• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.754-759
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• 1993

An adaptive model predictive control (AMPC) strategy using auto-regression moving-average (ARMA) models is presented. The characteristic features of this methodology are the small computer memory requirement, high computational speed, robustness, and easy handling of nonlinear and time varying MIMO systems. Since the process dynamic behaviors are expressed by ARMA models, the model parameter adaptation is simple and fast to converge. The recursive least square (RLS) method with exponential forgetting is used to trace the process model parameters assuming the process is slowly time varying. The control performance of the AMPC is verified by both comparative simulation and experimental studies on distillation column control.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• IE interfaces
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• v.18 no.3
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• pp.221-233
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• 2005

e-Manufacturing can be referred to as a system methodology enabling the integration of manufacturing operations and IT technologies to achieve objectives of an enterprise. It is recently regarded as a powerful solution to survive in a chaotic marketplace. While conducting an e-Manufacturing project, we first needed to capture collaborative processes conducted by multiple participants in order to define functions of a system supporting them. However, pervasive process modeling techniques including IDEF3, Petri nets, and UML are not sufficient for modeling collaborative processes. Therefore, we first briefly investigate several process modeling methods including aforementioned modeling methods and ARIS focusing on the collaboration point of view. Then, we propose a new modeling method referred to as Collaborative Process Modeling (CPM) to clearly describe collaborative processes. Also, we develop and illustrate a rule for transforming collaborative process models into Marked Graph models to use the analysis power of the Petri nets. Using CPM empowers us to develop collaborative process models with simple notations, understand and facilitate the realization of the collaboration, and verify models before rushing into the development.

• Journal of Industrial Technology
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• v.36
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• pp.23-31
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• 2016

This paper presents EMQ models in which some proportion of defective items are produced and some of them are converted to good items through rework process and items not converted are disposed. Numerical models are developed for three cases of disposal and optimal solution of each model is derived. In the first model, if a defective item is found during the production process, only re-workable items are stored and reworked after normal production is finished. Not re-workable items are disposed immediately during normal production. The second model deals with the case where all defective items are stored and items to be disposed are determined in rework process. In the third model, an additional inspection process exists before rework to determine rework or disposal. Numerical examples are presented to validate the proposed models.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.161-168
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• 2014

In mixture experiments with process variables, we consider the case that some of process variables are either uncontrollable or hard to control, which are called noise variables. Given the such mixture experimental data with process variables, first we study how to search for candidate models. Good candidate models are screened by the sequential variables selection method and checking the residual plots for the validity of the model assumption. Two methods, which use numerical optimization methods proposed by Derringer and Suich (1980) and minimization of the weighted expected loss, are proposed to find a cost-effective robust optimal condition in which the performance of the mean as well as the variance of the response for each of the candidate models is well-behaved under the cost restriction of the mixture. The proposed methods are illustrated with the well known fish patties texture example described by Cornell (2002).

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.1-14
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• 1997

This paper reviewed models to explain memory and neuropsychological tests to assess memory. Memory was explained in cognitive and neuroanatomical perspectives, Cognitive model describes memory as structure and process. In structure model, memory is divided into three systems: sensory memory, short-term memory(working memory), and long-term memory. In process model, there are broadly three categories of memory process: encoding, storage, and retrieval. Memory process work in memory structure. There are two prominent models of the neuroanatomy of memory, derived from the work of Mishkin and Appenzeller and that of Squire and Zola-Morgan. These two models are the most useful for the clinician in part because they take into account the connections between the limbic and frontal cortical regions. The major difference between the two models concerns the role of the amygdala in memory processess. Mishkin and his colleagues believe that the amygdala plays a significant role while Squire and his colleagues do not. The most popular and widely used tests of memory ability such as WMS-R, AVLT, CVLT, HVLT. RBMT, CFT, and BVRT-R, were reviewed.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.27-35
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• 2005

Due to the importance of lead time demand in the design of inventory management systems, researchers and practitioners have paid continuous attention and a few analytic models using the compound distribution approach have been reported. However, since the nature of compound distributions is hardly amenable, the analytic models have been done by non‐recognition of the compound nature of some components to reduce the analytic task. This study concerns some of the important aspects in the analytic models. Through the theoretic examination of the analytic model approach and the comparison with the rigid compound stochastic process approach, this study clarifies the assumptions implicitly made by the analytic models and provides some precautions in using the analytic models. Illustrative examples are also presented.

• Korean Journal of Mathematics
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• v.22 no.3
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• pp.529-552
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• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

• Journal of Korean Society for Quality Management
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• v.31 no.4
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• pp.219-225
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• 2003

Two­dimensional warranty data can be modelled using two different approaches： two­dimensional point process and one­dimensional point process with usage as a function of age. The first approach has three different models. First of all, bivariate model is appealing but is not appropriate for explaining warranty claims. Next, the rest of the two models (marked point process, and counting and matching on both directions independently) are more appropriate for explaining warranty claims. However, the second one (counting and matching on both directions independently) assumes that the two variables (variables representing the two­dimensions) are independent. Last of all, one­dimensional point process with usage as a function of age is also promising to explain the two­dimensional warranty claims. But the models or variations of them need more investigation to be applicable to real warranty claim data.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.4
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• pp.209-219
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• 2000

A method is developed to evaluate machining process and to determine the optimal machining conditions considering the environmental effects. The method Is based on the evaluation attributes from the general LCA programs and the analysis technique of AHP from HHS. To assist the analysis. the mass models of cutting energy, tools, and fluids are developed. The models may be used for both quantitative prediction of the uses and disposed masses of materials and optimization of the machining conditions. The algorithm with the mass models is applied to the milling process planning. The process to survey the environmental data, calculate the used mass, and evaluate the alternatives is demonstrated. This demonstration illustrates the of the change of process conditions of the decision making.