• Food Science of Animal Resources
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• 제28권3호
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• pp.269-275
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• 2008

The objective of this study was to determine the effects of 0.6% vitamin C (VC), 5% ethanol extract of B. polyfermenticus SCD (EB), and a mixture of 0.3% vitamin C and 2.5% B. polyfermenticus SCD ethanol extract (CB) on the physicochemical properties of cooked ground pork during storage. The changes in pH of VC, EB, and CB were smaller than was observed with the control (CON, no added antioxidant). VC, EB, and CB exhibited significantly lower TBARS values than CON during storage (p<0.05). Longer storage periods resulted in higher TBARS values (p<0.05). VBN values for VC, EB, and CB were significantly lower than CON (p<0.05). The L values of CON and VC were higher than EB and CB (p<0.05). The a value of VC was significantly lower than CON, EB, and CB during storage (p<0.05). The b values of all samples significantly increased during storage (p<0.05). The addition of vitamin C and B. polyfermenticus SCD to cooked ground pork did not significantly affect sensory evaluations during the storage period (p>0.05). Further studies are needed to develop other meat products containing B. polyfermenticus SCD with acceptable physicochemical properties.

• Food Science of Animal Resources
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• 제26권4호
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• pp.478-485
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• 2006

The treatments of Tteokgalbi studied were: control (no antioxidants); (T1) ethanol extracts of Bacillus polyfermenticus SCD (5%); (T2) ascorbic acid (0.06%); (T3) ascorbic acid (0.03%) + ethanol extracts of B. polyfermenticus SCD (2.5%). The pH of T1, T2, and T3 samples was significantly (p<0.05) lower than that of the control for up to 3 days of storage. Thereafter T1, T2 and T3 had a significantly higher (p<0.05) pH value than the control during storage. TBA values were significantly lower (p<0.05) in all treated samples relative to the control. The TBA value of the control rapidly increased after 6 days of storage, whereas the TBA values of the test samples did not sharply increase. T3 samples treated with vitamin C and Bacillus polyfermenticus SCD had a higher TBA value than T1 and T2 samples. The VBN values of T1, T2 and T3 sampleswere lower than that of the control (p>0.05). VBN values of the ground pork meat samples significantly increased (p<0.05) with storage time. The total microbial counts of each sample significantly increased with storage time (p<0.05). The $a^*$ values of T1 and T3 samples containing added vitamin C were significantly higher (p<0.05) than that of the control and T2 samples during storage. The $b^*$ value of T2 samples was significantly higher (p<0.05) than that of other ground pork meat products during storage.

• Korean Journal of Community Nutrition
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• 제12권3호
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• pp.259-271
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• 2007

The purpose of this study was to investigate the dietary habits and daily intake of nutrients in college students. This survey was conducted using a self-administered questionaire. The average heights and weights were 173.5 cm and 72.3 kg of male students and 161.8 cm and 57.2 kg of female students. The average of BMI was $24.2kg/m^2$ of male and $21.9kg/m^2$ of female, and the value of male students was higher than the value of female students. The response to the daily meals was 54.6% for '$2{\sim}3$ times/week'. The regularity of mealtime was 41.7% for irregular and the frequency eating after nine was 45.7% for '5-6 times/week', respectively. The repast was 72.2% for 'overeating and little eating' and was a significant difference of male and female students (p<0.05). The eating rate was higher '$10{\sim}20min$'. As for breakfast food eaten, skipping breakfast was 23.6% for 'no/week' and female students were higher than male students (p<0.05). The frequency of snacks was 36.0% for 'nothing' of males students and 34.8% for '3-4 times/week' of female students (p<0.05). The type of snack was a significant difference of males and females students (p<0.01), and was the highest 75.0% for carbonated drinks of males and 37.5% for snacks of females. The eating due to stress solution was a significant difference of male and female students (p<0.01), and was the highest 23.0% for 'frequency' of males and 44.7% for 'sometime' of females. As for food intake of male and female students, the meat intake was 66.7% for 'everything of male and female students. The fish intake was 68.1 % for '1-2times/week'. The milk, milk products, eggs and beans were each 40.3%, 58.3%, 56.9%, 47.2% for '1-2 times/week' (p<0.05). The fat intake was 55.6% for '$1{\sim}2$ times/week'. The average consumption of energy was 58% of male and 67% of female of estimated energy requirement (EER). Their mean ratio of carbohydrate: protein: fat was 57 : 15 : 28 of all subjects. The mean intakes of vitamin C and folic acid were 70% and 51% of males and 62% and 52% of females of recommended intake (RI). The mean intakes of Ca, P, Fe and Na were 71%, 140%, 146% of males and 72%, 122%, 76% of female of RI and 273% of males and 233% of females of adequate intake (AI). Therefore, nutritional education is necessary for college students to establish physicall and mentall optimal health conditions though nutritional intervention.

• Journal of the East Asian Society of Dietary Life
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• 제19권6호
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• pp.1049-1053
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• 2009

This study was carried out to investigate the effects of Eucommia ulmoides Oliver (Du-Zhong) as a main ingredient in a mixture of various medicinal herbal extracts on blood pressure, serum lipid concentrations, and hematological variables in hypertensive patients. After 4 weeks on drinking the mixed medicinal herbal teas, systolic blood pressure remarkably decreased from 167.14 to 134.86 mmHg and diastolic blood pressure decreased from 100.00 to 87.10 mmHg. In terms of serum lipid profiles, there were no differences in total-cholesterol, HDL-cholesterol, LDL-cholesterol level, and atherogenic index, However, the level of triglycerol was significantly reduced from 237.1 before the experiment to 145.00 mg/dL after 4 weeks on drinking the mixed medicinal herbal teas. Serum triglyceride were remarkably reduced from 237.1 to 145.00 mg/dL in the hypertensive patients who had taken the combined medicinal herbal extracts. Compared with data obtained from the patients before the experiment, there were no differences in hematological variables (RBC, WBC, hematocrit, hemoglobin, platlets and homocysteine) after the 4 weeks experiments, but mean corpuscular hemoglobin concentration was significantly increased by 2.70% (p<0.05) in the blood samples. Based on these findings, it can be presumed that the examined mixed medicinal herbal tea may be effective in lowering blood pressure and in increasing mean corpuscular hemoglobin concentration due to reduced serum triglyceride levels in hypertension patients.

• The Journal of the Korea institute of electronic communication sciences
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• 제13권4호
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• pp.765-776
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• 2018

Artificial Intelligence is a rapidly growing technology with the latest developments in computing technology and is considered as one of the next major technologies. Chat-Bot is a system that is designed to respond to user's input according to the rules that are set up in advance and it provides more services through simple and repetitive tasks such as counseling, ordering and others. Accordingly, the study aims to present a model of a celeb-bot using Artificial Intelligence. Celeb-Bot is a combination of Celeb, which are short for Celebrity and Chat-bot. Celeb-Bot provides a Chat-Bot service that allows people to talk to a celebrity. The celeb is the best thing to build a relationship and has the advantages of being accessible to anyone. At the same time, Artificial Intelligence is a technology that can be seen as a person, not a product. Based on this, we believe that Celeb's Characteristic and Chat-bot based on artificial intelligence technologies need to be combined, so variety of products can generate synergy. It is predicted that there will be variety of derivatives that utilize this technology, and it is going to present a celeb-bot model accordingly.

• Journal of Intelligence and Information Systems
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• 제28권4호
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.

• Journal of Intelligence and Information Systems
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• 제28권4호
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• pp.347-364
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• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.

• Journal of Service Research and Studies
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• 제12권2호
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• pp.80-93
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• 2022

This study is a study on a methodology that can extract various factors that affect purchase and use of products/services from the consumer's point of view through previous studies, and analyze the types and tendencies of consumers according to age and gender. To this end, we quantify factors in terms of general personal propensity, consumption influence, consumption decision, etc. to check the consistency of data, and based on these studies, we conduct research to suggest and prove data analysis methodologies of consumer types that are meaningful from the perspectives of startups and SMEs. did As a result, it was confirmed through cross-validation that there is a correlation between the three main factors assumed for data analysis from the consumer's point of view, the general tendency, the general consumption tendency, and the factors influencing the consumption decision. verified. This study presented a data analysis methodology and a framework for consumer data analysis from the consumer's point of view. In the current data analysis trend, where digital infrastructure develops exponentially and seeks ways to project individual preferences, this data analysis perspective can be a valid insight.

• Smart Media Journal
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• 제12권7호
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.505-508
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• 2022

Artificial intelligence itself has the value of advancing technology, and it is used in various industrial fields to enhance the added value of products and services produced in various industries. Therefore, regulations and policies related to artificial intelligence should be considered from a broader perspective. However, researchers have different understandings, and there is no agreement on how to regulate artificial intelligence. Therefore, we will examine the direction of government regulation on artificial intelligence technology in an exploratory manner. First, accountability, transparency, stability, and fairness are derived as the goals of artificial intelligence regulation, and the system itself, development process, and utilization process are set as the scope of regulation, and users and developers are subject to regulation. The academic significance of this study can be seen as analyzing the current level of artificial intelligence technology and laying the foundation for consistent discussions on artificial intelligence regulations in the future. Considering the life cycle from AI development to application, what is important is the balance of promotion policies to promote the artificial intelligence industry and regulatory policies to respond to the resulting risks. The goal of law related to artificial intelligence is to establish a system in which artificial intelligence can be accommodated in a positive direction to all participants, including developers, companies, and users.