• Horticultural Science & Technology
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• v.31 no.5
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• pp.531-537
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• 2013

The average annual and winter ambient air temperatures in Korea have risen by 0.7 and $1.4^{\circ}C$, respectively, during the last 30 years. Radish (Raphanus sativus), one of the most important cool season crops, may well be used as a model to study the influence of climatic change on plant growth, because it is more adversely affected by elevated temperatures than warm season crops. This study examined the influence of transplanting time, nitrogen fertilizer level, and climate parameters, including air temperature and growing degree days (GDD), on the performance of a radish cultivar 'Mansahyungtong' to estimate crop growth during the spring growing season. The radish seeds were sown from April 24 to May 22, 2012, at internals of 14 days and cultivated with 3 levels of nitrogen fertilization. The data from plants sown on April 24 and May 8, 2012 were used for the prediction of plant growth as affected by planting date and nitrogen fertilization for spring production. In our study, plant fresh weight was higher when the radish seeds were sown on $24^{th}$ of April than on $8^{th}$ and $22^{nd}$ of May. The growth model was described as a logarithmic function using GDD according to the nitrogen fertilization levels: for 0.5N, root dry matter = 84.66/(1+exp (-(GDD - 790.7)/122.3)) ($r^2$ = 0.92), for 1.0N, root dry matter = 100.6/(1 + exp (-(GDD - 824.8)/112.8)) ($r^2$ = 0.92), and for 2.0N, root dry matter = 117.7/(1+exp (-(GDD - 877.7)/148.5)) ($r^2$ = 0.94). Although the model slightly tended to overestimate the dry mass per plant, the estimated and observed root dry matter and top dry matter data showed a reasonable good fit with 1.12 ($R^2$ = 0.979) and 1.05 ($R^2$ = 0.991), respectively. Results of this study suggest that the GDD values can be used as a good indicator in predicting the root growth of radish.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.561-568
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• 2006

WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.

• Journal of Bio-Environment Control
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• v.28 no.3
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• pp.255-264
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• 2019

In this study, we conducted the hot box tests to compare the changes in thermal insulation for the four types of multi-layer thermal screens by the used period after collecting them from the greenhouses in the field when they were replaced at the end of their usage. The main materials for these four types of multi-layer thermal screens were matt georgette, non-woven fabrics, polyethylene (PE) foam, chemical cotton, etc. These materials were differently combined for each multi-layer thermal screen. We built specimens ($70{\times}70cm$) for each of these multi-layer thermal screens and measured the temperature descending rate, heat transmission coefficient, and thermal resistance for each specimen through the hot box tests. With regard to the material combinations of multi-layer thermal screens, thermal insulation can be increased by applying a multi-layered PE foam. However, it is considered that the multi-layered PE foam significantly less contributes to heat-retaining than chemical wool that forms an air-insulating layer inside multi-layer thermal screens. For the suitable heat-retaining performance of multi-layer thermal screens, basically, materials with the function of forming an air-insulating layer such as chemical cotton should be contained in multi-layer thermal screens. The temperature descending rate, heat transmission coefficient, and thermal resistance of multi-layer thermal screens were appropriately measured through the hot box tests designed in this study. However, in this study, we took into consideration only the four kinds of multi-layer thermal screens due to difficulties in collecting used multi-layer thermal screens. This is the results obtained with relatively few examples and it is the limit of this study. In the future, more cases should be investigated and supplemented through related research.

• Korean Journal of Poultry Science
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• v.45 no.4
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• pp.253-260
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• 2018

The hepatocyte nuclear factor 4 alpha ($HNF4{\alpha}$) gene is related to lipid transport, including abdominal fat and growth, in chickens. Interestingly, the A543G SNP within the $HNF4{\alpha}$ gene has previously been reported to be associated with body weight in both broilers and Korean native chickens (KNCs). However, its exact position within the HNF4 is not yet reported. This study aimed to identify the position of the A543G SNP and to identify additional SNPs that can be used as genetic markers in KNCs. A total of 128 KNCs were used for the sequencing and analysis of these genetic associations. As a result, A543G SNP was located in intron 4 of the $HNF4{\alpha}$ gene; it is reported as rs731246957 in the NCBI database. Fourteen SNPs were detected in the sequenced portion of the $HNF4{\alpha}$ gene; three of these, rs731246957, rs736159604 and new SNP, intron 6 (249), were significantly related with growth in the chickens. In this study, the TT genotype of rs731246957, previously reported as A543G SNP, the GG genotype of rs736159604 and GT of new SNP have are highly associated with body weight from birth to 40 weeks of age in KNCs (P<0.01). These results suggest that rs736159604, rs731246957 and intron 6 (249) SNPs within the $HNF4{\alpha}$ gene could function as growth-related markers in the selective breeding of KNCs.

• Journal of Dental Rehabilitation and Applied Science
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• v.35 no.1
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• pp.20-26
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• 2019

Purpose: Recently TF-adaptive movement is developed in order to increase the durability of TF files. The purpose of this study was to assess the effects of adaptive movement on durability and performance of twisted files. Materials and Methods: Resin blocks simulating artificial J-shape canals were used for this study. In TFC group, TF-adaptive ML-1 (25/.08 size) files were used to prepare the canals under continuous rotation 500 rpm/4.0 Ncm. In TFA group, TF-adaptive ML-1 (25/.08 size) files were used to prepare the canals under adaptive movement. After preparing each artificial canal, TF files were observed under dental microscope for assessing existence of unwinding, distortion, and fracture. If unwinding of flute was observed, the number of artificial canals until unwinding of flute occurs was recorded. Required time until instruments reach working length and distance of unwinded portion of files from D0 were measured. All test results were conducted by Mann-Whitney U test at a 0.05 level of significance. Results: No Ni-Ti instrument's separation was observed. Number of resin blocks until file unwinding happens and working time was significantly high in TFA group compared to TF group. Distance of distortion from D0 didn't show significant difference between TFA, TF groups. Conclusion: The number of resin blocks prepared until unwinding happens and working time were significantly high in TFA group. The location of unwinding showed no significant difference between 2 groups. Adaptive movement increased the number of canals prepared until unwinding occurs and working time of twisted files.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Korean journal of psychology:General
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• v.36 no.1
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• pp.81-107
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• 2017

The study aims to investigate the clinical utility of Bayley-III using US norm in Korea. A total of 98 preterm infants and 93 term infants were assessed with the K-Bayley-III. The performance pattern of preterm infants was analyzed with mixed design ANOVA which examined the differences of scaled scores and composite scores of Bayley-III between full term- and preterm- infant group and within preterm infants group. Then, We have investigated agreement between classifications of delay made using the BSID-II and Bayley-III. In addition, ROC plots were constructed to identify a Bayley-III cut-off score with optimum diagnostic utility in this sample. The results were as follows. (1) Preterm infants have significantly lower function levels in areas of 5 scaled scores and 3 developmental indexes compared with infants born at term. Significant differences among scores within preterm infant group were also found. (2) Bayley-III had the higher scores of the Mental Development Index and Psychomotor Developmental Index comparing to the scores of K-BSID-II, and had the lower rates of developmental delay. (3) All scales of Bayley-III, Cognitive, Language and Motor scale had the appropriate level of discrimination, but the cut-off composite scores of Bayley-III were adjusted 13~28 points higher than 69 for prediction of delay, as defined by the K-BSID-II. It explains the lower rates of developmental delay using the standard of two standard deviation. This study has provided empirical data to inform that we must careful when interpreting the score for clinical applications, identified the discriminating power, and proposed more appropriate cut-off scores. In addition, discussion about the sampling for making the Korean norm of Bayley-III was provided. It is preferable that infants in Korea should use our own validated norms. The standardization process to get Korean normative data must be performed carefully.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.169-178
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• 2021

The Korean girl group "Kara" has suffered the trilemma of its de facto failure to debut, the crisis of team breakup, and the CEO crisis of the agency. But the group has made an outstanding achievement in the history of Korean pop music after overcoming all odds. Their success strategy has never been disclosed by insiders involved in Kara's total music projects. This study has been carried out in the analysis of the strategy to provide academic implications and to honor the contribution of the late CEO Ho-yeon Lee and Kara's key member Ha-ra Gu. Therefore, between Nov. and Dec. 2020, we conducted in-depth interviews with managers, composers, stylists and Ha-ra Gu(Only in 2019, before her death) who took part in the project. The research model is set up by combining Porter's Competitive Advantage Strategy and the music value chain model into categories of "Product Innovation Differentiation (PD)" (producing, album production, performance activities) and "Marketing Differentiation (MD)" (market targeting, image specialization, promotion and communication). The analysis showed that the PD focused on complete rediscovered harmonization and revalued members' personality and sincerity with peppy songs and dainty dances as well as emission of "bright energy" which caused healing effects instead of mimicking other star singers recklessly. In terms of MD, they selected Japan's 10-20s as their main market, increasing intimacy with fans and media with the image of cute+pretty+classy+sexy. The result suggests that Poter's differentiation can function as a meaningful strategy frame in the fostering, hit, and revival of idol groups. In addition, it reaffirmed that spontaneous and passionate activities of early-stage or celebrity fan may serve as a valid catalyst for realizing differentiation, as Kara's caller of Japanese actor Gekidan Hitori caused a strong "priming effect" that drove Kara's unexpected wonderful success in Japan.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.30-36
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• 2022

A sensor is needed to continuously and automatically measure the change in HNS concentration in industrial facilities that directly discharge to the sea after water treatment. The basic function of the sensor is to be able to detect ppb levels even at room temperature. Therefore, a method for increasing the sensitivity of the existing sensor is proposed. First, a method for increasing the conductivity of a film using a conductive carbon-based additive in a nanoparticle thin film and a method for increasing ion adsorption on the surface using a catalyst metal were studied.. To improve conductivity, carbon black was selected as an additive in the film using ITO nanoparticles, and the performance change of the sensor according to the content of the additive was observed. As a result, the change in resistance and response time due to the increase in conductivity at a CB content of 5 wt% could be observed, and notably, the lower limit of detection was lowered to about 250 ppb in an experiment with organic solvents. In addition, to increase the degree of ion adsorption in the liquid, an experiment was conducted using a sample in which a surface catalyst layer was formed by sputtering Au. Notably, the response of the sensor increased by more than 20% and the average lower limit of detection was lowered to 61 ppm. This result confirmed that the chemical resistance sensor using metal oxide nanoparticles could detect HNS of several tens of ppb even at room temperature.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.