• Korean Journal of Food Science and Technology
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• v.29 no.5
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• pp.1016-1020
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• 1997

Treatments of irradiation alone and/or in combination with heat were investigated for the sterilization of Escherichia coli O157: H7. D values of the strain were 129.2 min at $50^{\circ}C$, 27.1 min at $55^{\circ}C$, and 2.4 min at $60^{\circ}C$. The survival effect of E. coli O157:H7 during heating at various media was investigated. On heating at temperature of $60^{\circ}C$ for 10 min, the strain was generally more resistant in the media containg such chemical substrates such as 0.03 M cysteine, 1% sodium citrate or 5% sucrose, whereas this strain was appeared weaker in the chemical substrates added group such as 1% meat extract, 1% casein or 1% casamino acid. In the case of irradiation alone, $D_{10}$ value of E. coli O157:H7 was 0.116 kGy, and inactivation factors were $17{\sim}25$ at doses of 2 to 3 kGy. Pre-and post-irradiation heating showed the same $D_{10}$ value about 0.07 kGy. And Inactivation factors were $25{\sim}41$ at doses of 2 to 3 kGy. Therefore, combination treatment with heat and irradiation significantly increased in inactivation rate by increasing radiation sensitivity of E. coli O157:H7.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• Smart Media Journal
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• v.12 no.7
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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.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.104-123
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• 2022

Style transfer based on neural network provides very high quality results by reflecting the high level structural characteristics of images, and thereby has recently attracted great attention. This paper deals with the problem of resolution limitation due to GPU memory in performing such neural style transfer. We can expect that the gradient operation for style transfer based on partial image, with the aid of the fixed size of receptive field, can produce the same result as the gradient operation using the entire image. Based on this idea, each component of the style transfer loss function is analyzed in this paper to obtain the necessary conditions for partitioning and padding, and to identify, among the information required for gradient calculation, the one that depends on the entire input. By structuring such information for using it as auxiliary constant input for partition-based gradient calculation, this paper develops a recursive algorithm for super high-resolution image style transfer. Since the proposed method performs style transfer by partitioning input image into the size that a GPU can handle, it can perform style transfer without the limit of the input image resolution accompanied by the GPU memory size. With the aid of such super high-resolution support, the proposed method can provide a unique style characteristics of detailed area which can only be appreciated in super high-resolution style transfer.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.166-178
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• 2023

This study aims to introduce and assess CNN Deep Learning methods to analyze visual landscape images on social media with embedded user perceptions and experiences. This study analyzed visual landscape images by focusing on a healing place. For the study, seven adjectives related to healing were selected through text mining and consideration of previous studies. Subsequently, 50 evaluators were recruited to build a Deep Learning image. Evaluators were asked to collect three images most suitable for 'healing', 'healing landscape', and 'healing place' on portal sites. The collected images were refined and a data augmentation process was applied to build a CNN model. After that, 15,097 images of 'healing' and 'healing landscape' on portal sites were collected and classified to analyze the visual landscape of a healing place. As a result of the study, 'quiet' was the highest in the category except 'other' and 'indoor' with 2,093 (22%), followed by 'open', 'joyful', 'comfortable', 'clean', 'natural', and 'beautiful'. It was found through research that CNN Deep Learning is an analysis method that can derive results from visual landscape image analysis. It also suggested that it is one way to supplement the existing visual landscape analysis method, and suggests in-depth and diverse visual landscape analysis in the future by establishing a landscape image learning dataset.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.79-92
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• 2024

ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.

• Smart Media Journal
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• v.13 no.4
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• pp.114-123
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• 2024

This paper proposes AR stone tower content, an experiential content based on wearable augmented reality (AR). Although wearable augmented reality is gaining attention, the acceptance of the technology is still focused on specialized applications such as industrial sites. On the other hand, the proposed AR stone tower content is based on the material of 'stone tower' so that general users can relate to it and easily participate in it, and it is organized to utilize space in a moving environment and find and stack stones based on natural hand gestures. The proposed AR stone tower content was implemented in the HoloLens 2 environment and evaluated by general users through a pilot exhibition in a small art museum. The evaluation results showed that the overall satisfaction with the content averaged 3.85, and the content appropriateness for the stone tower material was very high at 4.15. In particular, users were highly satisfied with content comprehension and sound, but somewhat less satisfied with object recognition, body adaptation, and object control. The above user evaluations confirm the resonance and positive response to the material, but also highlight the difficulties of the average user in experiencing and interacting with the wearable AR environment.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.279-285
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• 2018

The purpose of this study is to propose the role of future designers and capabilities to be developed in the age of A.I. Active and preliminary designers should prepare themselves to develop necessary capabilities. As a method of study, we investigated the meaning of design and the changing role of designers from the past to present. Additional research was conducted on generative design, design processes, and A.I based design software. Finally, based on the analysis, we proposed the role of future designers and their capabilities in the age of A.I. In conclusion, the role of future designer should lead social innovation through creativity by coworking with artificial intelligence based on understanding and empathy for users. Based on this research, designers are expected to develop unique humanities skills such as empathy and creativity and work with AI in response to $4^{th}$ industrial revolution.

• Analytical Science and Technology
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• v.12 no.6
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• pp.515-520
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• 1999

The spectrophotometric determination of Al(III) with thorin have been investigated. The optimum condition of pH, concentration of ligand and surfactant, and stability were evaluated. The thorin ligand offers selective separation of Al(III) from sample solution containing Fe(III), Ni(II), Cu(II), Pb(II) and Cu(II). Various surfactants were tested and Triton X-100 showed the best stability and the maximum absorbance in an aqueous solution of Al(III)-Thorin-Triton X-100 complex appears about 526 nm. The method was applied for the determination of Al(III) in mixed sample solution. Separation and preconcentration was performed with a short column filled with resorcinol-formaldehyde resin. Control of the pH during the column operation is essential because the adsorption capacities are very sensitive to change in pH. Their separation was carried out in 0.2 M acetic acid-sodium acetate buffer solution (pH 4.5) and 1.0 M $HNO_3$media.

• Journal of Nutrition and Health
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• v.26 no.1
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• pp.56-66
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• 1993

Interactive effects among eating behavior, obesity and serum lipid levels were studied in 117, 4~12 year old children residing suburban Seoul. Fasting blood samples are obtained and analyzed for serum triglycerides (TG), total cholesterol(TC), high density lipoprotein-cholesterol(HDL-C) and hematochrit. Obesity was determined by weight for length index(WLI)and the information on eating behavior including food habits and dietary intakes was obtained by questionaire using food record method for 2-consecutive days. Over 40% of children was classified overweight or obese by WIL and children's physical parameters were closely related to those of parents implying genetic influence on obesity. Although it did not reach the statistical significance, there was a tendency of higher TG, TC and low density lipoprotein-cholesterol(LDL-C)levels among girls compared to boys. Blood lipid levels of obese children were similar to those of other groups except TG, which was significantly higher(p<0.05) in obese group. Nutrient intakes seemed adequate in all subjects except iron, calcium and total calorie which were lower than RDAs. Lacking significant relationship between individual nutrient intake and obesity, there was significant correlation between food intake and blood lipid level especially in 10-12 year old group. Vegetable intake was negatively related to TG, LPH(LDL-C/HDL-C) and atherogenic index(AI), and positively to HDL-C. Skipping breakfast and frequent eating out appeared to cause imbalances in nutrient intake. These findings clearly revealed the influence of eating behavior on childhood obesity along with blood lipid profile. To ensure the proper growth and health of these children, devising method and developing media for nutrition education suited to our society should be accomplished first. With well-planned nutrition surveys and thorough intention, childhool obesity could be prevented from progress into adulthood obesity.