• Structural Engineering and Mechanics
• /
• v.76 no.6
• /
• pp.781-797
• /
• 2020

In recent years, there is an increasing interest to optimize the fuzzy logic controller with different methods. This paper focuses on the optimization of a fuzzy logic controller applied to a seismically excited nonlinear building. In most cases, this problem is formulated based on the linear behavior of the structure, however in this paper, four sets of objective functions are considered with respect to the nonlinear responses of the structure as the peak interstory drift ratio, the peak level acceleration, the ductility factor and the maximum control force. The Improved Charged System Search is used to optimize the membership functions and the rule base of the fuzzy controller. The obtained results of the optimized and the non-optimized fuzzy controllers are compared to the uncontrolled responses of the structure. Also, the performance of the utilized method is compared with various classical and advanced optimization algorithms.

• Earthquakes and Structures
• /
• v.25 no.6
• /
• pp.399-415
• /
• 2023

In this study, we investigate the use of a negative stiffness tuned inerter damper system to improve the performance of a base-isolated structure. The negative stiffness tuned inerter damper system consists of a tuned inerter damper connected in parallel with a negative stiffness element. To find the optimal parameters for the base-isolated structure with negative stiffness tuned inerter damper system, we develop an optimization method based on performance criteria. The objective of the optimization is to minimize the superstructure acceleration response ratio, while ensuring that the base displacement response ratio remains below a specified target value. We evaluate the proposed method by conducting numerical analyses on an eight-story building. The structure is modeled using both a simplified 3-degree-of-freedom system and a more detailed story-by-story shear-beam model. Lastly, a comparative analysis using time history analysis is performed to compare the performance of the base-isolated structure with negative stiffness tuned inerter damper system with that of the base-isolated structure and base-isolated structure with tuned inerter damper systems. The results obtained from the comparative analysis show that the negative stiffness tuned inerter damper system outperforms the tuned inerter damper system in reducing the dynamic seismic response of the base-isolated structure. Overall, this study demonstrates that the negative stiffness tuned inerter damper system can effectively enhance the performance of base-isolated structures, providing improved seismic response reduction compared to other systems.

• MALSORI
• /
• no.62
• /
• pp.149-161
• /
• 2007

In this paper we proposed a modified reliability function for improving bimodal speaker identification(BSI) performance. The convectional reliability function, used by N. Fox[1], is extended by introducing an optimization factor. We evaluated the proposed method in BSI domain. A BSI system was implemented based on GMM and it was tested using VidTIMIT database. Through speaker identification experiments we verified the usefulness of our proposed method. The experiments showed the improved performance, i.e., the reduction of error rate by 39%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.4835-4855
• /
• 2018

In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using Majorization-Minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.

• Journal of the Korean Institute of Gas
• /
• v.10 no.2 s.31
• /
• pp.55-60
• /
• 2006

Detailed modeling of PEMFCs has been getting considerable interest for predicting the fuel cell performance and also for use in various systems engineering activities. While CFD-based equipment models provide detailed analyses of the performance, they are very time-consuming to develop and run. The computations become quite complex when such models have to be embedded into the flowsheet-level optimization of fuel cell systems. In this paper, we present results about building and using NN-based reduced order models for quickly and repetitively predicting the flow of reactants in a PEMFC manifold.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1115-1120
• /
• 2003

The present paper shows the possible development of particle swarm optimization (PSO) based fuzzy-neural networks (FNN) which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs which can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by an user. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. Hidden Markov Model (HMM) based automatic speech recognizers are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system is successfully employed in a real life situation for motion control of a redundant manipulator.

• Computers and Concrete
• /
• v.22 no.4
• /
• pp.419-437
• /
• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• Journal of Korean Society for Quality Management
• /
• v.42 no.4
• /
• pp.685-700
• /
• 2014

Purpose: The purpose of this paper is to develop a systematic weighting procedure based on process capability indices method applying weighted mean squared error minimization (WMSE) approach to dual response surface optimization. Methods: The proposed procedure consists of 5 steps. Step 1 is to prepare the alternative vectors. Step 2 is to rank the vectors based on process capability indices in a pairwise manner. Step 3 is to transform the pairwise rankings into the inequalities between the corresponding WMSE values. Step 4 is to obtain the weight value by calculating the inequalities. Or, step 5 is to obtain the weight value by minimizing the total violation amount, in case there is no weight value in step 4. Results: The typical 4 process capability indices, namely, $C_p$, $C_{pk}$, $C_{pm}$, $C_{pmk}$ are utilized for the proposed procedure. Conclusion: The proposed procedure can provide a weight value in WMSE based on the objective quality performance criteria, not on the decision maker's subjective judgments or experiences.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.9 no.1
• /
• pp.11-24
• /
• 2017

The present work focuses multi-objective optimization of blade sweep for a Wells turbine. The blade-sweep parameters at the mid and the tip sections are selected as design variables. The peak-torque coefficient and the corresponding efficiency are the objective functions, which are maximized. The numerical analysis has been carried out by solving 3D RANS equations based on k-w SST turbulence model. Nine design points are selected within a design space and the simulations are run. Based on the computational results, surrogate-based weighted average models are constructed and the population based multi-objective evolutionary algorithm gave Pareto optimal solutions. The peak-torque coefficient and the corresponding efficiency are enhanced, and the results are analysed using CFD simulations. Two extreme designs in the Pareto solutions show that the peak-torque-coefficient is increased by 28.28% and the corresponding efficiency is decreased by 13.5%. A detailed flow analysis shows the separation phenomena change the turbine performance.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.1-10
• /
• 2022

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.