• Proceedings of the KSME Conference
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• 2001.06c
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• pp.170-175
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• 2001

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multiple objective genetic algorithm (MOGA), and a sample test case is presented to show the effects of optimizing the sequence with MOGA.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.451-451
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• 2000

This study aims for the optimization of process conditions in a fixed-bed catalytic reactor system with a circulating molten salt bath, in which partial oxidation of propylene to acrolein takes place. Two-dimensional pseudo-homogeneous model is adopted with estimation of suitable parameters and its validity is corroborated by comparing simulation result with experimental data. The temperature of the molten salt and the feed composition are found to exercise significant influence on the yield of acrolein and the magnitude of hot spot. The temperature of the molten salt is usually kept constant. This study, however, suggests that the temperature of the molten salt must be axially adjusted so that the abrupt peak of hot spot should not appear near the reactor entrance. The yield of acrolein is maximized and the position and the magnitude of hot spot are optimized by the method of the iterative dynamic programming (IDP).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.499-506
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• 2003

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several sub design structure matrices (DSMs) and processing them in parallel This paper proposes a new method for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multi-objective genetic algorithm and two sample test cases are presented to show the effect of the suggested decomposition method.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.501-506
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• 1998

A mathematical model is developed to describe the relationship between the manipulated variables (e.g. jacket inlet temperature and feed flow rate) and the important qualities (e.g conversion and weight average molecular weight (Mw)) in a continuous polymerization reactor. The subspace-based identification method for Wiener model is used to retrieve from the discrete sample data the accurate information about both the structure and initial parameter estimates for iterative parameter optimization methods. The comparison of the output of the identified Wiener model with the outputs of a non-linear plant model shows a fairly satisfactory degree of accordance.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.445-453
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• 2024

For Markov processes whose stationary probabilities are difficult to obtain in the analytical form, approximate solutions can be considered using numerical methods such as a matrix operation method or an iterative calculation method. In this paper we perform the study to verify the accuracy of a numerical iteration formula which calculate the stationary probabilities of Markov chains or processes. Especially, the convergence and accuracy of the numerical method are investigated by using Markov models for system availability. We compare the values of the system availability based on the numerical calculation and those based on the complicated but analytical solutions. We also calculate the iteration numbers necessary for the convergence of the numerical solutions. The accuracy and usefulness of the numerical iterative calculation method can be ascertained through this study.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1276-1279
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• 2002

In order to satisfy the requirement for various applications in an electronic device, many dynamically reconfigurable systems such as FPGAs have been used recently. This paper presents a pipelined scheduling for dynamically reconfigurable systems based on FPGAs. For reconfigurable systems conventional schedulings have reduced processing time by minimizing the number of reconfigurations. However, they are not effective enough for applications including many iterative processes such as digital signal processing. Our approach has been able to increase throughput of iterative applications on dynamically reconfigurable systems by using pipelined scheduling.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.376-381
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• 2001

This paper presents a methodology of machining error compensation by using Artificial Neural Network(ANN) model based on the inspection database of On-Machine-Measurement(OMM) system. First, the geometric errors of the machining center and the probing errors are significantly reduced through compensation processes. Then, we acquire machining error distributions from a specimen workpiece. In order to efficiently analyze the machining errors, we define two characteristic machining error parameters. These can be modeled by using an ANN model, which allows us to determine the machining errors in the domain of considered cutting conditions. Based on this ANN model, we try to correct the tool path in order to effectively reduce the errors by using an iterative algorithm. The iterative algorithm allows us to integrate changes of the cutting conditions according to the corrected tool path. Experimentation is carried out in order to validate the approaches proposed in this paper.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.4
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• pp.207-215
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• 2009

In recent digital communication systems, the performance of Turbo Code used as the error correction coding method depends on the interleaver size influencing the free distance determination and iterative decoding algorithms of the turbo decoder. However, some iterations are needed to get a better performance, but these processes require large time delay. Recently, methods of reducing the number of iteration have been studied without degrading original performance. In this paper, the new method combining ME (Mean Estimate) stopping criterion with SDR (sign difference ratio) stopping criterion of previous stopping criteria is proposed, and the fact of compensating each method's missed detection is verified Faster decoding realizes that reducing the number of iterative decoding about 1~2 times by adopting our proposed method into serially concatenation of both decoder. System Environments were assumed DS-CDMA forward link system with intense MAI (multiple access interference).

• Asian Journal of Innovation and Policy
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• v.7 no.3
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• pp.411-437
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• 2018

This paper articulates the STI strategy development principles and methodologies that have been elaborated through iterative processes of STI strategy development cases for the past ten years. The consultation cases include poverty traps in Nepal and Laos, African health challenges in Nigeria and Tanzania, and ASEAN global challenges in Indonesian Water, Vietnamese Green Energy, and Filipino Food, in partnership with some multilateral agencies.The iterative elaboration process has continued with consultation activities on Thailand and on Cambodia, Laos and Myanmar in planning partnership with Thailand. The principles were originally conceptualized from the benchmarking process of the Korean STI development experience. They were further incorporated as methodologies with which relevant planning bodies are guided to address individual and regional challenges through science, technology and innovation strategies. The methodologies are strong in providing plausible holistic perspective scenarios by which various stakeholders can be engaged in the planning and implementation process. But it is heuristic in nature and can be learned only through on-the-job training process. This is the structural limitation for scaling up.

• Nonlinear Functional Analysis and Applications
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• v.27 no.3
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• pp.449-469
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• 2022

This paper deals with the new iterative algorithm for approximating the fixed point of generalized 𝛼-nonexpansive mappings in a hyperbolic space. We show that the proposed iterative algorithm is faster than all of Picard, Mann, Ishikawa, Noor, Agarwal, Abbas, Thakur and Piri iteration processes for contractive mappings in a Banach space. We also establish some weak and strong convergence theorems for generalized 𝛼-nonexpansive mappings in hyperbolic space. The examples and numerical results are provided in this paper for supporting our main results.