• Journal of Power Electronics
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• v.4 no.1
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• pp.1-11
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• 2004

A stable adaptive sensorless speed controller for three-level inverter fed induction motor direct torque control (DTC) system using the radial-basis function network (RBFN) is presented in this paper. Torque ripple in the DTC system for high power induction motor could be drastically reduced with the foregoing researches of switching voltage selection and torque ripple reduction algorithms. However, speed control performance is still influenced by the inherent uncertainty of the system such as parametric uncertainty, external load disturbances and unmodeled dynamics, and its exact mathematical model is much difficult to be obtained due to their strong nonlinearity. In this paper, the inherent uncertainty is approximated on-line by the RBFN, and an additional robust control term is introduced to compensate for the reconstruction error of the RBFN instead of the rich number of rules and additional updated parameters. Control law for stabilizing the system and adaptive laws for updating both of weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov, and the stability proof of the whole control system is presented. Computer simulations as well as experimental results are presented to show the validity and effectiveness of the proposed system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.173-181
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• 2017

As the industries have developed, a myriad of big data have been produced and the inherent uncertainty in the data has also increased accordingly. In this paper, we propose an interval type-2 fuzzy clustering method to deal with the inherent uncertainty in the data and, using this method, design and optimize the fuzzy neural network. Fuzzy rules using the proposed clustering method are designed and carried out the learning process. Genetic algorithms are used as an optimization method and the model parameters are optimally explored. Experiments were performed with two pattern classification, both of the experiments show the superior pattern recognition results. The proposed network will be able to provide a way to deal with the uncertainty increasing.

• Journal of Powder Materials
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• v.14 no.6
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• pp.410-414
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• 2007

Carbon contamination from the binder resin is an inherent problem with the metal powder injection molding process. Residual carbon in the W-Cu compacts has a strong impact on the thermal and electric properties. In this study, uncertainty was quantified to evaluate determination of carbon in a W-15%Cu MIM body by the combustition method. For a valid generalization about this evaluation, uncertainty scheme applied even to the repeatability as well as the uncertainty sources of each analyse step and quality appraisal sources. As a result, the concentration of carbon in the W-Cu part were measured as 0.062% with expanded uncertainty of 0.003% at 95% level. This evaluation example may be useful to uncertainty evaluation for other MIM products.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1388-1392
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• 2008

Water resources management depends on dealing inherent uncertainties stemming from climatic and hydrological inputs and models. Dealing with these uncertainties remains a challenge. Streamflow forecasts basically contain uncertainties arising from model structure and initial conditions. Recent enhancements in climate forecasting skill and hydrological modeling provide an breakthrough for delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The approach here proposes integration and coupling of global climate models (GCM), multiple regional climate models, and numerous hydrological models to improve streamflow forecasting and characterize system uncertainty through generation of ensemble forecasts.

• Journal of ILASS-Korea
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• v.6 no.4
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• pp.31-38
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• 2001

The Holographic Particle Velocimetry system can be a promising optical tool for the measurements of three dimensional particle velocities. In this study, the holographic panicle velocimetry system was used to measure the sizes and velocities of droplets formed by a commercial full cone spray nozzle. Uncertainty analysis was performed to identify the sources of all relevant errors and to evaluate their magnitude. The droplet velocities ranged from 10.3 to 13.3 m/s with average uncertainty of ${\pm}1.6m/s$, which is ${\pm}14%$ of the mean droplet velocity. Compared with relatively small uncertainties of velocity components in the normal direction to the optical axis, the uncertainty of the optical axis component is ${\pm}3.6m/s$. This is due to the long depth of field of droplet images in the optical axis, which is inherent feature of holographic system using forward-scattering object wave of particles.

• Journal of Construction Engineering and Project Management
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• v.3 no.2
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• pp.21-34
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• 2013

Various risk factors affect construction projects. Due to the uncertainties created by risk factors, actual activity durations frequently deviate from the estimated durations in either favorable or adverse direction. For this reason, evaluation of schedule uncertainty is required to make decisions accurately when managing construction projects. In this regard, this paper presents a new computer simulation model - the Repetitive Schedule Risk Analysis Model (RSRAM) - to evaluate unit-based repetitive building project schedules under uncertainty when activity durations and risk factors are correlated. The proposed model utilizes Monte Carlo Simulation and a Critical Path Method based repetitive scheduling procedure. This new procedure concurrently provides the utilization of resources without interruption and the maintenance of network logic through successive units. Furthermore, it enables assigning variable production rates to the activities from one unit to another and any kind of relationship type with or without lag time. Details of the model are described and an example application is presented. The findings show that the model produces realistic results regarding the extent of uncertainty inherent in the schedule.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• The Journal of Industrial Distribution & Business
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• v.13 no.9
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• pp.11-26
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• 2022

Purpose: Manufacturers rely on interfirm governance mechanisms to reduce the risks inherent in uncertain environments; however, it is unclear which governance mechanisms are developed to manage relationships with suppliers. This study sought to enhance knowledge of how environmental uncertainty affects interfirm governance mechanisms under conditions reflecting varying levels of structural holes. To this end, the study investigated the relationships between manufacturers and major first-tier and sub-suppliers. In particular, the moderating effect of structural holes is examined. Research design, data and methodology: A questionnaire survey was conducted with a major first-tier supplier of a Korean engineering firm. Proposed hypotheses were tested using structural equation modeling. Results: The results show that while the relationship between environmental uncertainty and unilateral governance is positive but statistically insignificant, with bilateral governance is negative and statistically significant. The study also demonstrates that when structural holes are considered, the effects between environmental uncertainty and governance mechanisms are attenuated. Conclusions: This study suggests some theoretical and managerial contributions between exchange partners, especially, the results suggest that structural holes have a critical competitive advantage in uncertain environments. Therefore, manufacturers should carefully consider how they deal with environmental uncertainty when they make a business decision under structural holes situations.

• Journal of Mechanical Science and Technology
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• v.17 no.7
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• pp.1095-1103
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• 2003

The Holographic Particle Velocimetry system can be a promising optical tool for the measurements of three dimensional particle velocities. In this study, the holographic particle velocimetry system was used to measure the sizes and velocities of droplets produced by a commercial full cone spray nozzle. As a preliminary validation experiment, the velocities of glass beads on a rotating disk were measured with uncertainty analysis to identify the sources of all relevant errors and to evaluate their magnitude. The error of the particle velocity measured by the holographic method was 0.75 ㎧, which was 4.5% of the known velocity estimated by the rotating speed of disk. The spray droplet velocities ranged from 10.3 to 13.3 ㎧ with average uncertainty of ${\pm}$ 1.6 ㎧, which was ${\pm}$ 14% of the mean droplet velocity. Compared with relatively small uncertainty of velocity components in the normal direction to the optical axis, uncertainty of the optical axis component was very high. This is due to the long depth of field of droplet images in the optical axis, which is inherent feature of holographic system using forward-scattering object wave of particles.

• Journal of Environmental Impact Assessment
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• v.9 no.3
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• pp.185-202
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• 2000

Uncertainty and variability in Life Cycle Assessment(LCA) have been significant key issues in LCA methodology with techniques in other research area such as social and political science. Variability is understood as stemming from inherent variations in the real world, while uncertainty comes from inaccurate measurements, lack of data, model assumptions, etc. Related articles in this issues were reviewed for classification, distinguish and elaboration of probabilistic/stochastic health risk analysis application in LCA. Concept of focal zone, streamlining technique, scenario modelling and Monte Carlo/Latin Hypercube risk analysis were applied to the uncertainty/variability analysis of health risk in LCA. These results show that this general framework of multi-disciplinary methodology between probabilistic health risk assessment and LCA was of benefit to decision making process by suppling information about input/output data sensitivity, health effect priority and health risk distribution. There should be further research needs for case study using this methodology.