• Journal of Electrical Engineering and Technology
• /
• v.10 no.1
• /
• pp.227-242
• /
• 2015

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

• Structural Engineering and Mechanics
• /
• v.3 no.4
• /
• pp.373-382
• /
• 1995

The weighting strategy has received a great attention and has been widely applied to multicriterion optimization. This gaper examines a global criterion method (GCM) with the weighting objectives strategy in fuzzy structural engineering problems. Fuzziness of those problems are in their design goals, constraints and variables. Most of the constraints are originated from analysis of engineering mechanics. The GCM is verified to be equivalent to fuzzy goal programming via a truss design. Continued and mixed discrete variable spaces are presented and examined using a fuzzy global criterion method (FGCM). In the design process a weighting parameter with fuzzy information is introduced into the design and decision making. We use a uniform machine-tool spindle as an illustrative example in continuous design space. Fuzzy multicriterion optimization in mixed design space is illustrated by the design of mechanical spring stacks. Results show that weighting strategy in FGCM can generate both the best compromise solution and a set of Pareto solutions in fuzzy environment. Weighting technique with fuzziness provides a more relaxed design domain, which increases the satisfying degree of a compromise solution or improves the final design.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.323-335
• /
• 1999

In this paper, we propose a new hybrid model of artificial neural networks(ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not learn the patterns of data well, because they employed GA for simple use. In this study, we incorporate GA in a simultaneous manner to improve the learning and generalization ability of ANNs. In this study, GA plays role to optimize feature weighting and feature transformation simultaneously. Globally optimized feature weighting overcome the well-known limitations of gradient descent algorithm and globally optimized feature transformation also reduce the dimensionality of the feature space and eliminate irrelevant factors in modeling ANNs. By this procedure, we can improve the performance and enhance the generalisability of ANNs.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.2
• /
• pp.81-86
• /
• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

• 유체기계공업학회:학술대회논문집
• /
• 2003.12a
• /
• pp.397-403
• /
• 2003

In an attempt to solve multiobjective optimization problems, weighted sum method is most widely used for the advantage that a designer can consider the relative significance of each object functions by weight values but it can be highly sensitive to weight vector and occasionally yield a deviated optimum from the relative weighting values designer designated because the multiobjective function has the form of simple sum of the product of the weighting values and the object functions in traditional approach. To search the design solution well agree to the designer's weighting values, we proposed new multiobjective function which is the functional of each normalized objective functions and considered to find the design solution comparing the distance between the characteristic line and the ideal optimum. In this study, proposed multiobjective function was applied to design high efficiency and low noise axial flow fan and the result shows this approach will be effective for the case that the qualify of the design can be highly affected by the designer's subjectiveness represented as weighting values in multiobjective design optimization process.

• Phonetics and Speech Sciences
• /
• v.3 no.2
• /
• pp.65-70
• /
• 2011

Recently, audio-visual speech recognition (AVSR) has been studied to cope with noise problems in speech recognition. In this paper we propose a novel method of deciding weighting factors for audio-visual information fusion. We adopt the particle swarm optimization (PSO) to weighting factor determination. The AVSR experiments show that PSO-based normalized confidence measures (NCM) improve the rejection performance of mis-recognized words by 33%.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.11b
• /
• pp.295-300
• /
• 2002

In conventional model updating using single-objective optimization techniques, incompatible physical data are compared with each other using weighting factors. There are no general rules for selecting the weighting factors since they are not directly related with the dynamic behavior of an updated model. So one of the most difficult tasks, in model updating study, is 'balancing among the correlations' i.e. 'trade-off'. In this work, a multiobjecitive optimization technique called 'satisficing trade-off method' is introduced to extremize several correlations simultaneously. The absurd need for the weighting factors can be avoided using this technique. And the updated model with the most appropriate correlations is obtained easily in interactive way. Especially automatic trade-off is employed to increase the rate of convergence to the desired model. Its effectiveness is verified by application to a real engineering problem, HDD cover model updating.

• Structural Engineering and Mechanics
• /
• v.85 no.5
• /
• pp.607-620
• /
• 2023

The classical optimal control (COC) method has been widely used for linear quadratic regulator (LQR) problems of structural control. However, the equation of motion of the structure is incorporated into the optimization model as the constraint condition for the LQR problem, which needs to be solved through the Riccati equation under certain assumptions. In this study, an explicit optimal control (EOC) method is proposed based on the explicit time-domain method (ETDM). By use of the explicit formulation of structural responses, the LQR problem with the constraint of equation of motion can be transformed into an unconstrained optimization problem, and therefore the control law can be derived directly without solving the Riccati equation. To further optimize the weighting parameters adopted in the control law using the gradient-based optimization algorithm, the sensitivities of structural responses and control forces with respect to the weighting parameters are derived analytically based on the explicit expressions of dynamic responses of the controlled structure. Two numerical examples are investigated to demonstrate the feasibility of the EOC method and the optimization scheme for weighting parameters involved in the control law.

• The KSFM Journal of Fluid Machinery
• /
• v.7 no.2 s.23
• /
• pp.7-13
• /
• 2004

In an attempt to solve multiobjective optimization problems, weighted sum method is most widely used for the advantage that a designer can consider the relative significance of each object functions by weight values but it can be highly sensitive to weight vector and occasionally yield a deviated optimum from the relative weighting values designer designated because the multiobjective function has the form of simple sum of the product of the weighting values and the object functions in traditional approach. To search the design solution agree well to the designer's weighting values, we proposed new multiobjective function which was the functional of each normalized objective functions and considered to find the design solution comparing the distance between the characteristic line and the ideal optimum. In this study, proposed multiobjective function was applied to design high efficiency and low noise axial flow fan and the result shows this approach is effective for the case that the quality of the design can be highly affected by the designer's subjectiveness represented as weighting values in multiobjective design optimization process.

• 유체기계공업학회:학술대회논문집
• /
• 2005.12a
• /
• pp.287-294
• /
• 2005

Flow rate measurement uncertainties of the ultrasonic flow meter are generally influenced by many different factors, such as Reynolds number, flow distortion, turbulence intensity, wall surface roughness, velocity integration method along the acoustic paths, and transducer installation method, etc. Of these influencing factors, one of the most important uncertainties comes from the velocity integration method. In the present study, a optimization weighting factor method for 5-chord, which is given by a function of the chord locations of acoustic paths, is employed to obtain the mean velocity in the flow through a pipe. The power law profile is assumed to model the axi-symmetric pipe flow and its results are compared with the present weighting factor concept. For an asymmetric pipe flow, the Salami flow model is applied to obtain the velocity profiles. These theoretical methods are also compared with the previous Gaussian, Chebyshev, and Tailor methods. The results obtained show that for the fully developed turbulent pipe flows with surface roughness effects, the present weighting factor method is much less sensitive than Chebyshev and Tailor methods, leading to a better reliability in flow rate measurement using the ultrasonic flow meters.