• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.570-575
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• 2022

The MX-S2X, utilizing high-frequency broadband communication technology, provides a reliable connection between land, ship, and facilities. This technology is expected to be effectively utilized as a future maritime communication infrastructure in the upcoming mixed navigational situation among autonomous and manned and/or unmanned ships. Following the physical layer design and M&S-based performance analysis of the MX-S2X system to overcome maritime multipath fading, this paper confirms the optimized and detailed design of physical layer hardware and implemented it to verify the performance. The PER(Packet Error Rate) performance was then measured by configuring a test environment to verify the implemented hardware. The results showed that the performance degradation was 0.2 dB in the AWGN environment and 1.2 dB in the Multi-path Fading on Sea Environment, thus confirming the successful implementation of the physical layer.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.63-68
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• 2022

This After the debut of 5G, our lives have changed a lot. In particular, the proliferation of wireless network services through smartphones and LTE has completely changed the existing mobile communication services that are limited to voice/text communication between individuals and individuals, and new innovative services have emerged in all aspects of personal and corporate activities. This study verified the relationship between the social characteristics of 5G services and users' willingness to use 5G services. It analyzed the influence relationship between independent variables (social reality, subjective norms), media variables (perceived usefulness) and dependent variables (use intention), set hypotheses, and identified the media effects of perceived usefulness. The measurement items of variables are defined, and the research model of 5G service usage intention is designed. A questionnaire survey was conducted on the measurement items for users who have experience in using 5G services. Based on this result, among the social factors of users of 5G services, social reality and subjective norms are suitable factors to improve users' intentions. And through this research we put forward the enlightenment, discussed the limitations of the research and future research directions.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.2
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• pp.1-25
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• 2023

This study aims to propose implications for the development of archival content of archives management institutions in Korea by analyzing cases of the archival content on Instagram of the national archives in the Anglosphere. The basic information of the research target's Instagram account, including the creation date, content, and the number of followers, was investigated, and the posts' contents and interaction types with high user responses were analyzed. As a result, to spread the records information service using Instagram, producing images and short-form content that can be intuitively checked through mobile screens and creating content that will attract the attention of primary users are required. Moreover, it is necessary to develop content for informative communications that can be shared with other users. There is also a need to enhance the exposure and searchability of the institution's Instagram account by strengthening connections with the institution's existing online resources and enabling communications, such as using hashtags, following related institutional accounts, and providing feedback on the contents' comments with followers. This study is meaningful in that it examined cases of archival content for Instagram and suggested their applications, and it can be used as basic data to help plan archival contents to spread the archival culture.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• Archives of design research
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• v.18 no.2 s.60
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• pp.155-164
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• 2005

Home-service robots need to have sufficient spatial information about the surroundings for interacting with human intelligently and performing services efficiently. It is very important to investigate the efficient interaction style that supports map building task through human-robot collaboration. We first analyzed map building task with a cleaning robot and drew 4 design factors and tentative solutions, including map building procedure (task-preferred procedure/space- preferred procedure), LCD display installation (robot/robot+remote control), navigation method (push type/pull type), feedback modality(GUI/GUI+TTS). The design factors and tentative solutions were defined as independent variables and levels. This research investigated how those variables affect to the human task performance and behavior in map building tast. 8 kinds of experiment prototypes were built and usability test among 16 house wives was conducted for acquiring empirical data. As the experiment result, in terms of map building procedure, space-preferred procedure indicated better task performance than task-proffered procedure as we expected. For the LCD display installation factor, remote control with LCD display indicated higher task performance and subjective satisfaction. In robot navigation method, it was very difficult to find a significant difference between push type and pull type which contrary to our expectation. In fact, push type indicated higher subjective satisfaction. Also in feedback modality, we have acquired negative feedback an additional TTS operation guidance. It seems that robot's autonomy before achieving spatial information is rudiment condition which means users are just interacting with a mobile appliance. Thus they prefer remote-control-based interaction style in robot map building process as they used in traditional appliance control.

• Asia pacific journal of information systems
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• v.24 no.3
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• pp.299-323
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• 2014

The reason location based service is drawing attention recently is because smart phones are being supplied increasingly. Smart phone, basically equipped with GPS that can identify location information, has the advantage that it can provide contents and services suitable for the user by identifying user location accurately. Offering such diverse advantages, location based services are increasingly used. In addition, for use of location based services, release of user's personal information and location data is essentially required. Regarding personal information and location data, in addition to IT companies, general companies also are conducting various profitable businesses and sales activities based on personal information, and in particular, personal location data, comprehending high value of use among personal information, are drawing high attentions. Increase in demand of personal information is raising the risk of personal information infringement, and infringements of personal location data also are increasing in frequency and degree. Therefore, infringements of personal information should be minimized through user's action and efforts to reinforce security along with Act on the Protection of Personal Information and Act on the Protection of Location Information. This study aimed to improve the importance of personal information privacy by empirically analyzing the effect of perceived values on the intention to strengthen location information security and continuously use location information for users who received location-based services (LBS) in mobile environments with the privacy calculation model of benefits and risks as a theoretical background. This study regarded situation-based provision, the benefit which users perceived while using location-based services, and the risk related to personal location information, a risk which occurs while using services, as independent variables and investigated the perceived values of the two variables. It also examined whether there were efforts to reduce risks related to personal location information according to the values of location- based services, which consumers perceived through the intention to strengthen security. Furthermore, it presented a study model which intended to investigate the effect of perceived values and intention of strengthening security on the continuous use of location-based services. A survey was conducted for three hundred ten users who had received location-based services via their smartphones to verify study hypotheses. Three hundred four questionnaires except problematic ones were collected. The hypotheses were verified, using a statistical method and a logical basis was presented. An empirical analysis was made on the data collected through the survey with SPSS 12.0 and SmartPLS 2.0 to verify respondents' demographic characteristics, an exploratory factor analysis and the appropriateness of the study model. As a result, it was shown that the users who had received location-based services were significantly influenced by the perceived value of their benefits, but risk related to location information did not have an effect on consumers' perceived values. Even though users perceived the risk related to personal location information while using services, it was viewed that users' perceived value had nothing to do with the use of location-based services. However, it was shown that users significantly responded to the intention of strengthening security in relation to location information risks and tended to use services continuously, strengthening positive efforts for security when their perceived values were high.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.227-245
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• 2023

As offline company and online·mobile startups meet in an untact shopping environment, competition among companies in untact shopping is increasing. In this situation, companies need their own clear strategy to create customer value. In particular, it is very important to focus on 'customer experience' to establish such a strategy in an untact shopping environment. Customer experience refers to all processes in which consumers meet and experience a company or brand at a touch point. In this processes consumers decide whether to continue to use the company and brand. In this situation, it is thought that it will be meaningful for research to examine the customer experience of untact shopping. Therefore, this study aimed to examine the customer experience of untact shopping, which is used by all generations after COVID-19, through experience quality, and to examine the impact on the expectation-confirmation Model of untact shopping. The results of this study are as follows. First, as a result of examining whether interaction quality, information quality, and outcome quality affect expectation-confirmation it was found that all qualities except interaction quality affect expectation matching. Second, as a result of examining whether interaction quality, information quality, and outcome quality affect perceived usefulness, it was found that all qualities except interaction quality had an effect. Next, as a result of applying the expectation confirmation model to the untact shopping environment and examining whether the expectation confirmation has an effect on use satisfaction, it was found that there was a positive effect. As a result of examining whether perceived usefulness affects use satisfaction, it was found to have a positive effect. As a result of examining whether perceived usefulness affects expectation confirmation, it was found that there is a positive effect. Finally, as a result of examining whether perceived usefulness affects the intention to continue using untact shopping, it was found to be positive. Next, as a result of examining the effect of use satisfaction on trust, it was found that there was a positive effect. Finally, as a result of investigating whether trust has an effect on the intention to continue using, it was found that there is a positive effect. Looking at the important results especially, information quality was found to have the greatest influence.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.