• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.3
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• pp.151-157
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• 1993

In this paper, we investigate the effects of yield randomness for lot-sizing in a multi-stage production system. The practical importance of incorporating yield randomness into production models has been emphasized by many researchers. Yield randomness, especially in semiconductor manufacturing, poses a mojor challenge for production planning and control. The task becomes even more difficult if the demand for final product is uncertain. An attempt to meet the demand with a higher level of confidence forces one to release more input in the fabrication line. This leads to excessive work-in-process (WIP) inventories which cause jobs to spend unpredictably longer time waiting for the machines. The result is that it is more difficult to meet demand with exceptionally long cycle time and puts further pressure to increase the safety stocks. Due to this spiral effect, it is common to find that the capital tied in inventory is the msot significant factor undermining profitability. We propose a policy to determine the quantity to be processed at each stage of a multi-stage production system in which the yield at each stage may be random and may need rework.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.45 no.5
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• pp.493-498
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• 2012

We derived biological reference points for Pacific cod Gadus macrocephalus in southeastern Korean waters by applying a yield-per-recruit analysis based on a daily simulation that adopted size-dependent fecundity, growth, and natural mortality functions. This showed that the yield per recruit of Pacific cod can be maximized at an instantaneous rate of fishing mortality (F)=0.37 $yr^{-1}$ under the current regulations, where the minimum catch size ($L_c$)=30 cm in total length (TL). The maximum economic yield was estimated to be attained at $L_c$=35-45 cm TL, if F>1 $yr^{-1}$ but at $L_c$=35-40 cm TL, if F<1 $yr^{-1}$. Despite great uncertainty in the stock assessment, to develop fisheries management plans for the sustainable exploitation of Pacific cod in southeastern Korean waters, it is necessary to estimate F using capture-recapture or other expedient methods.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.413-413
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• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.521-528
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• 2005

The evaluation process of rock mass properties intrinsically contains some uncertainty due to the inhomogeneity of rock mass and the measurement error. Although various empirical methods for the determination of rock mass properties were suggested, there is no way of integrating various information on rock mass properties except averaging. For these reasons, this research introduces evidence theory which can model epistemic uncertainty and yield reasonable rock mass properties through combining various information such as empirical equations, in-situ test results, and so on. Through the application of evidence theory to the real site investigation and in situ experiment results, an interval of deformation modulus, cohesion and friction angle of rock mass were obtained. The ratios between lower and upper bound of those properties ranges from 1.6 to 3.6. Numerical analyses of circular hole using the properties for TYPE-2 rock mass were carried out. The magnitude or size of plastic region and radial displacement in case of lower bound properties is about 4 times larger than that of upper bound properties.

• Asian Journal of Business Environment
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• v.11 no.4
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• pp.17-29
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• 2021

Purpose: The aim of this study is to explore the dynamic linkage between conditional stock market volatility and macroeconomic uncertainty of Bangladesh. Research design, data, and methodology: This study uses monthly data covering the time period from January 2005 to December 2018. A comprehensive set of macroeconomic variables, namely industrial production index (IP), consumer price index (CPI), broad money supply (M2), 91-day treasury bill rate (TB), treasury bond yield (GB), exchange rate (EX), inflow of foreign remittance (RT) and stock market index of DSEX are used for analysis. Symmetric and asymmetric univariate GARCH family of models and multivariate VAR model, along with block exogeneity and impulse response functions, are implemented on conditional volatility series to discover the possible interactions and causal relations between macroeconomic forces and stock return. Results: The analysis of the study exhibits time-varying volatility and volatility persistence in all the variables of interest. Moreover, the asymmetric effect is found significant in the stock return and most of the growth series of macroeconomic fundamentals. Results from the multivariate VAR model indicate that only short-term interest rate significantly influence the stock market volatility, while conditional stock return volatility is significant in explaining the volatility of industrial production, inflation, and treasury bill rate. Conclusion: The findings suggest an increasing interdependence between the money market and equity market as well as the macroeconomic fundamentals of Bangladesh.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3073-3084
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• 2022

The propagation of radiation source uncertainties in spent nuclear fuel (SNF) cask shielding calculations is presented in this paper. The uncertainty propagation employs the depletion and source term outputs of the deterministic code STREAM as input to the transport simulation of the Monte Carlo (MC) codes MCS and MCNP6. The uncertainties of dose rate coming from two sources: nuclear data and modeling parameters, are quantified. The nuclear data uncertainties are obtained from the stochastic sampling of the cross-section covariance and perturbed fission product yields. Uncertainties induced by perturbed modeling parameters consider the design parameters and operating conditions. Uncertainties coming from the two sources result in perturbed depleted nuclide inventories and radiation source terms which are then propagated to the dose rate on the cask surface. The uncertainty analysis results show that the neutron and secondary photon dose have uncertainties which are dominated by the cross section and modeling parameters, while the fission yields have relatively insignificant effect. Besides, the primary photon dose is mostly influenced by the fission yield and modeling parameters, while the cross-section data have a relatively negligible effect. Moreover, the neutron, secondary photon, and primary photon dose can have uncertainties up to about 13%, 14%, and 6%, respectively.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.159-163
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• 2001

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. for constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an offset error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• Current Research on Agriculture and Life Sciences
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• v.31 no.4
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• pp.233-239
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• 2013

Understanding the temporal variability of agricultural parameters derived from historical climate data is important for planning in agriculture. Therefore, this study assessed the magnitude and recent trends of the transpiration ratio defined as the crop water use per harvested yield for the period from 1980 to 2010. The crop water use was estimated using the Food and Agriculture Organization's Crop Wat model for eight administrative provinces in Korea. The temporal trends and spatial uncertainty were explored using the Mann-Kendall and Theil Sen's methods. The regional average rice yield was $6.31t\;ha^{-1}$(range 5.9 to $6.9t\;ha^{-1}$). The results showed that the rice yield in Korea increased by $26kg\;ha^{-1}yr^{-1}$. Overall, the regional average transpiration ratio was $1,298m^3t^{-1}$ (range 1,162 to $1,470m^3t^{-1}$). From 1980 to 2010, the transpiration ratio decreased by $8.2m^3t^{-1}$ (range 2.7 to $14.4m^3t^{-1}$), largely as a result of the increasing yield. The statistical approach to historical data used in this study also provides a basis for simulating the future transpiration ratio.

• Journal of the Microelectronics and Packaging Society
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• v.26 no.1
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• pp.29-33
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• 2019

With shrinking form factor and improving performance of electronic packages, high input/output (I/O) density is considered as an important factor. Fan out wafer-level packaging (FO-WLP) has been paid great attention as an alternative. However, FO-WLP is vulnerable to warpage during its manufacturing process. Minimizing warpage is essential for controlling production yield, and in turn, package reliability. While many studies investigated the effect of process and design parameters on warpage using finite element analysis, they did not take uncertainty into consideration. As parameters, including material properties, chip positions, have uncertainty from the point of manufacturing view, the uncertainty should be considered to reduce the gap between the results from the field and the finite element analysis. This paper focuses on the effect of uncertainty of Young's modulus of chip on fan-out wafer level packaging warpage using finite element analysis. It is assumed that Young's modulus of each chip follows the normal distribution. Simulation results show that the uncertainty of Young's modulus affects the maximum von Mises stress. As a result, it is necessary to control the uncertainty of Young's modulus of silicon chip since the maximum von Mises stress is a parameter related to the package reliability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.59-66
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• 1992

This Paper, first, tries to optimize the output specifications with uncertain characteristics. And then aims to solve the problem not only by making use of transformed multiple regression equation which can yield objective function of output characteristics but also by formulating developed multiple fuzzy goal programming using fuzzy set theory which can treat uncertainty easily, and the efficiency of these techniques, will be also demonstrated through a case study.