Dynamic Encoding Algorithm For Searches Research Articles

Optimization of Solar/Fuel Cell Hybrid Energy System Using the Combinatorial Dynamic Encoding Algorithm for Searches (cDEAS)

This study proposes a computational design method for determining a hybrid power system’s sizing and ratio values that combines the national electric, solar cell, and fuel cell power sources. The inequality constraints associated with the ranges of power storage exchange and the stored energy are reflected as penalty functions in the overall cost function to be minimized. Using the energy hub model and the actual data for the solar cell power and the load of the residential sector in one Korean city for one hundred days, we optimize the ratio of fuel cell energy and solar cell energy to 0.46:0.54 through our proposed approach. We achieve an average cost-reduction effect of 19.35% compared to the cases in which the fuel-cell energy ratio is set from 0.1 to 0.9 in 0.1 steps. To optimize the sizing and the ratio of fuel-cell energy in the hybrid power system, we propose the modified version of the univariate dynamic encoding algorithm for searches (uDEAS) as a novel optimization method. The proposed novel approaches can be applied directly to any place to optimize an energy hub system model comprising three power sources, i.e., solar power, fuel cell, and power utility.

Min-Max Univariate Dynamic Encoding Algorithm for Searches (uDEAS) and Its Application to Optimal Design of Electric Machines

Optimal design of Interior Permanent Magnet Synchronous Machines (IPMSM) is to obtain the global design optimum in association with the characteristic analysis investigating nonlinear magnetic saturation based on Finite Element Analysis (FEA). Since characteristic analysis occupies most part of the convergence time, the number of objective function must be called minimally. Hence, in this paper, MuDEAS is newly implemented in order to compensate the excessive computation time and the reliability of convergence to the global optimum. MuDEAS has been verified through a benchmark function and applied to the optimal design of IPMSM for maximizing Maximum Torque Per Ampere (MTPA) indentified with FEA.

자기-자이로 유도 장치를 위한 MEMS형 자이로의 민감도 최적화

This paper presents a sensitivity optimization of a MEMS (microelectromechanical systems) gyroscope for a magnet-gyro system. The magnet-gyro system, which is a guidance system for a AGV (automatic or automated guided vehicle), uses a magnet positioning system and a yaw gyroscope. The magnet positioning system measures magnetism of a cylindrical magnet embedded on the floor, and AGV is guided by the motion direction angle calculated with the measured magnetism. If the magnet positioning system does not measure the magnetism, the AGV is guided by using angular velocity measured with the gyroscope. The gyroscope used for the magnet-gyro system is usually MEMS type. Because the MEMS gyroscope is made from the process technology in semiconductor device fabrication, it has small size, low-power and low price. However, the MEMS gyroscope has drift phenomenon caused by noise and calculation error. Precision ADC (analog to digital converter) and accurate sensitivity are needed to minimize the drift phenomenon. Therefore, this paper proposes the method of the sensitivity optimization of the MEMS gyroscope using DEAS (dynamic encoding algorithm for searches). For experiment, we used the AGV mounted with a laser navigation system which is able to measure accurate position of the AGV and compared result by the sensitivity value calculated by the proposed method with result by the sensitivity in specification of the MEMS gyroscope. In experimental results, we verified that the sensitivity value through the proposed method can calculate more accurate motion direction angle of the AGV.

UDEAS를 이용한 이동 로봇의 최적 전역 경로 계획

본 논문에서는 uDEAS(Univariate Dynamic Encoding Algorithm for Searches)를 이용하여 두 가지의 이동 로봇 최적 전역 경로 계획을 제안한다. 이동 로봇의 자율 주행을 위해서는 빠른 시간 내에 작업 공간에서의 최적 경로를 생성해야 한다. 따라서 본 논문에서는 이동 로봇의 실시간 최적 경로 계획을 위해 전역 경로 계획을 도입하여 장애물 지역과 비장애물 지역을 확인하고, 지도상의 노드점과 노트점을 이용하여 출발 지점과 도착 지점 사이의 기본 경로를 생성한다. 그리고 기본 경로를 사용하여 두 가지의 방법으로 경로를 생성하게 된다. 첫 번째 방법은 기본 경로에서 세 개의 노드점을 이용하여 대각선 경로를 생성하는 방법이다. 두 번째는 혼합 다항식의 파라미터를 uDEAS를 이용하여 탐색하고, 곡선 궤적을 생성하는 방법이다. 시뮬레이션을 통해 두개의 방법에 대해 비교 분석하였다. 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.

New Encoding Method of Parameter for Dynamic Encoding Algorithm for Searches (DEAS)

The dynamic encoding algorithm for searches (DEAS) is a recently developed algorithm that comprises a series of global optimization methods based on variable-length binary strings that represent real variables. It has been successfully applied to various optimization problems, exhibiting outstanding search efficiency and accuracy. Because DEAS manages binary strings or matrices, the decoding rules applied to the binary strings and the algorithm's structure determine the aspects of local search. The decoding rules used thus far in DEAS have some drawbacks in terms of efficiency and mathematical analysis. This paper proposes a new decoding rule and applies it to univariate DEAS (uDEAS), validating its performance against several benchmark functions. The overall optimization results of the modified uDEAS indicate that it outperforms other metaheuristic methods and obviously improves upon older versions of DEAS series.

Trajectory generation schemes for bipedal ascending and descending stairs using univariate dynamic encoding algorithm for searches (uDEAS)

In this paper, we propose new motion schemes for a biped robot to walk up and down a staircase using dynamic encoding algorithm for searches (DEAS) for optimization. Ascending and descending a staircase are scheduled by four phases for convenience of motion generation. Each phase mimics the natural gait of a human being and is easy to analyze and implement. Optimal trajectories of 10 joint motors in a lower extremity of a humanoid are computed to simultaneously satisfy stability conditions, walking constraints, and energy efficiency requirements, and the zero moment point condition. The performance of uDEAS is compared to genetic algorithm in finding 13 parameters with the result that uDEAS requires only 17 seconds, which is 12 times faster. The feasibility of the proposed motion schemes is validated with computer simulations and experiments successfully carried out for a small humanoid robot.

A New Trajectory Generation Scheme for Direction Turning in Biped Walking

This paper proposes a new trajectory generation scheme for direction turning during biped walking based on our previous study and experiences. For turning in walking, two joints in the sagittal plane should be added in the robot model, and each joint should rotate appropriately considering stability and landing precision. All the angular trajectories for 12 joints in the lower body are approximated by blending polynomials optimized by univariate Dynamic Encoding Algorithm for Searches (uDEAS). The proposed walking scheme is validated via simulation.

Special Issue on ISIS 2009, Dong-A University, Busan, Korea

The 10th International Symposium on Advanced Intelligent Systems 2009 (ISIS2009) held on August 17-19, 2009, at the Bumin Campus of Dong-A University (http://www.donga.ac.kr/) in Busan, Korea, was sponsored by the Korean Institute of Intelligent System Society (KIIS) and cosponsored technically by the Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) and the Taiwanese Association for Artificial Intelligence (TAAI). The international symposium focused on state-of-art accomplishments, innovations, and potential directions in intelligent systems. It also marked an epoch of innovation and the dissemination of research into many interesting fields. Its broad theme covered the latest in technical fields, including artificial intelligence, intelligent systems, Ambient Intelligence (AmI), bioinformatics, information technology, and their wide-ranging applications, from basic theoretical work to practical engineering applications. The 80 featured papers were presented by 120 participants. With so many papers submitted to JACIII, this special issue consists of just two strictly selected papers. The first, deals with emerging research trends in robotics, proposing a new trajectory generation using the univariate Dynamic Encoding Algorithm for Searches (uDEAS) in the turning of a biped walking robot. The second paper, presenting the latest findings in AmI, details a newly designed and implemented robust capacitive sensor with parasitic parameter modeling over a range of high 200 KHz frequencies based on an Unscented Kalman Filter (UKF) algorithm. I would like to thank Mr. Kunihiko Uchida, Mr. Shinya Wakai, Ms. Reiko Ohta, and Mr. Shinji Isokawa as editorial staff of Fuji Technology Press for editing these complex manuscripts into their final form. And I really thank to Prof. Kaoru Hirota, Editor-in-Chief of JACIII for inviting me to direct this special issue on ISIS

DEAS를 이용한 직접구동형 풍력발전기 최적설계

Optimal design of the direct-driven PM Wind Generator, combined with DEAS(Dynamic Encoding Algorithm for Searches) and FEM(Finite Element Method), has been proposed to maximize the Annual Energy Production(AEP) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, DEAS contributes to reducing the excessive computing time for the optimization process.

A Fast Computational Optimization Method: Univariate Dynamic Encoding Algorithm for Searches (uDEAS)

This paper proposes a new computational optimization method modified from the dynamic encoding algorithm for searches (DEAS). Despite the successful optimization performance of DEAS for both benchmark functions and parameter identification, the problem of exponential computation time becomes serious as problem dimension increases. The proposed optimization method named univariate DEAS (uDEAS) is especially implemented to reduce the computation time using a univariate local search scheme. To verify the algorithmic feasibility for global optimization, several test functions are optimized as benchmark. Despite the simpler structure and shorter code length, function optimization performance show that uDEAS is capable of fast and reliable global search for even high dimensional problems.

Parameter Identification of Induction Motors Using Dynamic Encoding Algorithm for Searches (DEAS)

A newly developed optimization algorithm, called the dynamic encoding algorithm for searches (DEAS), is introduced and applied to the parameter identification of an induction motor for vector control and fault detection. Digital simulations are conducted on startup with no load and normal operation with load perturbations. DEAS is compared with the continuous-time prediction error method and the genetic algorithm via identification performance using the startup signals. The capability of onload identification using the proposed technique is also verified with transient signals. Consequently, DEAS is shown to locate more precise parameter values than both the compared methods especially with much faster execution time than the genetical algorithm.