• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.820-822
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• 2010

Mobile WiMAX system provides various classes of traffic such as real-time and non-realtime services. These services have different QoS requirements and the QoS aware scheduling has been an important issue. Although many of scheduling algorithms for various services in OFDMA system have been proposed, it is needed to be modified to be applied to Mobile WiMAX system. Since Mobile WiMAX supports five kinds of service classes, it is important to take QoS characteristics of each class into consideration. In this paper, we propose an efficient packet scheduling algorithm to support QoS of each class. Proposed scheme selects a service class first considering QoS Characteristics of each class and choose an appropriate user in the selected class. Simulation results show that the proposed algorithm has better performance than the other algorithm.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• Journal of Communications and Networks
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• v.10 no.3
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• pp.351-361
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• 2008

This paper presents a delay-margin based traffic engineering (TE) approach to provide end-to-end quality of service (QoS) in multi-protocol label switching (MPLS) networks using differentiated services (DiffServ) at the link level. The TE, including delay, class, and route assignments, is formulated as a nonlinear optimization problem reflecting the inter-class and inter-link dependency introduced by DiffServ and end-to-end QoS requirements. Three algorithms are used to provide a solution to the problem: The first two, centralized offline route configuration and link-class delay assignment, operate in the convex areas of the feasible region to consecutively reduce the objective function using a per-link per-class decomposition of the objective function gradient. The third one is a heuristic that promotes/demotes connections at different links in order to deal with concave areas that may be produced by a trunk route usage of more than one class on a given link. Approximations of the three algorithms suitable for on-line distributed TE operation are also derived. Simulation is used to show that proposed approach can increase the number of users while maintaining end-to-end QoS requirements.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.141-151
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• 2008

In the paper, we analyze the performance of random access channels in multi-service multi-user OFDMA systems. The resource of the random access channels in OFDMA systems are available sub-channels and PN-codes. For given available sub-channels and PN-codes,' we analyze the performance of the random access channels of OFDMA systems in terms of the access success probability, the blocking probability, the access delay and the throughput per each service class. Further, we find the feasible region of the access probability of each service class in which the allowable minimum access success probability, the allowable maximum blocking probability and the allowable maximum access delay are satisfied. The results also can be used to find proper region of the access probabilities of each service class for differentiated quality of service(QoS)s, and for the system operations in multi-service multi-user OFDMA systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.383-386
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• 2007

Under the circumstance of Ubiquitous Society, service should be provided according to the users' location and environment. When specific service is demanded by user, the technology, which can help to detect whether there are devices performing the requested service in the ubiquitous network, is mandatory. This technology also should guarantee that users have the appropriate service through the most suitable device which is selected by this technology. The priority is an essential factor when multi service are asked by multi users simultaneouslyin the ubiquitous network, The paper focuses on realization of Smart Classroom which provides multi users, such as the professor and the students, with active services based on the Zigbee network and the Mobile platform.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.223-232
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• 2004

In this paper we propose the hierarchical-based dynamic bandwidth allocation algorithm for multi-class services in Ethernet-PON. The proposed algorithm consists of the high level scheduler in OLT and the low level scheduler in ONU. The hierarchical architecture is able to provide scalability and resource efficiency in Ethernet-PON which has the distributed nature of the scheduling domain, with queues and the scheduler located at a large distance from each other. We also propose three dynamic bandwidth allocation algorithms for the low level scheduler: Proportional Allocation algorithm, Maximum Request First Allocation (MRFA) algorithm and High Priority First Allocation (HPFA) algorithm. We implement the Ethernet-PON standardized in the IEEE 802,3ah using OPNET. We also evaluate and analyze the performance for the proposed algorithms in terms of channel utilization, queuing delay and the amount of remainder.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.3
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• pp.45-59
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• 2011

OFDMA systems have been expected to be widely used to provide multimedia services over wireless channels. To evaluate performance of the OFDMA system, power should be considered as system resource as well as subcarriers. This study propose a queueing traffic model incorporating two kinds of resources (power and subcarriers), and an extended model giving a priority to handoff calls over new calls. Some extensive experiments are conducted to illustrate the usefulness of the proposed traffic model.

• Journal of Communications and Networks
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• v.15 no.1
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• pp.15-24
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• 2013

Provisioning of quality of service (QoS) is a key issue in any multi-media system. However, in wireless systems, supporting QoS requirements of different traffic types is a more challenging problem due to the need to simultaneously minimize two performance metrics - the probability of dropping a handover call and the probability of blocking a new call. Since QoS requirements are not as stringent for non-real-time traffic, as opposed to real-time traffic, more calls can be accommodated by releasing some bandwidth from the already admitted non-real-time traffic calls. If the released bandwidth that is used to handle handover calls is larger than the released bandwidth that is used for new calls, then the resulting probability of dropping a handover call is smaller than the probability of blocking a new call. In this paper, we propose an efficient call admission control algorithm that relies on adaptive multi-level bandwidth-allocation scheme for non-realtime calls. The scheme allows reduction of the call dropping probability, along with an increase in the bandwidth utilization. The numerical results show that the proposed scheme is capable of attaining negligible handover call dropping probability without sacrificing bandwidth utilization.

• Journal of Families and Better Life
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• v.20 no.2
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• pp.83-95
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• 2002

The objectives of this study were 1) to analyze the attitudes about Elderly Assisted Living(EAL), 2) to consider the policy implications for elderly communal housing according to socio-economic status(income, occupation and education). The survey was undertaken from August to September, 2000 in Seoul and Kyunggi-Do(Ilsan, Bundang). The data were analyzed by SPSS Window program. The major findings were as follows: Most of the respondents prefered to live in EAL(over 56%), under 10 units and desired to be serviced(housework.leisure medical program.meal service). The upper class respondents preferred high-rise apartment style inner city or suburban and prefered paid services in EAL. They would willingly to pay over 800,000 won for living expenses and more than 200 million won for buying their own EAL. The middle class respondents prefered 5 stories multi-family house style with common facilities as a number. And they prefered to common facilities opening to public if the quality of facilities were better. The low class respondents prefered 5 stories multi-family house style and prefered to live within 30 minutes apart from their families and friends. They would pay under 300,000~600,000won for monthly living expenses and under 100 million won for their own EAL.

• Journal of Korean Academy of Nursing
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• v.20 no.1
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• pp.16-37
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• 1990

A cost analysis for hospitalized patients was carried out based upon Patient Classification System(PCS) in order to determine an appropriate nursing fee. The data were collected from 21 nursing units of three teaching hospitals from April 1 to June 30, 1989. first, all of the 22,056 inpatients were classified into mildly ill(Class Ⅰ), moderately ill(Class Ⅱ), acutely ill(Class Ⅲ), and critically ill(Class Ⅳ) by the PCS which had been carefully developed to be suitable for the Korean nursing units. Second. PCS cost accounting was applied to the above data. The distribution of inpatients, nursing costs, and nursing productivity were as follows : 1) Patient distribution ranged from 45% to class Ⅰ, 36% to class Ⅱ, 15% to class Ⅲ, and 4% to class Ⅳ, the proportion of class Ⅳ in ‘H’ Hospital was greater than that of the other two hospitals. 2) The proportion of Class Ⅲ and Ⅳ in the medical nursing units was greater than that of surgical nursing units. 3) The number of inpatients was greatest on Tuesdays, and least on Sundays. 4) The average nursing cost per hour was W 3,164 for ‘S’ hospital, W 3,511 for ‘H’ hospital and W 4,824 for ‘K’ hospital. The average nursing cost per patient per day was W 14,126 for ‘S’ Hospital, W 15,842 for ‘H’ hospital and W 21,525 for ‘K’ hospital. 5) The average nursing cost calculated by the PCS was W 13,232 for class Ⅰ, W 18,478 for class Ⅱ, W 23,000 for class Ⅲ, and W 25,469 for class Ⅳ. 6) The average nursing cost for the medical and surgical nursing units was W 13,180 and W 13,303 respetively for class Ⅰ, W 18,248 and W 18,707 for class Ⅱ, W 22,303 and W 23,696 for class Ⅲ, and W 24,331 and W 26,606 for class Ⅳ. 7) The nursing costs were composed of 85% for wages and fringe benefits, 3% for material supplies and 12% for overhead. The proportion of wages and fringe benefits among the three Hospitals ranged from 75%, 92% and 98% for the ‘S’, ‘H’, ‘K’ hospitals respectively These findings explain why the average nursing cost of ‘K’ hospital was higher than the others. 8) According to a multi- regression analysis, wages and fringe benefits, material supplies, and overhead had an equal influence on determining the nursing cost while the nursing hours had less influence. 9) The productivity of the medical nursing units were higher than the surgical nursing units, productivity of the D(TS) - nursing units was the lowest while the K(Med) - nursing unit was the highest in 'S' hospital. In ‘H’ hospital, productivity was related to the number of inpatients rather than to the characteristics of the nursing units. The ‘K’ hospital showed the same trend as ‘S’ hospital, that the productivity of the medical nursing unit was higher than the surgical nursing unit. The productivity of ‘S’ hospital was evaluated the highest followed by ‘H’ hospital and ‘K’ hospital. Future research on nursing costs should be extended to the other special nursing areas such as pediatric and psychiatric nursing units, and to ICU or operating rooms. Further, the PCS tool should be carefully evaluated for its appropriateness to all levels of institutions(primary, secondary, tertiary). This study took account only of the quantity of nursing services when developing the PCS tool for evaluating the productivity of nursing units. Future research should also consider the quality of nursing services including the appropriateness of nursing activities.