• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.152-161
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• 2014

In this paper, a multi-node real-time diagnostic and management system based on zigbee sensor network is proposed, which is to monitor and diagnose multiple nodes as well as to control the data generated from the various multiple sensors collectively. The proposed system is designed to transmit the collected wireless and wired data to the server for monitoring and controling efficiently the condition for multi-nodes by taking the corresponding actions according to the analysis. The system is implemented to make it possible to manage the sensor data by classifying them, of which data are issued from the clustered sources with a number of the remote sensors. In order to evaluate the performance of the proposed system, we measure and analyze both the transmission delay time according to the distance and the data loss rate issued from multiple sensors. The results shows that the proposed system has a good performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.207-214
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• 2011

In this paper, using Zigbee-based wireless sensor networks and Lego MindStorms NXT robot, a remote monitoring and navigation system for mobile robot has been developed. Mobile robot can estimate its position using encoder values of its motor, but due to the existing friction and shortage of motor power etc., error occurs. To fix this problem and obtain more accurate position of mobile robot, a ultrasound module on wireless sensor networks has been used in this paper. To overcome disadvantages of ultrasound which include straightforwardness and narrow detection coverage, we rotate moving node attached to mobile robot by $360^{\circ}$ to measure each distance from four fixed nodes. Then location of mobile robot is estimated by triangulation using measured distance values. In addition, images are sent via a network using a USB Web camera to smart phone. On smart phones we can see location of robot, and images around places where robot navigates. And remote monitoring and navigation is possible by just clicking points at the map on smart phones.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.137-143
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• 2017

This paper presents a smart parking guidance system based on IoT car-stoppers. The car-stopper embedding an IoT sensor module has the advantage of easy installation compared to conventional parking sensors buried in ground. The parking status data are transferred to the IoT gateway by the sequential point-to-point communication between the car-stoppers. The data transferred from the IoT gateway are stored in the web server, and parking spaces can be monitored remotely through the Android app in a smart device. An active/sleep cycle method using a watch dog timer is employed to reduce the power consumption of the battery powered car-stopper. The power consumption of the car-stopper is measured to be 80 and 25 mW at the active and sleep mode, respectively. A configuration of ultra-low-power IoT sensor module is proposed to minimize the power consumption in the sleep mode. The operation of the implemented system has been verified in a real-world parking lot.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.485-492
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• 2019

The observation system has been developed to investigate the rip currents at Haeundae beach using X-band marine radar. X-band radar system can observe shape, size, and velocity of rip currents, which is difficult to obtain through field observation by conventional device. Algorithms which automatically detect locations, shapes, and magnitudes of rip currents were developed using time averaged X-band radar sea clutter images. X-band sea clutter images are transformed through 3D FFT into 2D wave number spectrum and frequency spectrum. Rip current velocities were estimated using differences in wave-number spectra and wave frequency spectra due to Doppler shift. The algorithm was verified by drift experiments. At Haeundae beach, the radar system exactly located the rip currents and found to be sustained for 1-2 days at fixed locations.

• Journal of Environmental Impact Assessment
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• v.24 no.4
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• pp.367-374
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• 2015

To examine the effect of thermal discharge from nuclear power plants, Sea Surface Temperature (SST) is one of the most important variables measured by satellite remote sensing. However, the study was not much comparison of field data and satellite SST from operational Landsat 8 Thermal Infrared Sensor(TIRS) and Landsat 7 ETM+. The Landsat 8 TIRS have 2 spilt Thermal Infrared channels but ETM+ uses one channel for extracting of SST. In spite of that this research carried out that Landsat 7 ETM+ have more profitable for correction of SST than Landsat 8 TIRS. The used 15 Landsat 7 and 8 Thermal Infrared data of path/row 114-36 were processed by SST algorithm of ENVI and IDL. The in-situ SST data from KHOA(Korea Hydrographic and Oceanographic Administration) compared with satellite SST and the accuracy of extracted SST were assessed by each field sites in-situ point data with time series satellite SST.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.47-52
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• 2008

The visual implementation of 3-dimensional national environment is focused by the requirement and importance in the fields such as, urban planing, telecommunication facility deployment plan, railway construction, construction engineering, spatial city development, safety and disaster prevention engineering. The currently used DEM system based on the 2-D digital maps and contour lines has limitation in implementation in reproducing the 3-D spatial city. Currently, the LiDAR data which combines the laser and GPS skill has been introduced to obtain high resolution accuracy in the altitude measurement in the advanced country. In this paper, we first introduce the LiDAR based researches in advanced foreign countries, then we propose the data generation scheme and an solution algorithm for the optimal management of our 3-D spatial u-City construction. For this purpose, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional model with long distance for 3D u-city model generation.

• Aerospace Engineering and Technology
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• v.8 no.2
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• pp.105-112
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• 2009

Telemetry data is very important for the Launch Mission and Flight Safety Control during the Space Launch. In Naro Space Center, several telemetry stations such as a small station in the NARO space center, two stations in Jeju and a downrange station on a ship are deployed for the stable acquisition/receiving of the telemetry signals. In this paper, the Link Margin and Reliability for the telemetry are analyzed to evaluate the probability of the signal receiving of each station. Even though the proper analysis is to using the on-board EIRP(Effective Isotropic Radiation Power) values in the direction of the ground station considering the predicted flight trajectory and the locations of the stations, the global EIRP of 95% spatial coverage has been used for the analysis, due to the limitation of the available data.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.4
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• pp.412-419
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• 2010

Concurrent Multi Path-Differential Optical Absorption Spectroscopy (CMP-DOAS) is a novel active optical system to measure simultaneously ambient trace gases (such as $NO_2$, $SO_2$, $O_3$, and HCHO) present on several light paths. The CMP-DOAS system consists of a 2D CCD camera, spectrometer, receiving telescopes, and artificial light sources. The system receives spectra, which have been transported through several paths. It also covers wavelength ranges of which trace gases of interest share at the same time. This study presents the instrumental setup of a CMP-DOAS in detail. A field campaign for a comparative measurement was carried out at an urban site in Gwangju for a month on January 2009. $NO_2$ mixing ratios measured by the CMP-DOAS system and in-situ $NO_2$ analyzers were in good agreement by 83%. It demonstrates the high capacities of the CMP-DOAS technique to cover atmospheric trace gases dispersed across wide light paths.

• Journal of Astronomy and Space Sciences
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• v.13 no.2
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• pp.105-116
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• 1996

This study is based upon the thermal modeling, analysis and operational results of KITSAT-1 and KITSAT-2 microsatellites launched on August 11, 1992 and Septermber 26, 1993, respectively. As KITSAT-1/2 was designed to be launched as an auxiliary payload of ARIANE launcher, the constraints on volume, power consumption, and mass were required to adopt passive thermal control method controlling absorptivity, emissivity, and conductivities among adjacent modules. The main of KITSAT was to take Earth images using CCD cameras positioned at the bottom of spacecraft, in which the cameras were always pointing to the center of Earth. This study is concerned with orbital analysis, thermal modeling, simulation results, and its verification by utilizing in-orbit telemetry data of KITSAT-2. The results of telemetry analysis show that the thermal modeling is matched to actual temperature data within 10 degrees of error range in average.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.137-146
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• 2011

In order to support increased consumer awareness regarding energy consumption, we present new ways of monitoring and predicting with energy in electric appliances. The proposed system is a design of a common electrical power outlet called smart plug that measures the amount of current passing through current sensor at 0.5 second. To acquire data for training and testing the proposed neural network, weather parameters used include average temperature of day, min and max temperature, humidity, and sunshine hour as input data, and power consumption as target data from smart plug. Using the experimental data for training, the neural network model based on Back-Propagation algorithm was developed. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the proposed neural network model can predict the power consumption quite well with correlation coefficient was 0.9965, and prediction mean square error was 0.02033.