• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2676-2683
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• 2009

The objective of this thesis is to improve the quality of a software product based on the enhancement of a software design quality using a better error detecting method. Also, this thesis is based on a software design method called as MOA(Methodology for Object to Agents) which uses an ontology based ODES(A Method based on Ontology for Detecting Errors in the Software Design) model as a common information model. At this thesis, a new format of error detecting method was defined. The method is implemented during a transformation process from UML model to ODES model using a ODES model, a Inter-View Inconsistency Detection technique and a combination of ontologic property of consistency framework and related rules. Transformation process to ODES model includes lexicon analysis and meaning analysis of a software design using of multiple mapping table at algorithm for the generation of ODES model instance.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.284-295
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• 2003

The servo tracks of disk drives are constructed at the time of manufacture with the equipment of servo track writer. Because of the imperfection of servo track writer, disk vibrations and head fluctuations during servo track writing process, the constructed servo tracks might deviate from perfect circles and take eccentric shapes. The servo track writing error should be corrected because it might cause interference with adjacent tracks and irrecoverable operation error of disk drives. The servo track writing error is repeated every disk rotation and so is periodic time function. In this paper, we propose a new correction algorithm of servo track writing error based on iterative teaming approach. Our correction algorithm can learn iteratively the servo track writing error as accurately as is desired. Furthermore, our algorithm is robust to system model errors, is computationally simple, and has fast convergence rate. In order to demonstrate the generality and practical use of our work, we present the convergence analysis of our correction algorithm and some simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1083-1086
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• 1996

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. At cold rolling mill process, precalculation determines the mill settings before a strip actually enters the mill and is done by an outdated mathematical model. A corrective neural network model is proposed to improve the accuracy of the roll force prediction. Additional variables to be fed to the network include the chemical composition of the coil, its coiling temperature and the aggregated amount of processed strips of each roll. The network was trained using a standard backpropagation with 4,944 process data collected from no.1 cold rolling mill process from March 1995 through December 1995, then was tested on the unseen 1,586 data from Jan 1996 through April 1996. The combined model reduced the prediction error by 32.8% on average.

• Smart Structures and Systems
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• v.15 no.3
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• pp.717-733
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• 2015

Modal parameters of a structure are commonly used quantities for system identification and damage detection. With a limited number of studies on the statistics assessment of modal parameters, this paper presents procedures to properly account for the uncertainties present in the process of extracting modal parameters. Particularly, this paper focuses on how to deal with the measurement error in an ambient vibration test and the modeling error resulting from a modal parameter extraction process. A bootstrap approach is adopted, when an ensemble of a limited number of noised time-history response recordings is available. To estimate the modeling error associated with the extraction process, a model prediction expansion approach is adopted where the modeling error is considered as an "adjustment" to the prediction obtained from the extraction process. The proposed procedures can be further incorporated into the probabilistic analysis of applications where the modal parameters are used. This study considers the effects of the measurement and modeling errors and can provide guidance in allocating resources to improve the estimation accuracy of the modal data. As an illustration, the proposed procedures are applied to extract the modal data of a damaged beam, and the extracted modal data are used to detect potential damage locations using a damage detection method. It is shown that the variability in the modal parameters can be considered to be quite low due to the measurement and modeling errors; however, this low variability has a significant impact on the damage detection results for the studied beam.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2054-2060
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• 2008

This paper aims at the performance enhancement of tension controller for the yarn manufacturing process. The tension controller is required to keep the tension constant while the yarn is manufactured by a draw and twist machine, which is essential and critical for good quality production of yarn, steel, paper, etc. This paper proposes a linear model of tension control plant to develop a precise tension control system, which is derived by the close observation of the conventional mathematical model of motor driving and tension control systems. It is shown by experiments that the proposed control system precisely maintains the tension constant within the error bound of 0.05% while the conventional PI controller has about 0.2% error. The control performance of the system has been compared to that of conventional PI control not only for constant speed control but also for transient speed control experiments.

• Laser Solutions
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• v.3 no.3
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• pp.21-29
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• 2000

For the direct metal shape processing the powder feed device which is different from the widely used in rapid prototyping. is developed, The three dimensional object is shaped with the melting metal powder. The developed research has applied to rapid prototyping in ultraprecision for MEMS and medical science fields required of rapid manufacture of complex shape. The goal of this study make 3D model which has precision accuracy. Powder spreading apparatus has been more improved because that the control of powder spread is very important in layer manufacturing. It consists of the vibration motor, nozzle and tube which supplies various metal powder. This apparatus could control the spreading velocity that could control powder spreading thickness. Laser on/off switch was adapted because laser scanning velocity must be preserved constantly to prevent heat transformation of laser overheating. The error between sintered thickness md experimental one occurred by shrinkage in sintering melting process. The problem of heat transformation was solved by On/Off switching system.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.4
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• pp.735-739
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• 2013

The error motion of a machine tool spindle directly affects the surface errors of machined parts. Spindle motion errors such as three translational motions and two rotational motions are undesirable. These are usually due to the imperfectness of bearings, stiffness of spindle, assembly errors, and external force or unbalance of rotors. The error motions of the spindle need to be reduced for achieving the desired performance. Therefore, the level of error motion needs to be estimated during the design and assembly process of the spindle. In this study, an estimation method for five degree-of-freedom (5 DOF) error motions for a spindle with an angular contact ball bearing is suggested. To estimate the error motions of the spindle, the waviness of the inner-race of bearings and an external force model were used as input data. The estimation model considers the geometric relationship and force equilibrium of the five DOFs. To calculate the error motions of the spindle, not only the imperfections of the shaft and bearings but also driving elements such as belt pulley and direct driving motor systems are considered.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.86-89
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• 2002

In NC machining, the ability to automatically generate an optimal process plan is an essential step toward achieving automation, higher productivity, and better accuracy. For this ability, a system that is capable of simulating the actual machining process has to be designed. In this paper, a milling process simulation system for the general NC machining was presented. The system needs first to accurately compute the cutting configuration. ME Z-map(Moving Edge node Z-map) was developed to reduce the entry/exit angle calculation error in cutting force prediction. It was shorn to drastically improve the conventional Z-map model. Experimental results applied to the pocket machining show the accuracy of the milling process simulation system.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.351-359
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• 2002

In this paper, stochastically dependent single and sequential acceptance sampling plans are dealt when the process follows a Polya process model. A Monte-Cairo algorithm is used to find the acceptance and rejection probabilities of a lot. The number of defectives for the test to be accepted and rejected in a probability ratio sequential test can be found by using these probabilities. The formula to measure performance of these sampling plans is developed. Type I and II error probabilities are estimated by simulation. Dependent multiple acceptance sampling plans can be derived by extending the sequential sampling procedure. In numerical examples, single and sequential sampling plans of a Polya dependent process are examined and the characteristics are compared according to the change of the dependency factor.