• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.523-532
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• 2001

A Critical issue in the performance of a video-on-demand system is the required I/O bandwidth of the Video server in order to satisfy clients requests, and it is the crucial resource that may cause delay increasingly. Several approaches such as batching and piggybacking are used to reduce the I/O demand on the video server through sharing. Bathing approach is to make single I/O request for storage server by grouping the requests for the same object. Piggybacking is th policy for altering display rates of requests in progress for the same object to merge their corresponding I/O streams into a single stream, and serve it as a group of merged requests. In this paper, we propose a reservation-based hybrid disk bandwidth reduction policy that dynamically reserves the I/O stream capacity of a video server for popular videos according to the loads of video server in order to schedule the requests for popular videos immediately. The performance of the proposed policy is evaluated through simulations, and is compared with that of bathing and piggybacking. As a result, we know that the reservation-based hybrid disk bandwidth reduction policy provides better probability of service, average waithing time and percentage of saving in frames than batching and piggybacking policy.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.188-198
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• 2013

In order to increase its performance, NAND flash memory-based storage is composed of data buses that are shared by a number of flash memories and uses a parallel technique that can carry out multiple flash memory operations simultaneously. Since the storage performance is strongly influenced by the performance of each data bus, it is important to improve the utilization of the bus by ensuring effective scheduling of operations by the storage controller. However, this is difficult because of dynamic changes in buses due to the unique characteristics of each operation with different timing, cost, and usage by each bus. Furthermore, the scheduling technique for increasing bus utilization may cause unanticipated operation delay and wastage of storage resource. In this study, we suggest various dynamic operation scheduling techniques that consider data bus performance and storage resource efficiency. The proposed techniques divide each operation into three different stages and schedule each stage depending on the characteristics of the operation and the dynamic status of the data bus. We applied the suggested techniques to the controller and verified them on the FPGA platform, and found that program operation decreased by 1.9% in comparison to that achieved by a static scheduling technique, and bus utilization and throughput was approximately 4-7% and 4-19% higher, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.116-126
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• 2015

In a distributed heterogeneous computing system, the performance of a parallel application greatly depends on its task scheduling algorithm. Therefore, in order to improve the performance, it is essential to consider some factors that can have effect on the performance of the parallel application in a given environment. One of the most important factors that affects the total execution time is a critical path. In this paper, we propose the CLTS algorithm for a task scheduling. The CLTS sets the priorities of all nodes to improve overall performance by applying leveling method to improve parallelism of task execution and by reducing the delay caused by waiting for execution of critical nodes in priority phase. After that, it conditionally uses insertion based policy or duplication based policy in processor allocation phase to reduce total schedule time. To evaluate the performance of the CLTS, we compared the CLTS with the DCPD and the HCPFD in our simulation. The results of the simulations show that the CLTS is better than the HCPFD by 7.29% and the DCPD by 8.93%. with respect to the average SLR, and also better than the HCPFD by 9.21% and the DCPD by 7.66% with respect to the average speedup.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.143-155
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• 2009

A dynamic traffic assignment (DTA) has recently been implemented in many practical projects. The core of dynamic model is the inclusion of time scale. If excluding the time dimension from a DTA model, the framework of a DTA model is similar to that of static model. Similar to static model, with given exogenous travel demand, a DTA model loads vehicles on the network and finds an optimal solution satisfying a pre-defined route choice rule. In most DTA models, the departure pattern of given travel demand is predefined and assumed as a fixed pattern, although the departure pattern of driver is changeable depending on a network traffic condition. Especially, for morning peak commute where most drivers have their preferred arrival time, the departure time, therefore, should be modeled as an endogenous variable. In this paper, the authors point out some shortcomings of current DTA model and propose an alternative approach which could overcome the shortcomings of current DTA model. The authors substitute a traditional definition for time-dependent OD table by a new definition in which the time-dependent OD table is defined as arrival time-based one. In addition, the authors develop a new DTA model which is capable of finding an equilibrium departure pattern without the use of schedule delay functions. Three types of objective function for a new DTA framework are proposed, and the solution algorithms for the three objective functions are also explained.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.11-18
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• 2014

Most of the energy efficient MAC protocols for wireless sensor networks (WSNs) are based on duty cycling in a single channel and show competitive performances in a small number of traffic flows; however, under concurrent multiple flows, they result in significant performance degradation due to contention and collision. We propose a multi-channel pipelining (MCP) method for convergecast WSN in order to address these problems. In MCP, a staggered dynamic phase shift (SDPS) algorithms devised to minimize end-to-end latency by dynamically staggering wake-up schedule of nodes on a multi-hop path. Also, a phase-locking identification (PLI) algorithm is proposed to optimize energy efficiency. Based on these algorithms, multiple flows can be dynamically pipelined in one of multiple channels and successively handled by sink switched to each channel. We present an analytical model to compute the duty cycle and the latency of MCP and validate the model by simulation. Simulation evaluation shows that our proposal is superior to existing protocols: X-MAC and DPS-MAC in terms of duty cycle, end-to-end latency, delivery ratio, and aggregate throughput.

• Journal of the Korea Institute of Building Construction
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• v.18 no.3
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• pp.295-303
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• 2018

In the DBB delivery system, the design stage and the construction stage are separated. Because of this, design changes frequently occur, and problems such as construction cost overrun, schedule delay, and quality deterioration happen as well. Recently, in the construction industry CM at Risk(CM@R) delivery system, which can systematically solve the above-mentioned problems of DBB delivery system, meet various demands of clients, and overcome the limited cost and period. In the CM@R delivery system, the contractor negotiates for a maximum guaranteed price(GMP) with the client at the design stage, and the CM performer carries out the construction within the GMP. However, uncertainties are inherent in the GMP calculation because the calculation is based on unfinished drawings and documents. In this study, a Probabilistic Earlier GMP Calculation Method by combining a probabilistic tool of Monte Carlo simulation with a case based reasoning is proposed so that the uncertainty in GMP calculation is reflected. After the earlier GMP is calculated, a process to calculate the $2^{nd}$ GMP at the time of around 80 % of detailed deign and to negotiate with the client to fix the final GMP is proposed. The Probabilistic Earlier GMP Calculation Method is verified through the case study. In this study, researchers set the range of GMP through the proposed probabilistic GMP calculation and tried to reduce the risk through negotiation between the client and the CM performer. The proposed method and process would contribute to the successful introduction of CM@R in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.100-109
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• 2019

Maintainability indicates the extent to which maintenance can be done easily and quickly. The consideration of maintainability is crucial to reduce the operation and support costs of weapon systems, but if the maintainability is evaluated after the prototype production is done and necessitates design changes, it may increase the cost and delay the schedule. The evaluation should verify whether maintenance work can be performed, and support the designers in developing a design to improve maintainability. In previous studies, the maintainability index was calculated using the graph theory at the early design phase, but evaluation accuracy appeared to be limited. Analyzing the methods of evaluating the maintainability using fuzzy logic and 3D modeling indicate that the design of a system with good maintainability should be done in an integrated manner during the whole system life cycle. This paper proposes a method to evaluate maintainability using SysML-based modeling and simulation technique and fuzzy logic. The physical design structure with maintainability attributes was modeled using SysML 'bdd' diagram, and the maintainability was represented by an AHP matrix for maintainability attributes. We then calculated the maintainability using AHP-based weighting calculation and fuzzy logic through the use of SysML 'par' diagram that incorporated MATLAB. The proposed maintainability model can be managed efficiently and consistently, and the state of system design and maintainability can be analyzed quantitatively, thereby improving design by early identifying the items with low maintainability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.303-312
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• 2018

The university facility is made up of multiple buildings and has many maintenance items. In addition, administrative constraints need to be handled within a limited period. Most maintenance work is small scale and multi-work construction, such as waterproofing, needs to be organized. The facility manager makes annual unit price contract with a maintenance company and carries out the maintenance work. On the other hand, delay and rework is occurring because existing maintenance work performed without scheduling based on the manpower input. This study proposed a scheduling model that can support the facility manager to manage maintenance works of multiple university facilities at the program level. The model consists of three stages in order. In object analysis, details of the maintenance items were analyzed and the quantity is calculated based on the quantity takeoff sheet. In resource analysis, the craftsmen and construction period of detailed works are derived for the effective input of craftsmen. In scheduling, the priority of each work and the optimal manpower input are derived. The optimal schedule is selected according to the goodness of fit. The applicability and effectiveness of the prototype was evaluated through a case study and interviews with case participants. The model was found to be an effective tool to support the scheduling of maintenance works for the facility manager.

• Journal of Industrial Convergence
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• v.16 no.1
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• pp.21-26
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• 2018

The Seaborne Trade Volume accounts for 97% of the total. This means that the port operation management system can improve port efficiency, reducing operating costs, and the manager who manages all operations at the port needs to check and respond quickly when delays of work and equipment support is needed. Based on the real-time location information confirmation of yard automation equipment used the existing system GPS, the real-time location information confirmation system is a GPS system of the tablet, rather than a port operation system that monitors location information for the entered information, depending on the completion of the task or the start of the task. Network configurations also reduce container processing delays by using commercial LTE services that do not have shading due to containers in the yard also reduce container processing delays. Trough introduction of smart devices using Android or IOS and container processing scheduling utilizing artificial intelligence, we will build a minimum delay system with Smart Device usage of container processing applications and optimization of container processing schedule. The adoption of smart devices and the minimization of container processing delays utilizing artificial intelligence are expected to improve the quality of port services by confirming the processing containers in real time to consumers who are container information demanders.

• Explosives and Blasting
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• v.38 no.4
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• pp.16-25
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• 2020

Multi-setting smart-investigation of the ground and large uncharged hole boring (MSP) method to reduce the blast-induced vibration in a tunnel excavation is carried out over 50m of long-distance boring in a horizontal direction and thus has been accompanied by deviations in boring alignment because of the heavy and one-directional rotation of the rod. Therefore, the deviation has been adjusted through the boring machine's variable setting rely on the previous construction records and expert's experience. However, the geological characteristics, machine conditions, and inexperienced workers have caused significant deviation from the target alignment. The excessive deviation from the boring target may cause a delay in the construction schedule and economic losses. A deep learning-based prediction model has been developed to discover an ideal initial setting of the MSP machine. Dropout, early stopping, pre-training techniques have been employed to prevent overfitting in the training phase and, significantly improved the prediction results. These results showed the high possibility of developing the model to suggest the boring machine's optimum initial setting. We expect that optimized setting guidelines can be further developed through the continuous addition of the data and the additional consideration of the other factors.