• Journal of the Korean Society for information Management
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• v.23 no.4 s.62
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• pp.215-231
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• 2006

Recently there are many bibliometric studies attempting to utilize Pathfinder networks(PFNets) for examining and analyzing the intellectual structure of a scholarly field. Pathfinder network scaling has many advantages over traditional multidimensional scaling, including its ability to represent local details as well as global intellectual structure. However there are some limitations in PFNets including very high time complexity. And Pathfinder network scaling cannot be combined with cluster analysis, which has been combined well with traditional multidimensional scaling method. In this paper, a new method named as Parallel Nearest Neighbor Clustering (PNNC) are proposed for complementing those weak points of PFNets. Comparing the clustering performance with traditional hierarchical agglomerative clustering methods shows that PNNC is not only a complement to PFNets but also a fast and powerful clustering method for organizing informations.

• Journal of Energy Engineering
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• v.27 no.1
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• pp.21-32
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• 2018

In this paper, we reported the current technologies of water scaling removal and also water recovery from the flue gases, which are funded by Department of Energy (DOE), USA. Globally, water resources are limited due to the climate change. The potential impacts of climate change is food and water shortages. In the $21^{st}$ century, water shortages and pollution are expected to become more acute as populations grow and concentrate in cities. At present, the water stress increases over 62.0 ~ 75.8% of total water basin area and decreases over 19.7 ~ 29.0%. Many renewable energy sources demand secure water resources. Water is critical for successful climate change mitigation, as many efforts to reduce greenhouse gas emissions depend on reliable access to water resources. Water hardness is one of the major challenge to coal power plants. Department of energy (DOE) funded and encouraged for the development of advanced technologies for the removal of hardness of water (scaling) and also water recovery from the flue gases from coal power plants.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.65-68
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• 2001

RBFN has some problem that because the basis function isnt orthogonal to each others the number of used basis function goes to big. In this reason, the Wavelet Neural Network which uses the orthogonal basis function in the hidden node appears. In this paper, we propose the composition method of the actual function in hidden layer with the scaling function which can represent the region by which the several wavelet can be represented. In this method, we can decrease the size of the network with the pure several wavelet function. In addition to, when we determine the parameters of the scaling function we can process rough approximation and then the network becomes more stable. The other wavelets can be determined by the global solutions which is suitable for the suggested problem using the genetic algorithm and also, we use the back-propagation algorithm in the learning of the weights. In this step, we approximate the target function with fine tuning level. The complex neural network suggested in this paper is a new structure and important simultaneously in the point of handling the determination problem in the wavelet initialization.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• Journal of the Korean Society of Combustion
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• v.4 no.1
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• pp.131-139
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• 1999

A Numerical Analysis of NOx production in Hydrogen-Air flame is performed using the quasi-laminar reaction modelling. As results, in low global strain rate region, $U_F/D_F\;{\leq}\;50,000$, the quasi-laminar reaction modelling reproduces the experimentally observed EINOx half power scaling that the ratio of EINOx and flame residence time, $L_f^3(D_F^2U_F)$, is proportional to the square root of global strain rate. Thus, it suggests that turbulence-chemistry interaction has a minor impact on the trend of NOx production in low global strain rate region. However, the quasi-laminar reaction modelling predicts the higher temperature and NOx than experimentally observed. This overprediction may be due to the lack of radiation and quasi-laminar reaction modelling.

• Journal of the Korean Society of Combustion
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• v.7 no.1
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• pp.8-14
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• 2002

Measurements of global emissions, flame radiation and flame dimensions are presented for unconfined turbulent-jet and precessing-jet diffusion flames. Precessing jet flames are characterised by increases in global flame radiation and global flame residence time for methane and propane fuels, however a strong dependency of the NOx emission indices on the fuel type exists. The fuel type dependence is considered to be because soot radiation is more effective than gas-radiation at reducing global flame temperatures relative to adiabatic flame temperatures and reducing the NO production rate.

• Agribusiness and Information Management
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• v.4 no.2
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• pp.32-41
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• 2012

Studies have shown that the future of the wine market rests on the effective and efficient changes in technology to the supply chain used by most of the major global players. In today's wine industry, companies are faced with the ever-shifting demand for their products, strict regulation and increasing price competition. Even at that, mature companies in the wine industry are succeeding by scaling up production, streamlining their supply chains, expanding into new geographic areas, implementing more efficient processes, cleverly marketing products, and focusing on ever closer relationships with suppliers, partners and customers. However, this paper looks at supply chain challenges in the wine industry from a global perspective presented in the inbound, manufacturing and outbound processes as well as offer effective solutions in order for companies to gain a competitive advantage and succeed on a global level.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.648-653
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• 2010

The non-metric multidimensional scaling (nMDS) is a method for analyzing the relation among objects by mapping them onto the Euclidean space. The nMDS is useful when it is difficult to use the concept of distance between pairs of objects due to non-metric dissimilarities between objects. The nMDS can be regarded as an optimization problem in which there are many local optima. Since the conventional nMDS algorithm utilizes the steepest descent method, it has a drawback in that the method can hardly find a better solution once it falls into a local optimum. To remedy this problem, in this paper, we applied the simulated annealing to the nMDS and proposed a new optimization algorithm which could search for a global optimum more effectively. We examined the algorithm using benchmarking problems and found that improvement rate of the proposed algorithm against the conventional algorithm ranged from 0.7% to 3.2%. In addition, the statistical hypothesis test also showed that the proposed algorithm outperformed the conventional one.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.25-28
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• 2001

RBFN has some problem that because the basis function isn't orthogonal to each others the number of used basis function goes to big. In this reason, the Wavelet Neural Network which uses the orthogonal basis function in the hidden node appears. In this paper, we propose the composition method of the actual function in hidden layer with the scaling function which can represent the region by which the several wavelet can be represented. In this method, we can decrease the size of the network with the pure several wavelet function. In addition to, when we determine the parameters of the scaling function we can process rough approximation and then the network becomes more stable. The other wavelets can be determined by the global solutions which is suitable for the suggested problem using the genetic algorithm and also, we use the back-propagation algorithm in the learning of the weights. In this step, we approximate the target function with fine tuning level. The complex neural network suggested In this paper is a new structure and important simultaneously in the point of handling the determination problem in the wavelet initialization.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.1
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• pp.71-81
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• 2017

In this study, a series of full-scale extrapolation procedures based on the Granville's similarity scaling method, which was employed by Schultz (2007), is modified and then applied to compare the resistance performance between two different anti-fouling coatings. As an analysis example, the low frictional AF coating based on a novel skin-friction reducing polymer named FDR-SPC (Frictional Drag Reduction Self-Polishing Copolymer), which had been invented by the present author, is employed. The low frictional coating, which gives 25.4% skin frictional reduction in lab test, is estimated to give 18.2% total resistance reduction for a 176k DWT bulk carrier.