• Journal of the Korean Society of Safety
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• v.31 no.2
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• pp.10-17
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• 2016

The purpose of this paper is to deduct components that are in the group of highest risk(top 10%). the group is conducted for classification into groups by values according to risk priority through risk priority number(RPN) of FMEA(Failure modes and effects analysis) sheet. Top 10% of failure mode among total potential failure modes(72 failure modes) of ESS included 5 BMS(battery included) failure modes, 1 invert failure mode, and 1 cable connectors failure mode in which BMS was highest. This is because ESS is connected to module, try, and lack in the battery part as an assembly of electronic information communication and is managed. BMS is mainly composed of the battery module and communication module. There is a junction box and numerous connectors that connect these two in which failure occurs most in the connector part and module itself. Finally, this paper proposes RPN by each step from the starting step of ESS design to installation and operation. Blackouts and electrical disasters can be prevented beforehand by managing and removing the deducted risk factors in prior.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3C
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• pp.224-232
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• 2012

Priority MAC is an important issue in every communication system when we consider differentiated service applications. In this paper, we propose a mechanism to support priority MAC based on multi-parameters for IEEE 802.15.7 visible light communication (VLC). By using three parameters such as number of backoff times (NB), backoff exponent (BE) and contention window (CW), we provide priority for multi-level differentiated service applications. We consider beacon-enabled VLC personal area network (VPAN) mode with slotted version for random access algorithm in this paper. Based on a discrete-time Markov chain, we analyze the performance of proposed mechanism under non-saturation environments. By building a Markov chain model for multi-parameters, this paper presents the throughput and transmission delay time for VLC system. Numerical results show that we can apply three parameters to control the priority for VLC MAC protocol.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.4
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• pp.23-30
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• 2013

Reliability Centered Maintenance(RCM) is one of most widely used methods in the modern power system to schedule a maintenance cycle and determine the priority of inspection. A precedence study for the new structure of rearranged system should be performed due to introduction of additional installation. This paper proposes a new method to evaluate the priority of maintenance and inspection of the power system facilities. In order to calculate that risk index, it is required that the reliability block diagram should be analyzed for the power system. Additionally, a fault cause analysis is also performed through the event-tree analysis.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.145-153
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• 2017

Systemic management is required at each phase of the C4I system life cycle to achieve information advantage in the battlefield through stable operation of the C4I system under the NCOE. In particular, the maintenance phase is the longest period of the C4I system life cycle, and it is easy to utilize and analyze data such as faults that occur during system operation. However, according to the previous research, the maintenance is evaluated as a phase in which the definition and management of comprehensive indicators are insufficient compared to other phase. In this paper, we propose the method of C4I Maintenance Priority decision using Maintenance metric. As a result of modifying the Naval Tactical C4I System's preventive maintenance cycle according to the priority, the total number of faults is reduced and the maintenance efficiency is improved.

• Management Science and Financial Engineering
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• v.2 no.1
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• pp.123-145
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• 1996

We consider a priority-based multiclass queue with probabilistic feed-back. There are J service stations. Each customer belongs to one of the several priority classes, and the customers of each class arrive at each station in a Poisson process. A single server serves queued customers on a priority basis with a nonpreemptive scheduling discipline. The customers who complete their services feed back to the system instantaneously and join one of the queues of the stations or depart from the system according to a given probability. In this paper, we propose a new method to simplify the analysis of these queueing systems. By the analysis of busy periods and regenerative processes, we clarify the underlying system structure, and systematically obtain the mean for the sojourn time, i.e., the time from the arrival to the departure from the system, of a customer at every station. The mean for the number of customers queued in each station at an arbitrary time is also obtained simultaneously.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.191-199
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• 1996

In this paper, We assume that the closed computer system model composed of multiprocessor system is analyzed by BCMP queueing network theory with priority levels. In this system that contains terminals, processors and I/O devices, We show maximum throughput and the number of active terminals in the optimum multiprogramming levels. It is compared the performance with the other. In the result, it is obtained the optimum number of processors and active terminals. Therefore, the system model consisted of the optimum number of processor and multiprogramming level m is analyzed by the servers with a priority level. Each server is applied to the type of server which is characterized terminal, processor I/O device etc.. This model is analyzed by the server with a probability ditribution. Ideal state is proposed by the modeling for priority levels. Finally, we try to increase the performance in overload system.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.133-138
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• 2002

In this paper, we model a two-server queueing system with priority, to which we put a restriction of the number of servers for each customer class. A group of customers is divided into two different classes. The class 1 customers has non -preemptive priority over class 2 customers. We use the method of PGF depending on the state of server We find the PGF of the number of customers in queue, server utilization, mean queue length and mean waiting time for each class of customers.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.239-250
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• 2014

The purpose of this research is to implement and develop the Economic Cost Driver Size(ECDS) extended model to determine the optimal cash driver size with measurement complexity cost and allocation fail cost. ECDS model can be used to seek both measurement accuracy and time efficiency of the Activity-Base Costing (ABC). The study also develops Activity Priority Number (APN) to evaluate the importance of nonvalue-added activities improvement and to determine the representative cost driver of value-added activities when applying ECDS model. APN consists of Severity Priority Number (SPN), Undetectablitiy Priority Number (UPN) and Occurrence Priority Number (OPN). APN can be obtained from lower-stream activity, current activity, upper-stream activity in terms of hierarchical dependency of SIPOC (Supplier, Input, Process, Output, and Customer). In order to seek both efficiency of invested capital and reduction of overhead cost, the paper proposes the integrated ABC and Economic Value Added (EVA) model using redesigned ABC-based statement of comprehensive income and EVA-based statement of financial position. For a better understanding of the proposed ABC-EVA integrated model, numerical examples are demonstrated in this paper. Cost drivers of ABC and capital drivers of EVA in the proposed model can be used to reduce activity overhead cost from ABC-based statement of comprehensive income and to lessen activity capital charge from EVA-based statement of financial position.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.72-75
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• 2004

In this paper, the task assignment strategy considering communication delay and the priority of distributed tasks is proposed for the real-time network system in order to maximize the utilization of the system. For the task assignment strategy, the relationship among priority of tasks in network nodes, the calculation time of each task, and the end-to-end response time including the network delay is formulated firstly. Then, the task assignment strategy using the genetic algorithm is proposed to optimize the utilization of the system considering the LCM(Least Common Multiple) period. The effectiveness of proposed strategy is proven by the simulation for estimating the performance such as the utilization and the response time of the system in case of changing the number of tasks and the number of network nodes.