• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.67-75
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• 2022

When acquiring a product having an OS, it is very important to identify the exact kernel version of the OS. This is because the product's administrator needs to keep checking whether a new vulnerability is found in the kernel version. Also, if there is an acquisition requirement for exclusion or inclusion of a specific kernel version, the kernel identification becomes critical to the acquisition decision. In the case of the Linux kernel used in various equipment, sometimes it becomes difficult to pinpoint the device's exact version. The reason is that many manufacturers often modify the kernel to produce their own firmware optimized for their device. Furthermore, if a kernel patch is applied to the modified kernel, it will be very different from its base kernel. Therefore, it is hard to identify the Linux kernel accurately by simple methods such as a specific file existence test. In this paper, we propose a static method to classify a specific kernel version by analyzing function names stored in the symbol table. In an experiment with 100 Linux devices, we correctly identified the Linux kernel version with 99% accuracy.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.1
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• pp.47-53
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• 2022

Recently, intelligent packaging of foods has been increasingly developed in response to the growing interest of consumers in checking food quality. Indicators, an important element in intelligent packaging, change color to detect specific substances or indicate food quality changes. Gas indicators can be built into food packaging to detect volatile substances that are released when food quality changes. Ethylene gas is produced as climacteric fruits ripen. Climacteric fruit ripening results from a rapid increase in ethylene production and respiration. In the case of packaged fruits, the ethylene gas concentration in the headspace is closely related to the ripeness of each fruit variety. If an ethylene gas indicator that can be used in fruit packaging is available, the consumer will be able to eat the fruit at the optimal time. In this paper, the characteristics and pros and cons of the ethylene gas indicators developed so far were analyzed by reviewing various types of indicators such as metal reduction-based indicator, fluorescence-based indicator, pH indicator-based indicator, and liposome-based indicator.

• The Korean Journal of Franchise Management
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• v.13 no.2
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• pp.33-51
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• 2022

Purpose: The online market is growing the most in history due to the expansion of non-face-to-face commerce. In addition, as consumers' interest in health, food safety, and environment increases, interest in and consumption of eco-friendly agricultural products is also increasing. Therefore, in the case of a specialty store that sells eco-friendly organic agricultural products, a marketing strategy that can increase customer loyalty by reflecting these consumer needs is necessary. In this study, the store attributes of eco-friendly food stores are classified into location, assortment, price, quality, and employee service, and the effect of each store attribute on utilitarian and hedonic value is investigated. Research design, data, and methodology: The subjects of this study were customers who visited an eco-friendly food store. Of the 511 survey responses, 311 were used for statistical verification, excluding 200 who had not visited within the last 3 months. For statistical analysis, Smart PLS 3.0 was used, and after checking the validity and reliability of the items, hypothesis testing was performed. Result: As a result of the study, it was found that assortment, quality, and employee service among store attributes had a positive (+) effect on utilitarian and hedonic value. Second, location had no significant effect on utilitarian and hedonic value. Third, price did not appear to have a positive (+) effect on the utilitarian value, and it was found to have a positive (+) effect on the hedonic value. Fourth, It was investigated whether the presence or absence of delivery service had an effect on store attributes between utilitarian and hedonic value, and it was found that there was a significant effect between employee service and hedonic value. Conclusions: Among eco-friendly food store environment management will be required in order to provide food that meets the tastes and needs of consumers by diversifying the taste, standard, and quality grade of food, and to maintain or improve the quality. In order to unlike other stores, eco-friendly food stores have high price resistance from the point of view of consumers, so it is necessary to diversify promotional media such as YouTube and SNS to raise awareness of eco-friendly organic food.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.57-74
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• 2022

Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.

• Journal of the Korean Society of Food Culture
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• v.37 no.1
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• pp.1-12
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• 2022

This qualitative study analyzed various environmental factors and difficulties faced by school foodservices during the COVID-19 pandemic. Focus group interviews were conducted by enrolling 12 nutrition teachers and nutritionists. Data collected were subsequently analyzed for changes implemented during the pandemic, in hygiene management, diet management, and distribution management of the school meal. The content and method of delivery of information related to diet guidance and school foodservice by related organizations were also examined. Results of the survey show that personal hygiene (such as maintaining student-to-student distance, checking students for a fever, and hand disinfection) was duly applied, installation of table coverings and distancing between school cafeteria seats were conducted, and mandatory mask-wearing to prevent droplet transmission was enforced. Depending on the COVID-19 situation, the number of students having school meals was limited per grade, and time-spaced meals were provided. To prevent infection, menus that required frequent hand contact were excluded from the meal plan. Overall, it was difficult to manage the meal plan due to frequent changes in tasks, such as the number of orders and meal expenses. These changes were communicated by nutrition teachers and nutritionists wherein the numbers of school meals were adjusted, depending on situations arising from each COVID-19 crisis stage. Furthermore, in some schools, either face-to-face nutrition counseling was stopped entirely, or nutrition education was conducted online. Parent participation was disallowed in the monitoring of school meals, and the prohibition on conversations inside the school cafeteria resulted in the absence of communication among students, nutrition teachers, and nutritionists. Additionally, confusion in meal management was caused by frequent changes in the school meal management guidelines provided by the Office of Education and the School Health Promotion Center in response to COVID-19. In anticipation of the emergence of a new virus or infectious diseases caused by mutations in the years to come, it is suggested that a holistic, well-thought-out response manual for safe meal operation needs to be established, in close collaboration with schools and school foodservice-related institutions.

• Journal of the Korean Applied Science and Technology
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• v.39 no.1
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• pp.52-62
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• 2022

The purpose of this study was to find a way to improve the stability and quality of urinalysis by checking the changes in the measurement values of representative clinical chemistry test items according to the repeated freezing and thawing before the urine test and the thawing process. All subjects were 10 healthy males, and the freeze and thaw stability test was performed using their urine samples. In the case of micro-albumin and amylase, there was no statistical significance at 37℃ with time, but at 42℃ and 60℃, there was a statistically significant change in the results with time. There were statistically significant changes in BUN, creatinine, uric acid, and glucose. As a result of long-term stability, after 7 days, glucose mutation increased and amylase decreased at 60℃. In the case of glucose and amylase, there was a statistically significant change in the results over time. To obtain accurate test results, accurate standardization of urinalysis including appropriate collection, storage, and storage methods of urine samples is required and systematic study of conditions for securing stability for each biomaterial is required.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.3
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• pp.103-110
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• 2022

The purpose of this study is to experimentally evaluate the stiffness and strength reduction according to the reinforcing bar details of the vertically divided reinforced concrete shear walls. To confirm the effect of reducing strength and stiffness according to vertical division, four real-scale specimens were fabricated and repeated lateral loading tests were performed. As a result of the experiment, it was confirmed that the strength and stiffness were decreased according to the vertical division. In particular, as the stiffness reduction rate is greater than the strength reduction rate, it is expected that safety against extreme strength can be secured when the load is redistributed according to vertical division. As a result of checking the crack pattern, a diagonal crack occurred in the wall subjected to compression control among the divided walls. It was confirmed that two neutral axes occurred after division, and the reversed strain distribution appeared in the upper part, showing the double curvature pattern. In future studies, it is necessary to evaluate the stiffness reduction rate considering the effective height of the wall, to evaluate additional variables such as wall aspect ratio, and to conduct analytical studies on various walls using finite element analysis.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.45-55
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• 2022

BIM models allow building spaces to be instantiated and recognized as unique objects independently of model elements. These instantiated spaces provide the required semantics that can be leveraged for building code checking, energy analysis, and evacuation route analysis. However, theses spaces or rooms need to be designated manually, which in practice, lead to errors and omissions. Thus, most BIM models today does not guarantee the semantic integrity of space designations, limiting their potential applicability. Recent studies have explored ways to automate space allocation in BIM models using artificial intelligence algorithms, but they are limited in their scope and relatively low classification accuracy. This study explored the use of Graph Convolutional Networks, an algorithm exclusively tailored for graph data structures. The goal was to utilize not only geometry information but also the semantic relational data between spaces and elements in the BIM model. Results of the study confirmed that the accuracy was improved by about 8% compared to algorithms that only used geometric distinctions of the individual spaces.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.