• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1015-1016
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• 2015

VANET is a technology for building a robust ad-hoc network between mobile vehicles as well as, between vehicles and RSU. Vehicles are moving and they only stay in the RSU area for a short time. When the number of requests is increased, an important challenge is to implement a suitable scheduling algorithm which serves as more requests as possible. $D^*S$ algorithm uses a priority weight, DS_Value, for selection of a request to get service. Priority weight is influenced only by deadline and data size parameters. We propose a packet scheduling using multilevel queue and show that using this idea leads to higher service ratio compare to previous algorithms.

• The KIPS Transactions:PartC
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• v.15C no.5
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• pp.419-428
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• 2008

IEEE 802.15.4 reserves transmission time to support real-time transport by sending GTS request packets to the PAN coordinator in advance. This paper introduces GTS-FAT technique to reduce the reservation time by giving a higher sending priority to GTS request packets than data packets. Differently from the conventional scheme where these two kinds of packets share a single transmission queue, GTS-FAT scheme allocates two queues with two different contention window sizes like IEEE 802.11e. This paper also proposes an analytical GTS delay model by combining the two legacy models for 802.15.4 and 802.11e to accurately predict the GTS-FAT delay over a given network topology. Our analysis shows that GTS-FAT reduces GTS service delay by up to 50% at the expense of the data delay by only up to 6.1% when GTS request packets four times outnumber data packets.

• The Journal of the Korea Contents Association
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• v.2 no.1
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• pp.104-112
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• 2002

Dynamic Handoff Control Scheme (DHCS), which is propose d in this paper, suggests the method to request handoff at the optimal time. To accomplish this, DHCS measures the speed of the mobile station and sets the pilot strength for handoff request. When the pilot strength of the current base station is bigg or than the pilot strength for handoff request, which means the pilot strength of the current base station is big enough so the possibility of the call to be disconnected is low, DHCS doesn't request for the handoff even though the pilot strength of the adjacent base station is bigger than the pilot strength of the curent base station. DHCS guarantees the QoS (Quality of Service) by processing the handoff calls prior to new calls at the base station and providing continuous service for the mobile station by setting the priorities for the calls according to the queue waiting time transmitted from the mobile stations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2811-2817
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• 2010

In this paper we propose the QAC-MAC that supports Quality of Service(QoS) and saves energy resources of the sensor node, and hence prolonging the lifetime of the sensor network with multiple sink nodes. Generally, the nodes nearest to the sink node often experience heavy congestion since all data is forwarded toward the sink through those nodes. So this critically effects on the delay-constraint data traffics. QAC-MAC uses a hybrid mechanism that adapts scheduled scheme for medium access and scheduling and unscheduled scheme based on TDMA for no data collision transmission. Generally speaking, characteristics of the real-time traffic with higher priority tends to be bursty and has same destination. QAC-MAC adapts cross-layer concept to rearrange the data transmission order in each sensor node's queue, saves energy consumption by allowing few nodes in data transmission, and prolongs the network lifetime.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.5
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• pp.44-56
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• 2009

Multimedia streaming service is susceptible to loss and delay of data as it requires high bandwidth and real time processing. Therefore QoS cannot be guaranteed due to data loss caused by heavy network traffic and error of wireless channel. To solve these problems, studies about algorithms which improve the quality of multimedia by serving differently according to the priority of packets in multimedia stream. Two algorithms are proposed in this paper. The first algorithm proposed is WMS-1(Wireless Multimedia Scheduling-1) algorithm which acts like IWFQ when any wireless loss is occurred but assigns channels first in case of urgent situation like when the running time of multimedia runs out. The second algorithm proposed is WMS-2(Wireless Multimedia Scheduling-2) algerithm that assigns priority to multimedia flow and schedules flow that has higher priority according to type of frame first. The comparison with other existing scheduling algorithms shows that multimedia service quality of the proposed algorithm is improved and the larger the queue size of base station is, the better total quality of service and fairness were gained.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.3011-3018
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• 2000

On ATM networks, ABR services are provided using the remained bandwidth after allocating CBR and VBR traffic. Realtime services such as transmitting audio or video data may be provided using CBR and VBR which have a constrained transmission delay, but in these cases, the communications bandwidth may be wasted. This paper proposes an efficient bandwidth allocation algorithm to transfer real-time data using ABR service. The proposed algorithm guarantees MCR and allocates bandwidth to each connection proportional to its MCR. The proposed algorithm divides the connections in two groups - a satisfied state group and a bottlenecked state group - and enhances bandwidth utilization by allowing the remained bandwidth after allocating the connections in the satisfied state to be allocated to the connections in the bottlenecked state. Our algorithm uses a Queue control function proposed by Ghani[5] to keep the Queue length within some boundary, which makes the transmission delay constant. We simulate and compare the performance of the proposed algorithm with that of the algorithms proposed by ATM Forum[1] and Kalampoukas[2].

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.129-137
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• 2016

Transmission Control Protocol as a transport layer protocol provides steady data transfer service. There are some serious concerns about the performance of TCP over diverse networks. The vital concern in TCP network environment is congestion which may occur due to quick transmission rates or because of large number of new connections entering the network at the same time. Size of queues in routers grows thus resulting in packet drops. Retransmission of the dropped packets, and reduced throughput can prove costly. Explicit Congestion Notification (ECN) in conjunction with Active Queue Management mechanisms (AQM) such as Random early detection (RED) is used for packet marking rather than dropping. In IP packet header ECN bits can be added as a sign of congestion thus avoiding needless packet drops. The proposed ECN and AQM mechanism can be implemented with help of ns2 simulator and the performance can be tested on different TCP variants.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.99-108
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• 2014

WBAN(Wireless Body Area Network) is network to support medical and non-medical services. It is susceptible to loss and delay of data. WBAN is required to satisfy many kinds of demands such as a variety of data rate and a data priority for providing various service. In this paper scheduling algorithm, considering a data priority and transmission delay time, is proposed to improve service quality of WBAN. The proposed algorithm operates by allocating a channel to a flow with longer transmission delay. When a packet, in a queue of herb, is left within a certain period, the packet is assigned a channel and transmitted according to a data priority. Through the comparison with other existing scheduling algorithms, it is confirmed that QoS is improved due to higher arrival probability of medical data and less delay time in the proposed algorithm.

• Journal of Fisheries and Marine Sciences Education
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• v.10 no.1
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• pp.1-14
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• 1998

Transportation provides an infrastructure vital to economic growth, and it is also an integral part of production. As a port is regarded as the interface between the maritime transport and domestic transport sectors, it certainly play a key role in any economic development. Ship's delay caused by port congestion has recently has recently attracted attended with the analysis of overall operation in port. In order to analyse complicated port operation which contains large number of variable factors, queueing theory is needed to be adopted, which is applicable to a large scale transportation system in chiding ship's delay in Inchon port in relation to ship's delay problem. The overall findings are as follows ; 1. The stucture of queueing model in this port can be represented as a complex of multi-channel single-phase 2. Ship's arrival and service pattern were Poisson Input Erlangian Service. 3. The suitable formula to calculate the mean delay in this port, namely, $W_q={\frac{{\rho}}{{\lambda}(1-{\rho})}}{\frac{e{\small{N}}({\rho}{\cdot}N)}{D_{N-1}({\rho}{\cdot}N)}}$ Where, ${\lambda}$ : mean arrival rate ${\mu}$ : mean servicing rate N : number of servicing channel ${\rho}$ : utilization rate (l/Nm) $e{\small{N}}$ : the Poisson function $D_{(n-1)}$ : a function of the cumulative Poisson function 4. The utility rate is 95.0 percents in general piers, 75.39 percents in container piers, and watiting time 28.43 hours in general piers, 13.67 hours in container piers, and the length of queue is 6.17 ships in general piers, 0.93 ships in container piers, and the ship turnaround time is 107.03 hours in general piers, 51.93 hours in container piers.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.