• KNOM Review
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• v.24 no.1
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• pp.20-28
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• 2021

Extensive use of social media applications and mobile devices continues to increase data traffic. Social media applications generate an endless and massive amount of multimedia traffic, specifically video traffic. Many social media platforms such as YouTube, Daily Motion, and Netflix generate endless video traffic. On these platforms, only a few popular videos are requested many times as compared to other videos. These popular videos should be cached in the user vicinity to meet continuous user demands. MEC has emerged as an essential paradigm for handling consistent user demand and caching videos in user proximity. The problem is to understand how user demand pattern varies with time. This paper analyzes three publicly available datasets, MovieLens 20M, MovieLens 100K, and The Movies Dataset, to find the user request pattern over time. We find hourly, daily, monthly, and yearly trends of all the datasets. Our resulted pattern could be used in other research while generating and analyzing the user request pattern in MEC-based video caching scenarios.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.126-141
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• 2019

One of the Cooperative Intelligent Transportation System(C-ITS) priority services is collision prevention support service. Several studies have considered V2I2V communication-based collision prevention support services using Artificial Neural Networks(ANN). However, such services still show some issues due to a low penetration of C-ITS devices and large delay, particularly when loading massive traffic data into the server in the C-ITS center. This study proposes the Artificial Neural Network-based Collision Warning Service(ACWS), which allows upstream vehicle to update pre-determined weights involved in the ANN by using real-time sectional traffic information. This research evaluates the proposed service with respect to various penetration rates and delays. The evaluation result shows the performance of the ACWS increases as the penetration rate of the C-ITS devices in the vehicles increases or the delay decreases. Furthermore, it reveals a better performance is observed in more advanced ANN model-based ACWS for any given set of conditions.

• Journal of the Microelectronics and Packaging Society
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• v.27 no.2
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• pp.39-44
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• 2020

Recently, numerous researches are being conducted in flexible substrate to apply to wearable devices. Particularly, Conductive substrate researches that can implement the wearable devices on clothing are massive. In this study, we formed fiber substrate spraying CNT and Pd mixed solution on it and plated metal layer with electroless plating. Used SEM equipment and EDS analysis to analysis structure of the plated fiber substrate and discovered Ni layer was created. For check electrical properties, mapping was performed to check surface resistance and distribution of resistance of electroless plated fiber substrate with 4-point probe. It was confirmed that conductivity was improved as the duration of electroless plating was increased, and it was found that distribution of resistance by surface location was uniform. Changes in resistance due to mechanical stress were measured through tensile, bending, and twisting tests. As a result, it was confirmed that resistance change of flexible substrate gradually disappeared as plating time increased. Using UTM (Universal testing machine), it was analyzed mechanical properties of the electroless plated substrate with respect to changes in plating time were improved. In the case of conductive fiber substrate in which electroless plating was performed for 2 hours, tensile strength was increased by 16 MPa than fiber substrate. Based on these results, we found that Ni-CNT-Fabric flexible substrate is adequate for clothing-intergrated conductive substrate and we positively expect that this experiment shows flexible substrate can adapt to and develop not only a wearable device technology but also other fields needing flexibility such as battery, catalyst and solar cell.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.1163-1186
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• 2013

Big science such as nuclear fusion accelerators shares the characteristic of requiring long-term and massive budget input, human power, and related state-of-the-art technology. Because big science, by nature, thus requires large-scale budgets and facilities yet harbors the possibility of failure, most projects are led by the government. When the actual circumstances are examined, however, such projects are often implemented through the formation of cooperative relations with small and medium businesses (SMBs) possessing outstanding technological capacity. On the other hand, the reality is that the entry of corporations into the business ecosystem of big science is not easy and that even those that have once entered big science likewise fail to find sales outlets for technology that they have developed following the supply of single items, thus leading their technological capacity to lie idle. Consequently, based on an awareness of the problem, the present study seeks to propose strategies for activating the business ecosystem of nuclear fusion and accelerators and to present alternatives regarding which policy tasks must be pursued first by using the analytic hierarchy process (AHP) technique. The present study derived the four policy alternatives of approach, care, expansion, and infrastructures in accordance with the results of empirical analysis to activate the business ecosystem of nuclear fusion and accelerators and analyzed their priority in terms of urgency and effectiveness, the results of which were, in this order: care-approach-expansion-infrastructures. The significance of such research results lie in presenting the policy direction when the government determines which policy task must be pursued first and implements strategies for the activation of the business ecosystem of nuclear fusion and accelerators with limited financial resources in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4717-4737
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• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.113-120
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• 2018

In this study, we proposed a monitoring system for identifying and handling faulty sensing stream data on manufacturing equipments where low-cost sensors can be safely used. Low cost sensors will lessen the cost of implementing distributed monitoring system, but suffer from sensor noises and inaccurate sensed data. Therefore, a distributed monitoring system with low cost sensors should identify faulty signal data as either of sensor fault or machine fault, and filter out faulty signals from sensing fault. To this end, we adopted a fourier transform based diagnostic approach mixed with a weighed moving averaging method, in order to identify faulty signals. We measured how effective our approach is and found out our approach can filter out one-third faulty signals from our experimental environment. In addition, we attached wireless communication modules to reduce sensor and network installation cost. To handle massive sensor data efficiently, we employed unstructured data format with NoSQL based database.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.105-119
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• 2006

The car navigation system to be accompanied to the car on-board equipment system or the development of mobile communication technique, the demand in information communication which connects an interior and the car outside is coming to be high, As applications, ETC/VISC/AHS classes get deceived supply are advanced. The research of DSRC radio systems actively, with medium of communication between the automobile and road, is advanced. DSRC radio systems are appropriate in massive data transfer, in the case which the traffic accident evasion is urgent, the notarization of the preferential control function which is necessary to a medium of communication, guarantee and security are suitable in the high-speed network. Accompanied to the cellular phone which is to be supplied recently suddenly, By complementing and coexisting each other, and it will be developed simultaneously. However, in a connection of this kind of communication system and high-speed DSRC radio system, Hand-over technique (network, radio transmission hand-over), there is a technical subject of the high-speed transmission techniques against the mobile devices and the realization is expected to be difficult in near, until 2010 year is becoming the plan of putting to practical use. Also as the next generation DSRC with 5.8GHz built-on board equipment and the road-side equipment are expected in near. In this paper DSRC systems which will be developed are discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.13-20
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• 2020

One of the major issues surrounding big data is the availability of massive time-based or telemetry data. Now, the appearance of low cost capture and storage devices has become possible to get very detailed time data to be used for further analysis. Thus, we can use these time data to get more knowledge about the underlying system or to predict future events with higher accuracy. In particular, it is very important to define custom tailored contract offers for many households and businesses having smart meter records and predict the future electricity usage to protect the electricity companies from power shortage or power surplus. It is required to identify a few groups with common electricity behavior to make it worth the creation of customized contract offers. This study suggests big data transformation as a side effect and clustering technique to understand the electricity usage pattern by using the open data related to smart meter and KNIME which is an open source platform for data analytics, providing a user-friendly graphical workbench for the entire analysis process. While the big data components are not open source, they are also available for a trial if required. After importing, cleaning and transforming the smart meter big data, it is possible to interpret each meter data in terms of electricity usage behavior through a dynamic time warping method.

• Korean Journal of Ecology and Environment
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• v.42 no.3
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• pp.404-406
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• 2009

This study note wished to introduce special-purpose ship for algae removal that is developed by core technology of our country. The ship is consisted of main frame and adjuvant that can attach and detach as cross (+) shape of a character. The characteristics of ship are super light weight and low draft. That is consisted of four devices as suction, collection, filtration and recovering units. Among these, filtration used screen filter (mesh size 30 ${\mu}m$). Also, can separate and remove water and algae by compression air participle notion. Percentage of moisture content of concentrated algal particle was 85%. Water parted with algae finally is exhausted to water area. Removal efficiency that compare by chlorophyll-$\alpha$ concentration was about 57% (inflow: 83.2 ${\mu}g\;L^{-1}$, outflow: 35.8 $[\mu}g\;L^{-1}$) without physical and chemical pretreatment. Forward, need to achieve effect test in various conditions (algal biomass, flow etc.) for efficiency and technological elevation of exclusion device. We wished to contribute in presuppression system construction of massive algal development that manage blue-green algae occurrence area effectively, and prevents spread as lower part of reservoir.