• Korean Journal of Optics and Photonics
• /
• v.24 no.4
• /
• pp.196-200
• /
• 2013

Heretofore, it was enough that most of optical transceiver modules for automotive networks have the performance of data rate from 10 Mbps to 150 Mbps. As the required data rate in automotive infotainment systems has recently been increasing, the development of a new optical transceiver having high speed data rate over 1Gbps is now required. Therefore, we suggested a next-generation bi-directional optical transceiver module using vertical cavity surface emitting laser technology and plastic clad fiber technology, for the next-generation automotive optical network, MOST1000. We fabricated the high-speed and compact optical transceiver having 1 Gbps data rate and -22 dBm sensitivity satisfying bit error rate $10^{-12}$.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.2 s.46
• /
• pp.141-150
• /
• 2007

This paper proposes how to reduce the amount of data transmitted in each sensor and cluster head in order to lengthen the lifetime of sensor network. The most important factor of reducing the sensor's energy dissipation is to reduce the amount of messages transmitted. This paper proposed is to classify the node's cluster state into 6 categories in order to reduce both the number and amount of data transmission: Initial, Cluster Head, Cluster Member, Non-transmission Cluster Head, Non-transmission Cluster Member, and Sleep. This should increase the efficiency of filtering and decrease the inaccuracy of the data compared to the methods which enlarge the filter width to do more filtering. This method is much more efficient and effective than the previous work. We show through various experiments that our scheme reduces the network traffic significantly and increases the network's lifetime than existing methods.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.38 no.2
• /
• pp.75-93
• /
• 2010

Seen topographically, Busan is a city that is coastal and hilly. In the city, most parks have been formed around mountain areas that are not so useful. They also are unbalanced in location among different regions of the city. The purpose of this study is to find how to manage urban parks towards green network promotion. For the purpose, this researcher first analyzed physical and environmental characteristics of urban parks located within the main living spheres of Busan. Then, the researcher examined interactive relations between those parks and downtown areas surrounding them to classify types of the parks. In association, the researcher classified the entire of the city into inland and coastal regions. And the researcher examined mountainous and hilly urban parks that were 150 to 300 meters above sea level in the former region and 100 to 150 meters above sea level in the latter. Findings of the study can be summarized as follows. The above examination found that parks of Busan feature physically penetrating and overlapping with downtown areas of the city. How well the green zones of Busan in form of urban park are inter-connective and influential to each other heavily depends on shapes and functions that the downtown areas of the city have. In this study, urban parks of Busan were grouped according to their types and then analyzed. Based on results of the analysis, the researcher tried to find how to increase the utility of another urban parks that are expected to be formed and how to promote so-called the green network that integrates greens. Considering findings of the study, the researcher would make the following suggestions. In case of forming an urban park in a gently sloped green zone which is easily accessible and noticeable, it's important that the park should include a stream to which another green zone is converged or, if the park is located near a costal area, contribute to promote urban functions and openness. While, in a high-altitude green zone, it's more effective to form so-called the green way that consists of some limited usable site of the zone and greens behind it and then form a hub of regional community at the intersection between the main road and main gate to the urban park, contributing to the green network promotion.

• Korean journal of communication and information
• /
• v.60
• /
• pp.150-171
• /
• 2012

According to the result of the online survey of SNS users, SNS users' use motivations consist of 'information', 'participation', and 'interaction'. SNS use motivations explain characteristics of an individual SNS very well. SNS users that aim to collect information use much more the SNS for communication like 'Twitter' and 'Me2day' than other SNS. SNS users that aim to participate in communication through SNS use much more 'Cyworld' that is joined by the most subscriber. And SNS users that aim to interact with other users use much more the SNS for network like 'Facebook' and 'Cyworld'. This tendency can also be seen in the use hours and access times of SNS by SNS use motivations. Meanwhile, the SNS Users that aim to collect information and interact with other users positively rate SNS messages. On the other hand, the SNS Users that aim to participate in communication through SNS negatively rate those. This confirms that SNS use motivations affect SNS users' recognition of SNS messages.

• Journal of Digital Convergence
• /
• v.13 no.5
• /
• pp.139-150
• /
• 2015

Social Network Service is one of platforms rapidly growing in the environment of digital convergence. Most of researches regarding social network service(SNS) done up to the present time are concerning about technic and effectiveness characteristics of SNS. However, this study focuses on the roll of self-disclosure in the use of social network services(SNS). From the perspectives of personal and social motivation rather than the effectiveness and technic characteristics of SNS. the relations among self-disclosure, enjoyment, social ties and intention to use have been tested empirically. The results from this study shows that self-disclosure has not directly effects on the intention to use of SNS, but indirectly effects on it through personal enjoyment and desire to have social ties.

• Journal of Fisheries and Marine Sciences Education
• /
• v.28 no.3
• /
• pp.872-880
• /
• 2016

The purpose of this study is to find the research trends relating to flipped learning through keyword network analysis. For investigating this topic, final 100 papers (removed due to overlap in all 205 papers) were selected as subjects from the result of research databases such as RISS, DBPIA, and KISS. After keyword extraction, coding, and data cleaning, we made a 2-mode network with final 202 keywords. In order to find out the research trends, frequency analysis, social network structural property analysis based on co-keyword network modeling, and social network centrality analysis were used. Followings were the results of the research: (a) Achievement, writing, blended learning, teaching and learning model, learner centered education, cooperative leaning, and learning motivation, and self-regulated learning were found to be the most common keywords except flipped learning. (b) Density was .088, and geodesic distance was 3.150 based on keyword network type 2. (c) Teaching and learning model, blended learning, and satisfaction were centrally located and closed related to other keywords. Satisfaction, teaching and learning model blended learning, motivation, writing, communication, and achievement were playing an intermediary role among other keywords.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.93-107
• /
• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.1-7
• /
• 2022

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.

• Journal of KIISE:Information Networking
• /
• v.30 no.3
• /
• pp.329-342
• /
• 2003

Most of the existing multicast routing protocols for ad-hoc networks do not take into account the efficiency of the protocol for the cases when there are large number of sources in the multicast group, resulting in either large overhead or poor data delivery ratio when the number of sources is large. In this paper, we propose a multicast routing protocol for ad-hoc networks, which particularly considers the scalability of the protocol in terms of the number of sources in the multicast groups. The proposed protocol designates a set of sources as the core sources. Each core source is a root of each tree that reaches all the destinations of the multicast group. The union of these trees constitutes the data delivery mesh, and each of the non-core sources finds the nearest core source in order to delegate its data delivery. For the efficient operation of the proposed protocol, it is important to have an appropriate number of core sources. Having too many of the core sources incurs excessive control and data packet overhead, whereas having too little of them results in a vulnerable and overloaded data delivery mesh. The data delivery mesh is optimally reconfigured through the periodic control message flooding from the core sources, whereas the connectivity of the mesh is maintained by a persistent local mesh recovery mechanism. The simulation results show that the proposed protocol achieves an efficient multicast communication with high data delivery ratio and low communication overhead compared with the other existing multicast routing protocols when there are multiple sources in the multicast group.

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