• Journal of Aquaculture
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• v.16 no.1
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• pp.44-50
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• 2003

This study aimed to develop the methods of growing Enteromorpha prolifera natural seedlings in its natural habitat and artificial indoor seedlings by inducing spore release. Likewise, the study examined the possibility of mass production by developing cultivation techniques with cultivating examination. The natural seedling of E.prolifera thrived in a sea area composed of sand and mud, which Is its natural habitat. Growing of this alga on the seedling frame 20 cm high from the bottom at the intertidal zone in summer and 40 cm high in fall was found to be very effective. However, enabling the best attachment rate for artificial indoor seedling requires inducing spore release after drying the mature thalli in a dark place fur about 12∼24 hours and setting seedling nets in a dark water tank (spore solution) for 24 hours. Breeding E.prolifera in a pole-system farm is best done in shallow sea areas with mud or mud and sand geological feature. However, floating-system lam is better for deep-sea areas with fast current. Ideal farming places are sea areas with plenty of nutritional salt and safe places that protect the lam facilities against billows. Furthermore, an exposure method on seawater surface to produce larger output should be used.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.199-213
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• 2022

The unsupervised domain adaptation can solve the impractical issue of repeatedly collecting high-quality training data every year for annual crop classification. This study evaluates the applicability of deep learning-based unsupervised domain adaptation models for crop classification. Three unsupervised domain adaptation models including a deep adaptation network (DAN), a deep reconstruction-classification network, and a domain adversarial neural network (DANN) are quantitatively compared via a crop classification experiment using unmanned aerial vehicle images in Hapcheon-gun and Changnyeong-gun, the major garlic and onion cultivation areas in Korea. As source baseline and target baseline models, convolutional neural networks (CNNs) are additionally applied to evaluate the classification performance of the unsupervised domain adaptation models. The three unsupervised domain adaptation models outperformed the source baseline CNN, but the different classification performances were observed depending on the degree of inconsistency between data distributions in source and target images. The classification accuracy of DAN was higher than that of the other two models when the inconsistency between source and target images was low, whereas DANN has the best classification performance when the inconsistency between source and target images was high. Therefore, the extent to which data distributions of the source and target images match should be considered to select the best unsupervised domain adaptation model to generate reliable classification results.

• Journal of Bio-Environment Control
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• v.2 no.1
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• pp.53-57
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• 1993

This study was carried out to investigate the appropriate hydroponic system when cherry tomatoes were grown in summer. The base diameter of the trunk, leaf length, leaf width, and the length of cluster were good in deep flow culture(DFC), and not different between NFT and rockwool culture. The first time of flowering and the fruit coloring per cluster were not different among cultural systems, but the marketable yields were good in DFC. In DFC, ％ dry weight, firmness, the content of organic acid and sugar were low, and the ratio of sugar/organic acid and vitamin C were high. So DFC is recommended for the summer cultivation of cherry tomatoes.

• Journal of Educational Research in Mathematics
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• v.17 no.4
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• pp.333-357
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• 2007

One of the most important issues in mathematics education is to restore the educational foundation of school mathematics, which requires fundamental discussions about 'What are the reasons for teaching mathematics?'. This study begins with the problematic that mathematics education is generally pursued as an instrumental know-ledge, which is useful to solve everyday problems or develop scientific technology. This common notion cannot be overcome as long as the mathematics education is viewed as bringing up the learners' ability to work out practical problems. In this paper we discuss the value of mathematics education reflecting on the theory of 'two fold structure of mind'. And we examine the ideas pursued by mathematics educators analyzing the educational theory of Plato and Froebel. Furthermore, we review the mathematics educational theory of F. Klein, an educator who led the reformation of mathematics education in the early 20th century and established the basic modern philosophy and curriculum of mathematics education. In particular, reflecting on the 'two fold structure of mind,' we reexamine his mathematics educational theory in the aspect of the mind cultivation so as to elucidate his ideas more clearly. Moreover, for the more deep discussion about Klein's thoughts on the mathematics education, his viewpoint on tile teaching of 'functional thinking' for the mind cultivation is reexamined based on the research results found in the developments of mathematics education after Klein. As the result we show that under the current mathematics education, which regards mathematics as a practical tools for solving everyday problems and an essential device for developing science and technology, there is a more important value for cultivating the human mind, and argue that mathematics education should contribute to the mind cultivation by emphasizing such an educational value.

• Journal of Animal Science and Technology
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• v.60 no.2
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• pp.3.1-3.9
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• 2018

Background: Poultry breeding has increased by 306% in Korea, inevitably increasing the production of manure which may contribute to environmental pollution. The nutrients (NP) in the manure are essential for crop cultivation and soil fertility when applied as compost. Excess nutrients from manure can be accumulated on the land and can lead to eutrophication. Therefore, a nutrient load on the finite land should be calculated. Methods: This study calculates the nutrient production from Korean poultry by investigating 11 broiler and 16 laying hen farms. The broiler manure was composted using deep litter composting while for layer deep litter composting, drying, and simple static pile were in practice. The effect of weight reduction and storing period during composting was checked. Three weight reduction cases of compost were constructed to calculate nutrient loading coefficients (NLCs) using data from; i) farm investigation, ii) theoretical P changes (${\Delta}P=0$), and iii) dry basis. Results: During farm investigation of broiler and layer with deep litter composting, there was a 68 and 21% N loss whereas 77 and 33% P loss was found, respectively. In case of layer composting, a loss of 10-56% N and a 52% P loss was observed. Drying manure increased the P concentrations therefore NLCs calculated using dry basis that showed quite higher reductions (67% N; 53% P). Nutrient loss from farm investigation was much higher than reported by Korean Ministry of Environment (ME). Conclusions: Nutrients in manure are decreased when undergo storing or composting process due to microbial action, drying, and leaching. The nutrient load applied to soil is less than the fresh manure, hence the livestock manure management and conservation of environment would be facilitated.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1591-1604
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• 2020

This study is a study on classifying land cover by applying high-resolution satellite images to deep learning algorithms and verifying the performance of algorithms for each spatial object. For this, the Fully Convolutional Network-based algorithm was selected, and a dataset was constructed using Kompasat-3 satellite images, land cover maps, and forest maps. By applying the constructed data set to the algorithm, each optimal hyperparameter was calculated. Final classification was performed after hyperparameter optimization, and the overall accuracy of DeeplabV3+ was calculated the highest at 81.7%. However, when looking at the accuracy of each category, SegNet showed the best performance in roads and buildings, and U-Net showed the highest accuracy in hardwood trees and discussion items. In the case of Deeplab V3+, it performed better than the other two models in fields, facility cultivation, and grassland. Through the results, the limitations of applying one algorithm for land cover classification were confirmed, and if an appropriate algorithm for each spatial object is applied in the future, it is expected that high quality land cover classification results can be produced.

• Applied Biological Chemistry
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• v.47 no.4
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• pp.430-433
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• 2004

Lilium cv. Casablanca pollen grains stored at $-70^{\circ}C$ were grown in pollen germination medium with Agrobacterium tumefaciens LBA4404 cells harboring pBI121 for 18 hr at $27^{\circ}C$. Following this, cefotaxime (250 mg/L) was treated for 6 hr to eradicate the bacterial cells. Histochemical GUS analysis revealed that the transgenic pollen displayed deep blue color mostly from 12 hr after the co-cultivation. Presence of $200\;{\mu}M$ acetosyringone was determined not to be more effective for GUS transformation than its absence. GUS DNA integration in the transgenic pollen genomic DNA was clearly demonstrated by Southern blot analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.818-821
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• 2020

The artificial intelligence software market is developing in various fields world widely. In particular, there is a wide variety of applications for image recognition technology using deep learning. This study intends to apply image recognition technology to the 'Home Gardening' market growing rapidly due to COVID-19, and aims to build a small-scale smart farm in the house using artificial intelligence and IoT technology for convenient crop cultivation for busy people living in cities. This intelligent farm system includes an automatic image recognition function and recommendation function based on temperature and humidity sensor-based indoor environment analysis.

• The Korean Journal of Mycology
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• v.36 no.1
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• pp.31-35
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• 2008

To elucidate optimum light conditions for artificial cultivation of Grifola frondosa, the effects of light quality (blue, green, white) and light intensity (200, 500, 800, 1200 lux) on primordium formation, morphological properties and yield of fruiting bodies of G. frondosa using bag cultivation method were tested. Among three light sources, white light source ($400{\sim}620\;nm$) had a higher mushroom yield (242 g/bag) and a shorter cultivation period (52 days) than those of the others. In particular, blue light source ($400{\sim}560\;nm$) induced the morphology of wide and deep colored pilei in G. frondosa fruiting body. The experimental results on the appropriate light intensity indicated that 500 lux light was the most effective on mushroom production, whereas primordium formation was effective at 200 lux.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.983-991
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• 2012

These days, agricultural products cultivated in facilities occupy the highest percentage of agricultural output price. Specifically cucumbers have been one of the crops that farmers prefer to growing, because their prices were high. However, cucumber crop is sensitive to the soil and environments and it requires the exact crop management. In order to establish cultivation techniques for cucumbers, the current situation of cucumber cultivation was surveyed from ten cucumber farmlands; five farmlands of cucumber cultivation in forcing and five of semi-forcing practicing systems, respectably. The soil conditions were alluvial or valley in soil topology, moderately or poorly drainage in soil drainage classes, coarse loamy in soil texture family. Soil was managed with deep plowing combined with application of basal fertilizers such as compost, rice straw, oil cake, wood chip and chemical fertilizer. The whole soil was prepared in uniformly with rotary. Three major nutrients ($N-P_2O_5-K_2O$)of basal application were 815-464-529 kg $ha^{-1}$ in forcing and 197-135-151 kg $ha^{-1}$ in semi-forcing cultivation. Top dressing of fertilizer was supplied in fertigation system of macro and micro elements in 2~3 day interval with water irrigation. The average yields of cucumbers were $168t\;ha^{-1}$ with 381,000 thousand won $ha^{-1}$ in average gross profit (AGP) in forcing cultivation and $115t\;ha^{-1}$ with 177,000 thousand won $ha^{-1}$ in AGP in semi-forcing cultivation. Cucumber production during the winter season was considered to increase the gross profit because cucumber price tends to stay in high level during this time. The accumulation of soil chemicals like EC, available $P_2O_5$ and exchangeable cations could be controlled by rice straw application. The rice straw application increased soil temperature during the winter season, in exchange of soil air, and in extension of plant roots. In addition, the rice straw application somewhat affected decrease of salts accumulation.