• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.277-280
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• 2004

In the research of biomechanical engineering, robotics and neurophysiology, to clarify the mechanism of human bipedal walking is of major interest. It serves as a basis of developing several applications such as rehabilitation tools and humanoid robots Nevertheless, because of complexity of the neuronal system that Interacts with the body dynamics system to make walking movements, much is left unknown about the details of locomotion mechanism. Researchers were looking for the optimal model of the neuronal system by trials and errors. In this paper, we applied Genetic Programming to induce the model of the nervous system automatically and showed its effectiveness by simulating a human bipedal walking with the obtained model.

• Animal cells and systems
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• v.9 no.4
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• pp.211-213
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• 2005

In bird species that suffer brood parasitism, the question about which sex is responsible for egg rejection has important implications for determining the coevolutionary relationship between brood parasites and their hosts. In order to determine which sex rejects a parasitic egg in vinous-throated parrotbills (Paradoxornis webbianus) which have egg color dimorphism, we conducted model egg experiments and video-recorded the behavior of the focal pair. Both sexes showed rejection behavior to the parasitic eggs. It indicates that the vinous-throated parrotbill may have a high rejection rate and faster spread of any rejection alleles through out populations. However, further studies are still needed to confirm the egg recognition mechanism in this species, which will expand our knowledge of the evolutionary relationship between host and parasite.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2002.05b
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• pp.63-70
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• 2002

This paper is to illuminate the dynamic relationship between the technological learning mechanism and international political intervention. Besides conventional techno-economic factors, international political factors are emphasized as external factors in the technological learning mechanism. Being influenced by international political intervention, the evolutionary path of technological capabilities is not incrementally cumulated and organizational process is not autonomously performed, either. In order to mitigate the impact of the international political intervention, DCs make efforts to develop technological capabilities step-by-step in line with current and future civilian industrial demand.

• Advances in Computational Design
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• v.4 no.3
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• pp.197-210
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• 2019

Motion generation of a four-bar linkage is a type of mechanism synthesis that has a wide range of applications such as a pick-and-place operation in manufacturing. In this research, the use of meta-heuristics for motion generation of a four-bar linkage is demonstrated. Three problems of motion generation were posed as a constrained optimization probably using the weighted sum technique to handle two types of tracking errors. A simple penalty function technique was used to deal with design constraints while three meta-heuristics including differential evolution (DE), self-adaptive differential evolution (JADE) and teaching learning based optimization (TLBO) were employed to solve the problems. Comparative results and the effect of the constraint handling technique are illustrated and discussed.

• Molecules and Cells
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• v.28 no.2
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• pp.111-117
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• 2009

The CHRM3 gene is a member of the muscarinic acetylcholine receptor family that plays important roles in the regulation of fundamental physiological functions. The evolutionary mechanism of exon-acquisition and alternative splicing of the CHRM3 gene in relation to transposable elements (TEs) were analyzed using experimental approaches and in silico analysis. Five different transcript variants (T1, T2, T3, T3-1, and T4) derived from three distinct promoter regions (T1: L1HS, T2, T4: original, T3, T3-1: THE1C) were identified. A placenta (T1) and testis (T3 and T3-1)-dominated expression pattern appeared to be controlled by different TEs (L1HS and THE1C) that were integrated into the common ancestor genome during primate evolution. Remarkably, the T1 transcript was formed by the integration event of the human specific L1HS element. Among the 12 different brain regions, the brain stem, olfactory region, and cerebellum showed decreased expression patterns. Evolutionary analysis of splicing sites and alternative splicing suggested that the exon-acquisition event was determined by a selection and conservation mechanism. Furthermore, continuous integration events of transposable elements could produce lineage specific alternative transcripts by providing novel promoters and splicing sites. Taken together, exon-acquisition and alternative splicing events of CHRM3 genes were shown to have occurred through the continuous integration of transposable elements following conservation.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of Information Technology Services
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• v.12 no.3
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• pp.1-21
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• 2013

This study attempts to explain the mechanism behind the migration from vertically integrated value chain architecture to an innovation ecosystem consisting of horizontally separated layers in value chain. We first present a comprehensive framework based on the theoretical analysis of the drivers for migration to an innovation ecosystem, which are standard (institution), capability distribution, and transaction costs. The theoretical framework suggests that the migration to an innovation ecosystem is explained by the influence of standard on the evolutionary change of capability distribution and transaction costs. In particular, when the new de-jure standard competes with the de-facto standard, the new de-jure standard has the greatest impact on the distribution capabilities and the transaction costs. Based on this theoretical framework, we analyze the latest SDN (Software Defined Networking) case of the network industry. SDN standard has transformed the industry from a vertically integrated value chain architecture to a horizontally separated one with its influence on the distribution capabilities and the transaction costs in the industry.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.1-8
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• 1998

This paper proposes a reinforcement genetic programming based on the reinforcement learning method for the performance improvement of genetic programming. Genetic programming which has tree structure program has much flexibility of problem expression because it has no limitation in the size of chromosome compared to the other evolutionary algorithms. But worse results on the point of convergence associated with mutation and crossover operations are often due to this characteristic. Therefore the sizes of population and maximum generation are typically larger than those of the other evolutionary algorithms. This paper proposes a new method that executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. The validity of the proposed method is evaluated by appling it to the artificial ant problem.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.4
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• pp.537-545
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• 2013

The elastic recovery of a metal seal is a factor that can be used to assess its sealing performance. In this study, a compliant mechanism topology optimization has been performed to find a structure of a metal O-ring seal that can maintain excellent sealing performance with a maximized elastic recovery over extended operation. An evolutionary structural optimization (ESO) was used as a topology optimization algorithm with two different types of objective functions considering both flexibility and stiffness. In particular, a circular design domain was adopted to consider the outer shape of the metal O-ring seal. The elastic recovery of the optimal topology was calculated and compared to that of a commercial product.