• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2172-2178
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• 2007

In indoor environment, the combination of the two variations, large scale(path loss) and small scale(fading), leads to non-linear variation of RSSI(received signal strength indicator) values as distance varied. This has been one of the difficulties for indoor location estimation. This paper presents new findings on indoor RSSI characterization for more accurate model building. Experiments have been done statistically to find overall trend of RSSI values at different places and times within the same room. From experiments, it has been shown that the variation of RSSI values can be determined by both spatial and temporal factors. These two factors are directly indicated by the two main parameters of path loss model. The results show that all sensor nodes which are located at different places share the same characterization value for the temporal parameter whereas different values for the spatial parameters. The temporal parameter also has a large scale variation effect that is slowly time varying due to environmental changes. Using this relationship, the characterization for location estimation can be more efficient and accurate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.425-427
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• 2018

As the urban scale changes and the living space changes in three dimensions, the Ministry of Land, Infrastructure and Transport continuously builds various and high quality spatial information such as three-dimensional spatial information, indoor spatial information, underground spatial information and precision road map. However, although various disasters and safety accidents are rapidly increasing due to the enlargement and complexity of cities, safety management using spatial information is relatively insufficient. In this paper, we propose a data model for comparing and analyzing typical domestic and international spatial information construction cases and providing disaster safety services based on spatial information standards.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.1
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• pp.1-9
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• 2007

3D data are in great demand for pedestrian navigation recently. For pedestrian navigation, we needs to reconstruct 3D model in detail from people's eye. In order to present spatial features in detail for pedestrian navigation, it is indispensable to develop 3D model not only in outdoor environment but also in indoor environment such as underground shopping complex. However, it is very difficult to acquire 3D data efficiently by mobile mapping without GPS. In this research, 3D shape was acquired by Laser scanner, and texture by CCD(Charge Coupled Device) sensor. Continuous changes position and attitude of sensors were measured by IMU(Inertial Measurement Unit). Moreover, IMU was corrected by relative orientation of CCD images without GPS(Global Positioning System). In conclusion, Reliable, quick, and handy method for acquiring 3D data for indoor environment is proposed by a combination of a digital camera and a laser scanner with IMU.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.623-633
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• 2020

As the time spent in indoor space has increased, the demand for services targeting indoor spaces also continues to increase. To provide indoor spatial information services, the construction of indoor spatial information should be done first. In the study, a method of generation IndoorGML, which is the international standard data format for Indoor space, from existing BIM data. The characteristics of IFC objects were investigated, and objects that need to be converted to IndoorGML were selected and classified into objects that restrict the expression of Indoor space and internal passages. Using the proposed method, a part of data set provided by the BIMserver github and the IFC model of the 21st Century Building in University of Seoul were used to perform experiments to generate PrimalSpaceFeatures of IndoorGML. As a result of the experiments, the geometric information of IFC objects was represented completely as IndoorGML, and it was shown that NavigableBoundary, one of major features of PrimalSpaceFeatures in IndoorGML, was accurately generated. In the future, the proposed method will improve to generate various types of objects such as IfcStair, and additional method for automatically generating MultiLayeredGraph of IndoorGML using PrimalSpaceFeatures should be developed to be sure of completeness of IndoorGML.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.422-428
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• 2008

In this paper, we propose the robust object detection algorithm with spatial gradient information. To do this, first, we eliminate error values that appear due to complex environment and various illumination change by using prior methods based on hue and intensity from the input video and background. Visible shadows are eliminated from the foreground by using an RGB color model and a qualified RGB color model. And unnecessary values are eliminated by using the HSI color model. The background is removed completely from the foreground leaving a silhouette to be restored using spatial gradient and HSI color model. Finally, we validate the applicability of the proposed method using various indoor and outdoor conditions in a complex environments.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.49-62
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• 2008

This paper proposes an indoor location positioning system for the image recognition based LBS. The proposed system is a vision-based location positioning system that is implemented the augmented reality by overlaying the location results with the view of the user. For implementing, the proposed system uses the pattern matching and location model to recognize user location from images taken by a wearable mobile PC with camera. In the proposed system, the system uses the pattern matching and location model for recognizing a personal location in image sequences. The system is estimated user location by the image sequence matching and marker detection methods, and is recognized user location by using the pre-defined location model. To detect marker in image sequences, the proposed system apply to the adaptive thresholding method, and by using the location model to recognize a location, the system can be obtained more accurate and efficient results. Experimental results show that the proposed system has both quality and performance to be used as an indoor location-based services(LBS) for visitors in various environments.

• Asian Journal of Atmospheric Environment
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• v.2 no.1
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• pp.60-67
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• 2008

The spatial limitations of urban environments in general lead to invention and design of a wide range of underground transportation systems such as subways, underground roads and paths, etc. Among them, the application of subway systems in metropolitan cities is most commonly observed to ease those confronted difficulties on this purpose. It in turn leaves passengers and workers to be exposed to indoor air potentially polluted by various sources existing in this underground environment. Specifically when considering the IAQ in a subway station, there exist many IAQ-related parameters to be counted either as individual or as integrated exposures. In this study, a model system has been developed to manage the general IAQ in a subway station. Field survey and $CO_2$ measurements were initially conducted to analyze and understand the relationship between the indoor and outdoor air quality while considering the internal pollution sources such as passengers, subway trains, etc. The measurement data were then employed for the model development with other static information. For the model development, the algorithm of simple continuity was built and applied to model the subway IAQ concerned. In this paper, the recent updated draft version of model developed will be reported and demonstrated.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.47-49
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• 2010

최근 들어 복잡한 대규모의 실내공간이 늘어남에 따라, 실내공간에서의 생활이 늘어나고 있다. 이에 따라 실내공간에서의 편의와, 혹시 발생할지 모르는 사고에 대비하여 실내공간을 3차원으로 표현하고 분석하는 것은 매우 중요한 이슈가 되어왔다. 따라서 본 연구에서는 복잡한 실내공간을 표현하고, 실내공간 뿐만 아니라 실외공간과의 연계 표현이 가능한 실내공간 데이터 모델을 제시한다.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.50-54
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• 2009

Recently, many researches have been conducted to develop 3D Indoor/Outdoor Spatial Data Models. The 3D data created based on these data models have complex data structures. In order to manage these data efficiently, it is better to use a DBMS. There have been many researches to maintain the 3D data in Geo-DBMS, such that Oosterom (2002) and Arens (2005) developed a method to store 3D Building model, geometric and topological data of coverage in DBMSa. In this study, we propose a method to store the CityGML data into the RDBMS, Oracle Spatial 11g.