• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.16-20
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• 1999

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.62-69
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• 2012

This thesis proposes a new social network simulator, which can be used for the social network analysis (SNA). It is composed of three modules; initialization, network evolution, and output generation. For the network evolution module, we suggest a modified JGN (MJGN) based on JGN, the network evolution model developed by Jin, Girvan, and Newman. Arena, one of the most popular simulation tools, was used to model the agent based social network simulator. Lastly, some test results were presented to show the value of the proposed simulator when one performs SNA at the longitudinal point of view.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.275-284
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• 1999

The TTP(TimeTabling Problem) for a university which should be made every term is very difficult problem to schedule because each component must satisfy all fixed constraints. This TTP, therefore, is solved by several heuristic methods. In this paper, applying PPEP(Parallel Population Evolution Programs) for the TTP, we try to find the approximated optimal solution by maintaining independence of each subpopulation and by searching the wide feasible solution. In addition, we present the problem to be appeared when used PMX(Partially mapped crossover)operator for the long chromosomes and then propose GBX (Gene_Based crossover)operator which can complement it. Finally, experimental results are presented comparing EP(Evolution Programs) and PPEP, then GBX which has the random genes and the selected genes on a real TTP.

• Journal of Electrochemical Science and Technology
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• v.10 no.1
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• pp.22-28
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• 2019

To maximize the oxygen evolution reaction (OER) in the electrolysis of water, nano-grade $IrO_2$ powder with a low specific surface was prepared as a catalyst for a solid polymer electrolyte (SPE) system, and a membrane electrode assembly (MEA) was prepared with a catalyst loading as low as $2mg\;cm^{-2}$ or less. The $IrO_2$ catalyst was composed of heterogeneous particles with particle sizes ranging from 20 to 70 nm, having a specific surface area of $3.8m^2g^{-1}$. The anode catalyst layer of about $5{\mu}m$ thickness was coated on the membrane (Nafion 117) for the MEA by the decal method. Scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS) confirmed strong adhesion at the interface between the membrane and the catalyst electrode. Although the loading of the $IrO_2$ catalyst was as low as $1.1-1.7mg\;cm^{-2}$, the SPE cell delivered a voltage of 1.88-1.93 V at a current density of $1A\;cm^{-2}$ and operating temperature of $80^{\circ}C$. That is, it was observed that the over-potential of the cell for the oxygen evolution reaction (OER) decreased with increasing $IrO_2$ catalyst loading. The electrochemical stability of the MEA was investigated in the electrolysis of water at a current density of $1A\;cm^{-2}$ for a short time. A voltage of ~2.0 V was maintained without any remarkable deterioration of the MEA characteristics.

• IE interfaces
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• v.20 no.4
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• pp.438-447
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• 2007

This paper presents an aspect-oriented approach to simulation design and analysis in system design phase for integrated logistics system simulation. The integrated logistics system composed of AS/RS (Automated Storages and Retrieval System), AGVs (Automated Guided Vehicle System), STVs (Sorting Transfer Vehicle System) and Conveyor System is designed by using the aspect-oriented approach and UML (Unified Modeling Language). The multi-factorial design of experiments and regression analysis are used for design parameters of the system and Evolution Strategies is used to verify each parameter. Aspect-oriented approach for the integrated logistics system simulation shows the advantages of code reusability, extendible, modulation, easy improvement and a better design technique.

• IE interfaces
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• v.19 no.1
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• pp.9-18
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• 2006

In this paper, we explore the ubiquitous system that provides new business opportunities in the context of digital convergence. Exploiting the value network of the proposed ubiquitous system, we analyze strategic market drivers that define the speed and direction of the evolution path for ubiquitous industries. Motivated by the dynamic growth of ubiquitous industry, we design a set of ubiquitous business models that pave the way for firms to identify profitable business cases. In addition, we analyze the evolution path of the proposed business model such that the model can be implemented in the life-cycle of ubiquitous industry. Futhermore, we develop a modeling framework for selecting a suitable business model and for evaluating the performance of the selected business model.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 1993.11a
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• pp.20-21
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• 1993

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.2 no.1
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• pp.95-110
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• 1998

Evolutionary Algorithms (EAs) are population-based optimization methods based on the principle of Darwinian natural selection. The representative methodology in EAs is genetic algorithm (GA) proposed by J. H. Holland, and the theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the meaning of these foundational concepts, simple genetic algorithm (SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithm. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. And predator-prey co-evolution and symbiotic co-evolution, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. And the experimental results show a co-evolutionary algorithm works well in optimization problems even though in deceptive functions.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.113-119
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• 2021

With the increasing pressure of international competition, urban agglomeration cooperation and innovation had become an important means of regional economic development. This study analyzed the spatial characteristics of the Urban Cooperative Innovation Network in Guangdong-Hong Kong-Macao Greater Bay Area, found out the dynamic evolution law of innovation, provided suggestions for policy management departments, and effectively planned the industrial layout. According to the data of the State Intellectual Property Office of China, this study researched invention patents from 2005 to 2019. This paper constructed the urban cooperative innovation network, and took 11 cities in the bay area as the research objects, and used social network analysis to study the spatial structure and dynamic evolution of the urban innovation network. Every indicator reflected the urban cooperative innovation, but they all showed a certain decline in 2008-2010. And it is inferred that the innovation network space of each city will be "obvious fist advantages, significant spillover effect and weakening role of Hong Kong and Macao". This paper divided urban cooperative innovation of Guangdong-Hong Kong-Macao Greater Bay Area into three stages. Summing up the characteristics of each stage is helpful to recognize the changes of urban cooperative innovation and to do a good job in industrial layout planning.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.