• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.869-876
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• 2006

In the case of earthenware cultural heritages that are found in the form of fragments, the major task is quick and precise restoration. The existing method, which follows the rule of trial and error, is not only greatly time consuming but also lacked precision. If this job could be done by three dimensional scanning, matching up pieces could be done with remarkable efficiency. In this study, the original earthenware was modeled through three-dimensional pattern scanning and photogrammetry, and each of the fragments were scanned and modeled. In order to obtain images from the photogrammetry, we calibrated and used a Canon EOS 1DS real size camera. We analyzed the relationship among the sections of the formed model, efficiently compounded them, and analyzed the errors through residual and color error map. Also, we built a web-based three-dimensional simulation environment centering around the users, for the virtual museum.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.75-75
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• 2022

Traditional agriculture mostly focused on activity in the field, but current agriculture faces problems such as reduction of agricultural inputs, labor shortage and so on. Accordingly, traditional agricultural experiments generally considered the simple treatment effects, but current agricultural experiments need to consider the several and complicate treatment effects. To analyze such several and complicate treatment effects, data collection has the first priority. Remote sensing is a quite effective tool to collect information in agriculture, and recent easier availability of UAVs (Unmanned Aerial Vehicles) enhances the effectiveness. LAI (Leaf Area Index) is one of the most important information for evaluating the condition of crop growth. In this study, we utilized UAV with multispectral camera to evaluate plant-based LAI of sweetcorn in a small-scale field experiment and discussed the feasibility of a new experimental design to analyze the several and complicate treatment effects. The plant-based SR measured by UAV showed the highest correlation coefficient with LAI measured by a canopy analyzer in 2018 and 2019. Application of linear mix model showed that plant-based SR data had higher detection power due to its huge number of data although SR was inferior to evaluate LAI than the canopy analyzer. The distribution of plant-based data also statistically revealed the border effect in treatment plots in the traditional experimental design. These results suggest that remote sensing with UAVs has the advantage even in a small-scale experimental plot and has a possibility to provide a new experimental design if combined with various analytical applications such as plant size, shape, and color.

• Journal of Ginseng Research
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• v.47 no.6
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• pp.735-742
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• 2023

Background: Gintonin is a new material of ginseng that acts through the ginseng-derived lysophosphatidic acid (LPA) receptor ligand. The gintonin-enriched fraction (GEF) inhibits amyloid plaque accumulation in the cortex and hippocampus, improves cognitive dysfunction by increasing acetylcholine levels, and promoted hippocampal neurogenesis in an animal model of Alzheimer's disease. We evaluated the effect of the GEF on the cognitive performance of subjects with subjective memory impairment (SMI). Methods: In this eight-week, randomized, assessor- and participant-blinded, placebo-controlled study, participants with SMI were assigned to three groups receiving placebo, GEF 300 mg/day or GEF 600 mg/day. The Korean versions of the Alzheimer's Disease Assessment Scale (K-ADAS), Mini-Mental State Examination (K-MMSE), and Stroop color-word test (K-SCWT) were also evaluated along with the safety profiles. Results: One hundred thirty-six participants completed the study. After eight weeks, we analyzed intergroup differences in primary or secondary outcome score changes. When we compared the GEF group with the placebo group, we observed significant improvements in the K-ADAS and K-SCWT scores. The GEF group did not show a significant improvement in K-MMSE and BDI scores compared to the placebo group. No adverse events were observed in the gintonin and placebo groups for eight weeks. Conclusion: The GEF is safe and effective in improving subjective cognitive impairment related to both the K-ADAS and K-SCWT in this study. However, further large-scale and randomized controlled studies are warranted to secure other cognitive function tests besides the K-ADAS and K-SCWT, and to confirm the findings of the current study.

• Animal Bioscience
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• v.37 no.6
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• pp.1065-1076
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• 2024

Objective: This study aimed to assess the effects of dietary mulberry leaves on the growth, production performance, gut microbiota, and immunological parameters of poultry and livestock. Methods: The PubMed, Embase, and Scopus databases were systematically analyzed to identify pertinent studies up to December 2022. The effects of mulberry leaf diet was assessed using the weighted mean difference, and the 95% confidence interval was calculated using a random-effects model. Results: In total, 18 studies that sampled 2,335 poultry and livestock were selected for analysis. Mulberry leaves improved the average daily gain and reduced the feed/meat ratio in finishing pigs, and the average daily gain and average daily feed intake in chicken. In production performance, mulberry leaves lowered the half carcass weight, slaughter rate, and loin eye area in pigs, and the slaughter rate in chickens. Regarding meat quality in pigs, mulberry leaves reduced the cooked meat percentage, shear force, crude protein, and crude ash, and increased the 24 h pH and water content. In chickens, it increased the drip loss, shear force, 45 min and 24 h pH, crude protein, and crude ash. Mulberry leaves also affect the abundances of gut microbiota, including Bacteroides, Prevotella, Megamonas, Escherichia-Shigella, Butyricicoccus, unclassified Ruminococcaceae, Bifidobacterium, Lactobacillus, and Escherichia coli in poultry and livestock. Mulberry leaves at different doses were associated with changes in antioxidant capacity in chickens, and immune organ indexes in pigs. With respect to egg quality, mulberry leaves at different doses improved the shell strength, yolk color, eggshell thickness, and eggshell weight. However, moderate doses diminished the egg yolk ratio and the egg yolk moisture content. Conclusion: In general, dietary mulberry leaves improved the growth, production performance, and immunological parameters in poultry and livestock, although the effects varied at different doses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.126-146
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• 2024

Caladium bicolor is one of the most popular plants in Thailand. The original species of Caladium bicolor was found a hundred years ago. Until now, there are more than 500 species through multiplication. The classification of Caladium bicolor can be done by using its color and shape. This study aims to develop a model to classify Caladium bicolor using a transfer learning technique. This work also presents a proof of concept, GUI design, and web application deployment using the user-design-center method. We also evaluated the performance of the following pre-trained models in this work, and the results are as follow: 87.29% for AlexNet, 90.68% for GoogleNet, 93.59% for XceptionNet, 93.22% for MobileNetV2, 89.83% for RestNet18, 88.98% for RestNet50, 97.46% for RestNet101, and 94.92% for InceptionResNetV2. This work was implemented using MATLAB R2023a.

• Smart Media Journal
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• v.13 no.4
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• pp.57-64
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• 2024

Attributes such as material, color, and fit in fashion images are important factors for consumers to purchase clothing. However, the process of classifying clothing attributes requires a large amount of manpower and is inconsistent because it relies on the subjective judgment of human operators. To alleviate this problem, there is a need for research that utilizes artificial intelligence to classify clothing attributes in fashion images. Previous studies have mainly focused on classifying clothing attributes for either tops or bottoms, so there is a limitation that the attributes of both tops and bottoms cannot be identified simultaneously in the case of full-body fashion images. In this study, we propose a deep learning model that can distinguish between tops and bottoms in fashion images and classify the category of each item and the attributes of the clothing material. The deep learning models ResNet and EfficientNet were used in this study, and the dataset used for training was 1,002,718 fashion images and 125 labels including clothing categories and material properties. Based on the weighted F1-Score, ResNet is 0.800 and EfficientNet is 0.781, with ResNet showing better performance.

• Restorative Dentistry and Endodontics
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• v.49 no.3
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• pp.27.1-27.13
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• 2024

Objectives: This study aimed to develop whitening mouth rinses formulated with industrial mushrooms and compare them with over-the-counter whitening mouth rinses. Materials and Methods: Formulations with black shimeji mushrooms, mushroom substrates, and mushroom stalks were developed. Bovine enamel/dentin samples were divided into 7 groups (n = 10): Colgate Luminous White, Listerine Whitening Extreme (LWE), Listerine Cool Mint (LC), mushroom extract rinse (MEC), mushroom substrate rinse (MSB), mushroom stalk rinse (MTC), and artificial saliva. Samples were stained with black tea for 6 days, and then were immersed in 100 mL of each mouth rinse twice daily for 14 days. Color parameters (CIELAB [ΔE^*], CIEDE2000 [ΔE₀₀], whiteness index for dentistry [ΔWI_D]) and microhardness (Knoop hardness number [KHN]) were analyzed at T₁ (initial), T₂ (24 hours), and T₃ (7 days). Mouth rinse pH was measured, and enamel was examined using a scanning electron microscope. Data were analyzed using generalized linear models, and KHN with the generalized linear mixed model for repeated measures (p ≤ 0.05). Results: ΔE^* was higher in LW and MSB groups. No significant differences were found for ΔE₀₀ (p = 0.0982) and ΔWI_D (p = 0.2536). Experimental mouth rinses did not promote enamel whitening based on ΔE₀₀ and ΔWI_D. LWE and LC reduced KHN and had a more acidic pH, while MEC had higher KHN at T2. MEC, MSB, and MTC had alkaline pH, not altering the tooth surface. Conclusions: Black shimeji mushrooms are promising for mouth rinse development due to their alkaline pH and non-altering effect on surface microhardness.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.373-386
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• 2013

In this research, we specify the image consumers' preferred image quality ranges based on objective image quality evaluation factors and follow a method which measures preference of the natural image scenes. In other words, according to No-Reference, we select dynamic range, color, and contrast as factors of image quality measurements. For collecting sample images, we choose the preferred 200 landscapes which have over 30 recommendations by image consumers on the internet photo gallery. According to the scores of three objective factors of image quality measurements, the final expected score which means the image quality preference is measured and its total score is 100 points. In the main test, the actual image sample shows dynamic range 10 stop, LAB mean value L:54.7, A:2.96, B:-15.84, and RSC contrast 376.9. Total 200 image samples' normal distribution z value represents in dynamic range 0.21, LAB mean value L:0.15, A:0.38, B:0.13, and RSC contrast 0.08. In the standard normal distribution table, we can convert the z value as a percentage; dynamic range is 8.32%, LAB mean value is L:5.96%, A:14.8%, B:5.17%, and RSC contrast is 3.19%. And then, we convert the percentage values into the scores of 100; dynamic range is 91.68, LAB mean value is 91.36, and RSC contrast is 96.81. Therefore, we can conclude that the sample image's total mean score is 94.99 based on three objective image quality factors. Throughout our proposed image quality assessment model, we can measure the preference value of natural scenes. Also, we can specify the preferred image quality representation ranges and measure the expected image quality preference.

• The korean journal of orthodontics
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• v.31 no.6 s.89
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• pp.559-566
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• 2001

This study was undertaken to demonstrate the forces in the mandibular alveolar bone generated by activation of the mandibular posterior crossbite appliance in the treatment of buccal crossbite caused by lingual eruption of mandibular second molar. A three-dimensional photoelastic model was fabricated using a photoelastic material (PL-3) to simulate alveolar bone. We observed the model from the anterior to the posterior view in a circular polariscope and recorded photogtaphically before and after activation of the mandibular posterior crossbite appliance. The following results were obtained : 1. When the traction force was applied on the buccal surface of the mandibular second molar, stress was concentrated at the lingual alveolar crest and root apex area. The axis of rotation also was at the middle third of the buccal toot surface and the root apex, so that uncontrolled tipping and a buccal traction force for the mandibular second molar were developed. 2. When the traction force was applied on the lingual surface of the mandibular second molar more stress was observed as opposed to those situations in which the force application was on the buccal surface. In addition, stress intensity was increased below the loot areas and the axis of rotation of the mandibular second molar was lost. In result, controlled tipping and intrusive tooth movements were developed. 3. When the traction forte was applied on either buccal or lingual surface of the second molar, the color patterns of the anchorage unit were similar to the initial color pattern of that before the force application. So we can use the lingual arch for effective anchorage in correcting the posterior buccal crossbite. As in above mentioned results, we must avoid the rotation and uncontrolled tipping, creating occlusal interference of the malpositioned mandibular second molar when correcting posterior buccal crossbite. For this purpose, we recommend the lingual traction force on the second molar as opposed to the buccal traction.