• Journal of the Korean Regional Science Association
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• v.38 no.4
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• pp.59-74
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• 2022

We aim to explore how the COVID-19 affects commercial sales of traditional market in Seoul. We obtain data for commercial sales and several spatial variables that are related to commercial sales from the Seoul Open Data Plaza. In order to estimate the effect of COVID-19 occurrence on commercial sales, we employ fixed-effect panel data analysis models. Unlike our expectation, the empirical results show that the effect of the COVID-19 on commercial sales of traditional market is not significant. However, we found that the effects are significant in some types of businesses when we did the same analyses with subsamples. For example, service sectors are mostly negatively affected by COVID-19, and retail sectors are also second mostly affected by COVID-19. However, there is no significant relationship between COVID-19 and restaurant sectors. In addition, these effects vary by size of traditional market. Our results suggest that government should have a plan to help small businesses in traditional market because they do not have sufficient abilities to adjust to the unexpected economic shock, like COVID-19. Findings also suggest that strategies for helping them should be differentiated by business type and market size.

• Journal of the Korean Geotechnical Society
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• v.38 no.12
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• pp.91-101
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• 2022

The 1995 Kobe earthquake caused a massive damage to the Port of Kobe. Therefore, it was pointed out that it was impossible to design port structures for Level II (Mw 6.5) earthquakes with quasi-static analysis and Allowable Stress Design methods. In Japan and the United States, where earthquakes are frequent, the most advanced design standards for port facilities are introduced and applied, and the existing seismic design standards have been converted to performance-based design. Since 1999, the Korean Port Seismic Design Act has established a definition of necessary facilities and seismic grades through research on facilities that require seismic design and their seismic grades. It has also established a performance-based seismic design method based on experimental verification. In the performance-based seismic design method of the breakwater proposed in this study, the acceleration time history on the surface of the original ground was subjected to a fast Fourier transform, followed by a filter processing that corrected the frequency characteristics corresponding to the maximum allowable displacement with respect to performance level of the breakwater and the filtered spectrum. The horizontal seismic coefficient for the equivalent static analysis considering the displacement was calculated by inversely transforming (i.e., subjected to an inverse fast Fourier transform) into the acceleration time history and obtaining the maximum acceleration value. In addition, experiments and numerical analysis were performed to verify the performance-based seismic design method of breakwaters suitable for domestic earthquake levels.

• Journal of Platform Technology
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• v.10 no.2
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• pp.10-19
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• 2022

Moving assistant devices for people who are difficult to move are becoming electric-powered and automated. These moving assistant devices are not suitable for moving stairs at which the height between floor surfaces is different because these devices are designed and manufactured for flatland moving. An electric-powered and automated moving assistant device should change direction or stop when it approaches stairs in a movement direction. If the user or automatic control system does not change direction or stop in time, a moving assistant device can roll over or collide with stairs. In this paper, we propose a stairs detection method by using oblique distance measured by one sensor tilted to flatland. The method proposed in this paper can detect upward or downward stairs by using a difference between a predicted and measured oblique distance in considering a tilted angle of a sensor for measuring an oblique distance and installation height of the sensor on a moving object. Before the device enters a stairs region, if our proposed method provides information about detected stairs to a device's controller, the controller can do adequate action to avoid the accident.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.91-102
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• 2020

The purpose of this study is to analyze musical songs from an academic perspective by applying vocal techniques that can express songs in depth in three dimensions. Singing a musical song cannot be completed with just the musical part, rather, it should be accompanied by the analysis of various aspects such as the emotional state of the scenes and the characters. To this end, this study performed a multi-dimensional analysis of fields such as theatrical structure, lyrics, musical structure, and dynamics. In addition, the study explored and applied Estill Voice Training(EVT) that actors can best express songs with the emotions of the theater and music. EVT categorizes voice into six tones: speech, sob/cry, falsetto, twang, opera, and belting. In this study, in addition to these six sounds, the positions of vocal cords and larynx were also applied to seek ways to effectively express songs using "Gar Nichts" from the musical "Elisabeth" as a case study. "Gar Nichts" is a song sung by the protagonist Elisabeth, which expresses the self and the conflict at the peak of pain. Musically, this song requires various sound and voice-changing techniques to cover the range of "G#3-Gb5." As a result, it was confirmed that in order to embody the emotions of the characters and the songs in depth, the analysis of scenes and characters as well as various singing techniques need to be applied in harmony.

• Journal of the Korean Institute of Gas
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• v.26 no.4
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• pp.41-57
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• 2022

The global demand for carbon neutrality in response to climate change is in a situation where it is necessary to prepare countermeasures for carbon trade barriers for some countries, including Korea, which is classified as an export-led economic structure and greenhouse gas exporter. Therefore, digital transformation, which is one of the predictable ways for the carbon-neutral transition model to be applied, should be introduced early. By applying digital technology to industrial gas manufacturing facilities used in one of the major industries, high-tech manufacturing industry, and hydrogen gas facilities, which are emerging as eco-friendly energy, abnormal detection, and diagnosis services are provided with cloud-based predictive diagnosis monitoring technology including operating knowledge. Here are the trends. Small and medium-sized companies that are in the blind spot of carbon-neutral implementation by confirming the direction of abnormal diagnosis predictive monitoring through optimization, augmented reality technology, IoT and AI knowledge inference, etc., rather than simply monitoring real-time facility status It can be seen that it is possible to disseminate technologies such as consensus knowledge in the engineering domain and predictive diagnostic monitoring that match the economic feasibility and efficiency of the technology. It is hoped that it will be used as a way to seek countermeasures against carbon emission trade barriers based on the highest level of ICT technology.

• Clean Technology
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• v.28 no.3
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• pp.203-209
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• 2022

An environmental evaluation was conducted by employing LCA methodology for a mechanical seal manufacturing process that uses recycled silicon recovered from end-of-cycle PV modules. The recycled silicon was purified and reacted with carbon to synthesize β-SiC particles. Then the particles underwent compression molding, calcination and heat treatment to produce a product. Field data were collected and the potential environmental impacts of each stage were calculated using the LCI DB of the Ministry of Environment. The assessment was based on 6 categories, which were abiotic resource depletion, acidification, eutrophication, global warming, ozone depletion and photochemical oxidant creation. The environmental impacts by category were 45 kg CO₂ for global warming and 2.23 kg C₂H₄ for photochemical oxide creation, and the overall environmental impact by photochemical oxide creation, resource depletion and global warming had a high contribution of 98.7% based on weighted analysis. The wet process of fine grinding and mixing the raw silicon and carbon, and SiC granulation were major factors that caused the environmental impacts. These impacts need to be reduced by converting to a dry process and using a system to recover and reuse the solvent emitted to the atmosphere. It was analyzed that the environmental impacts of resource depletion and global warming decreased by 53.9% and 60.7%, respectively, by recycling silicon from end-of-cycle PV modules. Weighted analysis showed that the overall environmental impact decreased by 27%, and the LCA analysis confirmed that recycling waste modules could be a major means of resource saving and realizing carbon neutrality.

• Clean Technology
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• v.28 no.3
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• pp.232-237
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• 2022

Biorefineries, in which renewable resources are utilized, are an eco-friendly alternative based on biomass feedstocks. Alginic acid, a major component of brown algae, which is a type of marine biomass, is widely used in various industries and can be converted into value-added chemicals such as sugars, sugar alcohols, furans, and organic acids via catalytic hydrothermal decomposition under certain conditions. In this study, ruthenium-supported activated carbon and magnesium oxide were mixed and applied to the depolymerization of alginic acid in a batch reactor. The addition of magnesium oxide as a basic promoter had a strong influence on product distribution. In this heterogeneous catalytic system, the separation and purification processes are also simplified. After the reaction, low molecular weight alcohols and organic acids with 5 or fewer carbons were produced. Specifically, under the optimal reaction conditions of 30 mL of 1 wt% alginic acid aqueous solution, 100 mg of ruthenium-supported activated carbon, 100 mg of magnesium oxide, 210 ℃ of reaction temperature, and 1 h of reaction time, total carbon yields of 29.8% for alcohols and 43.8% for a liquid product were obtained. Hence, it is suggested that this catalytic system results in the enhanced hydrogenolysis of alginic acid to value-added chemicals.

• Journal of Korean Elementary Science Education
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• v.40 no.4
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• pp.460-479
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• 2021

The crisis of the COVID-19 pandemic has caused significant changes in education systems worldwide, including in Korea. Due to COVID-19's social distancing policies, the education system was suddenly switched to distance learning, resulting in many problems in primary schools without preparation. The purpose of this study was to investigate a teacher's awareness of science education techniques, responses to issues in science classes, including inquiry activities, advantages and disadvantages, and roles after experiencing distance learning. Survey and focus interviews were conducted for primary school teachers who had previously participated in distance learning, such as online content classes and real-time interactive classes. The study findings showed three conclusions: 1) Primary school teachers conducted one-way and interactive lectures in online classes. It is vital to improve a teacher's digital literacy to improve other teaching methods such as investigation and discussion in online classes. 2) Primary school teachers acknowledged the challenges of field feedback, inquiry item preparations, and safety in inquiry activities of science classes, by providing individual experimental packages and videos and using online discussion and feedback among teacher - student and student - student interactions. 3) Primary school teachers recognized that various types of classes using IT devices and individualized learning were possible as advantages of distance learning. As for disadvantages, it was acknowledged that inquiry activities, cooperative learning, immediate feedback, and interaction among students were challenging. Furthermore, learning gaps were wider in distance learning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.28-41
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• 2022

A road system with CV(Connected Vehicle)s, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides various road information to drivers using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed or change lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behaviors from a traffic safety point of view. Thus, empirically evaluating how a given in-vehicle warning information affects driving behaviors, and monitoring of the correlation between them are essential tasks for traffic operators. To quantitatively evaluate the effect of in-vehicle warning information, this study develops a method to calculate compliance rate of drivers where two groups of speed profile before and after road information is provided are compared. In addition, conventional indexes (e.g., jerk and acceleration noise) to measure comfort of passengers are examined. Empirical tests are conducted by using PVD (Probe Vehicle Data) and DTG (Digital Tacho Graph) data to verify the individual effects of warning information based on C-ITS constructed in Seoul metropolitan area in South Korea. The results in this study shows that drivers tend to decelerate their speed as a response to the in-vehicle warning information. Meanwhile, the in-vehicle warning information helps drivers to improve the safety and comport of passengers.

• Smart Media Journal
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• v.12 no.2
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• pp.66-75
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• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.