• Journal of Soil and Groundwater Environment
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• v.25 no.4
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• pp.87-97
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• 2020

In this study, the applicability of the geostatistical evolution strategy as an inverse analysis method of estimating hydraulic properties of small-scale basin was tested. The geostatistical evolution strategy is a type of data assimilation method that can effectively estimate aquifer hydraulic conductivity by combining a global optimization model of the evolution strategy and a local optimization model of the ensemble Kalman filtering. In the applicability test, the geometry, hydraulic boundary conditions, and the distribution of groundwater monitoring wells of Hanlim-Eup were employed. On the other hand, a synthetic hydraulic conductivity distribution was generated and used as the reference property for ease of estimation quality assessment. In the estimations, two different cases were tested where, in Case I, both groundwater levels and hydraulic conductivity measurements were assumed to be available, and only the groundwater levels were available, in Case II. In both cases, the reference and estimated hydraulic conductivity fields were found to show reasonable similarity, even though the prior information for estimation was not accurate. The ability to estimate hydraulic conductivity without accurate prior information suggests that this method can be used effectively to estimate mathematical properties in real-world cases, many of which little prior information is available for the aquifer conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.533-535
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• 2012

This research conduct exploratory case research to suggest evolution direction of platform based business model. To do this, we define platform business and business model based on prior literature respectively, select representative platform based business model, and suggest evolution factors of platform-based business model interaction with business model and emerging technologies.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.05a
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• pp.408-410
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• 2008

The evolution of texture and microstructure during recrystallization was tracked after different cold rolling of aluminum sheets. Texture of the sheet center were differentiated by different strain states due to prior deformation. The evolution of recrystallization texture was studied with the amount of shear applied during cold rolling. The final grain size after recrystallization annealing was varied due to the effective strain during deformation.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.773-782
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• 2014

The general path model (GPM) is one approach for performing degradation-based, or Type III, prognostics. The GPM fits a parametric function to the collected observations of a prognostic parameter and extrapolates the fit to a failure threshold. This approach has been successfully applied to a variety of systems when a sufficient number of prognostic parameter observations are available. However, the parametric fit can suffer significantly when few data are available or the data are very noisy. In these instances, it is beneficial to include additional information to influence the fit to conform to a prior belief about the evolution of system degradation. Bayesian statistical approaches have been proposed to include prior information in the form of distributions of expected model parameters. This requires a number of run-to-failure cases with tracked prognostic parameters; these data may not be readily available for many systems. Reliability information and stressor-based (Type I and Type II, respectively) prognostic estimates can provide the necessary prior belief for the GPM. This article presents the Bayesian updating framework to include prior information in the GPM and compares the efficacy of including different information sources on two data sets.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.144-154
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• 2003

In this paper we present Logical Evolution method which is a new teaming algorithm for the concepts expressed as binary logic function. We try to solve some problems of Inductive Learning algorithms through Logical Evolution. First, to be less affected from limited prior knowledge, it generates features using the gained informations during learning process and learns the concepts with these features. Second, the teaming is done using not the whole example set but the individual example, so even if new problem or new input-output variables are given, it can use the previously generated features. In some cases these old features can make the teaming process more efficient. Logical Evolution method consists of 5 operations which are selected and performed by the logical evaluation procedure for feature generation and learning process. To evaluate the performance of the present algorithm, we make experiments on MONK data set and a newly defined problem.

• Journal of Korean Elementary Science Education
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• v.25 no.4
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• pp.352-362
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• 2006

Many misconceptions regarding biology and evolution have been reported by students prior to being exposed to a formal education program of evolution which challenged them. This study sought to investigate and to analyze the misconception formation process of evolution originating from TV animation and science books for children. Firstly, to identify TV animation's influence on students' misconceptions of evolution, a questionnaire including TV animation characters was constructed and administered to 146 elementary school students, 161 middle school students, and 156 high school students. The data collected was analyzed. Secondly, 17 science books for children were sampled and the contents related to evolution were selected and analyzed in terms of five evolutionary explanations: creationism internal will explanation, teleological explanations, explanations of use and disuse, mutation and finally, natural selection. Children have understood 'growth' and 'metamorphosis' on TV animation as 'evolution'. The processes by which characters on TV animation undergo some forms of change, which are in fact a kind of metamorphosis has often been understood as 'evolution'. Many respondents have defined evolution incorrectly as the process of growing and changing shape. On the other hand, some science books fur children contained descriptions of evolution including' mutation and finally natural selection explanation'; however, most of the science books fur children sampled in this study were written through the perspectives of alternative evolutionary views such as 'teleology view', 'internal will view', and 'use and disuse view'. It is apparent that TV animation and science books fur children influence the formation of various misconceptions regarding evolution by children.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.331-340
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• 2008

Feature Selection is one of methods to improve the classification accuracy of data in the field of machine learning. Many feature selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. Bio-inspired algorithms are well-known evolutionary algorithms based on the principles of behavior of organisms, and very useful methods to find the optimal solution in optimization problems. Bio-inspired algorithms are also used in the field of feature selection problems. So in this paper we proposed new improved bio-inspired algorithms for feature selection. We used well-known bio-inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), to find the optimal subset of features that shows the best performance in classification accuracy. In addition, we modified the bio-inspired algorithms considering the prior importance (prior relevance) of each feature. We chose the mRMR method, which can measure the goodness of single feature, to set the prior importance of each feature. We modified the evolution operators of GA and PSO by using the prior importance of each feature. We verified the performance of the proposed methods by experiment with datasets. Feature selection methods using GA and PSO produced better performances in terms of the classification accuracy. The modified method with the prior importance demonstrated improved performances in terms of the evolution speed and the classification accuracy.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.69-72
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• 2002

본 논문에서 화자공간모델 evolution에 기반한 continuous density hidden Markov model (CDHMM)의 online 적응에 대한 새로운 기법을 제안한다. 학습화자의 a priori knowledge을 나타내는 화자공간모델은 factor analysis (FA) 또는 probabilistic principal component analysis (PPCA)와 같은 은닉변수모델(latent variable model)에 의해 효과적으로 나타내어진다. 은닉 변수모델은 화자공간모델뿐아니라 CDHMM 파라메터의 ajoint prior분포를 표시함으로, maximum a posteriori(MAP)적응기법에 직접 적용되어진다. 화자공간모델의 hyperparameters와 CDHMM파라메터를 동시에 순차적으로 적응하기 위해 quasi-Bayes (QB)추정 기술에 기반한 online 적응기법을 제안한다. 연속숫자음 인식과 관련된 화자적응 실험을 통해 제안된 기법은 적은 적응데이터에서 좋은 성능을 나타내며, 데이터가 증가함에 따라 성능이 지속적으로 증가함을 보여준다.

• Journal of Industrial Convergence
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• v.14 no.1
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• pp.39-45
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• 2016

This paper deals with the consideration of mathematical models with regards to growth of cluster and firms by reviewing the Metcalf and Breuner's articles. prior studies have been argued the phenomenon of local industrial clusters and districts. Several concepts have been adopted to support the success of and changes to these clusters and firm growth. Through the review of two papers, evolution of both cluster and firm growth may be achieved in terms of utilizations of the different local aspects and mechanisms. This paper supports the theoretical back bone with regards to the regional cluster policy implementing in Korea for the purpose of regional developments. In particular, a mathematical model that, on a more abstract level, captures the fundamental dynamic structure of all the observed mechanisms. On the basis of this model, the emergence and evolution of local clusters can be described. Also this model has given that the knowledge sharing between firms has an important role to firms and cluster' growth.

• 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.