• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.127-135
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• 2011

It is required that an e-learning system has a content recommendation component which helps a learner choose an item. In order to predict items concerning learner's interest, collaborative filtering and content-based filtering methods have been most widely used. The methods recommend items for a learner based on other learner's interests without considering the knowledge level of the learner. So, the effectiveness of the recommendation can be reduced when the number of overall users are relatively small. Also, it is not easy to recommend a newly added item. In order to address the problem, we propose a content recommendation method based on the similarity and the difficulty of an item. By using a recommendation function that reflects both characteristics of items, a higher-level leaner can choose more difficult but less similar items, while a lower-level learner can select less difficult but more similar items, Thus, a learner can be presented items according to his or her level of achievement, which is irrelevant to other learner's interest.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1877-1893
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• 2009

Folksonomy is a user-generated taxonomy to organize information by which a user assigns tags to resources published on the web. Triadic datas that indicate relations of between users, tags, and resources, are created by collaborative tagging from many users in folksonomy-based system. Such the folksonomy data has been utilized in the field of the semantic web and web2.0 as metadata about web resources. In this paper, we propose FCA-based folksonomy data mining approach in order to extract the useful information from folksonomy data with various points of view. And we developed tool for supporting our approach. In order to verify the usefulness of our proposed approach and FMT, we have done some experiments for data of del.icio.us, which is a popular folksonomy-based bookmarking system. And we report about result of our experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2241-2248
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• 2007

This paper proposes the web-based virtual collaboration among experts. The proposed system supports the presentation tool using Synchronous Multimedia Integration Language(SMIL) which is the easy and efficient way of adding multimedia to presentations. The presentation gives essential information to the participants before actual discuss. The participants use texts in discussing over the presented medical image. The spatial elements such as point or line, and some type of marker with their relative participants' comments can be set or removed dynamically to represent areas of interest in digital images. XML files are used for recording experts' opinions as well as the spatial elements that are associated with digital images during the discussion and stored for future reference. The participants can also set and reset a polygon in the image to select the interested area and refer to the stored relating information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.916-937
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• 2023

Most of the existing Distributed Denial-of-Service mitigation schemes in Software-Defined Networking are only implemented in the network domain managed by a single controller. In fact, the zombies for attackers to launch large-scale DDoS attacks are actually not in the same network domain. Therefore, abnormal traffic of DDoS attack will affect multiple paths and network domains. A single defense method is difficult to deal with large-scale DDoS attacks. The cooperative defense of multiple domains becomes an important means to effectively solve cross-domain DDoS attacks. We propose an efficient multi-domain DDoS cooperative defense mechanism by integrating blockchain and SDN architecture. It includes attack traceability, inter-domain information sharing and attack mitigation. In order to reduce the length of the marking path and shorten the traceability time, we propose an AS-level packet traceability method called ASPM. We propose an information sharing method across multiple domains based on blockchain and smart contract. It effectively solves the impact of DDoS illegal traffic on multiple domains. According to the traceability results, we designed a DDoS attack mitigation method by replacing the ACL list with the IP address black/gray list. The experimental results show that our ASPM traceability method requires less data packets, high traceability precision and low overhead. And blockchain-based inter-domain sharing scheme has low cost, high scalability and high security. Attack mitigation measures can prevent illegal data flow in a timely and efficient manner.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.274-284
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• 2016

The increasing penetration of distributed energy resources on the distribution networks have brought a number of technical impacts where voltage and thermal variations have been identified as the dominant effects. Active network management in distribution networks aims to integrate distributed energy resources with flexible network management so that distributed energy resources are organized to make better use of existing capacity and infrastructure. This paper propose active solutions which aims to solve the voltage and thermal issues in a distributed manner utilizing a collaborative approach. The proposed algorithms have been fully tested on a distribution network with distributed generation units.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.951-953
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• 2012

The industry field training course are offered a opportunities for industrial experience to students and Companies can take advantage of advanced workforce with minimum cost. Universities are taking a new collaborative educational systems that require on-site industry for the 21st century. Therefore, in this thesis, We intended to analyze factors influencing satisfaction of people studying in a situation that industry field training has been required to be a mainstream system of education in the midst of trend of changing paradigm of university education recently, and to suggest some possible solutions for industry field training at industrial complex campus.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.37-43
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• 2012

In this paper, we analysis wideband spectrum sensing with distance based weight combining for Cognitive Radio (CR) systems. CR systems is implemented the spectrum of the Primary User(PU) by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate (CFAR) algorithm for energy detection. The signal of PU is BPSK signal and the wireless channel between a PU and CR systems is modeled as Gaussian channel. From the simulation results, the wideband sensing with distance based and Distance based weight Combing (DWC) methods shows higher spectrum sensing performance than single CR user spectrum sensing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.690-692
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• 2014

Conventional one-way cramming education at schools has the disadvantage of poor student interest in learning, immersion, and learning efficiency as well as a limitation in realizing collective intelligence and collaborative learning. Therefore, an educational content service platform using a near-field communication(NFC) technology is required as a tool for encouraging the voluntary learning participation of students and increasing learning effectiveness through self-directed studying. This study focuses on the development of an educational content production system that creates high-quality education contents suitable for smart schools. In these schools, students and teachers generally communicate through an electronic blackboard using Bluetooth, which is an NFC technology. Further, the lecture notes of individual students are reproduced and collected as big data, which will facilitate the sharing of these notes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1687-1707
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• 2022

With the development of multiuser online meetings, more group-oriented technologies and applications for instance collaborative work are becoming increasingly important. Authenticated Group Key Agreement (AGKA) schemes provide a shared group key for users with after their identities are confirmed to guarantee the confidentiality and integrity of group communications. On the basis of the Public Key Cryptography (PKC) system used, AGKA can be classified as Public Key Infrastructure-based, Identity-based, and Certificateless. Because the latter type can solve the certificate management overhead and the key escrow problems of the first two types, Certificateless-AGKA (CL-AGKA) protocols have become a popular area of research. However, most CL-AGKA protocols are vulnerable to Public Key Replacement Attacks (PKRA) due to the lack of public key authentication. In the present work, we present a CL-AGKA scheme that can resist PKRA in order to solve impersonation attacks caused by those attacks. Beyond security, improving scheme efficiency is another direction for AGKA research. To reduce the communication and computation cost, we present a scheme with only one round of information interaction and construct a CL-AGKA scheme replacing the bilinear pairing with elliptic curve cryptography. Therefore, our scheme has good applicability to communication environments with limited bandwidth and computing capabilities.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.55-62
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• 2017

The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.