• Korean Journal of Food Science and Technology
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• v.27 no.1
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• pp.112-118
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• 1995

The effect of various packaging methods on kimchi quality was investigated in order to develop the packaging techniques for preventing commercial kimchi products from inflation and explosion, due to fermentative gas evolved during storage and distribution. Kimchi was packaged in different methods; 1) atmospheric packaging(AP), 2) check valve packaging(CV), 3) double packaging(DP), and 4) vacuum packaging(VP). The quality of kimchi during storage at $10^{\circ}C$ was evaluated in terms of gas composition, free volume, pH, titratable acidity color index and sensory properties. The gas composition inside packages showed different curves according to the packaging methods. Due to fermentative gas accumulation in both AP and CV, $CO_2$ concentration increased by 2 stepwise pattern, while $O_2$, concentration decreased exponentially. In DP, $O_2$ concentration remained constant, but $CO_2$ concentration increased by 2 stepwise pattern and then decreased. In contrast, VP produced low $O_2$ and high $CO_2$ concentrations only at the end of storage. The free volume in both AP and CV showed typical sigmoidal curves similar to $CO_2$ concentration changes. It remained constant in DP, but started to increase at the late stage of storage in VP. There was no significant effect of packaging methods on pH changes of kimchi. In titratable acidity, DP maintained relatively higher than others. Regarding to the color change of crushed kimchi juice in all packages, L and b values decreased exponentially but a value remained constant during storage. Color index(L b/a) of crushed kimchi juice decreased exponentially and remained constant at the end of storage. The growth of lactic acid bacteria was VP, CV, AP, DP in increasing order. In sensory test, the sourness scores of DP were fairly higher than those of others, but the texture was not significantly affected by the packaging methods. The preference for kimchi showed VP>AP, CV>DP in order of score. In this study, it could be proposed to employ DP and VP method as the effective packaging techniques for preventing commercial kimchi products from inflation.

• Food Science and Preservation
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• v.19 no.3
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• pp.337-343
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• 2012

The objective of this study was to characterize three different commercial (A, B and C) and two handmade (HM-AP, atmospheric pressure; HM-RP, reduced pressure) strawberry jams in relation to soluble solids, pH, total acid, total polyphenol, anthocyanin, color values, texture properties, and sensory evaluation. The soluble solid contents varied from 62.33 to 68.33 $^{\circ}Brix$, and the pH ranged from 3.59 to 3.70. The color L and a values were the highest in the HM-RP strawberry jam (p<0.05). The total polyphenol contents of commercial jams A, B, and C were 56.10, 97.59, and 105.85 mg GAE/100 g, respectively, and those of the HM-AP and HM-RP of handmade jams were 156.13 and 189.94 mg GAE/100 g. The anthocyanin contents of A, B, and C commercial jams were 1.51, 0.95, and 0.80 mg/100 g, respectively, and those of the HM-AP and HM-RP handmade jams were 2.64 and 9.16 mg/100 g. The phenolic contents of the HM-RP jam were significantly much higher than those of the other jams. The hardness ranged from $5.67{\times}10^3$ (HM-AP jam) to $41.91{\times}10^3$ (jam B) dyne/$cm^2$, the jelly strength ranged from 40.08 (HM-AP jam) to 180.33 (jam B) dyne, and the strength ranged from 83.84 (jam C) to 302.93 (jam B) g. The sensory evaluation of the color, flavor, sweetness, sourness, viscosity and overall acceptability of the HM-RP jam showed higher values than those of the other jams. Especially, the highest value of the color score was found in the HM-RP jam. The electon donating abilities of jams A, B, and C and of the HM-AP and HM-RP jams were 44.27, 41.70, 53.06, 69.08, and 73.21%, respectively. These results indicated that the HM-RP strawberry jam prepared with reduced pressure using micro-oxygen technology was a good source of phenolic compounds, total polyphenols and anthocyanin, and had a high level of antioxidant activity.

• Horticultural Science & Technology
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• v.28 no.3
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• pp.423-428
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• 2010

The quality change of musk melons, divided into ripened (90 days) and over-ripened (92 days) set by the formal day maturing melons, was investigated during marketing period at both 10 and $25^{\circ}C$. The rate of weight loss was increased in all samples as the storage period passed and greater in ripened melons than over-ripened melon. The hardness decreased in both well and over-ripened melon as the storage period passed. Furthermore, changes in hardness were prevented in fruit stored at $10^{\circ}C$ compared to fruit stored at $25^{\circ}C$. Immediately after harvest, the solid solubility of over-ripened melon was 14.6%, while that of ripened fruit was 12.8%. The respiration rate of both well and over-ripened melon increased temporarily when stored at $25^{\circ}C$, which is characteristic of climacteric fruits during the first day of storage; however, no change in respiration rate was observed in fruit stored at $10^{\circ}C$. When sensory evaluation was conducted, there were no differences observed in flavor and taste among samples. However, with the exception of over-ripened melon, the texture of all samples increased significantly with storage time when melon was stored at $25^{\circ}C$. The score of overall acceptability remained high for 12 days in both well and over-ripened melon, while that of ripened melon stored at $10^{\circ}C$ and over-ripened melon stored $25^{\circ}C$ remained high for 7 and 5 days, respectively (p<0.05).

• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.296-307
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• 2016

Ecological restoration is an eco-technology, which heals the nature damaged by human activity by imitating organization and function of the integrate nature and thereby provide an inhabitable space for diverse organisms. Such an ecological restoration has to be carried out by applying restoration plan prepared based on the results of diagnostic evaluation discussed in the diversified respects. This study aims to prepare an ecological restoration plan of the damaged forest ecosystem in Mt. Moodeung National Park. To arrive at the goal, first of all, we diagnosed quality of forest landscape established in Mt. Moodeung National Park based on natural (topography, climate, and distribution of vegetation) and artificial (land use, linear landscape element) factors. In addition, we evaluated the integrity of each zone divided by linear landscape element quantitatively based on geometric property and land use intensity. As the result of analysis, topography of Mt. Moodeung National Park tended to be depended on weathering property of parent rock and vegetation zones were divided to three vegetation zones. Based on land use pattern, deciduous broad-leaved forest, evergreen needle-leaved forest, and mixed forest occupied about 90% of Mt. Moodeung National Park. Mean score of forest landscape quality was shown in $69.86{\pm}11.41$. As a result, forest landscape elements in Mt. Moodeung National Park were influenced greatly by human activity and the degree was depended on topographic condition. This study suggested the synthetic restoration plan to improve ecological quality of Mt. Moodeung National Park based on the results of diagnostic evaluation.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.4
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• pp.57-65
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• 2016

Maeul-soop(Village forest) is a key element of Korean traditional village landscape historically and culturally. However, a number of Maeul-soops have been lost or declined due to various influences since the modern age. For this Maeul-soop that has a variety of conservation values including historical, cultural and ecological ones, attention and efforts for a systematic conservation and restoration of Maeul-soop are needed. The purpose of the present study is to provide information on ecological restoration and sustainable use and management of Maeul-soops based on component plant species, habitat and location characteristics of 499 Maeul-soops spread throughout Korea. Major six categories of threat factors to Maeul-soop ecosystem were identified and the influence of each factor was evaluated. For the evaluation of weight by threat factors for the influence on the vulnerability of Maeul-soop ecosystem, more three-dimensional analysis was conducted using Analytic Hierarchy Process (AHP) analysis method. In the results of evaluation using AHP analysis method, reduction of area, among six categories, was spotted as the biggest threat to existence of Maeul-soops. Next, changes in topography and soil environment were considered as a threat factor of qualitative changes in Maeul-soop ecosystem. Influence of vegetation structure and its qualitative changes on the loss or decline of Masul-soop was evaluated to be lower than that of changes in habitat. Based on weight of each factor, the figures were converted with 100 points being the highest score and the evaluation of vulnerability of Maeul-soop was conducted with the converted figures. In the result of evaluation of vulnerability of Maeul-soops, grade III showed the highest frequency and a normal distribution was formed from low grade to high grade. 38 Maeul-soops were evaluated as grade I which showed high naturality and 10 Maeul-soops were evaluated as grade V as their maintenance was threatened. Also in the results of evaluation of vulnerability of each Maeul-soop, restoration of Maeul-soop's own area was found as top priority to guarantee the sustainability of Maeul-soops. It was confirmed that there was a need to prepare a national level ecological response strategy for each vulnerability factor of Maeul-soop, which was important national ecological resources.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• Journal of the Korean Society of Radiology
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• v.15 no.6
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• pp.891-897
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• 2021

With the development of Amyloid PET Tracer, the accuracy of Alzheimer's diagnosis can be improved through the identification of beta-amyloid neurites. However, the long image acquisition time of 20 minutes can be difficult for the patient. PET/CT scans are sensitive to patient movement and may partially affect test results. In this study, we studied the proper image acquisition time without affecting the quantitative evaluation of the image through the list mode acquisition method according to the time of the distribution of radioactive drugs in the body. The list mode includes information about time compared to the existing frame mode, and it is easy to analyze data because it can reconstruct images about the time that researchers want. The research method obtained a reconstructed image by time using a list mode of 5min frame/bed, 10min frame/bed, 15min frame/bed, and 20min frame/bed to compare the difference between signal-to-pons take ratio (SNR) and lesion-to-pons uptake ratio (LPR) and the difference in reading time to obtain an appropriate image. As a result of quantitative analysis, when measuring in list mode, SUVmean values decreased in 6 regions of interest as the image acquisition time increased, but showed the largest difference in 5 min/bed images, followed by 10 min/bed and 15 min/bed. As a result, the difference in SUVmean values decreased. Therefore, it was found that SUVmean values at 15 min/bed did not differ enough to not affect image evaluation. There was no difference in LPR values. As a result of the qualitative analysis, there was no change in the reading findings according to the PET image acquisition time and there was no significant difference in the qualitative analysis score of the image reconstruction according to time. As a result of the study, there is no significant difference between 15 min/bed and 20 min/bed images during the ¹⁸F-flutemetamol PET/CT test, so it can be said that it is clinically useful to reduce the image acquisition time selectively using 15 min/bed via list mode depending on the patient's condition.

• Journal of Dental Rehabilitation and Applied Science
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• v.34 no.4
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• pp.270-279
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• 2018

Purpose: The aim of this study was to assess the patients' perception, acceptance, and preference of the difference between a conventional impression and digital impression through questionnaire survey. Materials and Methods: Thirteen (6 male, 7 female) subjects who experienced both digital and conventional impression at the same day were enrolled in this study. Conventional impression were taken with polyvinylsiloxane and digital impression were performed using a newly developed intra-oral scanner. Immediately after the two impressions were made, a survey was conducted with the standardized questionnaires consisting of the following three categories; 1) general dental treatment 2) satisfaction of conventional impression 3) satisfaction of digital impression. The perceived source of satisfaction was evaluated using Likert scale. The distribution of the answers was assessed by percentages and statistical analyses were performed with the paired t-test, and P < 0.05 was considered significant. Results: There were significant differences of the overall satisfaction between two impression methods (P < 0.05). Digital impression showed high satisfaction in less shortness of breath and odor to participants compared to conventional impression. The use of an oral scanner resulted in a discomfort of TMJ due to prolonged mouth opening and in lower score of the scanner tip size. Conclusion: It was confirmed that the preference for the digital impression using intraoral scanner is higher than the conventional impression. Most survey participants said they would recommend the digital impression to others and said they preferred it for future prosthetic treatment.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.