• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.385-396
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• 2024

To enhance the accuracy of sinkhole detection using GPR, this study derived a convolutional neural network that can optimally extract sinkhole characteristics from GPR B-scan grayscale images. The pre-trained convolutional neural network is evaluated to be more than twice as effective as the vanilla convolutional neural network. In pre-trained convolutional neural networks, fast feature extraction is found to cause less overfitting than feature extraction. It is analyzed that the top-1 verification accuracy and computation time are different depending on the type of architecture and simulation conditions. Among the pre-trained convolutional neural networks, InceptionV3 are evaluated as most robust for sinkhole detection in GPR B-scan grayscale images. When considering both top-1 verification accuracy and architecture efficiency index, VGG19 and VGG16 are analyzed to have high efficiency as the backbone for extracting sinkhole feature from GPR B-scan grayscale images. MobileNetV3-Large backbone is found to be suitable when mounted on GPR equipment to extract sinkhole feature in real time.

• Journal of Animal Science and Technology
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• v.47 no.5
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• pp.867-876
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• 2005

The microbial contamination of ice cream product commercially available in Korea was determined using ice bar, ice cream, ice milk and non-milk fat ice cream. Irradiation effect on enhancement of microbiological safety was also investigated at doses of 1, 3, and 5 kGy. In all products, yeast and molds were not detected, however, total aerobic and coliform bacteria were detected at 1-2 and 1-1.5 Log CFU/g level, respectively. According to the different flavor used in ice cream, total aerobic bacteria were detected as 2.30, 2.90, and 3.32 Log CFU/g level in vanilla, chocolate, and strawberry ice cream, respectively. Yeast and mold was not detected in vanilla ice cream but 2.30 and 2.70 Log CFU/g in chocolate and strawberry ice cream, respectively. Coliforms were also detected 1-2 Log CFU/g in the ice cream with different flavors. Listeria inocua and Escherichia coli were detected from 3 commercial samples but Salmonella spp. was not detected using API kit. Gamma irradiation significantly reduced the level of the contaminated total aerobic bacteria, yeast and molds and coliform population in the ice creams. These results indicated that irradiation(5kGy or less) is effective to ensure safety of ice cream.

• Knowledge Management Research
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• v.21 no.3
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• pp.27-44
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• 2020

In order to solve the user's information overload problem, recommender systems infer users' preferences and suggest items that match them. The collaborative filtering (CF), the most successful recommendation algorithm, has been improving performance until recently and applied to various business domains. Visual information, such as book covers, could influence consumers' purchase decision making. However, CF-based recommender systems have rarely considered for visual information. In this study, we propose VizNCS, a CF-based deep learning model that uses visual information as additional information. VizNCS consists of two phases. In the first phase, we build convolutional neural networks (CNN) to extract visual features from image data. In the second phase, we supply the visual features to the NCF model that is known to easy to extend to other information among the deep learning-based recommendation systems. As the results of the performance comparison experiments, VizNCS showed higher performance than the vanilla NCF. We also conducted an additional experiment to see if the visual information affects differently depending on the product category. The result enables us to identify which categories were affected and which were not. We expect VizNCS to improve the recommender system performance and expand the recommender system's data source to visual information.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.29-42
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• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.202-206
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• 2014

In this study, the bacteriological survey was examined on ice creams at manufacturing factories in Korea during the summer season of 2011. The nineteen selected among 166 samples by preliminary test were collected from 11 different manufacturing factories in four major manufacturers in May 2011. Samples from ice milk, ice creams, sherbets, and non milk fat ice creams were tested for the total aerobic bacteria, coliform bacteria, and five food borne pathogens, respectively. The results showed that the coliforms including E. coli O157:H7, Salmonella spp., Staphylococcus aureus, Clostridium perfringens, and Listeria monocytogenes were not detected on all the ice creams. The total aerobic bacteria of the packed samples examined ranged between $2.5{\times}10^3$ and $5.5{\times}10^5cfu/g$. One ice cream, two sherbets, and four ice milk samples exceeded the acceptable limits of total aerobic bacteria according to the Korean standards for ice cream ($1.0{\times}10^5cfu/g$) and others ($5.0{\times}10^4cfu/g$). The levels of these microorganisms from ice creams were higher in three original equipment manufacturers than seven self-manufacturers. Three of ten ice creams (30.0%), three of six ice milks (50.0%), and one of two sherbets (50%) exceeded the acceptable limits of total aerobic bacteria, respectively. The personnel hygiene procedures with chocolate and vanilla chip addition from the manufacturing process were the main sources of the microbial contamination of stick-bar type ice creams when being produced in a factory. Improvement of the hazard analysis critical control points (HACCP) system should be introduced into the ice cream factory to improve the microbial quality of the ice cream products in Korea.

• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.8
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• pp.1407-1413
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• 2004

This study was performed to investigate the usage and management of flavorings inside or outside (Europe, Japan, JECFA and USA) for that establish a legislation about the flavoring management in Korea. Also, this study contributed to prevent confusion when manufacturers produce flavorings used in food industry. 6,434 among 8,386 flavorings authorized by Korea Food and Drug administration are compound flavorings, and 618 among 6,434 compound flavorings are synthetic flavorings. Many other substances except for flavorings are using as solvent in flavoring manufacture. Flavorings used in food industries of Korea are listed at least one among FEMA, JECFA, CoE and JFFMA except for isooctyl acetate and tricyclene. 493 items out of total 618 synthetic flavorings have completed safety evaluation by JECFA. 106 synthetic flavorings out of the rest listed FEMA as GRAS and 20 synthetic flavorings used in Japan. The replier answered that the most frequently used flavorings are strawberry, grape, orange, plum, lemon and vanilla flavor and that the usage of flavoring added to foodstuffs is less than 0.5%.