• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2037-2051
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• 2012

Cognitive radio (CR) is envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, the main concern is to reliably sense the presence of primary users (PUs) to attain protection against harmful interference caused by potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize the total sensing decision error at the common soft data fusion (SDF) centre of a structurally-centralized cognitive radio network (CRN). Using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed methods are compared with each other as well as with other conventional deterministic algorithms such as maximal ratio combining (MRC) and equal gain combining (EGC). Computer simulations confirm the superiority of the PSO-based scheme over the GA-based and other conventional MRC and EGC schemes in terms of detection performance. In addition, the PSO-based scheme also shows promising convergence performance as compared to the GA-based scheme. This makes PSO an adequate solution to meet real-time requirements.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.10 s.253
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• pp.1209-1218
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• 2006

Precision servomechanisms are widely used in machine tool, semiconductor and flat panel display industries. It is important to improve contouring accuracy in high-precision servomechanisms. In order to improve the contouring accuracy, cross-coupled control systems have been proposed. However, it is very difficult to select the controller parameters because cross-coupled control systems are multivariable, nonlinear and time-varying systems. In this paper, in order to improve contouring accuracy of a biaxial servomechanism, a cross-coupled controller is adopted and an optimal tuning procedure based on an integrated design concept is proposed. Strict mathematical modeling and identification process of a servomechanism are performed. An optimal tuning problem is formulated as a nonlinear constrained optimization problem including the relevant controller parameters of the servomechanism. The objective of the optimal tuning procedure is to minimize both the contour error and the settling time while satisfying constraints such as the relative stability and maximum overshoot conditions, etc. The effectiveness of the proposed optimal tuning procedure is verified through experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.8
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• pp.598-608
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• 2002

This paper shows the optimal design methodology for the fluid engine mount by the artificial life algorithm. The design has been commonly modified by trial and error because there is many design parameters that can be varied in order to minimize transmissibility at the desired fundamental resonant and notch frequencies. The application of trial and error method to optimization of the fluid mount is a great work. Many combinations of parameters are possible to give us the desired resonant and notch frequencies, but the question is which combination Provides the lowest resonant peak and notch depth. In this study the enhanced artificial life algorithm is applied to get the desired fundamental resonant and notch frequencies of a fluid mount and to minimize transmissibility at these frequencies. The present hybrid algorithm is the synthesis of and artificial life algorithm with the random tabu (R-tabu) search method. The hybrid algorithm has some advantages, which is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all globa1 optimum solutions. The results show that the performance of the optimized mount compared with the original mount is improved significantly.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1760-1767
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• 2004

Sequential sampling approaches of a metamodel that sampling points are updated sequentially become a significant consideration in metamodeling technique. Sequential sampling design is more effective than classical space filling design of all-at-once sampling because sequential sampling design is to add new sampling points by means of distance between sampling points or precdiction error obtained from metamodel. However, though the extremum points can strongly reflect the behaviors of responses, the existing sequential sampling designs are inefficient to approximate extremum points of original model. In this research, new sequential sampling approach using the sensitivity of Kriging model is proposed, so that new approach reflects the behaviors of response sequentially. Various sequential sampling designs are reviewed and the performances of the proposed approach are compared with those of existing sequential sampling approaches by using mean squared error. The accuracy of the proposed approach is investigated against optimization results of test problems so that superiority of the sensitivity approach is verified.

• Structural Engineering and Mechanics
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• v.12 no.5
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• pp.527-540
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• 2001

A new sort of learning algorithm named whole learning algorithm is proposed to simulate the nonlinear and dynamic behavior of RC members for the estimation of structural integrity. A mathematical technique to solve the multi-objective optimization problem is applied for the learning of the feedforward neural network, which is formulated so as to minimize the Euclidean norm of the error vector defined as the difference between the outputs and the target values for all the learning data sets. The change of the outputs is approximated in the first-order with respect to the amount of weight modification of the network. The governing equation for weight modification to make the error vector null is constituted with the consideration of the approximated outputs for all the learning data sets. The solution is neatly determined by means of the Moore-Penrose generalized inverse after summarization of the governing equation into the linear simultaneous equations with a rectangular matrix of coefficients. The learning efficiency of the proposed algorithm from the viewpoint of computational cost is verified in three types of problems to learn the truth table for exclusive or, the stress-strain relationship described by the Ramberg-Osgood model and the nonlinear and dynamic behavior of RC members observed under an earthquake.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.40-44
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• 2010

The timing error detector(TED) employed in the closed loop type timing synchronization scheme for an MQASK all digital receiver suffers from the selfnoise-induced timing jitter. To eliminate the timing jitter a prefilter can be added in front of the TED. The prefilter method, however, degrades the stability and timing acquisition performance due to the loop delay and increases the complexity of the synchronizer. This paper proposes a polyphase filter type resampler approach to optimize the performance and architecture of the synchronizer simultaneously. The proposed scheme uses two resamplers which performs matched filtering and matched prefiltering so that the loop delay is minimized with minimal hardware resources. Simulation results showed an excellent acquisition performance with reduced timing jitter.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.31-31
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• 1999

In the preliminary design stage of an automotive interior, human sensibility is first analyzed and applied to design parameters for satisfying consumers needs using optimization and engineering judgement. Then designers try to design components that meet these needs using empirical and trial-and-error procedures. This process usually yields poor results because it is difficult to find a feasible design that satisfies the targets by trial-and-error (a feasible design is one that satisfies consumers needs and design constraints). To improve this process, we need tools to link the human sensibility with the design parameters that define the geometry of the components of an automotive interior. A methodology is presented for developing a tool for design guidance of an automotive interior. This tool translates the human sensibility into the design parameters that define the geometry of the components of an automotive interior. This tool, called interpreter, rapidly predicts the human sensibility of a given automotive interior and presents design parameters that meet or exceed given human sensibility to satisfy consumers needs and design constraints. The methodology is demonstrated on the interior design of an actual automotive.

• Journal of Power Electronics
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• v.10 no.4
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• pp.419-427
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• 2010

A parallel control strategy in capacity proportion frequency allocation mode for shunt active power filters (APFs) is proposed to overcome some of the difficulties in high power applications. To improve the compensation accuracy and overall system stability, an improved selective harmonic current control based on multiple synchronous rotating reference coordinates is presented in a single APF unit, which approximately implements zero steady-state error compensation. The combined decoupling strategy is proposed and theoretically analyzed to simplify selective harmonic current control. Improved selective harmonic current control forms the basis for multi-APF parallel operation. Therefore, a parallel control strategy is proposed to realize a proper optimization so that the APFs with a larger capacity compensate more harmonic current and the ones with a smaller capacity compensate less harmonic current, which is very practical for accurate harmonic current compensation and stable grid operation in high power applications. This is verified by experimental results. The total harmonic distortion (THD) is reduced from 29% to 2.7% for a typical uncontrolled rectifier load with a resistor and an inductor in a laboratory platform.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.10
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• pp.1079-1086
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• 2013

Optimal mount position of a front pillar trim considering heat resistant characteristics can be determined by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3061-3080
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• 2012

To improve the sensing performance, cooperation among secondary users can be utilized to collect space diversity. In this paper, we focus on the optimization of cooperative spectrum sensing in which multiple cognitive users efficiently cooperate to achieve superior detection accuracy with minimum sensing error probability in multiple cross-over cognitive radio networks. The analysis focuses on two fusion strategies: soft information fusion and hard information fusion. Under soft information fusion, the optimal threshold of the energy detector is derived in both noncooperative single-user and cooperative multiuser sensing scenarios. Under hard information fusion, the optimal randomized rule and the optimal decision threshold are derived according to the rule of minimum sensing error (MSE). MSE rule shows better performance on improving the final false alarm and detection probability simultaneously. By simulations, our proposed strategy optimizes the sensing performance for each cognitive user which is randomly distributed in the multiple cross-over cognitive radio networks.