• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.19-43
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• 2021

The prediction of box office performance in performing arts institutions is an important issue in the performing arts industry and institutions. For this, traditional prediction methodology and data mining methodology using standardized data such as cast members, performance venues, and ticket prices have been proposed. However, although it is evident that audiences tend to seek out their intentions by the performance guide poster, few attempts were made to predict box office performance by analyzing poster images. Hence, the purpose of this study is to propose a deep learning application method that can predict box office success through performance-related poster images. Prediction was performed using deep learning algorithms such as Pure CNN, VGG-16, Inception-v3, and ResNet50 using poster images published on the KOPIS as learning data set. In addition, an ensemble with traditional regression analysis methodology was also attempted. As a result, it showed high discrimination performance exceeding 85% of box office prediction accuracy. This study is the first attempt to predict box office success using image data in the performing arts field, and the method proposed in this study can be applied to the areas of poster-based advertisements such as institutional promotions and corporate product advertisements.

• Journal of Platform Technology
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• v.8 no.3
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• pp.10-21
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• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.

• Knowledge Management Research
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• v.22 no.1
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• pp.243-267
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• 2021

Sentiment represents one's own state through the process of change to stimulus, and emotion represents a simple psychological state felt for a certain phenomenon. These two terms tend to be used interchangeably, but their meaning and usage are different. In this study, we try to find out how it affects the helpfulness of reviews by classifying sentiment and emotion through online reviews written by online consumers after purchasing and using various products and services. Recently, online reviews have become a very important factor for businesses and consumers. Helpful reviews play a key role in the decision-making process of potential customers and can be assessed through review helpfulness. The helpfulness of reviews is becoming increasingly important in practice as it is utilized in marketing strategies in business as well as in purchasing decision-making issues of consumers. And academically, the importance of research to find the factors influencing the helpfulness of reviews is growing. In this study, Yelp.com secured reviews on restaurants and conducted a study on how the sentiment and emotion of online reviews affect the helpfulness of reviews. Based on the prior research, a research model including sentiment and emotions for online reviews was built, and text mining analyzes how the sentiment and emotion of online reviews affect the helpfulness of online reviews, and the difference in the effects on emotions It was verified. The results showed that negative sentiment and emotion had a greater effect on review helpfulness, which was consistent with the negative bias theory.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.352-363
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• 2021

This study aimed to examine the different factors affecting the intention to purchase meal kits between at-home concept meal kits and eating-out concept meal kits. An online survey was conducted with 565 Korean participants including meal kit experienced (n=412) and non-experienced (n=153). Meal kits selection attributes were organized into five factors, health, price, environmental protection, convenience, and familiarity. To verify the hypothesis, SPSS Statistics 23 was used. The main results can be summarized as follows. First, in the case of the at-home menu, convenience(p < .01), environmental protection(p < .05), and familiarity(p < .01) show positive influences on willingness to buy. Second, for the eating-out menu, health(p < .001) and convenience(p < .001) have positive effects on willingness to buy, but familiarity(p<.01) has a negative effect. This is the first study to categorize the menu of meal kits and investigate each factor affecting willingness to buy. Therefore, the results can offer useful guidelines to meal kit marketers letting them know the consumers' purchase behaviors.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.441-453
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• 2021

Recently, the untact healthcare industry due to COVID-19 has been attracting attention, and the telemedicine industry based on medical information has become a field of the healthcare industry receiving attention. However, in Korea, due to obstacles in the legal system, telemedicine is still illegal between doctors and patients. In the case of neighboring China, the reality is the opposite of the recent rapid growth of the telemedicine industry under the leadership of the government. This study looks at this from the perspective of the industrial innovation system on the grounds that telemedicine is an industry and innovative technology needs to be changed to clarify the difference between domestic and Chinese telemedicine industries. As a result of analyzing China's telemedicine industry on the seven sub-divisions of demand conditions, innovators, networks et al., Such as seizing appropriate opportunities for demand driving effects and appropriate communication between economic actors were identified as major success factors. This researcher proposes the following suggestions. first, it conforms to the current digital New Deal policy flow, and conducts a demand survey on the change in demand for medical services in the 4th Industrial Revolution and the Untact Era. For the introduction, a plan to conduct a demand survey for the public and second, second, a plan to grow and intensively foster digital high-tech medical care as a new industry was suggested.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.97-112
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• 2021

This study introduces lessons learned from investigation and analysis of major domestic and overseas cases of marine chemical incidents involving hazardous and noxious substances (HNS) during maritime transportation by chemical tankers carrying petrochemical products in bulk. The study then suggests plans to improve domestic marine HNS training curricula based on these lessons. Lessons learned from six incident cases are classified into the following six categories: 1) incident-related information, 2) safety, 3) pollution, 4) response, 5) salvage and 6) others. Based on these six categories, it is suggested that the curriculum provided by the Marine Environment Research & Training Institute for marine pollution prevention managers aboard noxious liquid substance carriers should be changed from the existing two-day training of eight subjects (16 h) to a three-day training of sixteen subjects (24 h). In addition, it is proposed that the marine chemical incident response course of the Korea Coast Guard Academy should be changed from the existing five-day training of fifteen subjects (35 h) to a six-day training of thirty-two subjects (48 h). These results are expected to contribute to sharing experiences and lessons learned about response to marine chemical incidents and to be used as basic data for improving the education and training courses for response personnel in preparedness for marine HNS incidents.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.83-88
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• 2022

The purpose of this study is to present an analysis and implications for the metaverse-based virtual real estate transaction service. Through blockchain-based technology and metaverse, the world we live in is expanding naturally. Therefore, changes in the environment and perceptions of market participants are also very important factors. The concept and thinking about the existing asset value change and investment are also changing. This means that you can generate profits through value and investment in intangible assets. The service user aspect is a case of investing in the future value of virtual real estate that if more users participate rather than the present value, the principle of supply and demand will be applied to increase the number of consumers and the price will naturally rise according to the principle of scarcity. The service provider provides a technical platform for the service to directly transact the portion of the virtual area considered of interest directly through the virtual real estate purchase business. As the number of participants increases as well as funds and transaction fees, various revenue models such as advertisements can be discovered and provided. It plays the role of providing jobs and information through new services. As a stakeholder, governments can exploit the emergence of new technologies and products to create people and services and secure economic benefits. Of course, various institutional supports should be provided so that new services can settle in the market while mitigating risk factors. This study is meaningful in that it contributes to the establishment of a domestic metaverse-based environment and related research and is utilized in the study of virtual space real estate services.

• Journal of Korea Entertainment Industry Association
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• v.14 no.2
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• pp.165-176
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• 2020

Customized medical care and services timely providing effective prevention and treatment by collecting and using individuals' biomedical data are recently possible and utilized for users' health care. They are developed as the real-time health care services and information is provided to individuals by using smart phones, PC, tablet, etc. Interactive communication is supported by informing managers of analysis data and results, through collected data. It is therefore the time for constructing health care. This study attempts to prepare for patent applications of technical development at this time, by analyzing the tendency of smart wearable health care technologies, including biological signal-based health care devices and real-time health care system. Patents regarding smart wearable health care technologies were reported to have the relatively higher concentration of research development. Korea focuses on patent activities for real-time health care systems across the intervals of analysis, while U.S and European countries actively make efforts for patent activities regarding health care devices Japan conduct patent activities across health care devices and systems, based on bio-technologies. Korea has recently dominated the market of patents for bio-technologies-based health care devices and real-time health care devices and also appears to secure patents for the technologies and the market, so entry barriers to the market of smart wearable health care technologies are determined to be higher in Korea. It is important to establish the portfolios of patents, by securing patent rights for the figures of products, manufacturing methods and other related technical systems, if technologies are planned to be commercialized.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.429-437
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• 2022

Recently, the demand for a QA (Question Answering) system for human-machine communication has increased. Among the QA systems, a closed domain QA system that can handle spatial-related questions is called GeoQA. In this study, a new type of graph database, LPG (Labeled Property Graph) was used to overcome the limitations of the RDF (Resource Description Framework) based database, which was mainly used in the GeoQA field. In addition, GraphQL (Graph Query Language), an API-type query language, is introduced to address the fact that the LPG query language is not standardized and the GeoQA system may depend on specific products. In this study, database was built so that answers could be retrieved when spatial-related questions were entered. Each data was obtained from the national spatial information portal and local data open service. The spatial relationships between each spatial objects were calculated in advance and stored in edge form. The user's questions were first converted to GraphQL through FOL (First Order Logic) format and delivered to the database through the GraphQL server. The LPG used in the experiment is Neo4j, the graph database that currently has the highest market share, and some of the built-in functions and QGIS were used for spatial calculations. As a result of building the system, it was confirmed that the user's question could be transformed, processed through the Apollo GraphQL server, and an appropriate answer could be obtained from the database.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.485-496
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• 2022

This study is for the software implementation to generate atmospheric and surface reflectance products from RapidEye satellite imagery. The software is an extension based on Orfeo Toolbox (OTB) and an open-source remote sensing software including calibration modules which use an absolute atmospheric correction algorithm. In order to verify the performance of the program, the accuracy of the product was validated by a test image on the Radiometric Calibration Network (RadCalNet) site. In addition, the accuracy of the surface reflectance product generated from the KOMPSAT-3A image, the surface reflectance of Landsat Analysis Ready Data (ARD) of the same site, and near acquisition date were compared with RapidEye-based one. At the same time, a comparative study was carried out with the processing results using QUick Atmospheric Correction (QUAC) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) tool supported by a commercial tool for the same image. Similar to the KOMPSAT-3A-based surface reflectance product, the results obtained from RapidEye Extension showed accuracy of agreement level within 5%, compared with RadCalNet data. They also showed better accuracy in all band images than the results using QUAC or FLAASH tool. As the importance of the Red-Edge band in agriculture, forests, and the environment applications is being emphasized, it is expected that the utilization of the surface reflectance products of RapidEye images produced using this program will also increase.