• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.131-145
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• 2010

The framework for making a coordinated decision for large-scale facilities has become an important issue in supply chain(SC) management research. The competitive business environment requires companies to continuously search for the ways to achieve high efficiency and lower operational costs. In the areas of production/distribution planning, many researchers and practitioners have developedand evaluated the deterministic models to coordinate important and interrelated logistic decisions such as capacity management, inventory allocation, and vehicle routing. They initially have investigated the various process of SC separately and later become more interested in such problems encompassing the whole SC system. The accurate quotation of ATP(Available-To-Promise) plays a very important role in enhancing customer satisfaction and fill rate maximization. The complexity for intelligent manufacturing system, which includes all the linkages among procurement, production, and distribution, makes the accurate quotation of ATP be a quite difficult job. In addition to, many researchers assumed ATP model with integer time. However, in industry practices, integer times are very rare and the model developed using integer times is therefore approximating the real system. Various alternative models for an ATP system with time lags have been developed and evaluated. In most cases, these models have assumed that the time lags are integer multiples of a unit time grid. However, integer time lags are very rare in practices, and therefore models developed using integer time lags only approximate real systems. The differences occurring by this approximation frequently result in significant accuracy degradations. To introduce the ATP model with time lags, we first introduce the dynamic production function. Hackman and Leachman's dynamic production function in initiated research directly related to the topic of this paper. They propose a modeling framework for a system with non-integer time lags and show how to apply the framework to a variety of systems including continues time series, manufacturing resource planning and critical path method. Their formulation requires no additional variables or constraints and is capable of representing real world systems more accurately. Previously, to cope with non-integer time lags, they usually model a concerned system either by rounding lags to the nearest integers or by subdividing the time grid to make the lags become integer multiples of the grid. But each approach has a critical weakness: the first approach underestimates, potentially leading to infeasibilities or overestimates lead times, potentially resulting in excessive work-inprocesses. The second approach drastically inflates the problem size. We consider an optimized ATP system with non-integer time lag in supply chain management. We focus on a worldwide headquarter, distribution centers, and manufacturing facilities are globally networked. We develop a mixed integer programming(MIP) model for ATP process, which has the definition of required data flow. The illustrative ATP module shows the proposed system is largely affected inSCM. The system we are concerned is composed of a multiple production facility with multiple products, multiple distribution centers and multiple customers. For the system, we consider an ATP scheduling and capacity allocationproblem. In this study, we proposed the model for the ATP system in SCM using the dynamic production function considering the non-integer time lags. The model is developed under the framework suitable for the non-integer lags and, therefore, is more accurate than the models we usually encounter. We developed intelligent ATP System for this model using genetic algorithm. We focus on a capacitated production planning and capacity allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using an evolutionary system to solve it efficiently. This method makes it possible for the population to reach the approximate solution easily. Moreover, we designed and utilized a representation scheme that allows the proposed models to represent real variables. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solutions converge to the optimum quickly.

• Journal of Astronomy and Space Sciences
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• v.23 no.3
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• pp.245-258
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• 2006

In this paper, an automation algorithm of analyzing and scheduling the station-keeping maneuver is presented for Communication, Ocean and Meteorological Satellite (COMS). The perturbation analysis for keeping the position of the geostationary satellite is performed by analytic methods. The east/west and north/south station-keeping maneuvers we simulated for COMS. Weekly east/west and biweekly north/south station-keeping maneuvers are investigated for a period of one year. Various station-keeping orbital parameters are analyzed. As the position of COMS is not yet decided at either $128.2^{\circ}E\;or\;116.0^{\circ}E$, both cases are simulated. For the case of $128.2^{\circ}E$, east/west station-keeping requires ${\Delta}V$ of 3.50m/s and north/south station-keeping requires ${\Delta}V$ of 52.71m/s for the year 2009. For the case of $116.0^{\circ}E,\;{\Delta}V$ of 3.86m/s and ${\Delta}V$ of 52.71m/s are required for east/west and north/south station-keeping, respectively. The results show that the station-keeping maneuver of COMS is more effective at $128.2^{\circ}E$.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.174-182
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• 2002

The object-oriented design methods and their CASE tools are widely used in practice by many real-time software developers. However, object-oriented CASE tools require an additional step of identifying tasks from a given design model. Unfortunately, it is difficult to automate this step for a couple of reasons: (1) there are inherent discrepancies between objects and tasks; and (2) it is hard to derive tasks while maximizing real-time schedulability since this problem makes a non-trivial optimization problem. As a result, in practical object-oriented CASE tools, task identification is usually performed in an ad-hoc manner using hints provided by human designers. In this paper, we present a systematic, schedulability-aware approach that can help mapping real-time object-oriented models to multi-threaded implementations. In our approach, a task contains a group of mutually exclusive transactions that may possess different periods and deadline. For this new task model, we provide a new schedulability analysis algorithm. We also show how the run-time system is implemented and how executable code is generated in our frame work. We have performed a case study. It shows the difficulty of task derivation problem and the utility of the automated synthesis of implementations as well as the Inappropriateness of the single-threaded implementations.

• The KIPS Transactions:PartA
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• v.18A no.6
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• pp.255-264
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• 2011

A sensor network is a special network that makes physical data sensed by sensor nodes and manages the data. The sensor network is a technology that can apply to many parts of field. It is very important to transmit the data to a user at real-time. The core of the sensor network is a sensor node and small operating system that works in the node. TinyOS developed by UC Berkeley is a sensor network operating system that used many parts of field. It is event-driven and component-based operating system. Basically, it uses non-preemptive scheduler. If an urgent task needs to be executed right away while another task is running, the urgent one must wait until another one is finished. Because of that property, it is hard to guarantee real-time requirement in TinyOS. According to recent study, Priority Level Scheduler, which can let one task preempt another task, was proposed in order to have fast response in TinyOS. It has restrictively 5 priorities, so a higher priority task can preempt a lower priority task. Therefore, this paper suggests Preemptive EDF(Earliest Deadline First) Scheduler that guarantees a real-time requirement and reduces average respond time of user tasks in TinyOS.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.297-306
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• 2017

Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2484-2496
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• 2000

High speed switches have been developing to interconnect a large number of nodes. It is important to analyze the switch performance under various conditions to satisfy the requirements. Queueing analysis, in general, has the intrinsic problem of large state space dimension and complex computation. In fact, The petri net is a graphical and mathematical model. It is suitable for various applications, in particular, manufacturing systems. It can deal with parallelism, concurrence, deadlock avoidance, and asynchronism. Currently it has been applied to the performance of computer networks and protocol verifications. This paper presents a framework for modeling and analyzing ATM switch using stochastic activity networks (SANs). In this paper, we provide the ATM switch model using SANs to extend easily and an approximate analysis method to apply A TM switch models, which significantly reduce the complexity of the model solution. Cell arrival process in output-buffered Queueing A TM switch with finite buffer is modeled as Markov Modulated Poisson Process (MMPP), which is able to accurately represent real traffic and capture the characteristics of bursty traffic. We analyze the performance of the switch in terms of cell-loss ratio (CLR), mean Queue length and mean delay time. We show that the SAN model is very useful in A TM switch model in that the gates have the capability of implementing of scheduling algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.1922-1931
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• 1999

A multicast traffic’s feature is the function of providing a point to multipoints cell transmission, which is emerging from the main function of ATM switch. However, when a conventional point-to-point switch executes a multicast function, the excess load is occurred because unicast cell as well as multicast cell passed the copy network. Additionally, due to the excess load, multicast cells collide with other cells in a switch. Thus a deadlock that losses cells raises, extremely diminishes the performance of switch. An input queued switch also has a defect of the HOL (Head of Line) blocking that less lessens the performance of the switch. In the proposed multicast switch, we use shared memory switch to reduce HOL blocking and deadlock. In order to decrease switch’s complexity and cell's processing time, to improve a throughput, we utilize the method that routes a cell on a separated paths by traffic pattern and the scheduling algorithm that processes a maximum 2N cell at once in the control part. Besides, when cells is congested at an output port, a cell loss probability increases. Thus we use the Output Memory (OM) to reduce the cell loss probability. And we make use of the method that stores the assigned memory (UM, MM) with a cell by a traffic pattern and clears the cell of the Output memory after a fixed saving time to improve the memory utilization rate. The performance of the proposed switch is executed and compared with the conventional policy under the burst traffic condition through both the analysis based on Markov chain and simulation.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.7
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• pp.370-379
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• 2006

Computational grid provides the environment which integrates v 따 ious computing resources. Grid environment is more complex and various than traditional computing environment, and consists of various resources where various software packages are installed in different platforms. For more efficient usage of computational grid, therefore, some kind of integration is required to manage grid resources more effectively. In this paper, a global scheduler is suggested, which integrates grid resources at meta level with applying various scheduling policies. The global scheduler consists of a mechanical part and three policies. The mechanical part mainly search user queues and resource queues to select appropriate job and computing resource. An algorithm for the mechanical part is defined and optimized. Three policies are user selecting policy, resource selecting policy, and executing policy. These can be defined newly and replaced with new one freely while operation of computational grid is temporarily holding. User selecting policy, for example, can be defined to select a certain user with higher priority than other users, resource selecting policy is for selecting the computing resource which is matched well with user's requirements, and executing policy is to overcome communication overheads on grid middleware. Finally, various algorithms for user selecting policy are defined only in terms of user fairness, and their performances are compared.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.431-435
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• 2011

This paper presents optimized searching technique to improve the performance of H.264/AVC. The proposed CAVLC encoder uses forward and backward searching algorithm to compute the parameters. By zero-block skipping technique and pipelined scheduling, the proposed CAVLC encoder can obtain better performance. The experimental result shows that the proposed architecture needs only 66.6 cycles on average for each $16{\times}16$ macroblock encoding. The proposed architecture improves the performance by 13.8% than that of previous designs. The proposed CAVLC encoder was implemented using VerilogHDL and synthesized with Megnachip $0.18{\mu}m$ standard cell library. The synthesis result shows that the gate count is about 15.6K with 125Mhz clock frequency.

• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.