We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

The management of blood inventory is very important within the medical care system. The efficient management of blood supplies and demands for transfusion is of great economic and social importance to both hospitals and patients. Fro any blood type, there is a complex interaction among the optimal inventory level, daily demand level , daily supply level, transfusion to crossmatch ratio, crossmatch release period, issuing policy and the age of arriving units that determine the shortage and outdate rate. In this paper, we develop an efficient decision rule for blood inventory management in a hospital blood bank which can support efficient hospital blood inventory management using simulation, The primary use of the efficient decision rule will be to establish minimum cost function which consists of inventory levels , period in inventory, outdate and shortage rate for whole blood and various component inventories for a hospital blood bank or a transfusion service, If the adminstrator compute the mean daily demand for each blood type, the mean daily supply for each blood type, the length of the crossmatch release period and the average transfusion to crossmatch ratio , then it is possible to apply the efficient decision rule to compute the optimal inventory level, inventory period , outdate and shortage rate. This rule can also be used as a decision support system that allows the blood bank adminstrator to do sensitivity analysis related to controlled blood inventory parameters.

This paper describes a new automated simulation system for micromachines whose size range $10^{-6}$ to $10^{-3}$ m. An automic finite element (FE) mesh generation technique, which is bases on the fuzzy knowledge processing and computation al geometry technique, is incorporated into the system, together with one of commerical FE analysis codes, MARC ,and one of commerical solid modelers, Designbase. The system allows a geometry model of concern to be automatically converted to different FE models, depending on physical phenomena of micromachines to be analyzed , i,e. electrostatic analysis, stress analysis, modal analysis and so on. The FEmodels are then automatically analyzed using the FE analysis code, Among a whole process of analysis, the definition of a geometry model, the designation of local node patterns and the assignment of material properties and boundary conditions onto the geometry model are only the interactive process to be done by a user. The interactive operations can be processed in a few minutes. The other processes which are time consuming and labour-intensive in conventional CAE systems are fully automatically performed in a popular engineering workstation environment. This automated simulation system is successfully applied to evaluate an electrostatic micro wobble actuator.

This paper describes a new automated simulation system for micromachines whose size range $10^{-6}$ to $10^{-3}$ m. An automic finite element (FE) mesh generation technique, which is bases on the fuzzy knowledge processing and computation al geometry technique, is incorporated into the system, together with one of commerical FE analysis codes, MARC, and one of commerical solid modelers, Designbase. The system allows a geometry model of concern to be automatically converted to different FE models, depending on physical phenomena of micromachines to be analyzed, i,e. electrostatic analysis, stress analysis, modal analysis and so on. The FE models are then automatically analyzed using the FE analysis code. Among a whole process of analysis, the definition of a geometry model, the designation of local node patterns and the assignment of material properties and boundary conditions onto the geometry model are only the interactive process to be done by a user. The interactive operations can be processed in a few minutes. The other processes which are time consuming and labour-intensive in conventional CAE systems are fully automatically performed in a popular engineering workstation environment. This automated simulation system is successfully applied to evaluate an electrostatic micro wobble actuator.

2-propanol /acetone/hydrogen 반응계로 구성된 화학적 열펌프 시스템은 낮은 온도(82.5~$90^{\circ}C$)에서의 2-propanol의 탈수소화 반응과 높은 온도(약 $200^{\circ}C$ 부근)에서의 acetone 의 수소화반응을 이용하여 열을 고품위화 시키는 장치이다. 본 연구에서는 이 시스템의 해석 및 설계를 목적으로 이 시스템에 대한 수치적인 모델들을 세우고 Sequential modular approach를 이용하여 시스템의 모사를 수행하였다. 또한 에너지 효율을 최대화하기 위하여 열펌프 시스템에서의 환류비의 영향을 규명하였다. 모사결과 이 시스템의 scale up을 위한 정량적인 정상상태 운전조건들을 구할수 있었으며 두 반응의 반응 전화율이 다르더라도 반응물의 유량의 차이를 통하여 두 반응열이 거의 같아지는 것을 알수 있었다. 아울러 주어진 운전조건에서 증류의 환류비는 최소환류비 근처의 최적값이 존재함을 알수 있었다.

Container handling facilities in Korean ports have increased rapidly according to Korean industrialization and the worldwide containerization. Over 98% of total containers handles in Korean ports are handled in Puan ports. This paper presents the estimation method of annual container handling capacity of container terminals by the computer simulation models. Simulation models are developed utilizing SIMAN IV simulation package. Annual handling capacity of real container terminals such as BCTOC and PECT was estimated by the proposed simulation models. Also, Annual handling capaicty of planned or expected terminals in Puan port was estimated. The comparisons between container forecast demand and estimated handling capacity of Pusan port from 1996 through 2001 were made. It showed that Pusan port will have over two million TEU handling capacity shortage during that period and will face enormous port congestion. Lastly, mid term and long-term capacity expansion plansof container terminals in korean ports were discussed.

This paper proposes an intelligent simulation generator, called INSIMS, for manufacturing systems. The INSIMS provides an interactive dialogue interactive dialogue interface and code generation tools for modeling and code genration of manufacturing systems. The interactive dialogue interface helps non-expert simulation modeler specify manufacturing systems with ease. After completing the model construction processes, the modeling specifications are automatically converted into SLAM II codes by code generation tools. To validate the usefulness of INSIMS and to illustrate the modeling processes, an example system has been presented.

In this paper, we propose a new events ordering mechanism for the optimistic distributed simulation of DEVS models. To simulate DEVS models in a distributed environment, a synchronization protocol is required for correct simulation. Time Warp is the most well-known optimistic synchronization protocol for distributed simulation. However, employing the Time Warp protocol in distributed simulation of DEVS models incurs events ordering problem due to the semantic difference between Time Warp and DEVS, Thus, to resolve such semantic difference, we devise the time-and-priority-stamp and $\varepsilon$ -delay schemes. The proposed schemes can order simultaneous events correctly in Time Warp-based distributed simulation of DEVS models.Time Warp and DEVS, Thus, to resolve such semantic difference, we devise the time-and-priority-stamp and $\varepsilon$ -delay schemes. The proposed schemes can order simultaneous events correctly in Time Warp-based distributed simulation of DEVS models.

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.