• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.387-393
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• 2022

The article reflects short-term perspectives on the use of information and communication technologies in the training of teachers for higher education. Education is characterized by conservatism, so aspects of systematic development of the industry are relevant to this cluster of social activity. Therefore, forecasting the introduction of innovative elements of ICT training is in demand for the educational environment. Forecasting educational trends are most relevant exactly in the issues of training future teachers of higher education because these specialists are actually the first to implement the acquired professional skills in pedagogical activities. The article aims to consider the existing potential of ICT-based learning, its implementation in the coming years, and promising innovative educational elements that may become relevant for the educational space in the future. The tasks of scientific exploration are to show the optimal formats of synergy between traditional and innovative models of learning. Based on already existing experience, extrapolation of conditions of educational process organization with modeling realities of using information and communication technologies in various learning dimensions should be carried out. Educational trends for the next 3 years are a rather tentative forecast because, as demonstrated by the events associated with the COVID-19 pandemic, the socio-cultural space is very changeable. Consequently, the dynamism of the educational environment dictates the need for a value-based awareness of the information society and the practical use of technological advances. Thus, information and communication technologies are a manifestation of innovative educational strategies of today and become an important component along with traditional aspects of educational process organization. Future higher education teachers should develop a training strategy taking into account the expediency of the ICT component.

• Korean Association of Arts Management
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• no.59
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• pp.5-31
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• 2021

The purpose of this study is to analyze social perceptions and discourses about creating arts in the era of artificial intelligence with making an implication of responding to the emergence of artificial intelligence. We conceptually understand the principles and limitations of creating visual arts using artificial intelligence whilst this paper addresses ai art in the social context by borrowing the theoretical lens from the sociology of arts. This article considers 472 newspapers about artificial intelligence art as the main data, which are interpreted through semantic network analysis. The analysis of this research shows that it is a controversial issue regarding who/which creates the artworks between humans and computers. However, judging from the dominant influence of a group of words representing the recognition of intellectual property rights, we have detected that social awareness is formed around the perspective of considering artificial intelligence creates visual arts rather than artists. In addition, based on the close relationship between the cluster and the cluster reflecting institutional support, we confirm that the discourse about artificial intelligence art is limited to technological development and legal system maintenance. Thus, this study suggests the need for defining artificial intelligence as the medium of art and constructing policy discourses on artificial intelligence art as an artistic genre.

• Journal of KIISE:Databases
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• v.37 no.3
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• pp.127-136
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• 2010

With rapid growth of information and computer communication technologies, the numbers of digital documents including multimedia data have been recently exploded. In particular, news video database and news video mining have became the subject of extensive research, to develop effective and efficient tools for manipulation and analysis of news videos, because of their information richness. However, many research focus on browsing, retrieval and summarization of news videos. Up to date, it is a relatively early state to discover and to analyse the plentiful latent semantic knowledge from news videos. In this paper, we propose the news video mining system based on multi-modal approach and text mining, which uses the visual-textual information of news video clips and their scripts. The proposed system systematically constructs a taxonomy of news video stories in automatic manner with hierarchical clustering algorithm which is one of text mining methods. Then, it multilaterally analyzes the topics of news video stories by means of time-cluster trend graph, weighted cluster growth index, and network analysis. To clarify the validity of our approach, we analyzed the news videos on "The Second Summit of South and North Korea in 2007".

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2037-2040
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• 2004

In this paper we introduce a new prototype based classifiers for overlapping data, where training pattern can be overlap on the feature space. The proposed classifier is based on the prototype from neural network classifier (NNC)[1] for overlap data. The method automatically chooses the initial center and two radiuses for each class. The center is used as a mean representative of training data for each class. The unclassified pattern is classified by measure distance from the class center. If the distance is in the lower (shorter radius) the unknown pattern has the high percentage of being in this class. If the distance is between the lower and upper (further radius), the pattern has the probability of being in this class or others. But if the distance is outside the upper, the pattern is not in this class. We borrow the words upper and lower from the rough set to represent the region of certainty [3]. The training algorithm to find number of cluster and their parameters (center, lower, upper) is presented. The clustering result is tested using patterns from Thai handwritten letter and the clustering result is very similar to human eyes clustering.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.42-48
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• 2015

Generally, the current commercial cloud system is hosted by the centralization large datacenter. However, the other clouding forms existed and one of them is the P2P cloud. The P2P Cloud is a distributed systems, is freedom to join and leave and is possible to provide the IaaS. Commonly, P2P Cloud System is based on Pure-P2P System. Since a number of connection paths exist, it has a high reliability and easily scalability of the network. However, there are disadvantages of the slow rate of route because of looking up the all peers in order to search for the peer. In this paper, it proposes the P2P cloud system based on super-peer P2P. Super-peer P2P system has fast routing time, because of searching for cluster unit and it also can has high reliability and high scalability. We designs the super Peer cloud service and proposes the system model and Resource Allocation Algorithm for IaaS in Super peer P2P environment.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.501-518
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• 2017

Reducing the time it takes for researchers to acquire knowledge and introduce them into research activities can be regarded as an indispensable factor in improving the productivity of research. The purpose of this research is to cluster the information usage patterns of KOSEN users and to suggest optimization method of personalized recommendation service algorithm for grouped users. Based on user research activities and usage information, after identifying appropriate services and contents, we applied a Spark based big data analysis technology to derive a personal recommendation algorithm. Individual recommendation algorithms can save time to search for user information and can help to find appropriate information.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.59-67
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• 2013

Compared to the conventional Hardware-oriented base stations, Software Defined Radio (SDR)-based base station provides various advantages especially in flexibility and expandability. It enables the multimode capability required in 4th-generation (4G) environment which aims at a convergence network of various kinds of communication standards. However, since a single base station processes all data required in various multiple waveforms, the SDR base station faces a problem of data processing speed. In this paper, we propose a new concept of SDR base station system which adopts a parallel processing technology of clustering environment. We implemented a WiMAX system with SDR concept which adopts the Message Passing Interface (MPI) technology which enables the speed-up operations. In order to maximize the efficiency of parallel processing in signal processing, we analyze how the algorithm at each of modules is related to data to be processed. Through the implemented system, we show a drastic improvement in operation time due to parallel processing using the proposed MPI technology. In addition, we demonstrate a feasibility of SDR system for 4G or even beyond-4G as well.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.252-258
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• 2013

As WSN is energy constraint so energy efficiency of nodes is important. Because avoiding long distance communication, clustering operating in rounds is an efficient algorithm for prolonging the lifetime of WSN and its performance depends on duration of a round. A short round time leads to frequent re-clustering while a long round time increases energy consume of cluster heads more. So existing clustering schemes determine proper round time, based on the parameters of initial WSN. But it is not appropriate to apply the round time according to initial value throughout the whole network time because WSN is very dynamic networks nodes can be added or vanished. In this paper we propose a new algorithm which calculates the round time relying on the alive node number to adapt the dynamic WSN. Simulation results validate the proposed algorithm has better performance in terms of energy consumption of nodes and loss rate of data.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.11
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• pp.1305-1317
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• 1999

NUMA 구조는 원격 메모리에 대한 접근이 불가피한 구조적 특성 때문에 상호 연결망이 성능을 좌우하는 큰 변수가 된다. 기존에 대중적으로 사용되던 버스는 물리적 확장성 및 대역폭에서 대규모 시스템을 구성하는 데 한계를 보인다. 이를 대체하는 고속의 지점간 링크를 사용한 링 구조는 버스가 가지는 확장성 및 대역폭의 한계라는 단점을 개선하였으나, 많은 클러스터가 연결되는 경우에는 전송 지연시간이 증가하는 문제점을 가지고 있다. 본 논문에서는 스누핑 프로토콜이 적용된 링 구조에서 클러스터 개수 증가에 따른 지연시간 증가의 문제점을 보완하기 위해 계층적 링 구조로의 확장을 제안하고, 이 구조에 효과적인 캐쉬 일관성 프로토콜을 설계하였다. 전역 링과 지역 링을 연결하는 브리지는 캐쉬 프로토콜을 관리하며 이 프로토콜에 의해 지역 링의 부하를 줄일 수 있도록 트랜잭션을 필터링하는 역할도 담당함으로써 시스템의 성능을 향상시킨다. probability-driven 시뮬레이터를 통해 계층적 링 구조가 시스템의 성능 및 링 이용률에 미치는 영향을 알아본다. Abstract Since NUMA architecture has to access remote memory, interconnection network performance determines performance of NUMA architecture. Bus, which has been used as popular interconnection network of NUMA, has a limit to build a large-scale system because of limited physical scalability and bandwidth. Ring interconnection network, composed of high-speed point-to-point link, made up for bus's defects of scalability and bandwidth. But, it also has problem of increasing delay as the number of clusters is increased. In this paper, we propose a hierarchical expansion of snoop-based ring architecture in order to overcome ring's defects of increasing delay. And we also design an efficient cache coherence protocol adopted to this architecture. Bridge, which connects local ring and global ring, maintains cache coherence protocol and does snoop-filtering which reduces local ring and cluster bus utilization. Therefore bridge can improve performance of this system. We analyze effects of hierarchical architecture on the performance of system and utilization of point-to-point links using probability-driven simulator.

• The Journal of the Korea Contents Association
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• v.14 no.6
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• pp.93-107
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• 2014

Recently Korean film industry has continuously experienced a rapid growth and expanded its influence to foreign countries. Accordingly, there have been numerous studies in various research areas to investigate the characteristics of the industry. In this study, we applied social network analysis based on the attributes such as genres, ratings, distributors, nationalities, sizes, and profitability to divide the movies into several clusters with respect to their similarity. Results suggested that the ratings and nationality rather than the genre are the major factors to divide the motion picture market into clusters and the profitability also varies much across the clusters. Furthermore, estimation of the diffusion model showed the positive relationship between the success of a movie and the word-of-mouth effect, while the relatively unsuccessful titles exhibited a monotonic decreasing diffusion pattern with the high initial demand and the early peak time.