• Journal of Korea Technology Innovation Society
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• v.2 no.2
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• pp.275-289
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• 1999

This study aims at investigating the stylized facts of the complex system products innovation in Korea. It reviewed the preceeding studies on the innovation patterns of complex system products and selected the issues to be examined in investigating Korean case. In addition, the stylized facts of innovation patterns of Korean complex system products were suggested. The peculiar characteristics of Korean cases are as follows: Korea has succeeded in integrating foreign technologies and components into commercial complex system products and in substituting the foreign system products in the domestic market. However, it faces a great difficulty in upgrading the products to the next generation. It lacks capabilities which facilitate the evolution of system products competing in the world market.

• Archives of design research
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• v.14 no.4
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• pp.137-146
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• 2001

The purpose of the study is to explore and understand the product development through the theory of evolution. For the purpose of the study, an evolution mechanism was set up in which the products were applied with. The results showed three distinguishable types of product evolution 'the evolution of functions','the evolution of forms'and 'the evolution of symbols what the products represent'. In order to answer the research questions, , , , the research was carried out in three ways: firstly, some ideas of product evolution were looked closely through existing researches; secondly, the study explored the idea of evolution mechanism being a natural process like an organic system; finally by applying various products with the evolution mechanism, different patterns of product evolution were classified. The results of the study can be summarised as follows: ${\bullet}$ The evolution mechanism in this study can be defined as an 'organic system'that consists of the causes of evolution'(based on the needs and the willingness to have pleasure), 'the point of evolving'(based on a degree of imperfection) and 'the motive of evolution'(based on the readiness to make profit). ${\bullet}$ The evolution mechanism seems to suggest three different patterns in product evolution,'the evolution of functions', 'the evolution of forms' and 'the evolution of symbols'

• Industrial Engineering and Management Systems
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• v.15 no.2
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• pp.181-195
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• 2016

In a product environment where various product-service systems (PSSs) are already being provided, the provision of a different type of PSS is difficult for second movers but necessary for their sustainability and differentiation. Despite the importance of providing distinguishing PSSs to market, prior PSS studies have not effectively considered the influence of existing PSSs in their methods. In response, we suggest an approach to generate new PSS concepts by employing general needs (GNs) and business system evolution patterns (BSEPs). Our approach 1) identifies customer GNs fulfilled by existing PSSs, 2) generates advanced PSS ideas from an evolutionary perspective by mapping the existing PSSs onto BSEPs, and 3) selects PSS ideas that can meet the unfulfilled or insufficiently considered GNs using a GN-PSS linking matrix, thereby generating new PSS concepts based on the selected ideas. The workings and practicability of this approach are illustrated using a PSS case study of furniture industry. This approach would provide PSS planners with an ability to generate the differentiated PSS concepts that handle the customer needs that have been untapped throughout a product's lifecycle. In addition, this approach as a module will have a synergetic effect when incorporated with other PSS methodologies.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.29-37
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• 1999

In general, the information processing capability of a neural network is determined by its architecture and efficient training patterns. However, there is no systematic method for designing neural network and selecting effective training patterns. Evolutionary Algorithms(EAs) are referred to as the methods of population-based optimization. Therefore, EAs are considered as very efficient methods of optimal system design because they can provide much opportunity for obtaining the global optimal solution. In this paper, we propose a new method for finding the optimal structure of neural networks based on competitive co-evolution, which has two different populations. Each population is called the primary population and the secondary population respectively. The former is composed of the architecture of neural network and the latter is composed of training patterns. These two populations co-evolve competitively each other, that is, the training patterns will evolve to become more difficult for learning of neural networks and the architecture of neural networks will evolve to learn this patterns. This method prevents the system from the limitation of the performance by random design of neural networks and inadequate selection of training patterns. In co-evolutionary method, it is difficult to monitor the progress of co-evolution because the fitness of individuals varies dynamically. So, we also introduce the measurement method. The validity and effectiveness of the proposed method are inspected by applying it to the visual servoing of robot manipulators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.111-114
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• 1997

Usually, Evolutionary Algorithms are considered more efficient for optimal system design, However, the performance of the system is determined by fitness function and system environment. In this paper, in order to overcome the limitation of the performance by this factor, we propose a co-evolutionary method that two populations constantly interact and coevolve. In this paper, we apply coevolution to neural network's evolving. So, one population is composed of the structure of neural networks and other population is composed of training patterns. The structure of neural networks evolve to optimal structure and, at the same time, training patterns coevolve to feature patterns. This method prevent the system from the limitation of the performance by random design of neural network structure and inadequate selection of training patterns. In this time neural networks are trained by evolution strategies that are able to apply to the unsupervised learning. And in the coding of neural networks, we propose the method to maintain nonredundancy and character preservingness that are essential factor of genetic coding. We show the validity and the effectiveness of the proposed scheme by applying it to the visual servoing of RV-M2 robot manipulators.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5A
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• pp.421-428
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• 2009

Inter-cell coordination has been an emerging issue for mitigating inter-cell interference in broadband wireless access networks such as IEEE802.16 and 3GPP LTE (Long Term Evolution). This paper proposes uplink/downlink hybrid inter-cell coordination patterns for a TDD (Time Division Duplex)/MC-CDMA (Multi-Carrier Code Division Multiple Access) system. For the performance analysis, closed forms of inter-cell interferences are derived when uplink and downlink transmissions coexist over a multi-cell environment. In the analysis, we find an optimal ratio of downlink transmit powers of BSs (Base Stations) based on the target outage probability and the performance according to ratios of uplink/downlink transmit powers of MSs (Mobile Stations)/BSs is explored. Our numerical results show that interference mitigation utilizing the characteristics of the uplink and downlink power ratio is very effective in improving system performance in terms of QoS.

• Korean Institute of Interior Design Journal
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• v.19 no.6
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• pp.100-111
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• 2010

'Pattern' is the term that is frequently used in the aspects of history, society, and science. It always appears in the remains or relics of the age of civilization when recording was started, and its evaluation and value differ by time. Patterns in the ancient civilization were symbolic, social, and spatially crucial. However, after the modernization, they were considered to be immoral and unnecessary, so the range of their significance came to reduce. Due to the development of science, ornament patterns lost the limitation of its range of use along with new interpretation of them. Especially with the advent of new scientific theories such as the evolution theory from the biological aspect, quantum mechanics, and super string theory, morphological possibilities more than the human scale perceived by men came to be discovered. Living organisms maintain their lives through patterns, structures, and processes in order to produce a system alive. Among them, patterns are the organization of relations determining the characteristics of the system. The present patterns may correspond to this meaning. The pattern in a space is the matter of how to relate the components after all. In a space, however, there are numerous components mingled with one another. If these tasks are conducted as analogue work, it will take a lot of time and effort. However, if digital media are utilized to perform the tasks like analysis, generation, or fabrication, it will produce a result with higher precision and efficiency. In this sense, parametric modeling is quite useful media. Opening morphological variation, it realizes more possibilities, connects conveniently the relations between complex components composing a space, and helps produce creative patterns.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2193-2196
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• 1998

Cellular automata are dynamical systems in which space and time are discrete, where each cell has a finite number of states and updates its states by interactive rules among the cell-neighborhood. From the characteristics of self-reproduction and self- organization, it is possible to create a neural network which has the specific patterns or structures dynamically. CAM-Brain is a kind of such neural network system which evolves its structure by adopting evolutionary computations like genetic algorithms (GA). In this paper, we suggest the evolution strategies for the structure of neural networks based on cellular automata.

• Korean Institute of Interior Design Journal
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• v.21 no.2
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• pp.35-45
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• 2012

The development of digital media made the change of architectural paradigm from tectonic to the surface and pattern. This means the transition to the new kind of materiality and the resurrection of ornament. This study started as an aim to apply biological pattern to architectural design from the new perception of pattern. Architectural patterns in the early era appeared as ladders, steps, chains, trees, vortices. But since 21st century, we can find patterns in nature like atoms and molecular structures, fluid forms of dynamics and new geometrical pattern like fractal and first of all biological patterns like viruses and micro-organisms, Voronoi cells, DNA structure, rhizomes and various hybrids and permutations of these. Pattern became one of the most important elements and themes of contemporary architecture through the change of materiality and resurrection of ornament with the new perception of surface in architecture. One of the patterns that give new creative availability to the architectural design is biological pattern which is self-organized as an optimum form through interaction with environment. Biological patterns emerge mostly as self-replicating patterns through morphogenesis, certain geometrical patterns(in particular triangles, pentagons, hexagons and spirals). The architectural application methods of biological patterns are direct figural pattern of organism, circle pattern, polygon pattern, energy-material control pattern, differentiation pattern, parametric pattern, growth principle pattern, evolutionary ecologic pattern. These patterns can be utilized as practical architectural patterns through the use of computer programs as morphogenetic programs like L-system, MoSS program and genetic algorithm programs like Grasshoper, Generative Components with the help of computing technology like mapping and scripting.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.