• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.21-26
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• 2020

An autonomous driving system is based on the deep learning system built by big data which are obtained by various IoT sensors. The miniaturization and high performance of the IoT sensors are needed for diverse devices including the autonomous driving system. Specially, the miniaturization of the sensors leads to compel the miniaturization of the fixer structures. In the viewpoint of the miniaturization, metallic structure is a best solution to attach the small IoT sensors to the main body. However, it is hard to manufacture the small metallic structure with a conventional machining process or manufacturing cost greatly increases. As one of solutions for the problems, in this work, metallic FDM (Fused depositon modeling) based on metallic filament was proposed and the FDM process was investigated to fabricate the small metallic structure. Final part was obtained by the post-process that consists of debinding and sintering. In this work, the relationship between infill rate and the density of the part after the post-process was investigated. The investigation of the relationship is based on the fact that the infill rate and the density obtained from the post-processing is not same. It can be said that this work is a fundamental research to obtain the higher density of the printed part.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.19-27
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• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.289-298
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• 2019

In this present study, the effects of Signal to Noise Ratio (SNR), Full Width Half Maximum (FWHM), Aerosol Optical Depth (AOD), $O_3$ Vertical Column Density ($O_3$ VCD), and Solar Zenith Angle (SZA) on the accuracy of sulfur dioxide Vertical Column Density ($SO_2$ VCD) retrieval have been quantified using the Differential Optical Absorption Spectroscopy (DOAS) method with the ground-based direct-sun synthetic radiances. The synthetic radiances produced based on the Beer-Lambert-Bouguer law without consideration of the diffuse effect. In the SNR condition of 650 (1300) with FWHM = 0.6 nm, AOD = 0.2, $O_3$ VCD = 300 DU, and $SZA=30^{\circ}$, the Absolute Percentage Difference (APD) between the true $SO_2$ VCD values and those retrieved ranges from 80% (28%) to 16% (5%) for the $SO_2$ VCD of $8.1{\times}10^{15}$ and $2.7{\times}10^{16}molecules\;cm^{-2}$, respectively. For an FWHM of 0.2 nm (1.0 nm) with the $SO_2$ VCD values equal to or greater than $2.7{\times}10^{16}molecules\;cm^{-2}$, the APD ranges from 6.4% (29%) to 6.2% (10%). Additionally, when FWHM, SZA, AOD, and $O_3$ VCD values increase, APDs tend to be large. On the other hand, SNR values increase, APDs are found to decrease. Eventually, it is revealed that the effects of FWHM and SZA on $SO_2$ VCD retrieval accuracy are larger than those of $O_3$ VCD and AOD. The SZA effects on the reduction of $SO_2$ VCD retrieval accuracy is found to be dominant over the that of FWHM for the condition of $SO_2$ VCD larger than $2.7{\times}10^{16}molecules\;cm^{-2}$.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.5
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• pp.749-759
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• 2012

A field experiment was conducted to selection of ground-based remote sensor and reflectance indices to estimate rice production, estimation of suitable season for ground-based remote sensor and N top dressing fertilizer application rate in 2010. Fertilizer application was determined by "Fertilizer management standard for crops" (National Academy of Agricultural Science, 2006). Four levels of N-fertilizer were applied as 0%, 70%, 100% and 130% by base N-fertilizer application and were fertilized as 70% of basal dressing and 30% as top dressing. Rice (Oryza sativa L.) of Chucheong and Joonam (Korean cultivar) were planted on May 22, 2010 in sandy loam soil and harvested on October 6, 2010. Reflectance indices were measured 7 times from July 5 to August 23 by Crop circle-amber and red version and GreenSeeker-green and red version. Remote sensing angle from the sensor head to the canopy of rice was adjusted to $45^{\circ}$, $70^{\circ}$ and $90^{\circ}$ degree because of difference in the density of plant and the sensing angle. The reflectance indices obtained ground-based remote sensor were correlated with the biomass of rice at the early growth stage and at the harvest with $70^{\circ}$ and $90^{\circ}$ degree of sensor angle. The reflectance indices at the 52th Day After Transplanting (DAT) and the 59th DAT, critical season, were positively correlated with dry weight and nitrogen uptake. Specially NDVI at the 59th was significantly correlated with the mentioned parameters. Based on the result of this study, rNDVI by GreenSeeker on $70^{\circ}$ degree of angle at the 59th DAT in Chucheong and rNDVI by Crop Circle on $70^{\circ}$ degree of angle and gNDVI by GreenSeeker on $70^{\circ}$ degree of angle at the 59th DAT in Joonam can be useful for estimation of dry weight and nitrogen uptake. Moreover, sufficiency index estimated by reflectance index at the 59th DAT can be useful for the estimation of N-fertilizer level application and can be used as a model for N-top dressing fertilizer management.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.4
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• pp.309-316
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• 2015

Numerical analysis and experimental verification of a thermal liquid mass flow meter (LMFM) were performed. The configuration of the LMFM was the same as a gas mass flow meter (GMFM), but the opposite results in temperature difference between upstream and downstream thermistors occurred. In the case of the gas, the convection depending on the flow of thermal mass was small and comparable to the conduction through the sensor tube wall. The temperature difference was proportional to the mass flow rate due to their interaction. For the liquid flow, the convection overwhelmed the wall conduction because of the large flow of thermal mass caused by high density. The temperature difference in this case was inversely proportional to the mass flow rate. The tube diameter and heater wiring width are important design parameters, and the optimized sensor can be used to measure and control the infinitesimal liquid flow rate.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.258-266
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• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.

• Journal of Information Display
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• v.11 no.1
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• pp.8-11
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• 2010

In this study, a new thin films passivation technique using Zn with high electronegativity and $MgF_2$, a fluorine material with better optical transmittance than the sealing film materials that have thus far been reported was proposed. Targets with various ratios of $MgF_2$ to Zn (5:5, 4:6 and 3:7) were fabricated to control the amount of Zn in the passivation films. The Mg-Zn-F films were deposited onto the substrates and Zn was located in the gap between the lattices of $MgF_2$ without chemical metathesis in the Mg-Zn-F films. The thickness and optical transmittance of the deposited passivation films were approximately 200 nm and 80%, respectively. It was confirmed via electron dispersive spectroscopy (EDS) analysis that the Zn content of the film that was sputtered using a 4:6 ratio target was 9.84 wt%. The Zn contents of the films made from the 5:5 and 3:7 ratio targets were 2.07 and 5.01 wt%, respectively. The water vapor transmission rate (WVTR) was determined to be $38^{\circ}C$, RH 90-100%. The WVTR of the Mg-Zn-F film that was deposited with a 4:6 ratio target nearly reached the limit of the equipment, $1\times10^{-3}\;gm^2{\cdot}day$. As the Zn portion increased, the packing density also increased, and it was found that the passivation films effectively prevented the permeation by either oxygen or water vapor. To measure the characteristics of gas barrier, the film was applied to the emitting device to evaluate their lifetime. The lifetime of the applied device with passivation was increased to 25 times that of the PLED device, which was non-passivated.

• Bulletin of the Korean Chemical Society
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• v.30 no.12
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• pp.2993-2998
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• 2009

An enzyme-linked immunosorbent assay (ELISA)-on-a-chip (EOC) biosensor combined with cell concentration technology based on immuno-magnetic separation (IMS) was investigated for use as a potential tool for early screening of Listeria monocytogenes (L. monocytogenes) in food products. The target analyte is a well-known pathogenic foodborne microorganism and outbreaks of the food poisoning typically occur due to contamination of normal food products. Thus, the aim of this study was to develop a rapid and reliable sensor that could be utilized on a daily basis to test food products for the presence of this pathogenic microorganism. The sensor was optimized to provide a high detection capability (e.g., 5.9 ${\times}\;10^3$ cells/mL) and, to eventually minimize cultivation time. The cell density was condensed using IMS prior to analysis. Since the concentration rate of IMS was greater than 100-fold, this combination resulted in a detection limit of 54 cells/mL. The EOC-IMS coupled analytical system was then applied to a real sample test of fish intestines. The system was able to detect L. monocytogenes at a concentration of 2.4 CFU/g after pre-enrichment for 6 h from the onset of cell cultivation. This may allow us to monitor the target analyte at a concentration less than 1 CFU/g within a 9 h-cultivation provided a doubling time of 40 min is typically maintained. Based on this estimation, the EOC-IMS system can screen and detect the presence of this microorganism in food products almost within working hours.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.19-30
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• 2007

Modeling 3D buildings is an essential process to revive the real world into a computer. There are two ways to create a 3D building model. The first method is to use the building layer of 1:1000 digital maps based on high density point data gained from airborne laser surveying. The second method is to use LiDAR point data with digital images achieved with LiDAR. In this research we tested one sheet area of 1:1000 digital map with both methods to process a 3D building model. We have developed a process, analyzed quantitatively and evaluated the efficiency, accuracy, and reality. The resulted differed depending on the buildings shape. The first method was effective on simple buildings, and the second method was effective on complicated buildings. Also, we evaluated the accuracy of the produced model. Comparing the 3D building based on LiDAR data and digital image with digital maps, the horizontal accuracy was within ${\pm}50cm$. From the above we derived a conclusion that 3D building modeling is more effective when it is based on LiDAR data and digital maps. Using produced 3D building modeling data, we will be utilized as digital contents in various fields like 3D GIS, U-City, telematics, navigation, virtual reality and games etc.