• Korean Journal of Soil Science and Fertilizer
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• v.48 no.6
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• pp.571-581
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• 2015

Soil moisture content is generally accepted as an important factor to understand the process of crop growth and is the basis of earth system models for analysis and prediction of the crop condition. To continuously monitor soil moisture changes at kilometer scale, it is demanded to create high resolution data from the current, several tens of kilometers. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Content (SMC) from 10 km to 30 m resolution using a soil texture and field measurements that have a high correlation with the SMC. As a result, the soil moisture variations of both data (before and after downscaling) were identical, and the Root Mean Square Error (RMSE) of SMC exhibited the low values. Also, time series analyses showed that three kinds of SMC data (field measurement, original AMSR2, and downscaled AMSR2) had very similar temporal variations. Our method can be applied to downscaling of other soil variables and can contribute to monitoring small-scale changes of soil moisture by providing high resolution data.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.167-167
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• 2023

Soil organic carbon (SOC) is a critical component of soil health and is crucial in mitigating climate change by sequestering carbon from the atmosphere. Accurate estimation of SOC storage is essential for understanding SOC dynamics and developing effective soil management strategies. This study aimed to investigate the factors influencing the spatial distribution of SOC storage in South Korea, using bulk density (BD) prediction to estimate SOC stock. The study utilized data from 393 soil series collected from various land uses across South Korea established by Korea Rural Development Administration from 1968-1999. The samples were analyzed for soil properties such as soil texture, pH, and BD, and SOC stock was estimated using a predictive model based on BD. The average SOC stock in South Korea at 30 cm topsoil was 49.1 Mg/ha. The study results revealed that soil texture and land use were the most significant factors influencing the spatial distribution of SOC storage in South Korea. Forested areas had significantly higher SOC storage than other land use types. Climate variables such as temperature and precipitation had a relative influence on SOC storage. The findings of this study provide valuable insights into the factors influencing the spatial distribution of SOC storage in South Korea.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.spc
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• pp.8-13
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• 2009

Mathematical models have become increasingly popular in both research and management problems involving flow and transport processes in the subsurface. Rosetta is a program to estimate unsaturated hydraulic properties from surrogate soil data such as soil texture data and bulk density. Models of this type are called pedotransfer functions (PTFs) as an alternative measurements since they translate basic soil data into hydraulic properties. These functions may be either measured directly or estimated indirectly through prediction from more easily measured data based using quasi-empirical models.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.373-381
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• 2021

In this paper, we propose a classification model based on convolutional neural network(CNN) for predicting 2-year recurrence in non-small cell lung cancer(NSCLC) patients using preoperative chest CT images. Based on the region of interest(ROI) defined as the tumor internal and external area, the input images consist of an intratumoral patch, a peritumoral patch and a peritumoral texture patch focusing on the texture information of the peritumoral patch. Each patch is trained through AlexNet pretrained on ImageNet to explore the usefulness and performance of various patches. Additionally, ensemble learning of network trained with each patch analyzes the performance of different patch combination. Compared with all results, the ensemble model with intratumoral and peritumoral patches achieved the best performance (ACC=98.28%, Sensitivity=100%, NPV=100%).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.765-770
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• 2012

In this paper, we proposed an effective hardware structure using DCT-based inra-prediction mode selection to reduce computational complexity caused by intra mode decision. In this hardware structure, the input block is transformed at first and then analyzed to determine its texture directional tendency. the complexity has solved by performing intra prediction in only predicted edge direction. $4{\times}4$ DCT is calculated in one cycle using Multitransform_PE and Inta_pred_PE calculates one prediction mode in two cycles. Experimental results show that the proposed Intra prediction encoding needs only 517 cycles for one macroblock encoding. This architecture improves the performance by about 17% than previous designs. For hardware implementation, the proposed intra prediction encoder is implemented using Verilog HDL and synthesized with Megnachip $0.18{\mu}m$ standard cell library. The synthesis results show that the proposed architecture can run at 125MHz.

• Food Engineering Progress
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• v.14 no.1
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• pp.75-79
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• 2010

Partial least square regression (PLSR) was executed on curve data of force-deformation from back extrusion test and sensory data for commercial instant noodles. Sensory attributes considered were hardness (A), springiness (B), roughness (C), adhesiveness to teeth (D), and thickness (E). Eight and two kinds of fried and non-fried instant noodles respectively were used in the tests. Changes in weighted regression coefficients were characterized as three stages: compaction, yielding, and extrusion. Correlation coefficients appeared in the order of E>D>A>B>C, root mean square error of prediction D>C>E>B>A, and relative ability of prediction D>C>E>B>A. Overall, 'D' was the best in the correlation and prediction. 'A' with poor prediction ability but high correlation was considered good when determining the order of magnitude.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.311-323
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• 2023

Beef consumers valued meat quality traits such as texture, tenderness, juiciness, flavor, and meat color that determining consumers' purchasing decision. Most research on meat quality has focused on marbling, a key characteristic related to meat eating quality. However, other important traits such as meat texture, tenderness, and color have not much studied in cattle. Among these traits, meat tenderness and texture of cattle are among the most important factors affecting quality evaluation of consumers. Collagen is the main component of connective tissues.It greatly affects meat tenderness. The objective of this study was to determine significant variants and candidate genes associated with collagen contents trait (total collagen) through genome-wide association studies (GWAS). Phenotypic and genomic data from 135 Hanwoo were used. The BLUPF90 family program and GRAMMAR method for GWAS were applied in this study. A total of 73 potential single nucleotide polymorphisms (SNPs) showed significant associations with collagen content. They were located in or near 108 candidate genes. TMEM135 and ME3 genes were identified to have the most significant SNPs associated with collagen contents trait. Data indicated that these genes were related to collagen. Biological processes and pathways for the prediction of biological functions of candidate genes were confirmed. We found that candidate genes were involved in positive regulation of CREB transcription factor activity and actin cytoskeleton related to tenderness and texture of beef. Three genes (CRTC3, MYO1C and MYLK4) belonging to these biological functions were related to tenderness. These results provide a basis for improving genomic characteristics of Hanwoo for the production of tender beef. Furthermore, they could be used they could be used as an index to select desired traits for consumers.

• Journal of Magnetics
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• v.2 no.3
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• pp.101-104
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• 1997

Vector Moving Preisach model has been applied to the unoriented Co-based alloy thin film media. In the model, the out-of plane easy axis distribution of the particles was derived directly from the texture coefficient phkl obtained from XRD analysis, which corresponds to the fraction of the grains that have the {hkl} plane lying parallel to in-plane direction. The model was validated, by its prediction of a variety of responses, including major loop, minor loop, and the angular dependence of coercivities.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.314-321
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• 2022

Depth information used in SLAM and visual odometry is essential in robotics. Depth information often obtained from sensors or learned by networks. While learning-based methods have gained popularity, they are mostly limited to RGB images. However, the limitation of RGB images occurs in visually derailed environments. Thermal cameras are in the spotlight as a way to solve these problems. Unlike RGB images, thermal images reliably perceive the environment regardless of the illumination variance but show lacking contrast and texture. This low contrast in the thermal image prohibits an algorithm from effectively learning the underlying scene details. To tackle these challenges, we propose multi-channel remapping for contrast. Our method allows a learning-based depth prediction model to have an accurate depth prediction even in low light conditions. We validate the feasibility and show that our multi-channel remapping method outperforms the existing methods both visually and quantitatively over our dataset.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.8
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• pp.818-823
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• 2012

In order to predict necking behaviour of aluminium sheets, a crystal plasticity model is introduced in the finite element analysis of tensile test. Due to the computational limits of time and memory, only a small part of tensile specimen is subjected to the analysis. Grains having different orientations are subjected to numerical tensile tests and each grain is discretized by many elements. In order to predict the sudden drop of load carrying capacity after necking, a well-known Cockcroft-Latham damage model is introduced. The mismatch of grain orientation causes stress concentration at several points and damage is evolved at these points. This phenomenon is similar to void nucleation. In the same way, void growth and void coalescence behaviours are well predicted in the analysis. For the comparison of prediction capability of necking, same model is subjected to finite element analysis using uniform material properties of polycrystal with and without damage. As a result, it is shown that the crystal plasticity model can be used in prediction of necking and fracture behavior of materials accurately.