• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.571-582
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• 2008

This paper analyzes global convergence of the univariate dynamic encoding algorithm for searches (uDEAS) and provides an application result to function optimization. uDEAS is a more advanced optimization method than its predecessor in terms of the number of neighborhood points. This improvement should be validated through mathematical analysis for further research and application. Since uDEAS can be categorized into the generating set search method also established recently, the global convergence property of uDEAS is proved in the context of the direct search method. To show the strong performance of uDEAS, the global minima of four 30 dimensional benchmark functions are attempted to be located by uDEAS and the other direct search methods. The proof of global convergence and the successful optimization result guarantee that uDEAS is a reliable and effective global optimization method.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.268-275
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• 2011

This paper proposes two optimal path planning methods of a mobile robot using uDEAS (univariate Dynamic Encoding Algorithm for Searches). Before start of autonomous traveling, a self-controlled mobile robot must generate an optimal global path as soon as possible. To this end, numerical optimization method is applied to real time path generation of a mobile robot with an obstacle avoidance scheme and the basic path generation method based on the concept of knot and node points between start and goal points. The first improvement in the present work is to generate diagonal paths using three node points in the basic path. The second innovation is to make a smooth path plotted with the blending polynomial using uDEAS. Effectiveness of the proposed schemes are validated for several environments through simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.840-848
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• 2009

This paper proposes a new multi-start scheme that generates guided random bits in selecting initial search points for global optimization with univariate dynamic encoding algorithm for searches (uDEAS). The proposed method counts the number of 1 in each bit position from all the previously generated initial search matrices and, based on this information, generates 0 in proportion with the probability of selecting 1. This rule is simple and effective for improving diversity of initial search points. The performance improvement of the proposed multi-start is validated through implementation in uDEAS and function optimization experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.43-50
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• 2007

Reheating furnace is an essential facility of a rod mill plant where a billet is heated to the required rolling temperature so that it can be milled to produce wire. Although it is very important to obtain information on billet temperatures, it is not feasible during furnace operation. Consequently, a billet temperature profile should be estimated. Moreover, this estimation should be done within an appropriate time interval for an on-line application. In this paper, a billet heat transfer model based on 2D FEM(Finite Element Method) with spatially distributed emission factors is proposed for an on-line billet temperature estimation and also a measurement is carried out for two extremely different furnace operation patterns. Finally, the difference between the model outputs and the measurements is minimized by using a new optimization algorithm named uDEAS(Univariate Dynamic Encoding Algorithm for Searches) with multi-step tuning strategy. The obtained emission factors are applied to a simulation for the data which are not used in the model tuning for validation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.863-868
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• 2004

Reheating furnace is an essential facility of a rod mill plant where a billet is heated to the required rolling temperature so that it can be milled to produce wire. Sometimes, it is also necessary to control a transient billet temperature pattern according to the material characteristics to prevent a wire from breaking. Though it is very important objective to obtain a correct information of a billet temperature during furnace operation. Consequently, a billet temperature profile must be estimated. In this paper, a billet heat transfer model based on FEM (Finite Element Method) with spatially distributed emission factors is proposed and a measurement is also carried out for two different furnace operation conditions. Finally, the difference between the model outputs and the measurements is minimized by using the new optimization algorithm named uDEAS(Univariate Dynamic Encoding Algorithm for Searches) with multi-step tuning strategy. Hence, the information of billet temperatures can be obtained by using proposed model on various furnace operation conditions.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.71-74
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• 2022

본 논문은 최적화알고리즘을 이용한 관절각 기반 3차원 자세 추정 기법의 정확도를 휴머노이드 로봇을 이용하여 검증하는 방법을 제안한다. 구글의 자세 추정 오픈소스 패키지인 MPP(MediaPipe Pose)로 특정자세를 취한 휴머노이드 로봇의 관절 좌표를 카메라의 픽셀 좌표로 추출한다. 추출한 픽셀 좌표를 전역최적화 방법인 uDEAS(univariate Dynamic Encoding Algorithm for Searches)를 통해 시상면과 관상면에서의 각도를 추정하고 휴머노이드 로봇의 실제 관절 각도와 비교하여 알고리즘의 정확도를 검증하는 방법을 제시한다.