• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.270-285
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• 2005

A joint design scheme is proposed to optimize the up/down scaler and the motion vector estimation module in the transcoder system. The proposed scheme first optimizes the resolution scaler for a fixed motion vector, and then a new motion vector is estimated for the fixed scaler. These two steps are iteratively repeated until they reach a local optimum solution. In the optimization of the scaler, we derive an adaptive version of a cubic convolution interpolator to enlarge or reduce digital images by arbitrary scaling factors. The adaptation is performed at each macroblock of an image. In order to estimate the optimal motion vector, a temporary motion vector is composed from the given motion vectors. Then the motion vector is refined over a narrow search range. It is well-known that this refinement scheme provides the comparable performance compared to the full search method. Simulation results show that a jointly optimized system based on the proposed algorithms outperforms the conventional systems. We can also see that the algorithms exhibit significant improvement in the minimization of information loss compared with other techniques.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.503-514
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• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Journal of the Korean Wood Science and Technology
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• v.32 no.2
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• pp.90-95
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• 2004

Pitch pine (Pinus rigida) has been planted in Korean forests for several decades, primarily for erosion control and use as a fuel supply. To enhance its value, and especially potential use as lamina for structural glued laminated timber (glulam), log quality and lumber yield of pitch pine were evaluated in this study. Trees from pure pitch pine stands with an average diameter at breast height of 32 cm were felled and bucked into 3.6m long 15 cm minimum butt-end diameter logs. Over 80% of the logs were classified to No.2 or No.3 visual grade group. Upon sawing total lumber yield was 55.2%, 39.9% for structural glulam lamina, 7.2% for louver, and 8.1% for miscellaneous use. The final lumber yield for manufacturing structural glulam, after cross-cutting to eliminate knots and finger jointing, was only 15.3%. To enhance this manufacturing yield requires that the rate of knot-included lumber used as lamina be raised. However arrangement of the knot-included lamina, whose mechanical properties need to be accurately evaluated, must be optimized to minimize any reduction to the structural glulam strength. The log quality and lumber yield of pitch pine evaluated in this study are expected to facilitate proper planning for wood product manufacture in the Korean lumbering and glulam industrial field, which has not previously dealt with this species.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.615-626
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• 2024

To accurately and efficiently monitor soil moisture (SM) across South Korea, this study developed a SM estimation model that integrates the cloud computing platform Google Earth Engine (GEE) and Automated Machine Learning (AutoML). Various spatial information was utilized based on Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and the global precipitation observation satellite GPM (Global Precipitation Measurement) to test optimal input data combinations. The results indicated that GPM-based accumulated dry-days, 5-day antecedent average precipitation, NDVI (Normalized Difference Vegetation Index), the sum of LST (Land Surface Temperature) acquired during nighttime and daytime, soil properties (sand and clay content, bulk density), terrain data (elevation and slope), and seasonal classification had high feature importance. After setting the objective function (Determination of coefficient, R² ; Root Mean Square Error, RMSE; Mean Absolute Percent Error, MAPE) using AutoML for the combination of the aforementioned data, a comparative evaluation of machine learning techniques was conducted. The results revealed that tree-based models exhibited high performance, with Random Forest demonstrating the best performance (R² : 0.72, RMSE: 2.70 vol%, MAPE: 0.14).

• Journal of agriculture & life science
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• v.50 no.2
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• pp.33-43
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• 2016

Pinellia tripartita(Blume) Schott is a herbaceous perennial plant belonging to the Araceae and distributed on Asia including of Korea, Japan, and China. P. tripartita is often used for gardening but has not been developed mass-breeding methods. In this study, we compared the tuber growth in different combinations of mixed soils used six compositions. Tubers used to study was cultured in vitro and divided into two groups. Type I was diameter more than 1cm and the group of Type II was diameter below than 1cm. Enlargement of tubers and growth of aerial parts were measuring the plant height, number of fresh leaves and dead leaves, number of bullets, tuber size, and fresh / dry weight. The size/weight and numbers of tubers from the mixed soil B (coir 68.0%, peat moss 14.7%, perlite 3.0%, vermiculite 7.0% and zeolite 7.0%) were the best grown up for eight weeks. In case of Type I, GI (Growth index) of tuber size and weight were 45% and 101%, respectively. The difference of growth was doubled compared to the bad growth treatment as the mixed soil E(Coir 14.3%, peat moss 14.3%, perlite 42.9%, vermiculite 14.3%, and zeolite 14.3%). These results could be used as the basic information for the similar experimental design for the P. ternata.