• Korean Journal of Environment and Ecology
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• v.34 no.6
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• pp.589-600
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• 2020

Effective conservation and management of protected areas require monitoring the settlement of invasive alien species and reducing their dispersion capacity. We simulated the potential distribution of invasive alien plant species (IAPS) using three representative species distribution models (Bioclim, GLM, and MaxEnt) based on the IAPS distribution in the forest genetic resource reserve (2,274ha) in Uljin-gun, Korea. We then selected the realistic and suitable species distribution model that reflects the local region and ecological management characteristics based on the simulation results. The simulation predicted the tendency of the IAPS distributed along the linear landscape elements, such as roads, and including some forest harvested area. The statistical comparison of the prediction and accuracy of each model tested in this study showed that the GLM and MaxEnt models generally had high performance and accuracy compared to the Bioclim model. The Bioclim model calculated the largest potential distribution area, followed by GLM and MaxEnt in that order. The Phenomenological review of the simulation results showed that the sample size more significantly affected the GLM and Bioclim models, while the MaxEnt model was the most consistent regardless of the sample size. The optimal model overall for predicting the distribution of IAPS among the three models was the MaxEnt model. The model selection approach based on detailed flora distribution data presented in this study is expected to be useful for efficiently managing the conservation areas and identifying the realistic and precise species distribution model reflecting local characteristics.

• Clinical and Experimental Reproductive Medicine
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• v.32 no.4
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• pp.315-324
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• 2005

Objectives: To compare the efficacy of GnRH antagonist multiple dose protocol (MDP) with that of GnRH agonist long protocol (LP) in controlled ovarian hyperstimulation for in vitro fertilization in patients with high basal FSH (follicle stimulating hormone) level or old age, a retrospective analysis was done. Methods: Two hundred ninety four infertile women (328 cycles) who were older than 41 years of age or had elevated basal FSH level (> 8.5 mIU/mL) were enrolled in this study. The patients had undergone IVF-ET after controlled ovarian hyperstimulation using GnRH antagonist multiple dose protocol (n=108, 118 cycles) or GnRH agonist long protocol (n=186, 210 cycles). The main outcome measurements were cycle cancellation rate, consumption of gonadotropins, the number of follicles recruited and total oocytes retrieved. The number of fertilized oocytes and transferred embryos, the clinical pregnancy rates, and the implantation rates were also reviewed. And enrolled patients were divided into three groups according to their age and basal FSH levels; Group A - those who were older than 41 years of age, Group B - those with elevated basal FSH level (> 8.5 mIU/mL) and Group C - those who were older than 41 years of age and with elevated basal FSH level (> 8.5 mIU/mL). Poor responders were classified as patients who had less than 4 retrieved oocytes, or those with $E_2$ level <500 pg/mL on the day of hCG injection or those who required more than 45 ampules of exogenous gonadotropin for stimulation. Results: The cancellation rate was lower in the GnRH antagonist group than in GnRH agonist group, but not statistically significant (6.8% vs. 9.5%, p=NS). The amount of used gonadotropins was significantly lower in GnRH antagonist group than in agonist group ($34.8{\pm}11.3$ ampules vs. $44.1{\pm}13.4$ ampules, p<0.001). The number of follicles > 14 mm in diameter was significantly higher in agonist group than in antagonist group ($6.7{\pm}4.6$ vs. $5.0{\pm}3.4$, p<0.01). But, there were no significant differences in clinical pregnancy rate (24.5% in antagonist group vs. 27.4% in agonist group, p=NS) and implantation rate (11.4% in antagonist group vs. 12.0% in agonist group, p=NS) between two groups. Mean number of retrieved oocytes was significantly higher in GnRH agonist LP group than in GnRH antagonist MDP group ($5.4{\pm}3.5$ vs. $6.6{\pm}5.0$, p<0.0001). But, the number of mature and fertilized oocytes, and the number of good quality (grade I and II) and transferred embryos were not different between two groups. In each group A, B, and C, the rate of poor response did not differ according to stimulation protocols. Conclusions: In conclusion, for infertile women expected poor ovarian response such as who are old age or has elevated basal FSH level, a protocol including a controlled ovarian hyperstimulation using GnRH antagonist appears at least as effective as that using a GnRH agonist, and may offer the advantage of reducing gonadotropin consumption and treatment period. However, much work remains to be done in optimizing the GnRH antagonist protocols and individualizing these to different cycle characteristics.

• The Korean Journal of Food And Nutrition
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• v.29 no.5
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• pp.618-627
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• 2016

This study evaluated the hydration, gelatinization, and saccharification properties of rice processing for beverage development. The properties of rice were studied on 10 rice cultivars (Samkwang, Ilpum, Seolgaeng, Anda, Dasan-1, Goami-4, Danmi, American rice, Chinese rice, and Thai rice) and employing four kinds of pre-treatment methods (dry grain, wet grain, dry flour, and wet flour). The results showed that moisture content of rice was between 11.88~15.26%. Increase in soaking time along with highest water absorption was noted in American rice cultivar (46.81%). The water binding capacity of Thai rice was higher when compared to that of other rice flours. In addition, solubility and swelling power of rice were 4.52~26.65% and 0.19~2.05%, respectively. The amylose content of Goami-4 was higher in rice processing. Using a rapid visco analyzer (RVA), the initial pasting temperature of Danmi cultivar was found to be the highest; the peak viscosities of Anda cultivar and Dasan-1 cultivar, and Chinese rice were higher than of those of other rice flours. After saccharification, the pH, soluble solids content, and reducing sugar content of rice processed through different pre-treatment methods were in the range of 6.22~7.08, $4.67{\sim}16.07^{\circ}Brix$, and 0.35~11.67% (w/w), respectively. In terms of color values, the L-value of dry grain, a-value of wet (grain, flour), and b-value of dry sample (grain, flour) were found to be the highest. Assessment of various factors and cultivars characteristics of the raw grains are of importance in the development of rice beverage.

• Radiation Oncology Journal
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• v.27 no.1
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• pp.35-41
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• 2009

Purpose: This study was designed to determine the optimum radiotherapy technique for internal mammary node (IMN) irradiation after breast-conserving surgery. Materials and Methods: We selected ten cases of early stage partial mastectomy for plan comparison. Five of the patients were treated to the right-side breast and the rest of the patients were treated to the left-side breast. For each case, four different treatment plans were made to irradiate the entire breast, IMNs and supraclavicular lymph nodes (SCLs). The four planning techniques include a standard tangential field (STF), wide tangential field (WTF), partially wide tangential field (PWT) and a photon-electron mixed field (PEM). We prescribed a dose of 50.4 Gy to the SCL field at a 3 cm depth and isocenter of the breast field. Results: The dose distribution showed clear characteristics depending on the technique used. All of the techniques covered the breast tissue well. IMN coverage was also good, except for the STF, which was not intended to cover IMNs. For the cases of the left-side breasts, the volume of the heart that received more than 30 Gy was larger (in order) for the WTF, PWT, PEM and STF. For radiation pneumonitis normal tissue complication probability (NTCP), the PWT showed the best results followed by the STF. Conclusion: Despite the variety of patient body shapes, the PWT technique showed the best results for coverage of IMNs and for reducing the lung and heart dose.

• Journal of dental hygiene science
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• v.16 no.4
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• pp.284-292
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• 2016

Water supplied through dental unit waterlines (DUWLs) has been shown to contain high number of bacteria. To reduce the contamination of DUWLs, it is essential to develop effective disinfectants. It is, however, difficulty to obtain proper DUWL samples for studies. The purpose of this study was to establish a simple laboratory model for reproducing DUWL biofilms. The bacteria obtained from DUWLs were cultured in R2A liquid medium for 10 days, and then stored at $-70^{\circ}C$. This stock was inoculated into R2A liquid medium and incubated in batch mode. After 5 days of culturing, it was inoculated into the biofilm formation model developed in this study. Our biofilm formation model comprised of a beaker containing R2A liquid medium and five glass rods attached to DUWL polyurethane tubing. Biofilm was allowed to form on the stir plate and the medium was replaced every 2 days. After 4 days of biofilm formation in the laboratory model, biofilm thickness, morphological characteristics and distribution of the composing bacteria were examined by confocal laser microscopy and scanning electron microscopy. The mean of biofilm accumulation was $4.68{\times}10^4$ colony forming unit/$cm^2$ and its thickness was $10{\sim}14{\mu}m$. In our laboratory model, thick bacterial lumps were observed in some parts of the tubing. To test the suitability of this biofilm model system, the effectiveness of disinfectants such as sodium hypochlorite, hydrogen peroxide, and chlorhexidine, was examined by their application to the biofilm formed in our model. Lower concentrations of disinfectants were less effective in reducing the count of bacteria constituting the biofilm. These results showed that our DUWL biofilm laboratory model was appropriate for comparison of disinfectant effects. Our laboratory model is expected to be useful for various other purposes in further studies.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.05a
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• pp.5-5
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• 2011

The research and development of hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) are intensified due to the energy crisis and environmental concerns. In order to meet the challenging requirements of powering HEV, PHEV and EV, the current lithium battery technology needs to be significantly improved in terms of the cost, safety, power and energy density, as well as the calendar and cycle life. One new technology being developed is the utilization of composite cathode by mixing two different types of insertion compounds [e.g., spinel $LiMn_2O_4$ and layered $LiMO_2$ (M=Ni, Co, and Mn)]. Recently, some studies on mixing two different types of cathode materials to make a composite cathode have been reported, which were aimed at reducing cost and improving self-discharge. Numata et al. reported that when stored in a sealed can together with electrolyte at $80^{\circ}C$ for 10 days, the concentrations of both HF and $Mn^{2+}$ were lower in the can containing $LiMn_2O_4$ blended with $LiNi_{0.8}Co_{0.2}O_2$ than that containing $LiMn_2O_4$ only. That reports clearly showed that this blending technique can prevent the decline in capacity caused by cycling or storage at elevated temperatures. However, not much work has been reported on the charge-discharge characteristics and related structural phase transitions for these composite cathodes. In this presentation, we will report our in situ x-ray diffraction studies on this mixed composite cathode material during charge-discharge cycling. The mixed cathodes were incorporated into in situ XRD cells with a Li foil anode, a Celgard separator, and a 1M $LiPF_6$ electrolyte in a 1 : 1 EC : DMC solvent (LP 30 from EM Industries, Inc.). For in situ XRD cell, Mylar windows were used as has been described in detail elsewhere. All of these in situ XRD spectra were collected on beam line X18A at National Synchrotron Light Source (NSLS) at Brookhaven National Laboratory using two different detectors. One is a conventional scintillation detector with data collection at 0.02 degree in two theta angle for each step. The other is a wide angle position sensitive detector (PSD). The wavelengths used were 1.1950 ${\AA}$ for the scintillation detector and 0.9999 A for the PSD. The newly installed PSD at beam line X18A of NSLS can collect XRD patterns as short as a few minutes covering $90^{\circ}$ of two theta angles simultaneously with good signal to noise ratio. It significantly reduced the data collection time for each scan, giving us a great advantage in studying the phase transition in real time. The two theta angles of all the XRD spectra presented in this paper have been recalculated and converted to corresponding angles for ${\lambda}=1.54\;{\AA}$, which is the wavelength of conventional x-ray tube source with Cu-$k{\alpha}$ radiation, for easy comparison with data in other literatures. The structural changes of the composite cathode made by mixing spinel $LiMn_2O_4$ and layered $Li-Ni_{1/3}Co_{1/3}Mn_{1/3}O_2$ in 1 : 1 wt% in both Li-half and Li-ion cells during charge/discharge are studied by in situ XRD. During the first charge up to ~5.2 V vs. $Li/Li^+$, the in situ XRD spectra for the composite cathode in the Li-half cell track the structural changes of each component. At the early stage of charge, the lithium extraction takes place in the $LiNi_{1/3}Co_{1/3}Mn_{1/3}O_2$ component only. When the cell voltage reaches at ~4.0 V vs. $Li/Li^+$, lithium extraction from the spinel $LiMn_2O_4$ component starts and becomes the major contributor for the cell capacity due to the higher rate capability of $LiMn_2O_4$. When the voltage passed 4.3 V, the major structural changes are from the $LiNi_{1/3}Co_{1/3}Mn_{1/3}O_2$ component, while the $LiMn_2O_4$ component is almost unchanged. In the Li-ion cell using a MCMB anode and a composite cathode cycled between 2.5 V and 4.2 V, the structural changes are dominated by the spinel $LiMn_2O_4$ component, with much less changes in the layered $LiNi_{1/3}Co_{1/3}Mn_{1/3}O_2$ component, comparing with the Li-half cell results. These results give us valuable information about the structural changes relating to the contributions of each individual component to the cell capacity at certain charge/discharge state, which are helpful in designing and optimizing the composite cathode using spinel- and layered-type materials for Li-ion battery research. More detailed discussion will be presented at the meeting.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Korean journal of food and cookery science
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• v.22 no.5 s.95
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• pp.642-649
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• 2006

The browning of soy sauce is caused by the reaction of amino-carbonyl between amino-compounds and reducing sugar. Only a few studies have investigated the formation of melanoidins in UHYUKJANG. The objectives of this study were to analyze the brown pigment of UHYUKJANG and to investigate the characteristics of UHYUKJANG in comparison with soy sauce and model melanoidins. The samples were ripened for 0, 60, 120, 180, 240, 300 and 360 days at 4$^{\circ}C$ and 20$^{\circ}C$. The pH, absorbance at 420 nm absorbance ratio of 400 to 500 nm and UV-VIS spectra as an index of color intensity were measured. Additionally, L, a and b values of the samples and the amount of 3-Deoxyglucosone(3DG) in the samples were measured. The pH of both soy sauce (from 6.26 to 5.52) and UHYUKJANG (from 6.13 to 5.11) rapidly decreased during the first 60 days of aging and was also affected by storage temperature. The absorbance of samples at 420 nm increased during the aging process, reaching its maximum after 180 days, regardless of sample and temperature. On the other hand, the intensity of brown color in the samples increased with increasing aging period according to the results of absorbance ratio (soy sauce: 1.37 to 5.29, UHYUKJANG: 1.37 to 5.02). The L value of soy sauce increased during the aging process and was maximized after 240 days at 4$^{\circ}C$ and 180 days at 20$^{\circ}C$, but decreased thereafter. There was no significant difference in L value of UHYUKJANG, regardless of aging period and temperature. On the other hand, the b value did not reveal any significant change during aging, but the a value increased until 120 days of aging in the other samples except for UHYUKJANG at 20$^{\circ}C$. The average amount of 3DG separated from soy sauce was 5.65 mg%, and from UHYUKJANG was 3.74 mg%. These results indicated that the browning of UHYUKJANG was also caused by melanoidins produced by the reaction of amino-carbonyl during the fermentation process.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.20-28
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• 2013

The purpose of this study was to compare the ultra-violet(UV) radiation under the landscaping shade facilities and tree with natural solar UV of the outdoor space at summer middays. The UVA+B and UVB were recorded every minute from the $20^{th}$ of June to the $26^{th}$ of September 2012 at a height of 1.1m above in the four different shading conditions, with fours same measuring system consisting of two couple of analog UVA+B sensor(220~370nm, Genicom's GUVA-T21GH) and UVB sensor(220~320nm, Genicom's GUVA-T21GH) and data acquisition systems(Comfile Tech.'s Moacon). Four different shading conditions were under an wooden shelter($W4.2m{\times}L4.2m{\times}H2.5m$), a polyester membrane structure ($W4.9m{\times}L4.9m{\times}H2.6m$), a Salix koreensis($H11{\times}B30$), and a brick-paved plot without any shading material. Based on the 648 records of 17 sunny days, the time serial difference of natural solar UVA+B and UVB for midday periods were analysed and compared, and statistical analysis about the difference between the four shading conditions was done based on the 2,052 records of daytime period from 10 A.M. to 4 P.M.. The major findings were as follows; 1. The average UVA+B under the wooden shelter, the membrane and the tree were $39{\mu}W/cm^2$(3.4%), $74{\mu}W/cm^2$(6.4%), $87{\mu}W/cm^2$(7.6%) respectively, while the solar UVA+B was $1.148{\mu}W/cm^2$. Which means those facilities and tree screened at least 93% of solar UV+B. 2. The average UVB under the wooden shelter, the membrane and the tree were $12{\mu}W/cm^2$(5.8%), $26{\mu}W/cm^2$(13%), $17{\mu}W/cm^2$(8.2%) respectively, while the solar UVB was $207{\mu}W/cm^2$. The membrane showed the highest level and the wooden shelter lowest. 3. According to the results of time serial analysis, the difference between the three shaded conditions around noon was very small, but the differences of early morning and late afternoon were apparently big. Which seems caused by the matter of the formal and structural characteristics of the shading facilities and tree, not by the shading materials itself. In summary, the performance of the four landscaping shade facilities and tree were very good at screening the solar UV at outdoor of summer middays, but poor at screening the lateral UV during early morning and late afternoon. Therefore, it can be apparently said that the more delicate design of shading facilities and big tree or forest to block the additional lateral UV, the more effective in conditioning the outdoor space reducing the useless or even harmful radiation for human activities.