• Journal of Convergence Society for SMB
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• v.5 no.2
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• pp.27-32
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• 2015

Recently, service-related products have gained more attention than general products on the existing social commerce sites. Based on the situation, the effect that the service quality of social commerce has on customer satisfaction was analyzed in this study. It is a study that analyzes how much the service quality affects the customer satisfaction after the purchase, targeting consumers who have made purchases of social commerce products. In the case of social commerce, it is well-known that the diversity and convenience of products have a significant effect on customer satisfaction. Social commerce is currently being dumped beyond the 900 sites and dozens of cases of news, real-time searches of popular portal sites appeared not to be bored enough to related sites to drive the popularity coming quickly dug into our everyday lives of human beings. Yet the perception of social commerce seems not properly established because of the new concept was suddenly going to go through penetration without a collective interpretation and acceptance process. Most of the companies that often mimic the syoseol commerce is large, the blame did not depart from the forms of social shopping. We believe that personal and exhibit their skills and talents, and to wonder to see the social rather than the individuals who make unilateral companies.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.2
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• pp.30-39
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• 2015

The English Landscape garden and picturesque aesthetics, which was in fashion during the 18th to early 19th century in England, has been accused of making people see the actual garden in terms of a static landscape painting without a synesthetic engagement in nature. As new optic devices such as diorama, panorama, photography, and cinematography were invented, ways of seeing nature transitioned from a perspective vision to a panoramic, that is, modern one. This study intends to uncover signs of this kind of modern vision in the picturesque aesthetics and visual representation of landscape gardener Humphry Repton. German garden theorist Christian Cay Lorenz Hirschfeld contended that the English landscape garden was a new style of designing landscape that followed the principle of the serpentine line, which produced movement in sightlines; thus, he considered garden art as a superior art form among all other genres. The signs of visual motion appear in Repton's sketches of "Red Books". Firstly, he designed systemic routes in his clients' properties by considering different types of movements between walks and drives. Secondly, he often used the visual effects of panoramic views for his sketches in order to allow his clients to experience the human visual field. Lastly, he constructed sequences of sketches in order to provide his clients with an illusion of movement; in other words, Repton's sketches functioned as potential visual media to produce the duration of time in a visual experience. Thus, the garden aesthetics of the time reflected the contemporary visual culture, that is to say, a panoramic vision pertaining to visual motion.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.855-865
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• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2169-2190
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• 2022

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.293-301
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• 2023

Unstandardized medical data collection and management are still being conducted manually, and studies are being conducted to classify CT data using deep learning to solve this problem. However, most studies are developing models based only on the axial plane, which is a basic CT slice. Because CT images depict only human structures unlike general images, reconstructing CT scans alone can provide richer physical features. This study seeks to find ways to achieve higher performance through various methods of converting CT scan to 2D as well as axial planes. The training used 1042 CT scans from five body parts and collected 179 test sets and 448 with external datasets for model evaluation. To develop a deep learning model, we used InceptionResNetV2 pre-trained with ImageNet as a backbone and re-trained the entire layer of the model. As a result of the experiment, the reconstruction data model achieved 99.33% in body part classification, 1.12% higher than the axial model, and the axial model was higher only in brain and neck in contrast classification. In conclusion, it was possible to achieve more accurate performance when learning with data that shows better anatomical features than when trained with axial slice alone.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.96-103
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• 2023

Generally, human stampedes and crowd collapses occur when people press against each other, causing falls that may result in death or injury. Particularly, crowd accidents have become increasingly common since the 1990s, with an average of 380 deaths annually. For instance, in Korea, a stampede occurred during the Itaewon Halloween festival on October 29, 2022, when several people crowded onto a narrow, downhill road, which was 45 meters long and between 3.2 and 4 meters wide. Precisely, this stampede was primarily due to the excessive number of people relative to the road size. Essentially, stampedes can occur anywhere and at any time, not just at events, but also in other places where large crowds gather. More specifically, the likelihood of accidents increases when the crowd density exceeds a turbulence threshold of 5-6 /m². Meanwhile, festivals and events, which have become more frequent and are promoted through social media, garner people from near and far to a specific location. Besides, as cities grow, the number of people gathering in one place increases. While stampedes are rare, their impact is significant, and the uncertainty associated with them is high. Currently, there is no scientific system to analyze the risk of stampedes due to crowd concentration. Consequently, to prevent such accidents, it is essential to prepare for crowd disasters that reflect social changes and regional characteristics. Hence, this study proposes using digital topographic maps and crowd-density risk simulations to develop a 3D model of the region. Specifically, the crowd density simulation allows for an analysis of the density of people walking along specific paths, which enables the prediction of danger areas and the risk of crowding. By using the simulation method in this study, it is anticipated that safety measures can be rationally established for specific situations, such as local festivals, and preparations may be made for crowd accidents in downtown areas.

• Korean Journal of Plant Resources
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• v.36 no.5
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• pp.527-539
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• 2023

Pepper (Capsicum annuum L.) is an important vegetable and spice crop that has been cultivated worldwide. Pepper fruits have unique taste and aroma, providing a variety of antioxidants and compounds important for human health, which makes a high economic value. In addition, there is a high demand for new pepper varieties, according to consumer's preference. However, pepper is a recalcitrant plant for in vitro tissue and organ differentiation and plant regeneration, which makes it difficult to develop demanded varieties using newly developed technologies such as genetic engineering and gene editing. In this study, tissue culture and regeneration conditions were investigated using seven pepper varieties that were obtained from the core-collection of Seoul National University. We observed callus and bud induction and shoot formation using several media composition composed of different cytokinins and auxin concentrations. As a result, it was found that there were differences in callus induction and shoot formation of each variety depending on the hormone composition, and the highest regeneration was shown when the medium containing Zeatin Riboside and the petioles of seedlings were used. In particular, out of seven pepper varieties, CMV980 exhibited a higher regeneration efficiency (approximately 48%) than other varieties, followed by Yuwolcho. Therefore, this study provides CMV980 and Yuwolcho as good candidates that can be used for pepper transformation, which might contribute to the development of various varieties through gene editing technology in the future.

• Journal of Platform Technology
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• v.11 no.6
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• pp.56-67
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• 2023

The increased demand for electronic components, spurred by the Fourth Industrial Revolution and the COVID-19 pandemic, has facilitated human life but also escalated the production of e-waste. Discussions on the impact of e-waste have primarily revolved around environmental, health, and social issues, with global legislations focusing on addressing these concerns. However, e-waste poses unique security risks, such as potential technological and personal information leaks, unlike conventional waste. Current discourse on e-waste security is notably insufficient. This study aims to empirically analyze the relatively overlooked trends in e-waste security, employing three methodologies. Firstly, it assesses the general trend in discussions on e-waste by analyzing year-wise documents and media reports. Secondly, it identifies key trends in e-waste security by examining documents on the subject. Thirdly, the study reviews national security guidelines related to e-waste disposal to assess the necessity of designing security strategies for e-waste management. This research is significant as it is one of the first in korea to address e-waste from a security perspective and offers a multi-dimensional analysis of e-waste security trends. The findings are expected to enhance domestic awareness of e-waste and its security issues, providing an opportunity for proactive response to these security risks.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.