• Korean Journal of Culture and Social Issue
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• v.19 no.2
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• pp.259-283
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• 2013

It seems that Internet based communication has been settled down in everyday life. Internet based communication studies also have been done and they proposed that internet based communication modal differs from other communications modal. One of the major themes about internet based communication was the effect of internet based communication on relationships. Early studies suggested that internet has negative effect on life and relationships, although it has positive effect on economics and information distribution. Because there is relative anonymity, People and Researchers thought that people easily could be exposed to negative situations like pornography, instant relationship, negative reply and soon. However,Recently there have been on going un-solving arguments about effect of internet based communication.From the negative perspective, Internet based communication is negative to relationship, because internet based communication could displace face to fact communication and old off-line relationships. However, from the positive perspective, researchers focused on the motivation and purpose of internet users. In this paradigm, people could expand their life and relationships using internet because internet could remove the various restrictions for relationship. Moreover they also suggested that people could enlarge their relationships because they could easily disclose theirselves in anonymity. However, No conclusion has been drawn yet and there needs some organization of two standpoints. Accordingly, This study is integrating the two perspectives and proposing future direction of internet based communication and relationship.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.787-799
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• 2023

As IoT devices are widely used at home, IoT automation system that is integrate IoT devices for users' demand are gaining populrity. There is automation rule in IoT automation system that is collecting event and command action. But attacker delay the packet and make time that real state is inconsistent with state recongnized by the system. During the time, the system does not work correctly by predefined automation rule. There is proposed some detection method for delay attack, they have limitations for application to IoT systems that are sensitive to traffic volume and battery consumption. This paper proposes a practical packet delay attack detection technique that can be applied to IoT systems. The proposal scheme in this paper can recognize that, for example, when a sensor transmits an message, an broadcast packet notifying the transmission of a message is sent to the Server recognized that event has occurred. For evaluation purposes, an IoT system implemented using Raspberry Pi was configured, and it was demonstrated that the system can detect packet delay attacks within an average of 2.2 sec. The experimental results showed a power consumption Overhead of an average of 2.5 mA per second and a traffic Overhead of 15%. We demonstrate that our method can detect delay attack efficiently compared to preciously proposed method.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.53-66
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• 2023

Recently, there has been a significant interest in the development of autonomous driving simulation environment based on digital twin. In the development of such digital twin-based simulation environment, many researches has been conducted not only performance and functionality validation of autonomous driving, but also generation of virtual training data for deep learning. However, such digital twin-based autonomous driving simulation system has the problem of requiring a significant amount of time and cost for the system development and the data construction. Therefore, in this research, we aim to propose a method for rapidly designing and implementing a digital twin-based autonomous driving simulation system, using only the existing 3D models and high-definition map. Specifically, we propose a method for integrating 3D model of FBX and NGII HD Map for the Busan EDC area into CARLA, and a method for adding and modifying CARLA functions. The results of this research show that it is possible to rapidly design and implement the simulation system at a low cost by using the existing 3D models and NGII HD map. Also, the results show that our system can support various functions such as simulation scenario configuration, user-defined driving, and real-time simulation of traffic light states. We expect that usability of the system will be significantly improved when it is applied to broader geographical area in the future.

• Information Systems Review
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• v.23 no.3
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• pp.201-223
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• 2021

Smart home is an advanced Internet of Things (IoT) service that enhances the convenience of human daily life and improves the quality of life at home. Recently, with the emergence of smart home products and services to which artificial intelligence (AI) technology is applied, interest in smart home is increasing. To gain a competitive edge in the smart home market, companies are providing "personalized service" to users, which is a key service that can promote smart home use. This study investigates the factors affecting the value awareness of personalized service and intention to use smart home. This research focuses on four-dimensional motivated innovativeness (cognitive, functional, hedonic, and social innovativeness) and privacy risk awareness as key factors that influence the value awareness of personalized service of smart home. In particular, this study conducts a comparative analysis between the generation MZ (young people in late teens to 30s), who are showing socially differentiated characteristics, and the generation X and baby boomers in 40s to 50s or older. Based on the analysis results, this study derives the distinctive characteristics of generation MZ that are different from the older generation, and provides academic and practical implications for expanding the use of smart home services.

• Information Systems Review
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• v.24 no.1
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• pp.1-19
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• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Information Systems Review
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• v.20 no.3
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• pp.17-31
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• 2018

In recent years, success of information system projects to possess competitive advantage in business has become very important for stakeholders. Stakeholders who are interested in the success of information system projects typically consist of users who need the system, developers who build it, and project managers who are responsible for project success. However, recently, there has been increasing in the number of business analysts engaged in bridging relationships among these stakeholders in information system projects. So far, there have been many researches on the competence of users, developers or project managers. But, the research on the competencies of business analysts has not been done much. So, in this study, what competencies are needed for business analysts who are engaged in information system projects are researched, and the level and difference of stakeholders' expectations and satisfaction with them are identified, using IPA techniques. The results of this study are expected to contribute greatly to providing basic information on the development of competency models or training programs needed for recruitment, evaluation and training of business analysts who are or will be engaged in information system projects.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.693-700
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• 2024

As of the end of March 2022, the total area of domestic industrial complexes is 606 km2, which is only about 0.6% of the total land area. However, as of 2018, the annual energy consumption of domestic industrial complexes is 110,866.1 thousand TOE, accounting for 53.5% of the country's total energy consumption and 83.1% of the entire industrial sector energy consumption. In addition, industrial complexes have a significant impact on the environment, accounting for 45.1% of the country's total greenhouse gas emissions and 76.8% of industrial sector greenhouse gas emissions. Under this background, in this study, in order to contribute to the energy efficiency of industrial complexes, a prediction study on energy demand and supply for an industrial complex in Korea using machine learning was conducted. In addition, a simulator UI screen was designed to more efficiently convey information on energy demand/supply prediction results and energy consumption status. Among the machine learning algorithms, Multi-Layer Perceptron (MLP) was used, and Bayesian Optimization was applied as an optimization technique for the prediction model. The energy prediction model for the industrial complex built in this study showed a prediction accuracy of 87.90% for compressed air demand and 99.54% for the flow rate available for the public air compressor.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.173-182
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• 2024

In recent, there has been a trend toward primarily utilizing data-driven models employing artificial intelligence technologies, such as machine learning, for flood prediction. These data-driven models offer the advantage of utilizing pre-training results, significantly reducing the required simulation time. However, it remains that a considerable amount of flood data is necessary for the pre-training in data-driven models, while the available observed data for application is often insufficient. As an alternative, validated simulation results from physically-based models are being employed as pre-training data alongside observed data. In this context, we developed a flood mapping accelerator to generate flood maps for pre-training. The proposed accelerator automates the entire process of flood mapping, i.e., estimating flood discharge using HEC-1, calculating water surface levels using HEC-RAS, simulating channel overflow and generating flood maps using RAS Mapper. With the accelerator, users can easily prepare a database for pre-training of data-driven models from hundreds to tens of thousands of rainfall scenarios. It includes various convenient menus containing a Graphic User Interface(GUI), and its practical applicability has been validated across 26 test-beds.

• The Transactions of the Korea Information Processing Society
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• v.13 no.8
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• pp.374-381
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• 2024

Documentation repositories on GitHub are used to share information that is helpful in performing various tasks. Popular documentation repositories have an advantage in attracting contributors who can help manage and extend documentation repository. Therefore, it is important to understand the characteristic of documentation repositories helpful to obtain popularity for developing strategies attracting attention of users. This paper presents a study on GitHub documentation repositories. To conduct the study, we collected 566 documentation repositories from GitHub and manually categorized their topic into 30 topics. Based on the stargazer score of the collected documentation repositories, we divided the collected documentation repositories into popular and unpopular documentation repository groups and investigated the topics in the popular documentation group. Then we statistically examined the differences in README characteristics of the popular and unpopular documentation repository groups. As a result, we found that the studied documentation repositories have 23 popular topics. We also found that the popular and unpopular documentation repository groups have differences in 5 README characteristics. The result of our study indicates that what documentation repository become popular in GitHub.