• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.365-372
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• 2000

he paper introduces evolutionary computing to Fractal Image Compression(FIC). In Fractal Image Compression(FIC) a partitioning of the image into ranges is required. As a solution to this problem there is a propose that evolution computation should be applied in image partitionings. Here ranges are connected sets of small square image blocks. Populations consist of $N_p$ configurations, each of which is a partitioning with a fractal code. In the evolution each configuration produces $\sigma$ children who inherit their parent partitionings except for two random neighboring ranges which are merged. From the offspring the best ones are selected for the next generation population based on a fitness criterion Collage Theorem. As the optimum image includes duplication in image data, it gets smaller in saving space more efficient in speed and more capable in image quality than any other technique in which other coding is used. Fractal Image Compression(FIC) using evolution computation in multimedia image processing applies to such fields as recovery of image and animation which needs a high-quality image and a high image-compression ratio.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.553-554
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• 2010

무수히 많은 정보가 쏟아져 나오는 시대에 살고 있는 웹 사용자에게 유용한 정보를 제공하기 위한 여과기법의 연구는 큰 중요성을 갖는다. 이런 기법엔 크게 내용 기반 여과방식과 협업적 여과방식 두 가지로 나눌 수 있다. 이들 각각은 서로 장, 단점을 가지고 있으며 따라서 이를 병합한 기법의 연구는 필수적이다. DB 의 WAL 기법과 진화알고리즘을 이용하여 좀 더 사용자에게 최적화된 추천을 가능하게 할 수 있다. 또한 폭소노미에 기반한 태깅기법 및 패턴인식, 온톨로지(ontology) 기법의 연구를 통해 기존의 한계를 보완하여 향후 더욱 개선된 여과 기법을 기대할 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.495-504
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• 2008

Aspect Oriented Programming(AOP) is a relatively new programming paradigm and has properties that other programming paradigms don't have. This new programming paradigm provides new modularization of software systems by cross-cutting concerns. In this paper, we propose a regression test method for program evolution by AOP. By using JoinPoint, we can catch a pointcut-name which makes it possible to test the incorrect pointcut strength fault and the incorrect aspect precedence fault. Through extending proof rules to aspect, we can recognize failures to establish expected postconditions faults. We can also trace variables using set() and get() pointcut and test failures to preserve state invariant fault. Using control flow graph, we can test incorrect changes in control dependencies faults. In order to show the correctness of our proposed method, channel management system is implemented and tested by using proposed methods.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.869-871
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• 2003

단백질 부분 구조는 일종의 단백질 패턴으로써 진화적인 성질을 띄고 있다. 본 논문에서는 단백질 간의 열 안정성과 이러한 단백질 부분 구조 간의 관련성에 대해서 알아보고자 한다. 또한 오류 허용 알고리즘 (FT-Apriori)의 성능을 향상시킬 수 있는 효과적인 기법을 제안한다. 이러한 기법을 단백질 부분 구조에 적용시킴으로써 실제 단백질 데이터에서 그 효용성을 일아본다.

• ICROS
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• v.21 no.4
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• pp.21-26
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• 2015

지능형 시스템의 수요가 증가하면서 영상인식의 중요성이 부각되고 있다. 사람이 직접 물체 인식 과정을 모델링하는 방식을 넘어 최근에는 기계학습을 이용하여 이를 자동화하는 방법이 주를 이루고 있다. 그 중 딥러닝은 빅데이터를 활용하는 각종 분야에서 놀라운 성능을 보이며 기계학습 수준을 한 단계 진화시킨 기술로 평가 받고 있으며 영상 인식의 다양한 분야에서 응용되고 있다. 본 글에서는 딥러닝을 이용한 물체 검출 기법의 동향을 살펴보고 이를 차량 전면부 인식에 적용한 사례를 소개한다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.277-279
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• 2001

HLR 데이터베이스 시스템은 이통 통신 망에서 지속적으로 변하는 가입자의 위치 정도를 관리한다. 본 논문에서는 이동 전화 번호를 위한 2단계 색인 기법과 단말번호를 위한 bucket 연결 hashing 기법을 제안하였다. 또한 HLR 시스템의 특성을 고려한 효율석인 백업 방법을 제안하였다. 아울러 IMT2000 서비스로의 진화에 필수적인 보안 기능 수행을 고려한 HLR 데이터베이스 Scheme을 제안하였다

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.682-691
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• 2005

We present a two-step evolution system that produces controllable virtual creatures in physically simulated 3D environment. Previous evolutionary methods for virtual creatures did not allow any user intervention during evolution process, because they generated a creature's shape, locomotion, and high-level behaviors such as target-following and obstacle avoidance simultaneously by one-time evolution process. In this work, we divide a single system into manageable two sub-systems, and this more likely allowsuser interaction. In the first stage, a body structure and low-level motor controllers of a creature for straight movement are generated by an evolutionary algorithm. Next, a high-level control to follow a given path is achieved by a neural network. The connection weights of the neural network are optimized by a genetic algorithm. The evolved controller could follow any given path fairly well. Moreover, users can choose or abort creatures according to their taste before the entire evolution process is finished. This paper also presents a new sinusoidal controller and a simplified hydrodynamics model for a capped-cylinder, which is the basic body primitive of a creature.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.27-37
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• 2009

Sensor nodes forming a sensor network have limited energy capacity such as small batteries and when these nodes are placed in a specific field, it is important to research minimizing sensor nodes' energy consumption because of difficulty in supplying additional energy for the sensor nodes. Clustering has been in the limelight as one of efficient techniques to reduce sensor nodes' energy consumption in sensor networks. However, energy saving results can vary greatly depending on election of cluster heads, the number and size of clusters and the distance among the sensor nodes. /This research has an aim to find the optimal set of clusters which can reduce sensor nodes' energy consumption. We use a Genetic Algorithm(GA), a stochastic search technique used in computing, to find optimal solutions. GA performs searching through evolution processes to find optimal clusters in terms of energy efficiency. Our results show that GA is more efficient than LEACH which is a clustering algorithm without evolution processes. The two-dimensional GA (2D-GA) proposed in this research can perform more efficient gene evolution than one-dimensional GA(1D-GA)by giving unique location information to each node existing in chromosomes. As a result, the 2D-GA can find rapidly and effectively optimal clusters to maximize lifetime of the sensor networks.