• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.133-144
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• 2019

The purpose of this study is to analyze the difference in perceptions of job applicants on core competency in the recruitment environment based on National Competence Standards(NCS), and also to obtain information relating to the seeking of employment and employment education plans. A survey and paired t-test were conducted on 355 job applicants of vocational colleges and four-year universities. Through these applicants, the difference between the current level and level of importance dependent on areas of core competency was analyzed. In addition, IPA analysis was conducted in order to confirm the educational necessity in this area. As a result, there were significant differences between the current level and level of importance in all 10 areas. IPA analysis confirmed that educational needs are prioritized. As a result, it was found that the areas which should be strengthened in both groups were interpersonal ability, communication ability, numerical ability, problem solving ability, and self-development ability. The area recognized as being in 'a state of excess' was resource management ability. As a result of checking the necessity levels of the two groups, it was found that job applicants of vocational colleges had to strengthen their problem solving ability, and the applicants of four-year universities had to intensify their communication ability, interpersonal ability, and information ability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.313-315
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• 2019

Although Korea's fashion market has negative growth, it has been growing again since 2018. This phenomenon means that people are becoming more interested in fashion. As interest in fashion grows, people visit various community sites for reference to find a suitable coordination for themselves. Most community sites, however, are manually categorizing each garment. Not only do these tasks take a lot of time, but they also make it difficult to search for multiple clothing at the same time. In other words, I can't choose what I want at the same time, and if I choose what I want, I have to look at what the model is wearing and refer to it. The problem with this may not help because the coordination in which the model provided is worn is more likely to be the one that the user does not want. In this paper, when the image is uploaded to improve the problem, the clothing is analyzed with AI analysis model and automatically classified and stored. Therefore, not only can you search for one clothes in the existing way, but you can also search for multiple clothes at the same time. The service is expected to allow more people to easily find and refer to the code for themselves.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.117-130
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• 2021

The government's social distancing policy and concerns about COVID-19 are increasing restrictions on outdoor leisure activities. Based on the decrease in outdoor leisure activities and the increase in indoor leisure activities, The purpose of this study is to examine the correlation between the degree of new confirmed cases of COVID-19 and the number of VOD views. This study conducted a time series analysis for 348 days from February 18, 2020 to January 31, 2021. Data were collected from the number of daily VOD views provided by the Korean Film Council and the number of new confirmed cases of COVID-19 provided by the Korea Centers for Disease Control and Prevention. The analysis showed that the number of confirmed COVID-19 cases has a significantly positive effect on the number of daily movie VOD views at the 5% significance level. This results indicate that the more confirmed cases of COVID-10, the more people watch movie VOD as indoor leisure activities. While previous studies examined the relationship between the confirmed cases of COVID-19 and indoor leisure activities in general, this study tried to academically contribute by analyzing the impact on specific indoor leisure activities. The practical implications of this study are as follows. The results of this study show that efficient promotions are possible based on significant social issues, such as infectious diseases. According to the results, promotions that respond quickly to changes are more effective than long-term promotions considering the climate or seasons. Due to the limitations of the data, the current study was conducted based only on PPV, but future research should also consider various billing forms such as PPM and SVOD.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.79-86
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• 2022

This study aims to analyze the research trends on the civic participation in a smart city and to present implications to policy makers, industry professionals and researchers. As rapid urbanization is defining development trend of modern city, urban problems such as transportation, environment, and energy are spreading and intensifying around the city. Countries around the world are introducing smart cities to solve these urban problems and to achieve sustainable development. Recently, many countries are modifying urban planning from top-down to down-up by actively engaging citizens to participate in the urban construction process directly and indirectly. Although the construction of smart cities is being promoted in Korea to solve urban problems, awareness of smart cities and civic participation are low. In order to overcome this situation, discussions on ideas and methods that can increase civic participation in smart cities are continuously being conducted. Therefore, in this study, by collecting publication containing both 'Smart Cities' and 'Participation (Engagement)' in Scopus DB, the topics of related studies were categorized and research trends were analyzed using topic modeling. Through this study, it is expected that it can be used as evidence to understand the direction of civic participation research in smart cities and to present the direction of related research in the future.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.317-321
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• 2022

In response to Incheon tap water accident in 2019, the Ministry of Environment has created the "Comprehensive Measures for Water Safety Management" to improve water operation management, provide systematic technical support, and respond to accidents. Accordingly, K-water is making a smart water supply management system for the entire process of tap water. In order to advance the response to water accidents, it is essential to secure the reliability of real-time water operation data such as flow rate, pressure, and water level, and to develop and apply a warning algorithm in advance using big data analysis techniques. In this paper, various statistical techniques are applied using water supply operation data (flow, pressure, water level, etc) to prepare the foundation for the selection of the optimal operating range and advancement of the monitoring and alarm system. In addition, the arrival time is analyzed through cross-correlation analysis of changes in raw water turbidity between the water intake and water treatment plants. The purpose of this paper is to study the model that predicts the raw water turbidity of a water treatment plant by applying raw water turbidity data considering the time delay according to the flow rate change.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.377-380
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• 2022

An application called an app can be downloaded and used on mobile devices. Among them, Android-based apps have the disadvantage of being implemented on an open source basis and can be exploited by anyone, but unlike iOS, which discloses only a small part of the source code, Android is implemented as an open source, so it can analyze the code. However, since anyone can participate in changing the source code of open source-based Android apps, the number of malicious codes increases and types are bound to vary. Malicious codes that increase exponentially in a short period of time are difficult for humans to detect one by one, so it is efficient to use a technique to detect malicious codes using AI. Most of the existing malicious app detection methods are to extract Features and detect malicious apps. Therefore, three ways to select the optimal feature to be used for learning after feature extraction are proposed. Finally, in the step of modeling with optimal features, ensemble techniques are used in addition to a single model. Ensemble techniques have already shown results beyond the performance of a single model, as has been shown in several studies. Therefore, this paper presents a plan to select the optimal feature and implement a learning model for Android app-based malicious code detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.358-360
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• 2022

Modern society is developing rapidly and technologies that provide convenience in living are developing day by day. On the other hand, the development of cyber attacks that threaten cybersecurity is developing faster, and it still adversely affects the industrial environment, and industrial damage is steadily occurring every year. Industrial security is an activity that safely protects major assets or technologies of companies and organizations from these attacks. Therefore, it is a situation that requires professional manpower for security. Currently, the manpower situation for security is staffed, but knowledge of the understanding and concept of industrial security jobs is insufficient. In other words, there is a lack of professional manpower for industrial security. It is the NCS that came out to solve this problem. NCS is the state standardized ability (knowledge, attitude, skills, etc.) necessary to perform duties in the industrial field. NCS can systematically design the curriculum using NCS as well as help in hiring personnel, and NCS can be applied to the national qualification system. However, in the field of industrial security, NCS has not yet been developed and is still having difficulties in hiring personnel and curriculum. Although the NCS system in the field of industrial security has not been developed, this paper proposes the industrial security NCS to solve the problem of hiring professionals later and to help the field of industrial security NCS to be established later.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.165-167
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• 2022

The proportion of cat cats among companion animals has been increasing at an average annual rate of 25.4% since 2012. Cats have strong wildness compared to dogs, so they have a characteristic of hiding diseases well. Therefore, when the guardian finds out that the cat has a disease, the disease may have already worsened. Symptoms such as anorexia (eating avoidance), vomiting, diarrhea, polydipsia, and polyuria in cats are some of the symptoms that appear in cat diseases such as diabetes, hyperthyroidism, renal failure, and panleukopenia. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia (drinking a lot of water), polyuria (a large amount of urine), and frequent urination (urinating frequently) more quickly. In this paper, 1) Efficient version of DeepLabCut for posture prediction running on an artificial intelligence server, 2) yolov4 for object detection, and 3) LSTM are used for behavior prediction. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the main server system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.