• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.2
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• pp.29-37
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• 2019

Purpose DMIDR(Discovery Molecular Imaging Digital Ready, General Electric Healthcare, USA) is a PET/CT scanner designed to allow application of PSF(Point Spread Function), TOF(Time of Flight) and Q.Clear algorithm. Especially, Q.Clear is a reconstruction algorithm which can overcome the limitation of OSEM(Ordered Subset Expectation Maximization) and reduce the image noise based on voxel unit. The aim of this paper is to evaluate the performance of reconstruction algorithms and optimize the algorithm combination to improve the accurate SUV(Standardized Uptake Value) measurement and lesion detectability. Materials and Methods PET phantom was filled with $^{18}F-FDG$ radioactivity concentration ratio of hot to background was in a ratio of 2:1, 4:1 and 8:1. Scan was performed using the NEMA protocols. Scan data was reconstructed using combination of (1)VPFX(VUE point FX(TOF)), (2)VPHD-S(VUE Point HD+PSF), (3)VPFX-S (TOF+PSF), (4)QCHD-S-400((VUE Point HD+Q.Clear(${\beta}-strength$ 400)+PSF), (5)QCFX-S-400(TOF +Q.Clear(${\beta}-strength$ 400)+PSF), (6)QCHD-S-50(VUE Point HD+Q.Clear(${\beta}-strength$ 50)+PSF) and (7)QCFX-S-50(TOF+Q.Clear(${\beta}-strength$ 50)+PSF). CR(Contrast Recovery) and BV(Background Variability) were compared. Also, SNR(Signal to Noise Ratio) and RC(Recovery Coefficient) of counts and SUV were compared respectively. Results VPFX-S showed the highest CR value in sphere size of 10 and 13 mm, and QCFX-S-50 showed the highest value in spheres greater than 17 mm. In comparison of BV and SNR, QCFX-S-400 and QCHD-S-400 showed good results. The results of SUV measurement were proportional to the H/B ratio. RC for SUV is in inverse proportion to the H/B ratio and QCFX-S-50 showed highest value. In addition, reconstruction algorithm of Q.Clear using 400 of ${\beta}-strength$ showed lower value. Conclusion When higher ${\beta}-strength$ was applied Q.Clear showed better image quality by reducing the noise. On the contrary, lower ${\beta}-strength$ was applied Q.Clear showed that sharpness increase and PVE(Partial Volume Effect) decrease, so it is possible to measure SUV based on high RC comparing to conventional reconstruction conditions. An appropriate choice of these reconstruction algorithm can improve the accuracy and lesion detectability. In this reason, it is necessary to optimize the algorithm parameter according to the purpose.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.138-145
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• 2021

Perilla contains more than 60% of fatty acids. Linolenic acid is effective in preventing heart disease, improving learning ability, treating allergies, and preventing cancer. This study was carried out to improve the cultivation method to aid the stable production of perilla by developing a suitable inter-cropping system with sesame in the central region as well as to report a suitable planting time. The test results are summarized as follows. As the planting time of perilla in the inter-cropping system with sesame was delayed, the number of clusters and capsules decreased. The perilla yields in this system showed significant differences compared to that with the previous crops (sesame varieties) and planting period. The yield of perilla was significantly lower in the characteristic-Type B variety than in the characteristic-Type A variety and decreased significantly as the planting time was delayed. With regards to the quality characteristics of perilla, such as crude protein, crude fat, etc., there were no differences between previous perilla crops and those inter-cropped with sesame. The perilla composition did not show any difference during the planting period; however, with delay in the planting time, crude protein content increased but crude fat content decreased. Yield of perilla was 38% higher in a two-row (40 x 40 cm) system, compared to a single-row cultivation (110 x 20 cm) of perilla inter-cropped with sesame. These results suggest that the suitable method for inter-cropping perilla with sesame in the central region is to sow the characteristic-Type A variety in early May, and cultivate the perilla in two lines (40 x 40 cm) in mid-June. This was judged to be the best cultivation method in the central region.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.198-209
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• 2021

This study evaluated the effects of the dimensional characteristics of containers on the nitrogen status of Quercus serrata, Fraxinus rhynchophylla, and Zelkova serrata in the container nursery stage. Seedlings were grown using 16 container types [four growing densities (100, 144, 196, and 256 seedlings/m²) × four cavity volumes (220, 300, 380, and 460 cm³/cavity)]. Two-way ANOVA was performed to test the differences in nitrogen concentration and seedling content among container types. Additionally, we performed multiple regression analyses to correlate container dimensions and nitrogen content. Container types had a strong influence on nitrogen concentration and the content of the seedling species, with a significant interaction effect between growing density and cavity volume. Cavity volumes were positively correlated with the nitrogen content of the three seedling species, whereas growing density negatively affected those of F. rhynchophylla. Further, nutrient vector analysis revealed that the seedling nutrient loading capacities of the three species, such as efficiency and accumulation, were altered because of the different fertilization effects by container types. The optimal ranges of container dimension by each tree species, obtained multiple regression analysis with nitrogen content, were found to be approximately 180-210 seedlings/m² and 410-460 cm³/cavity for Q. serrata, 100-120 seedlings/m² and 350-420 cm³/cavity for F. rhynchophylla, and 190-220 seedlings/m² and 380-430 cm³/cavity for Z. serrata. This study suggests that an adequate type of container will improve seedling quality with higher nutrient loading capacity production in nursery stages and increase seedling growth in plantation stages.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.97-111
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• 2021

Purpose: It is expected that various infrastructures diagnosis equipment will be needed as infrastructures management is strengthened to implement the Framework Act on Sustainable Infrastructure Management. It is necessary for a certification system to supply certified products of a reasonable level in accordance with market requirements for various convergence equipment. This paper deals with the introduction of certification system of inspection and diagnosis equipment for infrastructure using advanced technologies. Method: The basic elements, systems and procedures of certification system were reviewed through analyzing and comparing the existing similar certification system in Korea. In addition, a survey was conducted on a catalog method and the minimum performance criterion (sampling survey and complete enumeration survey) to equipment developers (manufacturers), clients and equipment users. Result: This survey showed that clients preferred complete enumeration method on the basis of minimum performance, and equipment users also preferred complete enumeration survey and sample survey, for minimum performance, at a similar rate. On the other hand, equipment developers preferred the catalog method. Conclusion: Clients and users who are the users of the diagnostic equipment preferred the minimum performance criterion because their trust in quality is important. On the other hand, developers(manufacturers) preferred the catalog method which adopts self certification because it is regulated in developing various products. There is no specific plan for the minimum performance standards required for the introduction of the method which users demand, at present. In addition, it is not desirable to force to introduce a certification system because it requires a considerable period of study to prepare the specific standards. Therefore, it is appropriate to operate the system for a certain period of time centering around the catalog method for the stable and continuous development of the infrastructure diagnosis and test equipment market in Korea. Also, it is effective to expand and develop the certification system to the extent that it minimizes the impact on the market when specific plans for the standards are prepared in the future.

• Composites Research
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• v.34 no.3
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• pp.192-198
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• 2021

Due to enormous market growing of electric vehicles without combustion engine, reducing unwanted BSR (buzz, squeak, and rattle) noise is highly demanded for vehicle quality and performance. Particularly, innerbelt weatherstrips which not only block wind noise, rain, and dust from outside, but also reduce noise and vibration of door glass and vehicle are required to exhibit high damping properties for improved BSR performance of the vehicle. Thermoplastic elastomers (TPEs), which can be recycled and have lighter weight than thermoset elastomers, are receiving much attention for weatherstrip material, but TPEs exhibit low material damping and compression set causing frictional noise and vibration between the door glass and the weatherstrip. In this study, high damping EPDM (ethylene-propylene-diene monomer)/PP (polypropylene) thermoplastic vulcanizates (TPV) were investigated by varying EPDM/PP ratio and ENB (ethylidene norbornene) fraction in EPDM. Viscoelastic properties of TPV materials were characterized by assuming that the material damping is directly related to the viscoelasticity. The optimum material damping factor (tanδ peak 0.611) was achieved with low PP ratio (14 wt%) and high ENB fraction (8.9 wt%), which was increased by 140% compared to the reference (tanδ 0.254). The improved damping is believed due to high fraction of flexible EPDM chains and higher interfacial slippage area of EPDM particles generated by increasing ENB fraction in EPDM. The stick-slip test was conducted to characterize frictional noise and vibration of the TPV weatherstrip. With improved TPV material damping, the acceleration peak of frictional vibration decreased by about 57.9%. This finding can not only improve BSR performance of electric vehicles by designing material damping of weatherstrips but also contribute to various structural applications such as urban air mobility or aircrafts, which require lightweight and high damping properties.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.3
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• pp.264-274
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• 1993

This study was carried out to assess the agronomic characters and physicochemical properties of floury and chalky-endosperm mutant lines induced by chemical mutagen treatment to rice varieties, Hwacheongbyeo and IR24. Linkage analysis of a floury-endosperm gene was carried out using linkage testers. The grain size of brown rice of the mutants was smaller than that of the original varieties. The l, 000-grain and 1$\ell$ weight were lighter in the mutants compared with those in the original varieties. The compound starch granules in the endosperm cell of the mutants showed a loosely-packed crystalline structure. Amylose contents in mutants ranged from 16.9 to 28.5%. Crude protein contents of the mutants were not significantly different from the original rice variety, Hwacheongbyeo, but white core mutant(line 47106) derived from IR24 showed higher protein(l1.32%) compared with IR24(8.30%). The mutants showed slightly harder gel characteristics, and much lower viscosity in Amylograph than original varieties. Steamed rice-cakes from mutant lines showed greater volume than those from original varieties. During the process of alcohol fermentation, Brix in the mutants(especially floury mutants) decreased faster and the alcohol production after 10-day fermentation was much greater in the mutants than in the original varieties. Three different gene loci for floury endosperm characteristics were identified from the allelism test among mutant lines, and the genes were tentatively symbolized as flo-a, flo-b and flo-c, respectively. A floury gene, flo-a, was linked with lg(liguleless) gene in the linkage group N, with R.V. 5.76$\pm$1.72%.

• Journal of Nutrition and Health
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• v.56 no.1
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• pp.97-111
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• 2023

Purpose: Childhood obesity has become a social problem due to the social distancing necessitated by the coronavirus disease 2019 pandemic. This study aimed to identify the dietary problems of obese children through various dietary assessment methods and to confirm the usefulness of each method. Methods: The subjects were 88 students in the 4th to 6th grade of elementary school who participated in the nutrition camp organised by the Busan Metropolitan Office of Education, 2020. To evaluate dietary problems and assess diet quality, 24-hour meal records, monthly food intake frequency, and Dietary Screening Test (DST) data were analyzed. Results: Of the subjects, 15.7%, 30.3%, and 53.9% were normal weight, overweight, and obese, respectively. The average age was 11.77 ± 0.77 years and the average body mass index was 23.96 ± 3.01 kg/m². It was observed from the 24-hour meal record method that the overweight and obese subject groups consumed fewer green vegetables (p < 0.001) and white vegetables (p < 0.01) than the normal weight group. In the monthly food intake frequency method, the consumption of ramen (p < 0.01), snacks (p < 0.05), and sausages (p < 0.05) were high in the obese group, and that of anchovies, broccoli, and sweet pumpkin was high in the normal group (p < 0.05). The comparative data from the DST revealed that the overweight and obese groups had less vegetable intake than the normal weight group (p < 0.01) and had higher intakes of dairy products, fast food, and sweet snacks (p < 0.05). Conclusion: The usefulness of each method in the dietary evaluation of obese children was confirmed. To address the problem of obesity, it is necessary to evaluate the dietary problem and approach it with a customized solution tailor-made for the individual subject.