• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of Korea Water Resources Association
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• v.47 no.6
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• pp.537-546
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• 2014

Surface image velocimetry was introduced as an efficient and sage alternative to conventional river flow measurement methods during floods. The conventional surface image velocimetry uses a pair of images to estimate velocity fields using cross-correlation analysis. This method is appropriate to analyzing images taken with a short time interval. It, however, has some drawbacks; it takes a while to analyze images for the verage velocity of long time intervals and is prone to include errors or uncertainties due to flow characteristics and/or image taking conditions. Methods using spatio-temporal images, called STIV, were developed to overcome the drawbacks of conventional surface image velocimetry. The grayscale-gradient tensor method, one of various STIVs, has shown to be effectively reducing the analysis time and is fairly insusceptible to any measurement noise. It, unfortunately, can only be applied to the main flow direction. This means that it can not measure any two-dimensional flow field, e.g. flow in the vicinity of river structures and flow around river bends. The present study aimed to develop a new method of analyzing spatio-temporal images in two-dimension using cross-correlation analysis. Unlike the conventional STIV, the developed method can be used to measure two-dimensional flow substantially. The method also has very high spatial resolution and reduces the analysis time. A verification test using artificial images with lid-driven cavity flow showed that the maximum error of the method is less than 10 % and the average error is less than 5 %. This means that the developed scheme seems to be fairly accurate, even for two-dimensional flow.

• Journal of Korea Foresty Energy
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• v.21 no.1
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• pp.65-75
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• 2002

The study was carried out to determine the growth and biomass production of major deciduous trees including Betula platyphylla var. japonica, Betula schmidtii, Zelkova serrata, Acer mono, Prunes sargentii, and Ligustrum obtusifolium subjected to artificial shade treatment in nursery field. The six deciduous trees seedlings grow for 2 years under different light intensity of 100%, 38-62%, 22-28%, 7-20%, and 2-6% of the full sun light intensity. The results were as follows; In the seedling heights and root collar diameters of shade intolerant species like Betula platyphylla var. japonica and Betula schmidtii, the relative growth rates of seedlings grown in full sun showed 2 times as compared with those subjected to the shade treatment of 2-6% light intensities of full sun. In the shade tolerant species like Acer mono ant Ligustrum obtusifolium, the growth performances were better in the seedlings grown in 38-62% light intensities of full sun. Total dry mass including the dry mass of leaves, shoot and root were as a whole decreased with shade treatment. The ratio of the dry mass of leaves and stem increased the dry mass of root. T/R ratio of the seedlings increased by decreasing the relative light intensity. And the T/R ratio of 2-6% light intensities of full sun was ranged from 1.1~5.0 were greater in the full sun light was ranged from 0.6~3.2. Light intensity by artificial shade treatment decreased in deciduous trees when compared on the whole, it showed tendency that SLA increases, increased that seeing resemblant tendency in LAR and LWR and changed of light intensity is strong, it increased that showed difference as statistical. But, LWR of Betula platyphylla var. japonica increased gradually and showed tendency that decreases rapidly in the shade treatment of 2-6% light intensities of full sun. This result is thought that biomass production decreased by shading treatment influenced in physiological characteristics such as leaf area and decrease of the leaf amount.

• Cartoon and Animation Studies
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• s.39
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• pp.261-295
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• 2015

Cyber singer appeared in the late 1990s has disappeared briefly appeared. although a few attempts in the 2000s, it did not show significant successes. cyber singer was born thanks to the technical development of the IT industry and the emergence of an idol training system in the music industry. It was developed by Vocaloid 'Seeyou' starting from 'Adam'. cyber singer that differenatiated typical digital characters in a cartoon or game may be subject to idolize to the music as a medium. They also feature forming a plurality of fandom. therefore, such attempts and repeated failures, this could be considered a fashion, but it flew content creation and ongoing attempts to take advantage of the new media, such as Vocaloid can see that there are expectations for a true Cyber-born singer. Early-Cyber singer is made only resemble human appearance, but 'Sciart' and 'Seeyou' has been evolving to becoming more like the human capabilities. in this paper, stylized cyber singer had disappeared in the past in the process of developing the technology to evolve into own artificial life does not end in failure cases, gradually led to a change in public perceptions of the image look looking machine was an attempt in that sense. With the direction of the evolution of the mechanical function to obtain a human, fun and human exchanges and mutual feelings. And it is equipped with an artificial life form that evolved with it only in appearance and function. in order to support this logic, I refer to the study of the coevolution of man and machine at every Bruce Mazlish. And, I have analyzed the evolution of cyber singer Bruce research from the perspective of the development process since the late 1990s, the planning of the eight singers who have appeared and design of the cyber character and important voices to be evaluated as a singer (vocal). The machine has been evolving coevolution with humans. cyber singer ambivalent development targets are recognized, but strive to become the new artificial creatures of horror idea of human desire and death continues. therefore, the new Cyber-organisms are likely to be the same style as 'Seeyou'. because, cartoon forms and whirring voice may not be in the form of a signifier is the real human desires, but this is because the contemporary public's desire to be desired and the technical development of this type can be created at the point where the cross-signifier.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.137-147
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• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• Journal of the Korean Ceramic Society
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• v.39 no.3
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• pp.259-264
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• 2002

In this study, sintering properties of Artificial Lightweight aggregates(ALAs) prepared from coal ash as a function of sintering temperature (900$^{\circ}$C, 1000$^{\circ}$C, 1100$^{\circ}$C) and time (2min, 5min, 10min) when limestone added as lightweight mineral was investigated. Increasing the sintering temperature resulted simultaneously from a decline of quartz mineral as well as growth of mullite mineral. Addition of limestone to ALAs newly formed sintered minerals such as clinoptilolite and plagioclase. Sintering effect of ALAs prepared from coal ash and limestone was more affected by a sintering temperature than time. As sintering temperature and time increae, transition of macropore to micropore and formation of closed pores were happened, consequently shrank the total pore volume of ALAs. The surface of ALAs sintered at 1000$^{\circ}$C for 5min was nearly not detected open pores due toe amalgamation effect of molten slag layer but homogeneous distributions of closed pores with micro-scale were examined in cross sectional area ALAs. Sintering temperature and time which present the most adequate state, in the preparation of ALAs, are corresponded to 1000$^{\circ}$C and 5min, respectively.

• Korean Journal of Plant Tissue Culture
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• v.22 no.5
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• pp.261-266
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• 1995

This study was carried out to develop new varieties which are dwarf and tolerant to winter cold in the Aurantioideae by intergeneric crossing. to do that, the reciprocal crosses of Hwanggeumyooza and trifoliate orange, yooza and trifoliate orange were done and in vitro immature ovule culture of their hybrid was carried out .The callus formation from immature ovule was good in order of Hwanggeumyooza, Hwanggeumyooza $\times$ tifoliate orange, yooza, and trifoliate orange and best at 1 to 3 mg/L NAA+0.5mg/L zeatin on MT medium. In vitro germination percentage of 20week old hybrid of Hwanggeumyooza $\times$ tifoliate orange and trifoliate orange $\times$ Hwanggeumyooza were 41.3% and 37.7, respectively. The phenotype of hybrid (95%) of Hwanggeumyooza $\times$ trifoliate orange and that (100%) of trifoliate orange $\times$ Hwanggeumyooza were similar to that of trifoliate orange. After Hwanggeumyooza was pollinated by pollens of trifoliate orange, the pollen tubes grew on stigma after 3h of pollination and entered into micropyle after about 24~28 h. One gamete in pollen was fused with polar nuclei after 2 days and other one fused with egg nucleus at 3days after pollination. The fruit set percentage by intergeneric crossing was 14.0% in Hwanggeumyooza $\times$ trtfoliate orange and 17.5% in trifoliate orange $\times$ Hwanggeumyooza. The fruit set percentages of Hwanggeumyooza. and trifoliate orange were 34.2% and 39.5% by artificial self-fertilization, 34.2% and 39.5% by artificial cross fertilization, 3.1% and 1.4% by parthenocarpy and 13.0% and 3.0% by natural fertilization, respectively. The somatic and gametic chromosome numbers of Hwanggeumyooza, yooza, and trifoliate orange were 2n=18 and n=9.

• The Journal of Korean Medicine
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• v.41 no.2
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• pp.58-79
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• 2020

Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics. Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting. Results: Prediction accuracy was increased from 1^st to 2^nd and 3^rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1^st (54.69%), 2^nd (73.52%), and 3^rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1^st (0.640), 2^nd (0.816), and 3^rd (0.939) prediction analysis based on reduced weight. Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.

• Journal of Forest and Environmental Science
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• v.15 no.1
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• pp.77-88
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• 1999

This experiment was carried out to compare the cultural characteristics of mycorrizal fungi associated with Pinus species, and to form mycorrhizal association with Pinus thunbergii by artificial inoculation of these fungi. Mycorrhizal fungi tested showed great variations in cultural characteristics. Most fungal isolates was best grown on MP medium, except PDA for Lepista sp.(Ln73/92). Hagem for Rhizopogon rubescens(FRI91017), and FDA for Paxillus sp.(Pa60/92). Optimum temperature for these fungi was $25^{\circ}C$, except $30^{\circ}C$ for Pisolithus tinctorius(FRI91004 and Pt1). The range of pH conditions favorable for these fungal isolates were also variable from weak acidic(pH5) to weak alkalic(pH8). Utilization of the carbon sources for these mycorrhizal fungi was different. Fructose, glucose, and maltose were all utilized well, while xylose was not utilized generally. Mycelial growth on the media supplemented with potassium nitrate was better than those on other media with urea, asparagine, or peptone as a nitrogen source, and the poor growth was observed on the media with urea. Pisolithus tinctorius(Pt1) among 7 mycorrhizal fungi artificially inoculated for the mycorrhizal synthesis on pinus thunbergii seedlings in the test tube containing a mixture of peat moss-vermiculite(2:1, v/v) formed mycorrhizae successfully after 3 months. P. tinctorius formed branched and unbranched roots covered with thick fungal mantle and radiating extemal hyphae. Mycorrhizal root cross-sectioned by hand, stained, and observed by Nomarski interference microscope showed typical characteristics of ectomycorrhizae: fungal mantle on epidermal cells and thick Hartig net hyphae around cortex cells.