• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.1
• /
• pp.21-27
• /
• 2022

As society develops and becomes more complex, new and diverse types of disasters such as fine dust and infectious diseases are occurring. However, in the past, there was no PA(Public Address) system that provided accurate information to prepare for such a disaster. In this paper, we propose a public address system that automatically broadcasts an alarm by analyzing polluted air quality data collected from public data and IoT sensors. The warning level varies depending on the air quality, and the information provided by public data may show a significantly different result from the guide area due to various factors such as the distance from the measuring station or the wind direction. To compensate for this, we are going to propose a method for broadcasting by comparing and analyzing data obtained from public data and data from on-site IoT sensors.

• Transactions of the Korean hydrogen and new energy society
• /
• v.30 no.6
• /
• pp.556-566
• /
• 2019

Recently, as the supply of residential polymer electrolyte membrane fuel cells (PEMFCs) increases, the durability and lifetime of the PEMFC system are becoming important. The related studies have been mainly focused on the durability and lifetime of materials while the research on the durability and maintenance of the system level is insufficient. In this paper, a model-based fault detection method is developed considering an air supply system that is dominant to the system performance and efficiency. A commercial 1 kW residential fuel cell system is built, and experiments are conducted under various operation loads and states (normal, 6 faults). From the experimental data, nominal models and residuals are generated. With the residual pattern obtained from real-time data, the detection and classification of various faults can be possible. The technical importance of this paper is to minimize extra sensor installation by using the empirical model rather than a complex mathematical model, and to decrease the number of models by using the applicable model at three loads. Finally, the model-based fault detection method for the air supply system of a PEMFC is established and is expected to be applicable to other subsystems.

• Journal of the Korean Society for Precision Engineering
• /
• v.26 no.11
• /
• pp.41-46
• /
• 2009

We present experimental data on the time response behavior of industrial platinum resistance thermometers (IPRT) to help with the selection of proper sensors in industry and research laboratories. Time constants of IPRTs were measured using a method specified in ASTM standards. Two different sensors of different protecting sheath diameters were tested in air, water and silicon oil at temperatures from $0^{\circ}C$ to $200^{\circ}C$. The time constant was the smallest in water and the highest in air. As the test temperature increased, time constants tended to decrease at all heat conducting media. For different diameters of sheath of IPRT at the same temperature, it was found that the IPRT of larger diameter showed higher time constant in air, but the opposite dependence was observed in water and oil. From the measured results, it was suggested that the sensor diameter and heat conducting medium should be considered if one wants to select proper thermometer to measure the dynamic temperature change in industry and research area.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.5
• /
• pp.1029-1036
• /
• 2018

This paper is a study on the development of optimal control system for lighting and air conditioning equipment to save the energy in campus environment. In the case of controling system developed through this research, lighting and air conditioner are controlled by both information on the number of people entering and leaving the room through the motion sensor and image processing, and the information on temperature and humidity. In addition, energy saving is enabled by the results that are controlled through data integration monitoring and command execution functions according to the control signals.

• Journal of Wellbeing Management and Applied Psychology
• /
• v.5 no.4
• /
• pp.19-31
• /
• 2022

Purpose: This study was conducted to identify indoor air quality in various living spaces using sensors that can measure noise, vibration, fine dust, and odor in real time and to propose optimal indoor air quality maintenance management using Internet of Things(IoT). Research design, data and methodology: Using real-time sensors to monitor physical factors and environmental air pollutants that affect the comfort of the residential environment, Noise, Vibration, Atmospheric Pressure, Blue Light, Formaldehyde, Hydrogen Sulfide, Illumination, Temperature, Ozone, PM10, Aldehyde, Amine, LVOCs and TVOCs were measured. It were measured every 1 seconds from 4 offices and 4 stores on a small scale from November 2018 to January 2019. Results: The difference between illuminance and blue light for each measuring point was found to depend on lighting time, and the ratio of blue light in total illumination was 0.358 ~ 0.393. Formaldehyde and hydrogen sulphide were found to be higher than those that temporarily attract people in an indoor office space that is constantly active, requiring office air ventilation. The noise was found to be 50dB higher than the office WHO recommendation noise level of 35 ~ 40dB. The most important factors for indoor environmental quality were temperature> humidity> illumination> blue light in turn. Conclusions: Various factors that determine the comfort of indoor living space can be measured with real-time sensors. Further, it is judged that the use of IoT can help maintain indoor air quality comfortably.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2000.04a
• /
• pp.109-112
• /
• 2000

The purpose of the present study is to evaluate the basic upper-limb involved in products manipulation. Upper-limb muscular deformations and electromyography (EMG) measurements are used as indexes for estimated motion: hand opening and closing, wrist extending and flexing, pronation and supination, grasping conditions. Measured values are analyzed by multivariate analysis and a regression equation is obtained for estimating the characteristics of upper-limb performance. Muscular deformation is defined as a change in shape, such as a pressure changes when the hand or wrist moves. hand opening and closing can be discriminated at a higher percentage of accuracy by muscular deformation data than by EMG data. Muscular deformation measurements using air-pack pressure sensors were verified to be effective in motion estimation applications.

• Journal of the Korean Society of Visualization
• /
• v.4 no.2
• /
• pp.76-80
• /
• 2006

The data for friction factor of the pipe correlated by Reynolds number and relative roughness have been reported well as a Moody chart. However, the results for corrugated shapes have been not investigated sufficiently. In this research, therefore, the pressure drop and friction factor are obtained. Flexible metal tubes with corrugations for the measurement are made of stainless steel plates. The kinds of tubes for the measurement are 5 annular types and helical types. The pressure drop & the velocity of the flow are obtained by micromanometer & digital pressure sensor, supplying dry air at several steps. Then the pressure drop is calculated for each tube, using the obtained data. The result shows that the pressure drop is strongly influenced by the viscous dissipation of kinetic energy due to the circulation of flows, rather than a viscous friction loss. The pressure drop increased consistently as the Reynolds number increases.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.103-106
• /
• 2003

Retrieval of ocean color information from Multispectral Camera (MSC) on KOMPSAT-2 was investigated to study and characterize small-scale biophysical features in the coastal oceans. Prior to the derivation of such information from space-acquired ocean color imageries, the atmospheric effects largely from path and the air-sea interface should be removed from the total signal recorded at the top of the atmosphere (T$_{TOA}$). In this study, the 'path-extraction' is introduced and demonstrated on the TM and SeaWiFS imageries of highly turbid coastal waters of Korea. The algorithms for retrieval of ocean color information were explored from the remote reflectance (R$_{rs}$) in the visible wavebands of MSC. The determination of coefficient (R$^{2}$) for log-transformed data [ N = 500] was 0.90. Similarly, the R$^{2}$ value for log-transformed data [ N = 500] was found to be 0.93.

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.39-49
• /
• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.1
• /
• pp.206-221
• /
• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.