameters can be derived by replacing specific environmental behavior influence factors, which can be applied to a wider range of scenarios and improve the efficiency of designers.

• The Mathematical Education
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• v.60 no.2
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• pp.133-157
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• 2021

Ill-structured problems have drawn attention in that they can enhance problem-solving skills, which are essential in future societies. The purpose of this study is to analyze and evaluate students' spatial reasoning(Intrinsic-Static, Intrinsic-Dynamic, Extrinsic-Static, and Extrinsic-Dynamic reasoning) and problem solving abilities(understanding problems and exploring strategies, executing plans and reflecting, collaborative problem-solving, mathematical modeling) that appear in ill-structured problem-solving. To solve the research questions, two ill-structured problems based on the geometry domain were created and 11 lessons were given. The results are as follows. First, spatial reasoning ability of sixth-graders was mainly distributed at the mid-upper level. Students solved the extrinsic reasoning activities more easily than the intrinsic reasoning activities. Also, more analytical and higher level of spatial reasoning are shown when students applied functions of other mathematical domains, such as computation and measurement. This shows that geometric learning with high connectivity is valuable. Second, the 'problem-solving ability' was mainly distributed at the median level. A number of errors were found in the strategy exploration and the reflection processes. Also, students exchanged there opinion well, but the decision making was not. There were differences in participation and quality of interaction depending on the face-to-face and web-based environment. Furthermore, mathematical modeling element was generally performed successfully.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.101-112
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• 1997

The purpose of this study was to examine usefulness of KEDI-Individual Basic Learning Skills Test as a diagnostic tool of learning disorders(LD). Learning disorder group consisted of two subgroups, verbal learning disorder group(VLD, n＝34) and nonverbal learning disorder group(NVLD, n＝14). Comparison group consisted of Dysthymia Disorder subgroup(n＝11) and Normal subgroup(n＝20). Performance of intelligence test and achievement test was examined in all 4 subgroups. In KEDI-WISC, VLD subgroup revealed primary problems in vocabulary, information and verbal-auditory attention test. NVLD group revealed primary problems in almost all performance tests such as visual acuity, psycho-motor coordination speed and visual-spatial organizations ability subtest. In KEDI-Individual Basic Learning Test, VLD group revealed primary problems in phonological coding process, word recognition and mathematics. For successful classification of LD children, the importance of achievement test and intelligence test was discussed by discriminant analysis and factor analysis. The results indicate that KEDI-Individual Basic Learning Skills is of considerable usefulness in diagnosing LD, but must be used in subtests, and additional tests must be conducted for thorough exploration of LD.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1107-1116
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• 2016

Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.

• Journal of The Korean Association For Science Education
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• v.35 no.2
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• pp.231-245
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• 2015

Flipped learning can be used as an innovative teaching method in science education. This study analyzes video clip produced by pre-service elementary school teachers for flipped learning and explore strategies to organize effective visualization. The pre-service elementary school teachers focused on providing information on macroscopic natural phenomenon using concrete case selection strategy for earth science class. They used marker and spatial transformation elements effectively, but their efforts to link the elements to the experience of students were not sufficient. In addition, it was very rare to put the contents into simplified drawing or provide extreme cases to enhance the imagery of students. In addition, it is necessary to provide specific case of multi-modal and link the material to the experience of students closely through familiar cases or analogical model to establish an effective visual teaching material. It may also be needed to present simplified drawing for enhancing imagery and provide extreme cases to make students have an opportunity to infer a new situation.