• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.67-95
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• 1998

This research addresses flow time prediction in the dynamic unbalanced job shop scheduling environment. The specific purpose of the research is to develop the job flow time prediction model in the dynamic unbalance djob shop. Such factors as job characteristics, job shop status, characteristics of the shop workload, shop dispatching rules, shop structure, etc, are considered in the prediction model. The regression prediction approach is analyzed within a dynamic, make-to-order job shop simulation model. Mean Absolute Lateness (MAL) and Mean Relative Error (MRE) are used to compare and evaluate alternative regression models devloped in this research.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3C
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• pp.219-233
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• 2008

Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. This paper introduces a Duplication based Task Scheduling with Communication Cost in Heterogeneous Systems (DTSC), which provides optimal results for applications represented by Directed Acyclic Graphs (DAGs), provided a simple set of conditions on task computation and network communication time could be satisfied. Results from an extensive simulation show significant performance improvement from the proposed techniques over the Task duplication-based scheduling Algorithm for Network of Heterogeneous systems(TANH) and General Dynamic Level(GDL) scheduling algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.63-72
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• 2011

Broadcast-based data dissemination has become a widely accepted approach of communication in the mobile computing environment. However, with a large set of data items, the expected delay of receiving a desired data increases due to the sequential nature of the broadcast channel. With the objective of minimizing this wait time, this paper explores the problem of data broadcast over multiple channels. In traditional approaches, data items are partitioned based on their access probabilities and allocated on multiple channels, assuming flat data scheduling per channel. If the data items allocated on the same channel are broadcast in different frequencies based on their access probabilities, the performance will be enhanced further. In this respect, this paper proposes a new broadcast scheduling scheme named two level dynamic programming(TLDP) which can reflect a variation of access probabilities among data items allocated on the same channel.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.310-314
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• 2016

Currently, several research studies are looking to improve the quality of DASH (Dynamic Adaptive Streaming over HTTP) based live streaming services. However, conventional DASH based streaming schemes cannot provide seamless playback while maintaining the low buffering and it adversely affects the QoE (Quality of Experience). To address this problem, we propose the QoE driven segment scheduling scheme. The proposed scheme adaptively schedules the segment request message according to the time and variation of segment fetching. Simulation results indicate that the proposed scheme improves the QoE of live streaming service by maintaining the low buffering delay and reducing the buffer underflow events.

• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.484-491
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• 2003

For most of linear time-varying (LTV) systems, it is difficult to design time-varying controllers in analytic way. Accordingly, by approximating LTV systems as uncertain linear time-invariant, control design approaches such as robust control have been applied to the resulting uncertain LTI systems. In particular, a robust control method such as quantitative feedback theory (QFT) has an advantage of guaranteeing the frozen-time stability and the performance specification against plant parameter uncertainties. However, if these methods are applied to the approximated linear. time-invariant (LTI) plants with large uncertainty, the resulting control law becomes complicated and also may not become ineffective with faster dynamic behavior. In this paper, as a method to enhance the fast dynamic performance of LTV systems with bounded time-varying parameters, the approximated uncertainty of time-varying parameters are reduced by the proposed QFT parameter-scheduling control design based on radial basis function (RBF) networks.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1171-1177
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• 2013

Since existing location-based services suggest a search result, not considering user's environment, intention, or preference, situational awareness is required to resolve this problem. In this paper, an Android-based situational-awareness dynamic scheduling will be suggested and a tourist information service application will be embodied and tested. In other words, a real-time tourist scheduling service that calculates an estimated travel time from user's location to destination and reminds a user before the expected departure time will be developed. In addition to tourist information, an added scheduler provides convenience and improves its functionality.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.28-37
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• 2008

In this paper, we investigate the problem of minimizing the average transmission power of users while guaranteeing the average delay constraints in time-varying uplink channels. We design a scheduler that selects a user for transmission and determines the transmission rate of the selected user based on the channel and backlog information of users. Since it requires prohibitively high computation complexity to determine an optimal scheduler for multi-user systems, we propose a low-complexity scheduling scheme that can achieve near-optimal performance. In this scheme, we reduce the complexity by decomposing the multiuser problem into multiple individual user problems. We arrange the probability of selecting each user such that it can be determined only by the information of the corresponding user and then optimize the transmission rate of each user independently. We solve the user problem by using a dynamic programming approach and analyze the upper and lower bounds of average transmission power and average delay, respectively. In addition, we investigate the effects of the user selection algorithm on the performance for different channel models. We show that a channel-adaptive user selection algorithm can improve the energy efficiency under uncorrelated channels but the gain is obtainable only for loose delay requirements in the case of correlated channels. Based on this, we propose a user selection algorithm that adapts itself to both the channel condition and the backlog level, which turns out to be energy-efficient over wide range of delay requirement regardless of the channel model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.646-652
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• 2015

In this paper, the system-level performance is evaluated for the feedback scheme on the pre-coding matrix index (PMI) and channel quality indication (CQI), which are required for user selection in the multi-user MIMO system. Our analysis demonstrates that the number of users, the number of selected users, and codebook size are the key factors that govern the performance of the best companion grouping (BCG)-based user scheduling. Accordingly, we have confirmed that the probability of forming the co-scheduled user group is determined by these factors, which implies that the number of PMI's and codebook size can be dynamically determined so as to maximize the average system throughput as the number of users varies in the cell.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.11
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• pp.982-993
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• 2000

This paper describes an optimal control scheduling of an encapsulated ice storage system with a chiller of nominal chiller 34RT(103,200kcal/hr) and an ice storage tank of 170RT-hrs(514,080 kcal). The optimization technique used in the study is dynamic programing. The objective function is summed cost during a day including charge and discharge periods. Control strategies being used commercially are chiller priority and storage priority control. In chiller priority control, the chiller is allowed to run at full capacity during the day, subject to limitations of the building load, and the ice is only melted when and if the load exceeds the chillers full capacity. In contrast to chiller priority control, the aim in storage priority control is to melt as much as ice as possible during the day time period. The system simulation calculates the operation costs for the three control strategies in the condition of the same cooling load and the same ice storage system. The simulation period is a day, assuming that initially the tank is stored fully and the cooling load is perfectly predicted for the scheduling. Also Final state of the tank is to be charged fully.

• Journal of Communications and Networks
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• v.6 no.1
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• pp.68-77
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• 2004

In the near future packet networks should support applications which can not predict their traffic requirements in advance, but still have tight quality of service requirements, e.g., guaranteed bandwidth, jitter, and packet loss. These dynamic characteristics mean that the sources can be made to modify their data transfer rates according to network conditions. Depending on the customer&; needs, network operator can differentiate incoming connections and handle those in the buffers and the interfaces in different ways. In this paper, dynamic QoS-aware scheduling algorithm is presented and investigated in the single node case. The purpose of the algorithm is in addition to fair resource sharing to different types of traffic classes with different priorities ?to maximize revenue of the service provider. It is derived from the linear type of revenue target function, and closed form globally optimal formula is presented. The method is computationally inexpensive, while still producing maximal revenue. Due to the simplicity of the algorithm, it can operate in the highly nonstationary environments. In addition, it is nonparametric and deterministic in the sense that it uses only the information about the number of users and their traffic classes, not about call density functions or duration distributions. Also, Call Admission Control (CAC) mechanism is used by hypothesis testing.