• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4B
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• pp.349-354
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• 2006

In 3G networks interworking with external IP-based networks, provision of end-to-end QoS to packet-based services is a critical issue. In this paper, we propose DiffServ-aware Multiple Protocol Label Switching(MPLS) as a new policy enforcement means. With the adoption of the proposed DiffServ-aware MPLS, it is feasible to provide differentiated QoS provision with the help of DiffServ as well as to improve network utilization by using multiple paths based on MPLS. We verify the effectiveness of our proposed policy enforcement means through a simulation in which realtime traffic and non-realtime traffic are served together.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.1A
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• pp.16-22
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• 2006

This papers described with the analysis for electric Field strength on ground level transmitted from DMB transponder in stratosphere HAPS. It is compare with horizontal propagated ground wave. Resultly we confirm the equal strength a electric field on ground level between hish altitude vertical propagated wave and horizontal ground wave, also, is only 1W compare with terrestrial facility as transmitted output power for the DMB transponder in stratosphere HAPS. It is corresponding to 1Kw as same power value in ground propagated wave. Lastly it is new material wave source and also we concluding remarks as ubiquitous communication networking media.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11A
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• pp.1035-1046
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• 2005

This paper analyzes the channel assignment techniques and their performance in the On Board Processing (OBP) satellite communication system. It suggests the new Continuous Rate Assignment (CRA) and Dynamic Rare Assignment (DRA) for improving the efficiency of channel assignment at the OBP switch. Mathematical analysis and simulation are given to evaluate the system performance. Aggregate real-time and non-real-time services are considered as different classes. Higher priority is given to voice and video real-time services to avoid delay variation. Onboard scheduler uses CRA and DRA ways to arrange the capacity allocation dynamically. An improved algorithm is given to make the channel more efficient by doing some evaluation of the switching matrix.

• Journal of KIISE:Information Networking
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• v.36 no.4
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• pp.351-359
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• 2009

A wireless MAC scheduler for CDMA2000 IxEV-DO that provides a high level of quality-of-service(QoS) for video-on-demand(VOD) applications while achieving a reasonable level of system throughput is proposed, The proposed scheduler that exists in the MAC layer utilizes the video data information that resides in the application layer such that it improves the QoS for VOD applications. We show by simulations that our approach is better than the previous scheduler which is also based on video traces for VBR videos that have high variability between video frames.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.85-96
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• 2003

To build traffic information providing services by ITS technology should be carried out effect analysis in the first step for social and individual advantages. The propose on this study is to make clear what influences of highway network by traffic information are, and what differences between drivers who use traffic information and drivers who do not use that for route choice are. For these propose. travel time and forecast error of travel time on network and traffic information dependence of driver are analyzed by simulation. As a result of analysis travel time and forecast error of travel time is that the efficiency and reliability of travel time were increased when getting more drivers using traffic information in network. Drivers who using traffic information had advantage of decrease of travel time and forecast error in only definite situation. traffic information dependence analysis presented that drivers are dependent upon information and reliability of traffic information is also increased when drivers using traffic information become on increasing in network. In conclusion, considering the range of the traffic information user ratio in this simulation, this study presents that the traffic information service provides an advantage to the highway network and the drivers, and increases the dependence of information.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.149-163
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• 2012

Logistics systems provide their service to customers by coordinating the resources with limited capacity throughout the underlying processes involved to each other. To maintain the high level of service under such complicated condition, it is essential to carry out the real-time monitoring and continuous management of logistics processes. In this study, we propose a method of estimating the service completion time of key processes based on process-state information collected in real time. We first identify the factors that influence the process completion time by modeling and analyzing an influence diagram, and then suggest algorithms for quantifying the factors. We suppose the container terminal logistics and the process of discharging and loading containers to a vessel. The remaining service time of a vessel is estimated using a decision tree which is the result of machine-learning using historical data. We validated the estimation model using container terminal simulation. The proposed model is expected to improve competitiveness of logistics systems by forecasting service completion in real time, as well as to prevent the waste of resources.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1153-1160
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• 2021

This paper proposes a routing algorithm that determines the optimal path using deep reinforcement learning in software-defined networks. The deep reinforcement learning model for learning is based on DQN, the inputs are the current network state, source, and destination nodes, and the output returns a list of routes from source to destination. The routing task is defined as a discrete control problem, and the quality of service parameters for routing consider delay, bandwidth, and loss rate. The routing agent classifies the appropriate service class according to the user's quality of service profile, and converts the service class that can be provided for each link from the current network state collected from the SDN. Based on this converted information, it learns to select a route that satisfies the required service level from the source to the destination. The simulation results indicated that if the proposed algorithm proceeds with a certain episode, the correct path is selected and the learning is successfully performed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.189-197
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• 2012

In this paper, we propose an optimal path search algorithm that applies the received signal strength between a mobile device and a satellite. Because the common path search algorithm is only based on the shortest path search, it is difficult to provide stable multimedia services for the satellite mobile devices. The proposed algorithm provides the stable communication environment for the satellite mobile devices based on received signal strength. In Satellite communications, changes in the radio quality are severe depending on the receiving environment. Therefore, an accurate analysis of the receiving environment characteristics is very important for providing stable multimedia services of satellite communications. The causes of radio attenuation are atmosphere attenuation, vegetation attenuation and buildings attenuation. These factors were applied to analyze the received signal strength. The proposed algorithm can search the optimal path in urban for stable satellite multimedia services.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Navigation and Port Research
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• v.41 no.3
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• pp.137-148
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• 2017

Port service level is a metric of competitiveness among ports for the operating/managing bodies such as the terminal operation company (TOC), Port Authority, or the government, and is used as an important indicator for shipping companies and freight haulers when selecting a port. Considering the importance of metrics, we developed software to objectively define and manage six important service indicators exclusive to container and bulk terminals including: berth occupancy rate, ship's waiting ratio, berth throughput, number of berths, average number of vessels waiting, and average waiting time. We computed the six service indicators utilizing berth 1 through berth 5 in the container terminals and berth 1 through berth 4 in the bulk terminals. The software model allows easy computation of expected ship's waiting ratio over berth occupancy rate, berth throughput, counts of berth, average number of vessels waiting and average waiting time. Further, the software allows prediction of yearly throughput by utilizing a ship's waiting ratio and other productivity indicators and making calculations based on arrival patterns of ship traffic. As a result, a TOC is able to make strategic decisions on the trade-offs in the optimal operating level of the facility with better predictors of the service factors (ship's waiting ratio) and productivity factors (yearly throughput). Successful implementation of the software would attract more shipping companies and shippers and maximize TOC profits.