• 한국컴퓨터정보학회논문지
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• 제22권4호
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• pp.33-39
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• 2017

In this paper, we expand the process of classification to an ensemble of fuzzy decision tree. For indoor space recognition, many research use Boosted Tree, consists of Adaboost and decision tree. The Boosted Tree extracts an optimal decision tree in stages. On each stage, Boosted Tree extracts the good decision tree by minimizing the weighted error of classification. This decision tree performs a hard decision. In most case, hard decision offer some error when they classify nearby a dividing point. Therefore, We suggest an ensemble of fuzzy decision tree, which offer some flexibility to the Boosted Tree algorithm as well as a high performance. In experimental results, we evaluate that the accuracy of suggested methods improved about 13% than the traditional one.

• 인터넷정보학회논문지
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• 제19권3호
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• pp.43-48
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• 2018

본 논문은 DNN(Deep Neural Network)와 슈퍼픽셀을 이용한 실내 공간 인식 알고리즘을 제안한다. 영상으로부터 실내 공간 인식을 위해 우선 영상 분할을 위한 세그멘테이션 프로세스가 필요하다. 이를 위해 본 논문에서는 적당한 크기로 나눌 수 있는 슈퍼 픽셀 알고리즘을 이용해 세그멘테이션을 수행한다. 각 세그먼트를 인식하기 위해 세그먼트마다 제안하는 방법을 이용하여 특징을 추출한다. 추출된 특징들을 DNN을 이용하여 학습하고, 학습으로부터 추출된 DNN모델을 이용하여 각 세그먼트를 인식한다. 실험 결과를 통해 제안하는 방법과 기존의 알고리즘과의 성능 비교 분석을 한다.

• 한국환경과학회지
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• 제32권8호
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• pp.573-583
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• 2023

This study was conducted to verify the effectiveness of improving indoor environmental awareness, relieving stress, and improving learning efficiency in school indoor green space, and suggest desirable ways to develop indoor green space in the future. As part of the study, a survey was conducted among 225 individuals across six schools in a metropolitan area with garden and panel-type indoor gardens inside the school building. The survey comprised the current status and use of indoor green spaces, the perception of indoor green spaces, improvement measures in indoor green spaces, and basic properties. Semantic Differential (SD) was used to investigate the impression of school indoor spaces. Resultantly, the more frequent the use of green spaces in the school, the more they feel the positive effects of indoor green spaces, such as improving the school's indoor environment, reducing stress, and improving learning efficiency. In addition, it appears that the more frequent contact with the natural environment, the more they feel the positive effects provided by indoor green space at school. Therefore, it is suggested that educational conditions must be improved by revitalizing various green welfare, including indoor green areas, at the school level.

• 대한건축학회논문집:계획계
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• 제34권3호
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• pp.3-10
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• 2018

The purpose of this study is to suggest the direction and recognition for applying to component of Emotion of the elementary school space with characteristics of child development. For the accomplishment of the study is to deduce types of emotional component and characteristics of child development based on literature and advanced research related to 'Child development and behavior', 'The elementary school space', and concept of 'children' and 'emotion'. In addition, The level of recognition of teachers and students about creation plan of school space by types of emotion component and preference and relationships of students on emotion component of elementary school space is investigated. The space environment has great influence in childhood going through big changes in physical, cognitive, emotional and social ways, Providing space environment built with emotion component such as 'affordance', 'diversity', 'territoriality', and 'relationships' considering characteristics of child development is most important of all, In particular, when building indoor space in elementary schools where students going through various development stages live, providing friendly environments for emotion of children put top priority on students in the decision-making process and guaranteed the participation of students is expected.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.445-450
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• 2009

This paper presents a new mobile robot localization method for indoor robot navigation. The method uses hexagon distributed color-coded patches on the ceiling and a camera is installed on the robot facing the ceiling to recognize these patches. The proposed "cell-coded map", with the use of only seven different kinds of color-coded landmarks distributed in hexagonal way, helps reduce the complexity of the landmark structure and the error of landmark recognition. This technique is applicable for navigation in an unlimited size of indoor space. The structure of the landmarks and the recognition method are introduced. And 2 rigid rules are also used to ensure the correctness of the recognition. Experimental results prove that the method is useful.

• 한국주거학회논문집
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• 제21권4호
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• pp.99-109
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• 2010

The purpose of this study is to provide basic data needed for planning apartment community spaces in order to vitalize rental apartments. Indoor community spaces of 12 rental apartments in Seoul and Kyunggi were examined. The results are as follows. First, the layout types of indoor community spaces in rental apartment complexes were found out to be mostly the building type planned in the piloties of the apartment, or the singular type placed in a singular building. Depending on the layout type, the spaces were mostly concentrated at the outskirt of the complex or the in-between space of the main building, thus lowering their recognition. Thereby, they were not satisfactory for utilization of the spaces and association of residents. Second, Indoor community space legal establishment standard and square measure did not reflect resident's feature except elderly spaces, and there was problem in activation of space. Third, as for the spatial planning of indoor community space, although each space was categorized by the users' age, the furniture and appliance planning considering users was not satisfactory. The area calculation by the type of space did not reflect the users' characteristics, thus causing problems in using the facilities. Fourth, as for the management and programs of the indoor community space, spaces were managed after categorized by the major user classes such as children, seniors, and adolescents. Depending on eagerness of program managers of each apartment complex, the level of program management varied. The survey results showed that, in most cases, almost no programs were used or merely basic management and programs were being provided.

• 대한임베디드공학회논문지
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• 제19권1호
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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.

• 방송공학회논문지
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• 제23권1호
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• pp.154-161
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• 2018

최근 스마트폰 사용자가 늘어남에 따라 실내 위치인식 서비스에 대한 연구의 중요성이 증가하고 있다. 실내 위치인식에는 주로 WiFi, Bluetooth 등이 연구되고 있으나, 본 연구에서는 대부분의 실내 공간에 설치되어 있고 스마트폰에 WiFi 기능이 탑재되어 있어 접근성이 좋은 WiFi를 사용한다. 본 연구에서는 수집된 WiFi의 수신신호세기를 이용하는 핑거프린트 기술과 다변량 분류법 중 Ensemble learning method인 랜덤포레스트 알고리즘을 사용한다. 핑거프린트의 데이터로는 수신신호세기와 더불어 Mac주소를 사용해 총 4개의 라디오 맵을 만들어 사용하였다. 실험은 제한된 실내공간에서 진행하였고 실험분석을 위해 본 연구에서 제안하는 방법과 유사한 기존의 랜덤포레스트를 사용하는 실내 위치인식 시스템과 비교 분석하였다. 실험 결과 기존의 랜덤포레스트를 사용하는 실내 위치인식 시스템보다 본 연구에서 제안하는 시스템의 위치인식 정확도가 약 5.8% 높고 학습 데이터 개수에 상관없이 위치인식 속도가 일정하게 유지 되며 기존 방식 보다 더 빠름을 입증하였다.

• 한국정보통신학회논문지
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• 제20권2호
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• pp.367-374
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• 2016

오늘날 많은 사람들은 공동의 실내 공간에서 학업이나 업무 등의 다양한 일을 수행한다. 그러나 이렇게 함께 공동으로 사용하는 공간은 여러 가지 오염 요인에 의해 업무의 효율 뿐 만아니라 건강에도 좋지 않은 영향을 미치게 될 수 있다. 그래서 무엇보다도 공동으로 사용하는 공간에 대한 쾌적한 환경의 유지가 중요한 요소로 인식되고 있다. 본 논문에서는 이러한 공동으로 사용되는 사무실이나 도서관, 강의실 등의 공간을 보다 쾌적한 환경으로 만들기 위해서 환경적으로 유해한 요소들을 분석하고, 이러한 유해 요소들을 관리하여 보다 좋은 생활환경을 제공하기 위한 통합 실내 환경 관제시스템을 설계 구현한다. 제안된 실내 환경 관제시스템은 실내 환경의 상태를 실시간으로 모니터링 할 수 있고 액추에이터를 구동시킴으로써 쾌적한 환경을 제공할 것이다. 또한, 각종 실내 공간에 적용할 수 있을 뿐만아니라 사람들의 실내 환경오염 인지도 역시 높이는 방안이 될 것이다.

• Journal of Positioning, Navigation, and Timing
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• 제12권2호
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• pp.91-100
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• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.