• KIEAE Journal
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• v.10 no.1
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• pp.65-72
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• 2010

The use of natural light has the potential for improving both the energy efficiency and indoor environmental quality in buildings. A light-pipe system can introduce daylight to spaces that would otherwise not be able to benefit from the advantages of daylight penetration. For the light-pipe system to be widely used in Korea, it is important to quantify its daylighting performance with due consideration regarding the effects imposed by the local climate conditions. This paper presents the evaluation results of existing semi-empirical models to predict daylighting performance of a light-pipe system. The evaluation of the existing models was based on the monitoring data obtained from a underground parking lot in which the light-pipe system was installed. Comparisons were made between the predicted and the monitored data obtained from the study. The results indicated that semi-empirical models which was developed using the experimental data obtained under the Korean climatic conditions had a good prediction performance. We also quantified the effects caused by sky conditions, solar altitudes, room dimensions, and the aspect ratio of a light-pipe system on both the daylighting performance of the light-pipe system and the indoor illuminance distributions of the space using the semi-empirical model. Finally, this paper provides the design guideline of the light-pipe system for its application to an underground parking lot space.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.205-212
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• 2019

Scene graph is a kind of knowledge graph that represents both objects and their relationships found in a image. This paper proposes a 3D scene graph generation model for three-dimensional indoor environments. An 3D scene graph includes not only object types, their positions and attributes, but also three-dimensional spatial relationships between them, An 3D scene graph can be viewed as a prior knowledge base describing the given environment within that the agent will be deployed later. Therefore, 3D scene graphs can be used in many useful applications, such as visual question answering (VQA) and service robots. This proposed 3D scene graph generation model consists of four sub-networks: object detection network (ObjNet), attribute prediction network (AttNet), transfer network (TransNet), relationship prediction network (RelNet). Conducting several experiments with 3D simulated indoor environments provided by AI2-THOR, we confirmed that the proposed model shows high performance.

• KIEAE Journal
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• v.17 no.5
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• pp.33-41
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• 2017

Purpose: Indoor air quality in residential buildings needs to be evaluated over the long term. In previous research, there has been an attempt to perform the health risk assessment of pollutants by using numerical models as a method of long-term evaluation. However, the numerical model of this precedent study has limitations that do not reflect the actual concentration distribution. Therefore, this study introduces the CRPS index, constructs a numerical model that can reflect the concentration distribution, and then presents a more accurate health risk assessment method using it. At this time, the pollutants are toluene, which is a typical material released from building materials. Method: CRPS index was applied to existing numerical model to reflect concentration distribution. This was used to calculate concentrations at adult breathing area and to use them for exposure assessment in a health risk assessment. After that, we entered adult data and conducted a health risk assessment of toluene. Results: The non-carcinogenic risk of toluene was calculated to be 0.0060. This is 5% smaller than the existing numerical model, meaning that it is more accurate to predict the pollutant risks. This value is also lower than the US EPA reference value of 1. Therefore, under the conditions of this study, long-term exposure of adults to toluene has no impact on health.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.177-182
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• 2012

We propose effective motion and observation models for the position of a WiFi-equipped smartphone user in large indoor environments. Three component motion models provide better proposal distribution of the pedestrian's motion. Our Gaussian interpolation-based observation model can generate likelihoods at locations for which no calibration data is available. These models being incorporated into the particle filter framework, our WiFi fingerprint-based localization algorithm can track the position of a smartphone user accurately in large indoor environments. Experiments carried with an Android smartphone in a multi-story building illustrate the performance of our WiFi localization algorithm.

• Journal of Communications and Networks
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• v.5 no.4
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• pp.303-308
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• 2003

We present a statistical model for the path loss of ultrawideband (UWB) channels in indoor environments. In contrast to our previously reported measurements, the data reported here are for a bandwidth of 6GHz rather than 1.25GHz; they encompass commercial buildings in addition to single-family homes (20 of each); and local spatial averaging is included. As before, the center frequency is 5.0GHz. Separate models are given for commercial and residential environments and, within each category, for lineof sight (LOS) and non-line-of-sight (NLS) paths. All four models have the same mathematical structure, differing only in their numerical parameters. The two new models (LOS and NLS) for residences closely match those derived from the previous measurements, thus affirming the stability of our path loss modeling. We find, also, that the path loss statistics for the two categories of buildings are quite similar.

• The Magazine of the IEIE
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• v.23 no.12
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• pp.115-125
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using one degree perspective Invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of a simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two locally parallel sloe-lines are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points. Feature points for cross ratio are extracted robustly using a vanishing point and intersection points between two locally parallel side-lines and vertical lines. Also the local position estimation problem has been treated when feature points exist less than 4points in the viewed scene. The robustness and feasibility of our algorithms have been demonstrated through real world experiments In Indoor environments using an indoor mobile robot, KASIRI-II(KAist Simple Roving Intelligence).

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.920-923
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using perspective invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of the simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two parallel walls are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points(V.P). Point features for computing cross ratios are extracted robustly using a vanishing point and the intersection points between floor and the vertical lines of door frames. The robustness and feasibility of our algorithms have been demonstrated through experiments in indoor environments using an indoor mobile robot, KASIRI-II(KAist SImple Roving Intelligence).

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.4
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• pp.299-306
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• 2007

Recently, the numerical analysis using person shape model for CFD (Computational Fluid Dynamic) has been researched widely for the thermal comfort and inhaled air quality of human body in the indoor environments. The purpose of this research is to examine the characteristic of airflow and thermal environment around human body by the experiment of displacement ventilation that assumes the indoor environment of natural convection. In this study, thermal manikin was used instead of real human body. The Airflow characteristic around human body was measured in precision by PIV (Particle Image Velocimetry). This experimental result will be used as data for CFD benchmark test using person shape model.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.624-627
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• 2023

Unmanned aerial vehicles are gaining in popularity with the development of science and technology, and are being used for a wide range of purposes, including surveillance, rescue, delivery of goods, and data collection. In particular, the ability to avoid obstacles during navigation without human oversight is one of the essential capabilities that a drone must possess. Many works currently have solved this problem by implementing deep reinforcement learning (DRL) model. The essential core of a DRL model is reward function. Therefore, this paper proposes a new reward function with appropriate action space and employs dueling double deep Q-Networks to train a drone to navigate in indoor environment without collision.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1219-1226
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• 2011

This paper proposes a novel approach to building an occupancy grid map using sonar data. It is very important for a mobile robot to recognize and construct its surrounding environments for navigation. However, the grid map constructed by ultrasonic sensors cannot represent a realistic shape of given environments due to incorrect sonar measurements caused by specular reflection. To overcome this problem, we propose an advanced sonar sensor model which consists of distance and shape factors used to determine the reliability of sensor data. Through this sensor model, a robot can build a high-quality grid map. The proposed method was verified by various experiments and showed that the robot could build an accurate map with sonar data in various indoor environments.