• Communications of the Korean Mathematical Society
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• v.14 no.3
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• pp.611-625
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• 1999

We consider ΜΑΡ/G/ 1 finite capacity queue with mul-tiple thresholds on buffer. The arrival of customers follows a Markov-ian arrival process(MAP). The service time of a customer depends on the queue length at service initiation of the customer. By using the embeded Markov chain method and the supplementary variable method, we obtain the queue length distribution ar departure epochs and at arbitrary epochs. This gives the loss probability and the mean waiting time by Little's law. We also give a simple numerical examples to apply the overload control in packetized networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3201-3223
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• 2020

Wireless communication has become the promising technology in the recent times because of its applications in Internet of Things( IoT) devices. The IEEE 802.15.4e has become the key technology for IoT devices which utilizes the Time-Slotted Channel Hopping (TSCH) networks for the communication between the devices. In this paper, we develop a Two Level Scheduling Algorithm (TLSA) for scheduling multiple packets with different arrival rate at the source nodes in a TSCH networks based on the link activated by a centralized scheduler. TLSA is developed by considering three types of links in a network such as link i with packets arrival type 1, link j with packets arrival type 2, link k with packets arrival type 3. For the data packets arrival, two stages in a network is considered.At the first stage, the packets are considered to be of higher priority.At the second stage, the packets are considered to be of lower priority.We introduce level 1 schedule for the packets at stage 1 and level 2 schedule for the packets at stage 2 respectively. Finally, the TLSA is validated with the two different energy functions i.e., y = e^ax - 1 and y = 0.5x² using MATLAB 2017a software for the computation of average and worst ratios of the two levels.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.3
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• pp.13-22
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• 2013

Queueing system with scheduled arrivals is useful in many fields where both customers' waiting time and servers' operation time (utilization) are important, and arrival time of customers is possible to be controlled. In this paper, we analyzed the operation of ship maintenance with the queueing system with scheduled arrival. Based on the model presented by Pegden and Rosenshine [8], who dealt with exponential service time, we extended the service time distributions to phase-type distribution which is able to include a wide range of real stochastic phenomena. Since most activities in the military are carried out under tight control and schedule, scheduled arrival queue has quite good applicability in this area. In this context, we applied queue with scheduled arrival to the optimal booking time decision for the ship maintenance in the navy.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.149-152
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• 2002

In this paper, an adaptive interference canceller (AIC) Is applied to the space-time coded DS-CDMA system in fading channels with arrival time difference from multiple transmit antennas In a CDMA system, arrival time difference causes not only inter-antenna and Inter-symbol interference but also multiple-access Interference even in a downlink. To mitigate the effect of the Interferences an AIC and ML decoding joint scheme Is proposed fur a space-time coded DS-CDMA system in which an adaptation process of tile AIC is merged in the Viterbi decoding algorithm. The performance of the proposed receiver is evaluated for the system with two transmit antennas. It is shown that the proposed receiver achieves significant performance improvement over the ML decoding receiver without the AIC

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.571-576
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• 2022

Due to the epochal development of the unmanned technology, the importance of LDT(: Location Detection Technology), which accurately estimates the location of a user or object, is dramatically increased. TOA(: Time of Arrival), which calculates a location by measuring the arrival time of signals, and TDOA(: Time Difference of Arrival) which calculates it by measuring the difference between two arrival times, are representative LDT methods. Based on the signals received from three or more base stations, TOA calculates an intersection point by drawing circles and TDOA calculates it by drawing hyperbolas. In order to improve the radio shadow area problem, a huge number of repeaters have been installed in the urban area, but the signals received through these repeaters may cause the serious error for estimating a location. In this paper, we propose an efficient location estimation technique using the signal received through the repeater. The proposed approach estimates the location of MS(: Mobile Station) employing TOA and TDOA methods, based on signals received from one repeater and two BS(: Base Station)s.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.36-43
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• 2002

We analyze bulk service $M/G^{b}/1$ queues using the arrival time approach of Chae et al. (2001). As a result, the decomposition property of the M/G/1 queue with generalized vacations is extended to the $M/G^{b}/1$ queue in which the batch size is exactly a constant b. We also demonstrate that the arrival time approach is useful for relating the time-average queue length PGF to that of the departure time, both for the $M/G^{b}/1$queue in which the batch size is as big as possible but up to the maximum of constant b. The case that the batch size is a random variable is also briefly mentioned.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.376-383
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• 2011

Since the first arrival path may not be the strongest path of UWB(Ultra Wide Band) multipath channels, this makes ToA(Time-of-Arrival) estimation becomes a challengeable issue. Furthermore, because of ultra bandwidth of received signals, the compressed sensing theory is employed to reduce the complexity caused by very high Nyquist sampling rate in coherent UWB receivers. In this paper, we propose a ToA estimation scheme which provides precise estimation performance, while exploiting the benefits of compressed sensing-based UWB receivers. Simulation results show that the proposed scheme can outperform other low complexity schemes in a wide range of signal-to-noise ratios.

• Geophysics and Geophysical Exploration
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• v.5 no.1
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• pp.18-22
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• 2002

As a single arrival traveltime, maximum energy arrival traveltime has been known as the most proper operator for Kirchhoff migration. In case of the model having the simple structure, both the first arrival traveltime and the maximum energy arrival traveltime can be used as the correct operators for Kirchhoff migration. However for some model having the complex and high velocity contrast structure, the migration using the first arrival traveltime can't give the correct depth section. That is, traveltime to be required in Kirchhoff migration is the maximum energy traveltime, but, needs considerably more calculation time than that of first arrival. In this paper, we propose the method for calculating the traveltime approximated to the maximum energy arrival using one-way wave equation. After defining the WAS(Wrap Around Suppression) factor to be used for calculating the first arrival traveltime using one-way wave equation as the function of lateral grid interval and depth and considering the delay time of source wavelet. we calculate the traveltime approximated to the maximum energy arrival. to verify the validity of this traveltime, we applied this to the migraion for simple structure and complex structure and compared the depth section with that obtained by using the first arrival traveltime.

• Journal of Trauma and Injury
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• v.30 no.4
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• pp.120-125
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• 2017

Purpose: The aim of this study was to identify clinical outcome and characteristics of trauma patients via emergency medical services (EMS). Methods: Medical records of the trauma patients visiting the emergency department were retrospectively collected and analyzed from January 2015 to June 2016 in the single institution. Of 529 registered patients, 371 patients were transported by - were enrolled. The parameters including age, gender, injury mechanism, Glasgow coma scale on arrival, presence of shock (systemic blood pressure <90 mmHg) on arrival, time to arrival from accident to emergency room (ER), need for emergency procedures such as operation or angioembolization, need for intensive care unit (ICU) admission, injury severity score (ISS), the trauma and injury severity score, revised trauma score (RTS), length of stay, and mortality rate were collected. The SAS version 9.4 (SAS Institute, Cary, NC, USA) was used for the data analysis. Results: Arrival time from the field to the ER was significantly shorter in EMS group. However, overall outcomes including mortalities, length of stay in the ICU and hospital were same between both groups. Age, ISS, RTS, and injury mechanisms were significantly different in both groups. ISS, RTS, and age showed significant influence on mortality statistically (p<0.05). Conclusions: The time to arrival of EMS was fast but had no effect on length of hospital stay, mortality rate. Further research that incorporates pre-hospital factors influence clinical outcomes should be conducted to evaluate the effectiveness of such a system in trauma care of Korea.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.3
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• pp.1-10
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• 2009

Bus delay time is occurred as the result of traffic condition and important factor to predict bus arrival time. In this paper, transition probability matrixes between bus stops are made by using Markov Chain and it is predicted bus delay time with them. As the results of study, it is confirmed a possibility of adapting the assumption which it has same bus transition probability between stops through paired-samples T-test and overcame the limitation of exiting studies in case there is no scheduled bus arrival time for each stops with using bus interval time. Therefore it will be possible to predict bus arrival time with Markov Chain.