• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.470-477
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• 2013

The aim of this study is to suggest a detection method of water surface location and its evaluation results of application for same vertical position in two successive images with time interval including both staff gauge and water surface. A specific rectangular inspection area is defined from the top of watermark and then the correlation coefficients for the inspection area of the same position of two images with short time interval is calculated. Accordingly, it is possible to identify differences between changing area and fixed area of pixel density by the water flow. The photographs taken in the laboratory were analyzed in order to validate the proposed technique. As the result of the experiment, it is identified that characteristic of correlation coefficients depends on the size of the inspection area. In the case that the inspection area is within the entire width of the watermark, water surface characteristic according to correlation coefficients is clearly noticeable. Thus, it is identified that the proposed technique can be utilized to search water surfaces. Besides, using corelation analysis of two images with time interval, it is identified that error range between 10 and 42cm was reduced in the level of 2.6cm or less in the contaminated photo of existing image stage gauge. Therefore, it is expected that the suggested method can be utilized to enhance image stage gauge performance improving the previous water surface detection method.

• Journal of IKEEE
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• v.23 no.1
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• pp.280-288
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• 2019

In the CR-enabled MANET, routing paths can be easily destroyed due to node mobility and channel unavailability (due to the emergence of the PU of a channel), resulting in significant overhead to maintain/recover the routing path. In this paper, network caching is actively used for route maintenance, taking into account the properties of the CR. In the proposed scheme, even if a node detects that a path becomes unavailable, it does not generate control messages to establish an alternative path. Instead, the node stores the packets in its local cache and 1) waits for a certain amount of time for the PU to disappear; 2) waits for a little longer while overhearing messages from other flow; 3) after that, the node applies local route recovery process or delay tolerant forwarding strategy. According to the simulation study using the OPNET simulator, it is shown that the proposed scheme successfully reduces the amount of control messages for path recovery and the service latency for the time-sensitive traffic by 13.8% and 45.4%, respectively, compared to the existing scheme. Nevertheless, the delivery ratio of the time-insensitive traffic is improved 14.5% in the proposed scheme.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.2
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• pp.21-34
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• 2002

In this paper we propos a new hardware architecture of modular exponentiation using a division chain method which has been proposed in (2). Modular exponentiation using the division chain is performed by receding an exponent E as a mixed form of multiplication and addition with divisors d=2 or $d=2^I +1$ and respective remainders r. This calculates the modular exponentiation in about $1.4log_2$E multiplications on average which is much less iterations than $2log_2$E of conventional Binary Method. We designed a linear systolic array multiplier with pipelining and used a horizontal projection on its data dependence graph. So, for k-bit key, two k-bit data frames can be inputted simultaneously and two modular multipliers, each consisting of k/2+3 PE(Processing Element)s, can operate in parallel to accomplish 100% throughput. We propose a new encoding scheme to represent divisors and remainders of the division chain to keep regularity of the data path. When it is synthesized to ASIC using Samsung 0.5 um CMOS standard cell library, the critical path delay is 4.24ns, and resulting performance is estimated to be abort 140 Kbps for a 1024-bit data frame at 200Mhz clock In decryption process, the speed can be enhanced to 560kbps by using CRT(Chinese Remainder Theorem). Futhermore, to satisfy real time requirements we can choose small public exponent E, such as 3,17 or $2^{16} +1$, in encryption and verification process. in which case the performance can reach 7.3Mbps.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of the Korean Applied Science and Technology
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• v.35 no.4
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• pp.1003-1012
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• 2018

This study is for checking the possibility of Lentinula edodes as cosmetic materials. For this we carried out biological active evaluation about anti-oxidant and anti-inflammatory effects by Lentinula edodes extracts. We extracted Lentinula edodes with water and 70% ethanol and then in order to evaluate anti-oxidant activity we treated samples by concentrations (100, 500, 1000) ${\mu}g/ml$ and carried out 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging, the activity of 2,2'-azino-bis ( 3-ethylbenzothiazoline-6-sulphonic acid )-diammonium salt (ABTS) cation radical scavenging and superoxide dismutase(SOD) like activity. Also, in order to evaluate effect of anti-inflammatory the samples in macrophages(RAW 264.7 cells), we carried out evaluation of cell viability, nitric oxide inhibitory activity western blot. The results of DPPH, $ABTS^+$ radical scavenging activity and SOD-like activity of the Lentinula edodes extracts increased in dose-dependent manner. The cytotoxic of samples by MTT assay showed no toxicity at the concentrations of 10, 25 and $50{\mu}g/ml$ of Lentinula edodes extract. Nitric oxide inhibition activity results showed that the extracts reduced NO productions in a concentration-dependent manner. Expression of inflammatory cytokines as $TNF-{\alpha}$, $PGE_2$ and $IL-1{\beta}$ decreased in a concentration-dependent manner and iNOS and COX-2 proteins expression rates were decreased significantly in western blot analysis. From the results of the experiment, it was comfirmed that the Lentinula edodes extracts had excellent anti-oxidant and anti-inflammatory effect and could be used as a safe natural cosmetic material in the future.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.105-111
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• 2021

Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1423-1444
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• 2022

In this study, the sensitivity of the mid-infrared radiance to atmospheric and surface factors was analyzed using the radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN6)'s simulation data. The possibility of retrieving the land surface temperature (LST) using only the mid-infrared bands at night was evaluated. Based on the sensitivity results, the LST retrieval algorithm that reflects various factors for night was developed, and the level of the LST retrieval algorithm was evaluated using reference LST and observed LST. Sensitivity experiments were conducted on the atmospheric profiles, carbon dioxide, ozone, diurnal variation of LST, land surface emissivity (LSE), and satellite viewing zenith angle (VZA), which mainly affect satellite remote sensing. To evaluate the possibility of using split-window method, the mid-infrared wavelength was divided into two bands based on the transmissivity. Regardless of the band, the top of atmosphere (TOA) temperature is most affected by atmospheric profile, and is affected in order of LSE, diurnal variation of LST, and satellite VZA. In all experiments, band 1, which corresponds to the atmospheric window, has lower sensitivity, whereas band 2, which includes ozone and water vapor absorption, has higher sensitivity. The evaluation results for the LST retrieval algorithm using prescribed LST showed that the correlation coefficient (CC), the bias and the root mean squared error (RMSE) is 0.999, 0.023K and 0.437K, respectively. Also, the validation with 26 in-situ observation data in 2021 showed that the CC, bias and RMSE is 0.993, 1.875K and 2.079K, respectively. The results of this study suggest that the LST can be retrieved using different characteristics of the two bands of mid-infrared to the atmospheric and surface conditions at night. Therefore, it is necessary to retrieve the LST using satellite data equipped with sensors in the mid-infrared bands.