• The Journal of Engineering Geology
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• v.31 no.4
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• pp.507-522
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• 2021

Specific survey objectives often cannot be met using only one geophysical method, as each method's results are influenced by the specific physical properties of subsurface materials. In particular, areas susceptible to geological hazards require investigation using more than one method in order to reduce risks to life and property. Instead of analyzing the results from each method separately, this work develops a four-quadrant criterion for classifying areas of levees as safe or weak. The assessment is based on statistically determined thresholds of seismic velocity (P-wave velocity from seismic refraction and S-wave velocity from multichannel analysis of surface waves) and electrical resistivity. Thresholds are determined by subtracting the standard deviation from the mean during performance testing of this correlation technique applied to model data of four horizontal and inclined fracture zones. Compared with results from the crossplot of resistivity and P-wave velocity, crossplot analysis using resistivity and S-wave velocity data provides more reliable information on the soil type, ground stiffness, and lithological characteristics of the levee system. A loose and sandy zone (represented by low S-wave velocity and high resistivity) falling within the second quadrant is interpreted to be a weak zone. This interpretation is well supported by the N values from standard penetrating test for the central core.

• Journal of the Korean Chemical Society
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• v.66 no.6
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• pp.493-508
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• 2022

Chemistry is the subject which includes properties, change, and composition of matter. Chemistry has the system which explains observable properties and change with microscopic level, it explains them using scientific theory and laws. In the national-level curriculum, the properties and changes of matter are continuously dealt with from elementary school to high school, and the curriculum are organized so that students could strengthen their understanding about matter. In other words, understanding of the properties and changes of matter is the base to explain everyday life with the view of chemistry, and these two are classified as domains of chemistry in the 2015 revised science curriculum. In this study, we confirmed students' understanding about properties of matter and change of matter, through the analysis about results of the National Assessment of Educational Achievement (NAEA). For that purpose, this study analyzed the 12 items about properties of matter, and 19 items about change of matter, which were used in the NAEA from 2015 to 2019. According to the results of classifying and analyzing questions according to the core concept, the understanding about the two domains significantly changed between the proficient achievement-level students and basic achievement-level students. Depending on the achievement-level, there was a difference in explaining the phenomenon by using the perspective of particles, and by associating scientific concepts and models, or there was a difference in understanding the inquiry related to these two domains. Based on this analysis, this study discussed some implications to be improved on teaching-learning for 'properties of matter', and 'change of matter'.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.209-235
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• 2022

Starting from 2010, blockchain technology, along with the development of artificial intelligence, has been in the spotlight as the latest technology to lead the 4th industrial revolution. Furthermore, previous research regarding blockchain's technological applications has been ongoing ever since. However, few studies have been examined the standards for classifying the blockchain economic ecosystem from a capital market perspective. Our study is classified into a collection of interviews of software developers, entrepreneurs, market participants and experts who use blockchain technology to utilize the blockchain economic ecosystem from a capital market perspective for investing in stocks, and case study methodologies of blockchain economic ecosystem according to application fields of blockchain technology. Additionally, as a way that can be used in connection with equity investment in the capital market, the blockchain economic ecosystem classification methodology was established to form an investment universe consisting of global blue-chip stocks. It also helped construct an intelligent portfolio through quantitative and qualitative analysis that are based on quant and artificial intelligence strategies and evaluate its performances. Lastly, it presented a successful investment strategy according to the growth of blockchain economic ecosystem. This study not only classifies and analyzes blockchain standardization as a blockchain economic ecosystem from a capital market, rather than a technical, point of view, but also constructs a portfolio that targets global blue-chip stocks while also developing strategies to achieve superior performances. This study provides insights that are fused with global equity investment from the perspectives of investment theory and the economy. Therefore, it has practical implications that can contribute to the development of capital markets.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.95-118
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• 2021

As the AI speaker business has risen significantly in recent years, the potential for numerous uses of AI speakers has gotten a lot of attention. Consumers have created an environment in which they can express and share their experiences with products through various channels, resulting in a large number of reviews that leave consumers with a variety of candid opinions about their experiences, which can be said to be very useful in analyzing consumers' thoughts. Using this review data, this study aimed to examine the factors driving the continued use of AI speakers. Above all, it was determined whether the seven characteristics associated with the intention to adopt AI identified in prior studies appear in consumer reviews. Based on customer review data on Amazon.com, text mining and social network analysis were utilized to examine Amazon eco-products. CONCOR analysis was used to classify words with similar connectivity locations, and Connection centrality analysis was used to classify the factors influencing the continuous use of AI speakers, focusing on the connectivity between words derived by classifying review data into positive and negative reviews. Consumers regarded personality and closeness as the most essential characteristics impacting the continued usage of AI speakers as a result of the favorable review survey. These two parameters had a strong correlation with other variables, and connectedness, in addition to the components established from prior studies, was a significant factor. Furthermore, additional negative review research revealed that recognition failures and compatibility are important problems that deter consumers from utilizing AI speakers. This study will give specific solutions for consumers to continue to utilize Amazon eco products based on the findings of the research.

• The Journal of KAIRB
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• v.4 no.2
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• pp.36-41
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• 2022

Purpose: The purpose of this study is to evaluate how university hospital Institutional Review Boards (IRBs) in Korea classify risk when reviewing clinical trial protocols. Methods: IRB experts (IRB chairman, vice chairman, IRB administrator) in the university hospitals obtaining a Human research protection program (HRPP) or IRB accreditation in Korea were asked to fill out the Google Survey from September 1, 2020 to October 10, 2020. Result: Among the 23 responder hospitals, 8 were accredited by the American Association for Human Research Protection Program (AAHRPP) and 8 were accredited by the HRPP of Ministry of Food and Drug Safety (MFDS). Seven were accredited by Forum for Ethical Review Committees in Asia and the Western Pacific or Korea National Institution for Bioethics Policy. Thirteen of 23 hospitals (56.5%) had 4 levels (less than minimal, low, moderate, high risk), 4 hospitals had 3 levels (less than, slightly over, over than minimal risk), 1 hospital had 5 levels (4 levels plus required data safety monitoring board), and 1 hospital had 2 levels (less than, over than minimal risk) risk classification system. Thirteen of 23 hospitals (56.5%) had difficulty classifying the risk levels of research protocols. Fourteen hospitals (60.9%) responded that different standards among hospitals for risk level determination associated with clinical trials will affect the subject protection. Six hospitals (26.1%) responded that it will not. Three hospitals (13.0%) responded that it will affect the beginning of the clinical trial. To resolve differences in standards between hospitals, 14 hospitals (60.9%) responded that either the Korean Association of IRB or MFDS needs to provide a guideline for risk level determination in clinical trials: 5 hospitals (21.7%) responded education for IRB members and researchers is needed; 3 hospitals (13.0%) responded that difference among institutions needs to be acknowledged; and 1 hospital (4.3%) responded that there needs to be communication among IRB, investigator, and sponsor. Conclusion: After conducting a nationwide survey on how IRB in university hospital determines risk during review of clinical trials, it is reasonable to use 4-level risk classification (less than minimal, low, moderate, high risk); the most utilized method among hospitals. Moreover, personal information and conflict of interest associated with clinical trials have to be considered when reviewing clinical trial protocols.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1148-1157
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• 2022

The maritime safety act defines a large vessel as a vessel of at least 200m in length overall. Since this standard was introduced in 1986, it has not been revised even though the marine traf ic environment has changed significantly. The length overall of 200m is equivalent to the handymax class for a dry bulker; therefore, classifying this as a modern large vessel size is difficult. Meanwhile, according to the maritime safety act, the specific sea area for traffic safety is established where large vessels frequently pass. Accordingly, by reviewing maritime-related laws, this study confirmed that standards for vessels larger than 200m in length overall were already introduced. Furthermore, by examining the statistics of vessels entering Korean ports, the existence of sea areas with a lot of traffic by large vessels, except the current 5 specific areas, was confirmed. Therefore, the following were suggested: the deletion of the term large vessel, a raise in the standard for length of a vessel related to a specific sea area in the maritime safety act.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.4
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• pp.285-292
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• 2022

The education and training system, the ISCO (International Standard Classification of Occupations), and the legal systems of Japan and the United States consider all ophthalmic optometry laboratory personnel as ophthalmologists. They encompass optometrists, orthoptists, optometric technicians, and ophthalmic medical technicians. Data retrieved from the KOSIS (Korean Statistical Information Service) revealed that the number of opticians associated with the department of ophthalmology in 2022 could be appraised by classifying their medical institutions; contrarily, the number of clinical laboratory technologists could not be assessed. However, the current research investigated a general tertiary hospital and determined that clinical laboratory technologists outnumber opticians. Classification in Korea is based on ophthalmic optometry laboratory personnel, ISCO, ISCED (International Standard Classification of Education), the medical service act, the act on medical service technologists, and the higher education act. These results cannot be compared to the optometrists evaluated in the United States. Ophthalmology is a suitable profession for optometric technologists and technicians who perform under the instructions of ophthalmologists and optometrists. The field of eye healthcare would be benefitted by assigning the management based on their qualification according to the requirement of the job title, such as 'Clinical Optometry Technologist' to be given to clinical laboratory technologists and opticians who work in the ophthalmic optometry laboratories after obtaining a private qualification endowed by the Korean Ophthalmological Society and the Korean Optometry Society.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.199-206
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• 2023

This study utilizes deep learning algorithms to automatically classify construction waste into three categories: wood waste, plastic waste, and concrete waste. Two models, VGG-16 and ViT (Vision Transformer), which are convolutional neural network image classification algorithms and NLP-based models that sequence images, respectively, were compared for their performance in classifying construction waste. Image data for construction waste was collected by crawling images from search engines worldwide, and 3,000 images, with 1,000 images for each category, were obtained by excluding images that were difficult to distinguish with the naked eye or that were duplicated and would interfere with the experiment. In addition, to improve the accuracy of the models, data augmentation was performed during training with a total of 30,000 images. Despite the unstructured nature of the collected image data, the experimental results showed that VGG-16 achieved an accuracy of 91.5%, and ViT achieved an accuracy of 92.7%. This seems to suggest the possibility of practical application in actual construction waste data management work. If object detection techniques or semantic segmentation techniques are utilized based on this study, more precise classification will be possible even within a single image, resulting in more accurate waste classification

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.759-766
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• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.