• Korean journal of applied entomology
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• v.54 no.4
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• pp.401-407
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• 2015

This study describes the development of a method for monitoring Aphis gossypii in greenhouse cucumber fields that was used during 2013 and 2014. The dispersion pattern of A. gossypii was determined by commonly used methods: Taylor's power law (TPL) and Iwao's patchiness regression (IPR). The sample unit was determined by linear regression analysis between mean density of sample unit versus whole plant. The optimum sample unit for different plant growth stages was two leaves (median and the lowest + 1 leaf) when the total number of leaves was less than nine, and three leaves (4th, 7th from canopy, and the lowest +1 leaf) when the total number of leaves was greater than nine. A. gossypii showed an aggregated distribution pattern, as the slopes of both TPL and IPR lines were greater than 1. TPL provided a better description of the mean-variance relationship than did IPR. The slopes and intercepts of TPL and IPR from leaf samples did not differ between the surveyed years. Fixed precision levels (D) for a sequential sampling plan were developed using Green's and Kuno's equations based on the number of aphid in a leaf sample. Green's method was more efficient than Kuno's to stop sampling. The number of samples needed to estimate the density of A. gossypii increased at higher D levels and lower mean densities. The cumulative number of aphids needed to stop sampling increased at higher D levels and with fewer plants sampled. Thus to estimate 10 aphids per leaf, 13 plants needed to be sampled, and the cumulative number of aphids to stop sampling was 131.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.422.1-422.1
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• 2016

Graphene quantum dots (GQDs), a new kind of carbon-based photo luminescent nanomaterial from chemically modified graphene oxide (CMGO) or chemically modified graphene (CMG), has attracted extensive research attention in the last few years due to its outstanding chemical, optical and electrical properties. To further extended its potential applications as optoelectronic devices, solar cells, bio and bio-sensors and so on, intensive research efforts have been devoted to the CMG. However, the CMG, a suspension of aqueous, have problematic since they are prone to agglomeration after drying a solvent. In this study, we synthesized the GQDs from graphite and deposited on silicon substrate by kinetic spray. The photo luminescent properties of deposited GQD films were analyzed and compared with initial GQDs suspension. In addition, its carbon properties were investigated with GQDs solution properties. The properties of deposited GQD films by kinetic spray were similar to that of the GQDs suspension in water. We could provide a pathway for silicon-based silicon based device applications. Finally, the well-adjusted GQD films with photo luminescence effects will show Energy-Down-Shift layer effects on silicon solar cells. The GQD layers deposited at nozzle scan speeds of 40, 30, 20, and 10 mm/s were evaluated after they were used to fabricate crystalline-silicon solar cells; the results indicate that GQDs play an important role in increasing the optical absorptivity of the cells. The short-circuit current density (Jsc) was enhanced by about 2.94 % (0.9 mA/cm2) at 30 mm/s. Compared to a reference device without a GQD energy-down-shift layer, the PCE of p-type silicon solar cells was improved by 2.7% (0.4 percentage points).

• Journal of the Korea Organic Resources Recycling Association
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• v.24 no.4
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• pp.39-48
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• 2016

An information management system for a solid waste landfill site was developed, in this study, to optimize the operation and management of solid waste landfill in real time in addition to provide the information of landfill status to the landfill operator, public official concerned and local residents. The landfill information management system is composed of two systems (Solid waste landfill history management system and landfill operation and performance management system). The solid waste landfill history management system based on automated RFID/LPR system allows landfill operators to provide information of waste collection vehicles and received waste. In addition, the system aids in the identification of 3-dimensional (3D) position for landfilled solid wastes. Using the landfill operation and performance management system based on 3D laser scanner delivers information about landfill volume, settlement, landfill density, and current landfill capacity to landfill operators in real time, resulting in optimum space utilization. Ultimately, this system would dramatically reduce exposure of landfill operators to hazardous materials and improve the productivity of landfill operations.

• Journal of the Korean Wood Science and Technology
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• v.41 no.3
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• pp.173-180
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• 2013

This study was carried to provide basic data for the research of the effect of PEG impregnation on preventing wood from cracking during heat treatment. Three popular softwood species were selected for investigating the PEG penetration rate and retention depending on PEG molecular weight, PEG retention after heat treatment and their EMCs. The average retentions of PEG400 were reversely proportional to the basic densities of three species and those of the other PEGs showed similar behaviour as well. It is obvious that PEG retention decreased as PEG molecular weight increased with a species. PEG impregnation increased or decreased the moisture contents of the specimens within 2%, and increased their basic densities by 16.8% as a maximum. The Weight Percentage Losses of PEG400 during heat treatment were the largest among three PEG levels, which implied that lower molecular weight PEG leached more than the highers. There was less difference in EMC between PEG impregnated and control specimens at low RH, but their difference increased at high RH.

• Korean Chemical Engineering Research
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• v.48 no.2
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• pp.147-156
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• 2010

There are four key factors for gas-phase biofilters; biocatalysts(microorganisms), packing materials, design/operating techniques, and diagnosis/management techniques. Biofilter performance is significantly affected by microbial community structures as well as loading conditions. The microbial studies on biofilters are mostly performed on basis of culture-dependent methods. Recently, advanced methods have been proposed to characterize the microbial community structure in environmental samples. In this study, the physiological, biochemical and molecular methods for profiling microbial communities are reviewed, and their applicability to biofilters is discussed. Community-level physiological profile is based on the utilization capability of carbon substrate by heterotrophic community in environmental samples. Phospholipid fatty acid analysis method is based on the variability of fatty acids present in cell membranes of different microorganisms. Molecular methods using DNA directly extracted from environmental samples can be divided into "partial community DNA analysis" and "whole community DNA analysis" approaches. The former approaches consist in the analysis of PCR-amplified sequence, the genes of ribosomal operon are the most commonly used sequences. These methods include PCR fragment cloning and genetic fingerprinting such as denaturing gradient gel electrophoresis, terminal-restriction fragment length polymorphism, ribosomal intergenic spacer analysis, and random amplified polymorphic DNA. The whole community DNA analysis methods are total genomic cross-DNA hybridization, thermal denaturation and reassociation of whole extracted DNA and extracted whole DNA fractionation using density gradient.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1605-1613
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• 2020

The tidal channel is a coastal sedimentary terrain that plays the most important role in the formation and development of tidal flats, and is considered a very important index for understanding and distribution of tidal flat sedimentation/erosion terrain. The purpose of this study is to understand the changes in tidal channels by a period after the opening of the floodgate of the seawall in the reclaimed land of Sihwa Lake using KOMPSAT high-resolution multispectral satellite image data and to evaluate the applicability and efficiency of high-resolution satellite images. KOMPSAT 2 and 3 images were used for extraction of the tidal channels' lineaments in 2009, 2014, and 2019 and were applied to supervised classification method based on Principal Component Analysis (PCA), Artificial Neural Net (ANN), Matched Filtering (MF), and Spectral Angle Mapper (SAM) and band ratio techniques using Normalized Difference Water Index (NDWI) and MF/SAM. For verification, a numerical map of the National Geographic Information Service and Landsat 7 ETM+ image data were utilized. As a result, KOMPSAT data showed great agreement with the verification data compared to the Landsat 7 images for detecting a direction and distribution pattern of the tidal channels. However, it has been confirmed that there will be limitations in identifying the distribution of tidal channels' density and providing meaningful information related to the development of the sedimentary process. This research is expected to present the possibility of utilizing KOMPSAT image-based high-resolution remote exploration as a way of responding to domestic intertidal environmental issues, and to be used as basic research for providing multi-platform-image-based convergent thematic maps and topics.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• Progress in Medical Physics
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• v.25 no.3
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• pp.143-150
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• 2014

In general radiotherapy, mega-voltage (MV) x-ray images are widely used as the unique method to verify radio-therapeutic fields. But, the image quality of MV images is much lower than that of kilo-voltage x-ray images due to scatter interactions. Since 1990s, studies for the scatter correction have performed with digital-based MV imaging systems. In this study, a novel method for the scatter correction is suggested using scatter to primary ratio (SPR), instead of conventional methods such as digital image processing or scatter kernel calculations. We measured two MV images with and without a solid water phantom describing a patient body with given imaging conditions, and calculated un-attenuated ratios. Then, we obtained SPR distributions for the scatter correction. For experimental validation, a line-pair (LP) phantom using several Al bars and a clinical pelvis MV image was used. As the result, scatter signals of the LP phantom image were successfully reduced so that original density distribution of the phantom was restored. Moreover, image contrast values increased after SPR correction at all ROIs of the clinical image. The mean value of increases was 48%. The SPR correction method suggested in this study has high reliability because it is based on actually measured data. Also, this method can be easily adopted in clinics without additional cost. We expected that the SPR correction can be an effective method to improve the quality of MV image guided radiotherapy.

• Journal of the Korean Magnetics Society
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• v.18 no.6
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• pp.211-216
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• 2008

The effects of Si impurity on electronic structures and magnetism of bcc Fe are investigated by using a first-principles method by considering spin-orbit coupling. In order to describe the Si impurity, a 27 atomic bcc Fe supercell has been considered. The Kohn-Sham equation was solved in terms of the all-electron full-potential linearized augmented plane wave (FLAPW) method within the generalized gradient approximation (GGA). The effects of spin-orbit coupling were calculated self-consistently by considering spin-diagonal terms based on second variation method. For the ferromagnetic (FM) state without considering SOC, the spin magnetic moment of the Si impurity was calculated to be $-0.143{\mu}B$, while the magnetic moments of Fe atoms were calculated to be $2.214{\mu}B$, $2.327{\mu}B$, and $2.354{\mu}B$ in away from the Si atom, respectively. However, the FM state with considering SOC, the spin magnetic moment of the Si impurity was calculated to be $-0.144{\mu}B$, which is not affected significantly by SOC, but the spin magnetic moments of Fe atoms were calculated $2.189{\mu}B$, $2.310{\mu}B$, and $2.325{\mu}B$, respectively, which are much reduced value compared to those of the FM state without SOC. Comparing the total charge density and spin density, those features are thought to be originated by the screening distortions of the Fe $t_{2g}$ orbital, which can be obtained by considering SOC.