• Journal of the Korea Society of Computer and Information
• /
• v.22 no.4
• /
• pp.33-39
• /
• 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.

• Journal of Internet Computing and Services
• /
• v.19 no.3
• /
• pp.43-48
• /
• 2018

In this paper, we propose an indoor-space recognition using DNN and super-pixel. In order to recognize the indoor space from the image, segmentation process is required for dividing an image Super-pixel is performed algorithm which can be divided into appropriate sizes. In order to recognize each segment, features are extracted using a proposed method. Extracted features are learned using DNN, and each segment is recognized using the DNN model. Experimental results show the performance comparison between the proposed method and existing algorithms.

• Journal of Environmental Science International
• /
• v.32 no.8
• /
• pp.573-583
• /
• 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.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.34 no.3
• /
• pp.3-10
• /
• 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.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.4
• /
• pp.445-450
• /
• 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.

• Journal of the Korean housing association
• /
• v.21 no.4
• /
• pp.99-109
• /
• 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.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.1
• /
• pp.47-55
• /
• 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 Broadcast Engineering
• /
• v.23 no.1
• /
• pp.154-161
• /
• 2018

As the number of smartphone users increases, research on indoor location recognition service is necessary. Access to indoor locations is predominantly WiFi, Bluetooth, etc., but in most quarters, WiFi is equipped with WiFi functionality, which uses WiFi features to provide WiFi functionality. The study uses the random forest algorithm, which employs the fingerprint index of the acquired WiFi and the use of the multI-value classification method, which employs the receiver signal strength of the acquired WiFi. As the data of the fingerprint, a total of 4 radio maps using the Mac address together with the received signal strength were used. The experiment was conducted in a limited indoor space and compared to an indoor location recognition system using an existing random forest, similar to the method proposed in this study for experimental analysis. Experiments have shown that the system's positioning accuracy as suggested by this study is approximately 5.8 % higher than that of a conventional indoor location recognition system using a random forest, and that its location recognition speed is consistent and faster than that of a study.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.2
• /
• pp.367-374
• /
• 2016

Recently, many people perform jobs including study and work within a common indoor space. Yet this space could have an adverse effect on operational efficiency as well as health because of many pollution factors. So maintaining a pleasant environment in the common space is important. In this thesis we study the integrated environment management system for better living conditions. This system analyzes and manages harmful environmental factors to make more pleasant environment in office, library or classroom. The proposed indoor environment management system will provide a pleasant environment by monitoring the indoor environment and driving the actuator in real time. In addition, it can be applicable to different types of indoor space to reach solutions to raise recognition of indoor environment pollution by people.

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.2
• /
• pp.91-100
• /
• 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.