• Journal of The Korea Institute of Healthcare Architecture
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• v.21 no.1
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• pp.7-15
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• 2015

Purpose : When natural disaster occurs, the victims are evacuated to temporary shelters such as indoor gymnasiums or large space buildings until their homes are recovered. If someone in this temporary shelter is infected with an airborne infectious disease, it becomes easier for the disease to spread to the other people in the shelter than it would be under normal conditions. Therefore, temporary shelters need to provide not only water and food but also hygienic indoor conditions. Methods : In this study, the use of mechanical systems such as ultraviolet germicidal irradiation (UVGI) systems and air cleaners were simulated using numerical analysis to find out how these systems can control airborne infection in temporary shelters. An indoor gymnasium was selected as a temporary shelter for the numerical simulation model considering Korea's post-disaster response system. Influenza A virus was assumed as an airborne infectious disease and the diffusion of the virus was made by one person in the shelter. Results : The result of this study showed that the UVGI systems disinfected the virus more effectively than the air cleaners by creating a more stable airflow after the disinfection process. The air cleaners could remove the virus but since it created an unstable airflow in the temporary shelter, the virus was condensed to a certain area to show a higher virus concentration level than the source location. Implications : In the temporary shelter, it is necessary to use UVGI systems or air cleaners for hygienic indoor conditions.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.69-75
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• 2024

This paper proposes an IoT-based indoor autonomous driving system that applies SLAM (Simultaneous Localization And Mapping) and Navigation techniques in a ROS (Robot Operating System) environment based on TurtleBot3. The proposed autonomous driving system can be applied to indoor autonomous wheelchairs and robots. In this study, the operation was verified by applying it to an indoor self-driving wheelchair. The proposed autonomous driving system provides two functions. First, indoor environment information is collected and stored, which allows the wheelchair to recognize obstacles. By performing navigation using the map created through this, the rider can move to the desired location through autonomous driving of the wheelchair. Second, it provides the ability to track and move a specific logo through image recognition using OpenCV. Through this, information services can be received from guides wearing uniforms with the organization's unique logo. The proposed system is expected to provide convenience to passengers by improving mobility, safety, and usability over existing wheelchairs.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1215-1220
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• 2019

Currently, each farm bears both the outbreak of foot-and-mouth disease and the damage caused by AI. In addition, complaints about odors in the livestock industry are constantly being recovered and are expected to occur in the future. The purpose of this study is to improve the indoor air quality of enclosed facilities such as barns, houses, pigsty, and etc. This paper develops low-temperature plasma waste heat ventilation system to be installed in ventilation unit location and standardizes heat exchange element, low-temperature plasma lamp, and ballast for enhanced air cleaning function. In addition, this study intends to develop a new control system so that the farmers can connect with existing weather systems, flow fans, and other facility equipment by incorporating ICT.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.810-815
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• 2013

In case Wi-Fi network based First responder's position tracking system is used, range measurement must be generated from RSSI finger print database. However, it is impossible to build up finger print database and to perform rescue operation at same time in the scene of rescue. In this paper, improvised Wi-Fi network without finger print database and pedestrian dead reckoning based first responders tracking system is proposed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2523-2529
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• 2014

Location estimation service can be provided outdoors using various location estimation system based on GPS. However, location estimation system is based on existing indoor resources as GPS cannot be used because of insufficient visible satellites and weak signals. The fingerprinting technique that uses WLAN signal, in particular, is good to use indoors because it uses RSSI provided by AP to estimate location. However, its accuracy may vary depending on how accurate data the offline stage used where the fingerprinting map is built. The study sampled various data at the stage that builds the fingerprinting map and suggested a location estimation system that enhances its precision by saving the data of high frequency among them to improve this problem. The suggested location estimation system based on majority sampling data estimates location by filtering RSSI data of the highest frequency at the client and server to be saved at a map, building the map and measuring a similar distance. As a result of the test, the location estimation precision stood at minimum 87.5 % and maximum 90.4% with the margin of error at minimum 0.25 to 2.72m.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.124-131
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• 2015

In this paper, we proposed indoor positioning system using RSS(Received Signal Strength) positioning method and TSS(Transmission Signal Strength). The main point in the paper is to improve reliability of accuracy positioning with the area recognition algorithm and probabilistic algorithm, which can be effectively used indoor. In the test in 1-dimensional or 2-dimensional spaces, also we checked effective positioning system considered environment of propagation that is changed by reflection, refraction and multipath in according to space form. It is necessary to find place where urgent situation happen and quickly to respond the situation for patients or the weak. Therefore, we expect the positioning system proposed can apply to the field of traffic IT.

• Journal of Navigation and Port Research
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• v.38 no.5
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• pp.457-462
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• 2014

Indoor-positioning systems are useful to various applications. Navigation system is one of the most popular applications, which needs the information of directions of nodes' movements. Specifically the applications should get the information in real-time to properly show the current moving position of a node. In this paper, simple vector-based algorithms are proposed to compute amount and direction of changes of azimuth of mobile nodes' heading directions using existing indoor positioning systems in indoor environments where azimuth sensors do not work properly. Previous algorithms calculate the azimuth changes by too many steps of topology-based formula. The algorithms proposed in this paper get the amount of changes of azimuth by simple formula based on vector, and determine the direction of changes by the sign of value of simple formula based on the previous movement of nodes. The algorithms are much simpler and less error-prone than previous ones, and then they can detect changes in many location-based applications as well. The performance of the algorithms is proved logically and mathematically.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.141-146
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• 2018

Recently, location recognition technology has attracted much attention, especially for locating people or objects in an indoor environment without being influenced by the surrounding environment GPS technology is widely used as a method of recognizing the position of an object or a person. GPS is a very efficient, but it does not allow the positions of objects or people indoors to be determined. RFID is a technology that identifies the location information of a tagged object or person using radio frequency information. In this study, an RFID system is constructed and the position is measured using tags. At this time, an error occurs between the actual and measured positions. To overcome this problem, a neural network is trained using the measured and actual position data to reduce the error. In this case, since the number of read tags is not constant, they are not suitable as input values for training the neural network, so the neural network is trained by converting them into center-of-gravity inputs and median value inputs. This allows the position error to be reduce by the neural network. In addition, different numbers of trained data are used, viz. 50, 100, 200 and 300, and the correlation between the number of data input values and the error is checked. When the training is performed using the neural network, the errors of the center-of-gravity input and median value input are compared. It was found that the greater the number of trained data, the lower the error, and that the error is lower when the median value input is used than when the center-of-gravity input is used.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.486-494
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• 2014

Intelligent homes consist of ubiquitous sensors, home networks, and a context-aware computing system. These homes are expected to offer many services such as intelligent air-conditioning, lighting control, health monitoring, and home security. In order to realize these services, many researchers have worked on various research topics including smart sensors with low power consumption, home network protocols, resident and location detection, context-awareness, and scenario and service control. This paper presents the real-time metabolic rate estimation method that is based on measured heart rate for human adaptive appliance (air-conditioner, lighting etc.). This estimation results can provide valuable information to control smart appliances so that they can adjust themselves according to the status of residents. The heart rate based method has been experimentally compared with the location-based method on a test bed.