• Journal of Digital Convergence
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• v.20 no.1
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• pp.33-39
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• 2022

This study aims to investigate the online class preference depending on students' gender and school level. To achieve this aim, the study conducted a survey on 4,803 elementary, middle, and high school students in 17 regions nationwide. The valid data of 4,524 were then analyzed using the Apriori algorithm to discern the associated patterns of the online class preference corresponding to their gender and school level. As a result, a total of 16 rules, including 7 from elementary school students, 4 from middle school students, and 5 from high school students were derived. To be specific, elementary school male students preferred software-based classes whereas elementary female students preferred maker-based classes. In the case of middle school, both male and female students preferred virtual experience-based classes. On the other hand, high school students had a higher preference for subject-specific lecture-based classes. The study findings can serve as empirical evidence for explaining the needs of online classes perceived by K-12 students. In addition, this study can be used as basic research to present and suggest areas of improvement for diversifying online classes. Future studies can further conduct in-depth analysis on the development of various online class activities and models, the design of online class platforms, and the female students' career motivation in the field of science and technology.

• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.607-619
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• 2023

This study, it was tried to evaluate the asphalt behavior under tensile loading conditions through indirect Brazilian and direct tensile tests, experimentally and numerically. This paper is important from two points of view. The first one, a new test method was developed for the determination of the direct tensile strength of asphalt and its difference was obtained from the indirect test method. The second one, the effects of particle size and loading rate have been cleared on the tensile fracture mechanism. The experimental direct tensile strength of the asphalt specimens was measured in the laboratory using the compression-to-tensile load converting (CTLC) device. Some special types of asphalt specimens were prepared in the form of slabs with a central hole. The CTLC device is then equipped with this specimen and placed in the universal testing machine. Then, the direct tensile strength of asphalt specimens with different sizes of ingredients can be measured at different loading rates in the laboratory. The particle flow code (PFC) was used to numerically simulate the direct tensile strength test of asphalt samples. This numerical modeling technique is based on the versatile discrete element method (DEM). Three different particle diameters were chosen and were tested under three different loading rates. The results show that when the loading rate was 0.016 mm/sec, two tensile cracks were initiated from the left and right of the hole and propagated perpendicular to the loading axis till coalescence to the model boundary. When the loading rate was 0.032 mm/sec, two tensile cracks were initiated from the left and right of the hole and propagated perpendicular to the loading axis. The branching occurs in these cracks. This shows that the crack propagation is under quasi-static conditions. When the loading rate was 0.064 mm/sec, mixed tensile and shear cracks were initiated below the loading walls and branching occurred in these cracks. This shows that the crack propagation is under dynamic conditions. The loading rate increases and the tensile strength increases. Because all defects mobilized under a low loading rate and this led to decreasing the tensile strength. The experimental results for the direct tensile strengths of asphalt specimens of different ingredients were in good accordance with their corresponding results approximated by DEM software.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.212-215
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• 2022

In the era of so-called digital transformation today, the need for the construction and utilization of big data in various fields has increased. Today, a lot of data is produced and stored in a digital device and media-friendly manner, but the production and storage of data for a long time in the past has been dominated by print books. Therefore, the need for Optical Character Recognition (OCR) technology to utilize the vast amount of print books accumulated for a long time as big data was also required in line with the need for big data. In this study, a system for digitizing the structure and content of a document object inside a scanned book image is proposed. The proposal system largely consists of the following three steps. 1) Recognition of area information by document objects (table, equation, picture, text body) in scanned book image. 2) OCR processing for each area of the text body-table-formula module according to recognized document object areas. 3) The processed document informations gather up and returned to the JSON format. The model proposed in this study uses an open-source project that additional learning and improvement. Intelligent OCR proposed as a system in this study showed commercial OCR software-level performance in processing four types of document objects(table, equation, image, text body).

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.381-388
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• 2023

Through artificial neural network education using spreadsheets, non-major undergraduate students can understand the operation principle of artificial neural networks and develop their own artificial neural network software. Here, training of the operation principle of artificial neural networks starts with the generation of training data and the assignment of correct answer labels. Then, the output value calculated from the firing and activation function of the artificial neuron, the parameters of the input layer, hidden layer, and output layer is learned. Finally, learning the process of calculating the error between the correct label of each initially defined training data and the output value calculated by the artificial neural network, and learning the process of calculating the parameters of the input layer, hidden layer, and output layer that minimize the total sum of squared errors. Training on the operation principles of artificial neural networks using a spreadsheet was conducted for undergraduate non-major students. And image training data and basic artificial neural network development results were collected. In this paper, we analyzed the results of collecting two types of training data and the corresponding artificial neural network SW with small 12-pixel images, and presented methods and execution results of using the collected training data for Orange machine learning model learning and analysis tools.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.41-48
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• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.

• Information Systems Review
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• v.21 no.4
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• pp.125-141
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• 2019

With the advent of deep learning technology, which is represented by AlphaGo, artificial intelligence (A.I.) has quickly emerged as a key theme of digital transformation to secure competitive advantage for businesses. In order to understand the trends of A.I. based digital transformation, a clear comprehension of the A.I. business ecosystem should precede. Therefore, this study analyzed the A.I. business ecosystem from the multi-actor network perspective and identified the A.I. platform strategy type. Within internal three layers of A.I. business ecosystem (infrastructure & hardware, software & application, service & data layers), this study identified four types of A.I. platform strategy (Tech. vertical × Biz. horizontal, Tech. vertical × Biz. vertical, Tech. horizontal × Biz. horizontal, Tech. horizontal × Biz. vertical). Then, outside of A.I. platform, this study presented five actors (users, investors, policy makers, consortiums & innovators, CSOs/NGOs) and their roles to support sustainable A.I. business ecosystem in symbiosis with human. This study identified A.I. business ecosystem framework and platform strategy type. The roles of government and academia to create a sustainable A.I. business ecosystem were also suggested. These results will help to find proper strategy direction of A.I. business ecosystem and digital transformation.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.1-12
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• 2024

As the service robot market grows among the food technology industry, the quality of robot service that affects consumer behavioral intentions in the restaurant industry has become important. Serving robots, which are common in restaurants, reduce employee work through order and delivery, but because they do not respond to service failures, they increase customer dissatisfaction as well as increase employee work. In order to improve the quality of service beyond the simple function of receiving and serving orders, functions of recovery effort, fairness, empathy, responsiveness, and certainty of the process after service failure, such as serving employees, are also required. Accordingly, we assumed the type of failure of restaurant serving service as two internal and external factors, and developed a serving robot with a vocational ethics module to respond with a professional ethical attitude when the restaurant serving service fails. At this time, the expression and action of the serving robot were developed by adding a failure mode reflecting failure recovery efforts and empathy to the normal service mode. And by recruiting college students, we tested whether the service robot's response to two types of service failures had a significant effect on evaluating the robot. Participants responded that they were more uncomfortable with service failures caused by other customers' mistakes than robot mistakes, and that the serving robot's professional ethical empathy and response were appropriate. In addition, unlike the robot's favorability, the evaluation of the safety of the robot had a significant difference depending on whether or not a professional ethical empathy module was installed. A professional ethical empathy response module for natural service failure recovery using generative artificial intelligence should be developed and mounted, and the domestic serving robot industry and market are expected to grow more rapidly if the Korean serving robot certification system is introduced.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.1
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• pp.10-20
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• 2024

RNA-sequencing (RNA-seq) is a technique used for providing global patterns of transcriptomes in samples. However, it can only provide the average gene expression across cells and does not address the heterogeneity within the samples. The advances in single-cell RNA sequencing (scRNA-seq) technology have revolutionized our understanding of heterogeneity and the dynamics of gene expression at the single-cell level. For example, scRNA-seq allows us to identify the cell types in complex tissues, which can provide information regarding the alteration of the cell population by perturbations, such as genetic modification. Since its initial introduction, scRNA-seq has rapidly become popular, leading to the development of a huge number of bioinformatic tools. However, the analysis of the big dataset generated from scRNA-seq requires a general understanding of the preprocessing of the dataset and a variety of analytical techniques. Here, we present an overview of the workflow involved in analyzing the scRNA-seq dataset. First, we describe the preprocessing of the dataset, including quality control, normalization, and dimensionality reduction. Then, we introduce the downstream analysis provided with the most commonly used computational packages. This review aims to provide a workflow guideline for new researchers interested in this field.

• Earthquakes and Structures
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• v.26 no.6
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• pp.417-431
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• 2024

The fundamental period of vibration is one of the most critical parameters in the analysis and design of structures, as it depends on the distribution of stiffness and mass within the structure. Therefore, building codes propose empirical equations based on the observed periods of actual buildings during seismic events and ambient vibration tests. However, despite the fact that infill walls increase the stiffness and mass of the structure, causing significant changes in the fundamental period, most of these equations do not account for the presence of infills walls in the structure. Typically, these equations are dependent on both the structural system type and building height. The different values between the empirical and analytical periods are due to the elimination of non-structural effects in the analytical methods. Therefore, the presence of non-structural elements, such as infill panels, should be carefully considered. Another critical factor influencing the fundamental period is the effect of Soil-Structure Interaction (SSI). Most seismic building design codes generally consider SSI to be beneficial to the structural system under seismic loading, as it increases the fundamental period and leads to higher damping of the system. Recent case studies and postseismic observations suggest that SSI can have detrimental effects, and neglecting its impact could lead to unsafe design, especially for structures located on soft soil. The current research focuses on investigating the effect of infill panels on the fundamental period of moment-resisting and eccentrically braced steel frames while considering the influence of soil-structure interaction. To achieve this, the effects of building height, infill wall stiffness, infill openings and soil structure interactions were studied using 3, 6, 9, 12, 15 and 18-story 3-D frames. These frames were modeled and analyzed using SeismoStruct software. The calculated values of the fundamental period were then compared with those obtained from the proposed equation in the seismic code. The results indicate that changing the number of stories and the soil type significantly affects the fundamental period of structures. Moreover, as the percentage of infill openings increases, the fundamental period of the structure increases almost linearly. Additionally, soil-structure interaction strongly affects the fundamental periods of structures, especially for more flexible soils. This effect is more pronounced when the infill wall stiffness is higher. In conclusion, new equations are proposed for predicting the fundamental periods of Moment Resisting Frame (MRF) and Eccentrically Braced Frame (EBF) buildings. These equations are functions of various parameters, including building height, modulus of elasticity, infill wall thickness, infill wall percentage, and soil types.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.3
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• pp.14-26
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• 2018

The Biotope Area Ratio (BAR) is a quantitative pre-planning index for sustainable development and an integrated indicator for the balanced development of buildings and outdoor spaces. However, it has been pointed out that there are problems in operations management: errors in area calculation, insufficiency in the underground soil condition and depth, reduction in biotope area after construction, and functional failure as a pre-planning index. To address these problems, this study proposes implementing LIM. Since the weights of the BAR are mainly decided by the underground soil condition and depth with land cover types, the study focused on the terrain and pavements. The model should conform to BIM guidelines and standards provided by government agencies and professional organizations. Thus, the scope and Level Of Detail (LOD) of the model were defined, and the method to build a model with BIM software was developed. An apartment complex on sloping ground was selected as a case study, a 3D terrain modeled, paving libraries created with property information on the BAR, and a LIM model completed for the site. Then the BAR was calculated and construction documents were created with the BAR table and pavement details. As results of the study, it was found that the application of the criteria on the BAR and calculation became accurate, and the efficiency of design tasks was improved by LIM. It also enabled the performance of evidence-based design on the terrain and underground structures. To adopt LIM, it is necessary to create and distribute LIM library manuals or templates, and build library content that comply with KBIMS standards. The government policy must also have practitioners submit BIM models in the certification system. Since it is expected that the criteria on planting types in the BAR will be expanded, further research is needed to build and utilize the information model for planting materials.