• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.2
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• pp.189-197
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• 2013

With the trend of technological advances in electronic communications and the advent of ubiquitous environments, the density of existing electronic equipment in the surroundings is increasing significantly. It is hence great importance to study the numerically efficient and fast algorithm for complex and large environments to identify their electromagnetic compatibility and interference characteristics of equipments installed in those structure. This paper introduces a statistical-based power balance method(PWB) for the analysis of these problems and considers its practical utility. The 2-dimensional lossy rectangular cavity was numerically revisited to clarify its relationship with the classical deterministic analysis solutions based on the Maxwell's equation. It can be shown that the statistical assumptions and analysis results from the power balance method correspond to the volume average over the realistic deterministic domain. This statistical power balance approach should be a sufficiently practical alternative to the electromagnetic problem of complex and large environment since it is apparent that the full-wave analysis methods have some severe limits of its computational burdens under the situation of complex and large environment.

• Journal of the Korean Institute of Gas
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• v.10 no.2 s.31
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• pp.7-13
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• 2006

In order to evaluate the fire and explosion involved and to ensure the safe and optimized operation of chemical processes, it is necessary to know combustion characteristics. The explosion limit, the heat of combustion, flame temperature and temperature dependence of the lower explosive limit are the major combustion characteristics used to determine the fire and explosion hazards of the flammable substances. The aim of this study is to investigate interrelationship of explosion characteristics and the temperature dependence of the lower explosion limit at elevated temperature for akylketones. By using the reference data, the empirical equations which describe the interrelationships of explosion properties of akylketones have been derived. Also, the new equations using the mathematical and statistical methods for predicting the temperature dependence of lower explosion limits of akylketones on the basis of the literature data are proposed. The values calculated by the proposed equations agreed with literature data within a few percent. From the given results, using the proposed methodology, it is possible to predict the explosion limits of the other flammable substances.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.273-280
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• 2013

This study aims to examine the influences of wearing an occlusal biteplane splint on balance of the body. 10 subjects were selected who had neither impaired occlusion and masticating systems nor difficulty in walking along with no medical history in temporomandibular joint. Measurements were conducted with the Fukuda stepping test, the limits of stability test. The level of significance, ${\alpha}$ to determine the statistical significance was .05. Fukuda stepping test and the limits of stability test were put to the Wilcoxon signed test. As a result of this study, there was a significant difference in the moving distance in the Fukuda stepping test (p<.05). In addition, there were significant differences in the results of the limits of stability test (p<.05). In conclusion, the results of this study suggest that wearing the occlusal biteplane splint has an positive influence on the balance of the boby.

• Journal of Korean Society for Quality Management
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• v.35 no.2
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• pp.37-44
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• 2007

Today, the statistical process control (SPC) in manufacture environment is an important role at the process by the productivity improvement of the manufacturing systems. The control chart in this statistical method is widely used as an important statistical tool to find the assignable cause that provoke the change of the process parameters such as the mean of interest or standard deviation. But the traditional SPC don't grasp the change of process according to the points fallen the near control limits because of monitoring the variance of process such as the fixed sampling interval and the sample size and handle the cost of the aspect of these sample point. The control chart can be divided into the statistical and economic design. Generally, the economic design considers the cost that maintains the quality level of process. But it is necessary to consider the cost of the process improvement by the learning effects. This study does the economic design in the VSI $\bar X$ control chart and added the concept of loss function of Taguchi in the cost model. Also, we preyed that the VSI $\bar X$ control chart is better than the FSI $\bar X$ in terms of the economic aspects and proposed the standard of the process improvement using the VSI $\bar X$ control chart.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.387-396
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• 2002

Batch processes are a significant class of processes in the process industry and play an important role in the production of high quality speciality materials. Examples include the production of semiconductors, chemicals, pharmaceuticals, and biochemicals. With on-line sensors connected to most batch processes, massive amounts of data are being collected routinely during the batch on easily measured process variables such as temperatures, pressures, and flowrates. In this paper, multivariate SPC charts for on-line monitoring of the progress of new batches are developed which utilize the information in the on-line measurements in real-time. We propose the formation of statistical model which describes the normal operation of a batch at each time interval during the batch operation. An on-line monitoring scheme based on the proposed method can handle both cross-correlation among process variables at any one time and auto-correlation over time. And the control limits for the monitoring charts are established from sound statistical framework unlike previous researches which use the external reference distribution. The proposed charts perform real-time, on-line monitoring to ensure that the batch is progressing in a manner that will lead to a high-quality product or to detect and indicate faults that can be corrected prior to completion of the batch. This approach is capable of tracking the progress of new batch runs, identifying the time periods in which the fault occurred and detecting underlying cause.

• Journal of Veterinary Clinics
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• v.28 no.1
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• pp.108-112
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• 2011

In clinical settings, researchers often want to assess agreement between two measurements (or tests) of the same continuous variable. For example, when new point-of-care analyzer for testing blood glucose level were introduced clinicians need to compare results from standard or established laboratory method of measurement to those of new or point-of-care analyzer. The question in a method-comparison study would either of two different methods be used to measure the same variable equivalently. In this paper common misuse of statistical methodologies seen in the medical literatures such as correlation coefficient and paired t-test are discussed. The Bland-Altman technique has been widely used for this purpose and provides a graphic in presentation of the findings from a method-comparison study, with a mean value of measurement, this bias and the limits of agreement. For ease of application and interpretation of this technique we discussed the analysis procedure and illustrated with two worked examples. Finally, a number of alternative ways in which data can be analysed and reported in such studies were reviewed.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.323-338
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• 1994

This paper outlines a framework for performing intelligent sensor validation for a diagnostic expert system while reasoning under uncertainty. The emphasis is on the algorithmic preprocess technique. A companion paper focusses on heuristic post-processing. Sensor validation plays a vital role in the ability of the overall system to correctly detemine the state of a plant monitored by imperfect sensors. Especially, several theoretical developments were made in understanding uncertain sensory data in statistical aspect. Uncertain information in sensory values is represented through probability assignments on three discrete states, "high", "normal", and "low", and additional sensor confidence measures in Algorithmic Sv.Upper and lower warning limits are generated from the historical learning sets, which represents the borderlines for heat rate degradation generated in the Algorithmic SV initiates a historic data base for better reference in future use. All the information generated in the Algorithmic SV initiate a session to differentiate the sensor fault from the process fault and to make an inference on the system performance. This framework for a diagnostic expert system with sensor validation and reasonig under uncertainty applies in HEATXPRT$^{TM}$, a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants.

• Tunnel and Underground Space
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• v.6 no.3
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• pp.209-222
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• 1996

Shut-in pressure, reopenting pressure and fracture orientation are very important parameters to be evaluated precisely in in-situ stress measurement by hydraulic fracturing. Graphical methods on pressure-time curves have been conventionally used, even though these are seriously dependent on subjectivity of interpreters. So there have been many demands on new method to objectivity in determining parameters. We have developed integrated hydrofracturing data processing program (HYDFRAC), based on nonlinear regression analysis and can be invoked under the Window graphical user interface. HYDFRAC consiste of three routines, that is shut-in pressure routine, reopening pressure routine, and fracture delineation routine. Each of routines include independent modules according to parameter determination methods. Its application to field tests ensured both objectivity and facility in determining of hydraulic fracturing parameters. Determining shut-in pressures at each pressurization cycles, we adopted the exponential pressure-decay method(EPD method), the bilinear pressure-decay-rate method (PDR method), and the tangent intersection method in order to find the pressurization-cyclic tendency of shut-in pressures. The estimated pressure by PDR method exists in the range of the upper and lower values by EPD method, and lies near to the upper value more than the lower. Being the pressurization cycle increased, the range of upper and lower limits come to be stabilized gradually. By graphical superposition method and bilinear pressure-accumulated volume method, reopening pressures were determined. Vertical and inclined fracture attitudes were determined by applying the directional statistics and sinusoidal curve fitting, respectively. The results of evaluation of hydrofracturing parameters showed that statistical methods could enhance the objectivity better than graphical methods.