• Archives of design research
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• v.15 no.4
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• pp.307-316
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• 2002

Metaphor thinking is a kind of intuitive thinking and plays a central role in design process. But there are not many researches on this topic because it happens in designer's mind during design problem solving. In this paper, we considered cognitive aspects of metaphorical thinking as they cropped up in the process of design concepts development. As a method of cognitive experiment we used a protocol analysis of the design review reports. At the end of this research we concluded that metaphorical thinking is engaged in restructuring of new frames and reconciliation of conflicting frames for the development of new design ideas and concepts. This role of metaphorical thinking makes the design thinking divergent and the design process creative.

• Archives of design research
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• v.13 no.3
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• pp.39-48
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• 2000

Under the ambiguous situation that design aim is not defined, this study would help designers with 1. overcoming the limitation of form generation ability by establishing visual application system, 2. accepting users'opinions by generating images dynamically, analysing and giving information on the preferred ones on the web on real time, 3. identifying tendency of preference so that they can generate preferred colors and images in future by updating image combination and dropping low-preferred ones. This system would play a role as an idea or form generation application in the product design development process.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.471-485
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• 2024

This study explores the effective utilization of generative AI for conducting in-depth interviews within the lean startup environment. With recent technological advancements, the application of generative AI in enhancing operational productivity has been on the rise across various organizations, and this trend extends to the lean startup milieu. The research develops specific guidelines and a guidebook aimed at assisting practitioners in lean startups to conduct in-depth interviews using AI, even amidst the constraints of limited time and capital. The proposed guidebook facilitates practitioners to swiftly design and conduct interviews, thereby promoting an agile and flexible working environment within lean startups. Moreover, this study investigates practical methods for applying text-based generative AI services like ChatGPT 4 and Luyten in the fields of design and interviewing, thereby contributing to the academic discussion and practical implementation in these areas. The significance of this research lies in its potential to broaden the horizon of scholarly debate and practical application of generative AI in lean startups.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.777-786
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• 2023

Generative AI systems are expected to be more widely utilized. However, relatively little attention has been paid to understanding how users perceive and accept generative AI results. To identify strategies for increasing the future use of generative AI and prepare for potential issues, we organized design workshop for the general user group and the designer group. They created artwork utilizing Novel AI and semi-structured interview was followed to evaluate their attitudes toward generative AI and its copyright. Results indicate that the general public views generative AI positively, while the design-related group views it quite negatively. The participants expressed concerns as to the misuse the system, specifically related to copyright issues. People who are likely to utilize generative AI outcomes have insisted more strongly that copyrights should be their own. Those working in the design field highly evaluated the possibility of using generative AI in their work. Copyright perceptions were not significantly influenced by users' satisfaction or their level of involvement in the creation process. We discuss design implications for interfaces using generative AI based on the findings.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.113-117
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• 2000

침입 탐지 시스템은 네트워크나 호스트에 대한 오용, 남용, 또는 허가되지 않은 접근을 탐지하는 기능을 갖는 시스템이다. 최근 침입들은 그 종류가 매우 다양화되고, 탐지하기가 매우 어려운 형태로 나타나고 있다. 이러한 침입으로 대표적인 것이 분산 공격과 스텔시 공격(Stealthy Attack)이 있다. 분산 공격은 침입자가 공간적으로 분산되어 이를 탐지하기 어렵게 하는 공격을 말하며, 스텔시 공격은 시간적으로 분산되어 이를 탐지하기 어려운 경우를 말한다. 침입 탐지 시스템의 모델링 및 시뮬레이션을 위해서는 침입 탐지 시스템 모델에 필요한 침입을 제공하고, 침입에 대한 탐지 능력을 평가하기 위한 experimental frame을 디자인 해야한다. 본 연구에서는 분산 공격과 스텔시 공격 기능을 갖는 침입 생성 모델링 방법을 소개하며, 침입 생성을 위해 요구되는 침입 정보 베이스의 역할 및 저장 정보를 소개한다. 또한, 침입에 대한 탐지 능력 평가를 위한 Transducer 모델의 디자인을 소개한다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.199-207
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• 2024

Today, AI image creation technology is being expanded and utilized across industries. Accordingly, various AI image creation programs optimized for the fashion industry are being developed and commercialized. In this study, we compared and analyzed the visual characteristics of fashion images created by AI image creation programs such as Playground, Midjourney, and The New Black to identify the characteristics of each program and point out areas where each program can be used and problems. The results are as follows: First, while Playground and Midjourney intuitively applied the contents of the command to create images that were different from actual fashion trends, Dannew Black created images that were relatively similar to fashion trends. Second, while Playground separates or combines images corresponding to the command content, Midjourny tends to create new images by adding and fusing various details. Third, in Playground, colors not included in the command appear randomly, and in The New Black, colors not included in the command appear coordinated, and Midjourney generates the color specified in the command relatively accurately. In conclusion, Midjourney can be used when seeking inspiration for developing unique and creative fashion designs, and The New Black can be helpful in referencing fashion trends or fashion styling. On the other hand, playgrounds can be somewhat confusing when it comes to color creation, so this is something to be careful about. It is expected that AI image creation tools can be used more efficiently in fashion design development.

• Journal of the Korea Computer Graphics Society
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• v.15 no.4
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• pp.13-21
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• 2009

Game level design is one of the important parts in the commercial game development. Because of its complexity in combining game components, game design work could be classified into a non-linear problem. In this paper, we propose a new automated game level design system by using genetic algorithms. With our system, a game designer easily generates an optimized game level by designating the key parameters m the initial stage of game design. Our system can be useful in reducing the trial-errors in the initial game level design process.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.555-564
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• 2002

Design patterns are used to utilize well-defined design information. As using these design patterns, we can get re-use in object-oriented paradigm, decrease the time of development and improvement the quality of software. Although these design patterns are widely used among practice, most of design patterns information is manually used, inconsistent and its utilization could be very low. Because the design patterns information that a designer applies does not appear in software, it is sometimes difficult to track them. In this paper, we propose a tool support for design pattern-oriented software development. This tool supports design pattern management, software design and automatic source code generation. The design pattern management has the function for storing, managing and analyzing the existing design pattern and registering new design pattern. The software design has the function for software design with UML and automatically generate design pattern elements. By using this design information, this system can automatically generate source code. In the result to include the tracking design pattern element that is not Included In the existing CASE tools into design information, we can build the stable and efficient system that provides to analyse software, manage design pattern and automatically generate source code.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.163-170
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• 2021

Smart-textronics technology which enables functional textiles has recently been applied in various fields such as smart clothes, smart home and smart health care, and a variety of smart-textronics products have been developed. In this context, the smart-textronics product development process is proposed based on the systematic participatory design method in this paper. The proposed method consists of two phases: in-depth interviews and analyzing. In the phase of in-depth interviews, participants are asked to create journey maps that include activities, pain points and emotional status and to generate solution ideas with sketches and simple prototypes. In the analyzing phase, design researchers investigate the participants' journey maps, and create personas by identifying critical characteristics with the behavior pattern analysis. Then, each persona's needs are linked with value elements of the E3 value framework. Finally, pre-survey was conducted to identify smart-textronics market and a smart sofa design is proceeded as the case study to show the applicability of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.11-18
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• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.