• Journal of the Korean School Mathematics Society
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• v.22 no.3
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• pp.277-302
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• 2019

In this study, we identified the effects of Self-Reflective Note(SRN) strategy, which used on 'college mathematics' courses, operated as a liberal arts curriculum course in university. For this purpose, we used SRN strategy on 'college mathematics' 3 classes, 'college mathematicsII' 1 class enrolled 95 students, and then analyzed the data. For identifying a change of students' learning, we conducted surveys related to the affective domain, core competencies, satisfaction. From this, we identified the followings. First, the interest, self-confidence, future expectation of students who attended classes in which SRN strategy is used are positively changed. Second, core competencies(self-directed ability, communication ability) of students who attended classes in which SRN strategy is used are improved. Third, the students who attended classes in which SRN strategy is used evaluated such as mathematics learning using the strategy help their mathematics study. Fourth, the students who attended classes in which SRN strategy is used evaluated such as the strategy improved their learning habit, supplemented their weakness, and activate realistic communication between professor and them.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.45-52
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• 2009

In this paper, we develop queueing network models of communication networks with reliability model considering link failures. The reliability of a communication network with a virtual connection exposed to link failures is analyzed. Stochastic Reward Nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. To get the performance index, appropriate reward rates are assigned to its SRN. It is shown that SRN modeling is well suited to specify, automatically generate and solve for reliability under rerouting. Markov models using SRN are developed and solved to depict various rerouting caused by link failures and reliability analysis in communication networks.

• International Journal of Contents
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• v.4 no.3
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• pp.20-28
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• 2008

To obtain realistic performance measures for wireless networks, one should consider changes in performance due to failure related behavior. In performability analysis, simultaneous consideration is given to both pure performance and performance with failure measures. SRN is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, a new methodology to model and analyze performability based on stochastic reward nets (SRN) is presented. Composite performance and availability SRN models for wireless handoff schemes are developed and then these models are decomposed hierarchically. The SRN models can yield measures of interest such as blocking and dropping probabilities. These measures are expressed in terms of the expected values of reward rate functions for SRNs. Numerical results show the accuracy of the hierarchical model. The key contribution of this paper constitutes the Petri nets modeling techniques instead of complicate numerical analysis of Markov chains and easy way of performance analysis for channel allocation under SRN reward concepts.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.12
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• pp.529-538
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• 2002

Conventional method to reduce the handoff call blocking probability(PBH) in mobile communication system is to reserve a predetermined number of channels only for handoff calls. To determine the number of reserved channels, an optimization problem, which is generally computationally heavily involved, must be solved. In this Paper, we propose a call admission control (CAC) scheme that can be used to reduce the PBH without reserving channels in advance. For this, we define a new measure, gain, which depends on the state of the system upon the arrival of a new call. The proposed CAC decision rule relies on the gain computed when a new call arrives. SRN, an extended stochastic Petri nets, provides compact modeling facilities for system analysis can be calculated performance index by appropriate reward to the model. In this Paper, we develop SRN models which can perform the CAC with gain. The SRN models are 2 level hierarchical models. The upper layer models are the structure state model representing the CAC and channel allocation methods considering QoS with multimedia traffic The lower layer model Is to compute the gain under the state of the upper layer models.

• Journal of Korea Multimedia Society
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• v.5 no.6
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• pp.704-711
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• 2002

Multimedia communication systems are characterized by supporting three different typer of services such as circuit switched services, and packet switched real Lime and non real time services. The wireless channels in a cell ate allocated by calls of these different service classes and the different service requirements have to be met. SRN is an extension of stochastic Petri nets and provides compact Modeling facilities for system analysis. To get the performance index, appropriate reward rates are assigned to its SRN. In this paper, we present a SRN model for performance analysis of channel allocation of multimedia mobile communication systems. The key contribution of this paper constitutes the Petri nets modeling techniques instead of complicate numerical analysis of Markov chains and easy way of performance analysis for channel allocations under SRN rewards concepts.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.300-307
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• 2008

Power management (PM) depends on the power state transition and system workload. The system model is composed of corresponding stochastic models of the power state and system queue. In this paper, stochastic models which can handle various PM techniques are developed. SRN (Stochastic Reward Nets), an extended Petri-Net, has facilities that represent system queue and various modelling functions. The SRN is employed for developing PM models. An adaptive timeout PM model is also introduced and the power consumption and performance of this model are compared with other existing PM techniques models such as greedy and N-Policy techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.355-358
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• 2007

In wireless mobile communication systems, priority of voice service through high speed data and multimedia transmission requires increased service diversification. Research is being carried out in this environment, on the call admission control techniques to guarantee the diversified service's QoS. SRN (Stochastic Reward Net) is an extended version of Petri nets, well know modeling and analysis tool. In this paper, we develop SRN call admission control model considering the 4 classes of services in the 4th generation IEE 802.16e mobile communication Technology.

• The Journal of the Korea Contents Association
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• v.3 no.4
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• pp.55-62
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• 2003

In this paper, a call admission contro(cac) technique is proposed to reduce the dropping probabilities of handoff calls in wireless networks while guaranteeing QoS to the users. The proposed technique is based on the estimated effective load for the target eel if a call is accepted. When the estimated effective load is higher than a predetermined threshold, a nu call is blocked and a handoff call is queued irrespective of the availability channels. The SRN, an extended Stochastic Petri Net, modes are constructed to compare the performance of the techniques. The SRN uses rewards concepts instead of the complicate numerical analysis required for the Markov chain modes. As a result, the SRN modeling techniques provide an easier way to carry out performance analysis for call admission control and channel allocation.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.147-155
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• 2005

In this paper, communication channels for the transmission of multimedia packets are modeled and evaluated. The multimedia packet traffic characterized by on-off and MMPP process for voice and data, respectively, dynamic channel allocation, queueing of data packets due to unavailability of channels and dropping of queued data packets over timeout, and guard channel for voice packets are modeled. The performance indices adopted in the evaluation of SRN model includes blocking and dropping probabilities. The SRN uses rewards concepts instead of the complicate numerical analysis required for the Markov chain. It is shown that our SRN modeling techniques provide an easier way to carry out performance analysis.

• The KIPS Transactions:PartC
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• v.8C no.1
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• pp.97-104
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• 2001

In this paper, we present a new hierarchical model for performance analysis of channel allocation and packet service protocol in wireless n network. The proposed hierarchical model consists of two parts : upper and lower layer models. The upper layer model is the structure state model representing the state of the channel allocation and call service. The lower layer model, which captures the performance of the system within a given structure state, is the wireless packet retransmission protocol model. These models are developed using SRN which is an modeling tool. SRN, an extension of stochastic Petri net, provides compact modeling facilities for system analysis. To get the performance index, appropriate reward rates are assigned to its SRN. Fixed point iteration is used to determine the model parameters that are not available directly as input. That is, the call service time of the upper model can be obtained by packet delay in the lower model, and the packet generation rates of the lower model come from call generation rates of the upper model.