• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.8
• /
• pp.56-64
• /
• 1997

In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.655-657
• /
• 1995

Variable Structure Controller is well known to be a robust controller. Recently, Evolution Strategy is used as a effective search algorithm. In this paper, we propose a Variable Structure Controller combined with Evolution Strategy. Evolution Strategy is used to estimate the unknown parameters, the control gain and the thickness of saturation function boundary layer of Variable Structure Controller. From the experiment, we found the proposed Variable Structure Controller shows accurate tracking ability and robust performance in the BLOC motor position control system.

• Journal of The Korean Astronomical Society
• /
• v.36 no.3
• /
• pp.223-239
• /
• 2003

It is now a well established fact that galaxies undergo significant morphological transformation during their lifetimes, manifesting as an evolution along the Hubble sequence from the late to the early Hubble types. The physical processes commonly believed to be responsible for this observed evolution trend, i.e. the major and minor mergers, as well as gas accretion under a barred potential, though demonstrated applicability to selected types of galaxies, on the whole have failed to reproduce the most important statistical and internal properties of galaxies. The secular evolution mechanism reviewed in this paper has the potential to overcome most of the known difficulties of the existing theories to provide a natural and coherent explanation of the properties of present day as well as high-redshift galaxies.

• Publications of The Korean Astronomical Society
• /
• v.30 no.2
• /
• pp.511-512
• /
• 2015

Star formation activities dominate the evolution of galaxies. Elliptical galaxies are believed to be old galaxies in the Hubble sequence, and elliptical galaxies at different evolution epochs might have different star formation activities and/or morphologies. We investigate the connection between star formation rates and the morphology of elliptical galaxies. With the Sloan Digital Sky Survey (SDSS) and the Galaxy Zoo, we select a sample of elliptical galaxies by morphology and consider their infrared emission as an index of star formation rate to study the relation between the star formation rates and their morphological properties, such as ellipticities. In addition, we select some nearby spiral galaxies with very low MIR emission to probe the mechanisms of these red spiral galaxies. We display our preliminary results and discuss their implication on the evolution of galaxies in this poster.

• Publications of The Korean Astronomical Society
• /
• v.30 no.2
• /
• pp.485-486
• /
• 2015

We use light-curve fitting models (MLCS2k2, SALT2, and SNooPy) as implemented in SNANA to make the YOnsei Nearby Supernova Evolution Investigation (YONSEI) Supernova Catalogue. The catalogue consists of several hundred Type Ia supernovae (SNe Ia) in the redshift range from 0.01 to 1.35, and provides distance moduli, light-curve shape parameters, and color or extinction values for each supernova. This data set will be used to study the dependence of SNe Ia luminosities on the host galaxy morphologies. In this paper, we present the YONSEI Supernova Catalogue and preliminary systematic tests for the catalogue.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.420-426
• /
• 1998

Evolutionary algorithms are probabilistic optimization algorithms based on the model of natural evolution. Recently the efforts to improve the performance of evolutionary algorithms have been made extensively. In this paper, we introduce the research for improving the convergence rate and search faculty of evolution algorithms by using reinforcement learning. After providing an introduction to evolution algorithms and reinforcement learning, we present adaptive genetic algorithms, reinforcement genetic programming, and reinforcement evolution strategies which are combined with reinforcement learning. Adaptive genetic algorithms generate mutation probabilities of each locus by interacting with the environment according to reinforcement learning. Reinforcement genetic programming executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. Reinforcement evolution strategies use the variances of fitness occurred by mutation to make the reinforcement signals which estimate and control the step length.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1110-1112
• /
• 1996

In this paper, we propose a new approach for robust auto-tuning of PID gains using Evolution Strategy. Evolution Strategy is searching algorithm which imitate the principles of natural evolution as a method to solve parameter optimization problem and easy to use without any other special mathematical theory. Through the simulation of the speed control of a series-connected de motor, our proposed method shows more improved performance by finding optimal parameters of PID controller than a classical Ziegler-Nichols method.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.4
• /
• pp.686-693
• /
• 2013

Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Opposition-based Differential Evolution (ODE) algorithms have been applied to the GEP problem. The original GEP problem has been modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units have been considered. The results have been compared with Dynamic Programming (DP) method. The ODE performs well and converges faster than DE.

• Journal of information and communication convergence engineering
• /
• v.19 no.3
• /
• pp.155-160
• /
• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• 중국학논총
• /
• no.65
• /
• pp.287-302
• /
• 2020

Social Evolution was the most influential idea in modern China. Chinese intellectuals, who had made the survival of their country and people a top priority in the face of threats from Western powers, accepted the theory of social evolution as an idea calling for national unity. For Liang Qi chao, the theory of social evolution was a reason to raise the modern nation-state and the new people, along with the need for reform. This article examines that philosophical content and meaning modern Chinese social evolution has around his concept of "nation". His ideas, which are regarded as the origin of Asian nationalism, reflect his belief in and will toward a nation-state, and occupy a unique position in the political history of modern China.