• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• Progress in Superconductivity and Cryogenics
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• v.18 no.1
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• pp.50-54
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• 2016

In this paper, a hybrid method is proposed to design an air-core superconducting solenoid system for 6 T axial uniform magnetic field using Niobium Titanium (NbTi) superconducting wire. In order to minimize the volume of conductor, the hybrid optimization method including a linear programming and a nonlinear programming was adopted. The feasible space of solenoid is divided by several grids and the magnetic field at target point is approximated by the sum of magnetic field generated by an ideal current loop at the center of each grid. Using the linear programming, a global optimal current distribution in the feasible space can be indicated by non-zero current grids. Furthermore the clusters of the non-zero current grids also give the information of probable solenoids in the feasible space, such as the number, the shape, and so on. Applying these probable solenoids as the initial model, the final practical configuration of solenoids with integer layers can be obtained by the nonlinear programming. The design result illustrates the efficiency and the flexibility of the hybrid method. And this method can also be used for the magnet design which is required the high homogeneity within several ppm (parts per million).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.393-400
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• 2011

In a large scale survey, cluster sampling design in which a set of observation units called clusters are selected is often used to satisfy practical restrictions on time and cost. Especially, a two stage cluster sampling design is preferred when a strong intra-class correlation exists among observation units. The sample Primary Sampling Unit(PSU) and Secondary Sampling Unit(SSU) size for a two stage cluster sample is determined by the survey cost and precision of the estimator calculated. For this study, we derive the optimal sample PSU and SSU size when the population SSU size across the PSU are di erent by extending the result obtained under the assumption that all PSU have the same number of SSU. The results on the sample size are then applied to the $4^{th}$ Korea Hospital Discharge results and is compared to the conventional method. We also propose the optimal sample SSU (discharged patients) size for the $7^{th}$ Korea Hospital Discharge Survey.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.170-179
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• 2003

In order to classify Land Use/Land Cover using multispectral images, we have to give consequence to defining proper classes and selecting training sample with higher class separability. The process of satellite hyperspectral image which has a lot of bands is difficult and time-consuming. Furthermore, classification result of hyperspectral image with noise is often worse than that of a multispectral image. When selecting training fields according to the signatures in the study area, it is difficult to calculate covariance matrix in some clusters with pixels less than the number of bands. Therefore in this paper we presented an overview of feature extraction methods for classification of Hyperion data and examined effectiveness of feature extraction through the accuracy assesment of classified image. Also we evaluated the classification accuracy of optimal meaningful features by class separation distance, which is also a method for band reduction. As a result, the classification accuracies of feature-extracted image and original image are similar regardless of classifiers. But the number of bands used and computing time were reduced. The classifiers such as MLC, SAM and ECHO were used.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.551-562
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• 2009

In this paper, I would present a using method for The Fishing Operation Information(FOI) of National Federation of Fisheries Cooperatives(NFFC) through the availabilities analysis and put out the similarities by the section of the sea through classifying characteristics of fishing patterns by their locations. As a result, although the catch of FOI is nothing more than 33% level to National Fishery Production Statistics(NFPS), FOI data is useful in understanding the patterns of fishing operation by the location because both patterns and correlation were very similar in the usability analysis, comparing the FOI data with NFPS. So I classified optimal clusters for catch, the number of fishing days and the number of fishing vessels through 2 step cluster analysis by the big marine zone and divided fishing patterns.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.13 no.2
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• pp.45-56
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• 1980

Farming density of oyster cultured in Hansan-Geoje Bay was studied to obtain the optimal farming density based on the biosedimentation analysis and the annual yield data from 1970 to 1979. Farming density of oyster extrapolated by means of pollution grade of sediment is significantly correlated to COD and phaeophytin content of the bottom mud of the bay. Pollution grade is linearly related to the number of oyster clusters suspended in the unit area. Optimal farming density was $0.12\;string/m^2$ in case of raft culture, and it was $0.12\;string/m^2$ in case of long-line culture. Farming density was well expressed by the number of strings per raft and the area covered by a raft. As strings per raft increased from 350 to 558, total yield from a raft increased and when occupied sea area per raft ranged from $1.000\;m^2\;to\;6,000\;m^2$, the yield per raft linearly increased as the area increased. This analysis suggests that the optimal density be 0.11 string per unit area $(m^2)$. As increasing the number of strings per $m^2$ the yield per string decreases, and this is well dipicted by a linear function. At this time the yield per unit area increases when the number of string increases up to the density of $0.13\;strings/m^2$. From the point of these three comprehensive analyses the optimal density was $0.11\~0.13\;string/m^2$ in case of raft culture and $0.25\;strings/m^2$ in case of long-line culture in Hansan-Geoje Bay. The maximum expected yield of oyster in Hansan-Geoje Bay is approximately 5,600 tons when maintained the string density at $0.11\~0.13\;string/m^2$.

• Mycobiology
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• v.37 no.4
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• pp.258-266
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• 2009

Pleurotus eryngii, known as king oyster mushroom has been widely used for nutritional and medicinal purposes. This study was initiated to screen the suitable conditions for mycelial growth and to determine the phylogenetic relationship of the selected strains. Optimal mycelial growth was observed at $30{^{\circ}C}$ and minimum mycelial growth observed at $10{^{\circ}C}$. This mushroom tolerates a broad pH range for mycelial growth, with most favorable growth observed at pH 6. Results also indicated that glucose peptone, yeast malt extract and mushroom complete media were favorable growth media, while Hennerberg and Hoppkins media were unfavorable. Dextrin was the best and xylose the least effective carbon sources. Results revealed that inorganic nitrogen sources were less effective than organic sources for the mycelial growth of P. eryngii. Investigation of genetic diversity is necessary to identify the strains. The ITS region of rDNA were amplified using PCR. The size of the ITS1 and ITS2 regions of rDNA from the different strains varied from 214 to 222 bp and 145 to 236 bp, respectively. The sequence of ITS2 was more variable than that of ITS1, and the 5.8S sequences were identical. A phylogenetic tree based on the ITS region sequences indicated that selected strains could be classified into six clusters. Fourteen IUM and ATCC- 90212 strains were also analyzed by RAPD with 20 arbitrary primers. Fourteen of these primers were efficiently amplified the genomic DNA. The number of amplified bands varied with the primers and strains, with polymorphic fragments in the range from 0.2 to 2.3 kb.

• Journal of Plant Biotechnology
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• v.30 no.3
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• pp.263-267
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• 2003

This study was conducted to evaluate the effect of thidazuron(TDZ) on shoot proliferation and growth from axillary buds of 20-years-old Corylopsis coreana. Shoots proliferation was effectively achieved on WPM(Woody Plant Medium) supplemented with 0.03∼0.1mg/L TDZ. The highest shoot number(6.5$\pm$0.7) was obtained on 0.1mg/L TDZ treatment. On the TDZ medium shoots formed as clusters less than 1cm in height and therefore needed to subculture on GA$_{3}$ containing medium to induce elongation. In consecutive cultures, phenolic compounds were excreted at the proximal part of the explants and inhibited growth of the explants. Growth inhibition by the compounds was overcome using liquid and paper bridge culture system. About 60％ of the elongated shoots rooted on half- strength MS medium containing IBA. Generally, IBA was mire effective on in vitro rooting than NAA with optimal range of 0.5mg/L to 1.0mg/L. Rooted plantlets were transferred in an artificial soil(vermculite) and acclimatized in high humidity greenhouse condition. Survival rate differed greatly depending on rooting types of the explants. Two types of rooting were observed. The first type was direct rooting from the explants. The second type was callus formation followed by rooting from the callus. The explants showing the 1st type rooting survived can be multiplicated in vitro by TDZ treatment followed by elongation with GA$_{3}$ and rooting with IBA.