• Journal of the Korea Convergence Society
• /
• v.12 no.7
• /
• pp.169-179
• /
• 2021

This study shows that the two-stage k-means clustering method can improve prediction performance by predicting the stock price, To this end, this study introduces the two-stage k-means clustering algorithm and tests the prediction performance through comparison with various machine learning techniques. It selects the cluster close to the prediction target obtained from the k-means clustering, and reapplies the k-means clustering method to the cluster to search for a cluster closer to the actual value. As a result, the predicted value of this method is shown to be closer to the actual stock price than the predicted values of other machine learning techniques. Furthermore, it shows a relatively stable predicted value despite the use of a relatively small cluster. Accordingly, this method can simultaneously improve the accuracy and stability of prediction, and it can be considered as the new clustering method useful for small data. In the future, developing the two-stage k-means clustering is required for the large-scale data application.

• Lingua Humanitatis
• /
• v.6
• /
• pp.321-357
• /
• 2004

In this article the structure of modern Russian humor is analyzed on the basis of some theories: bi-sociation theory (Koestler 1964), semantic script theory of verbal humor, using the concept of semantic presupposition, pragmatic felicity condition (Searle 1969; Levinson 1983) and grammatical rules (Chomsky 1965). Up to now the listed former theories were not examined and less analyzed by the semantic structure in the study of the structure of Russian humor(HcaeBa 1969; 3 $a_{OPHOB}$ 1991; 1992). Kreps (1981), who analyzed the works of Zoschenko, presented 21 types of humor, using the term 'humoreme'(Kpenc 1981, 36-37). These types are the list of the available means of humor that work not in the base of semantic criteria, but in the base of means of literary rhetoric. Kreps presented types of humor means, such as contradiction, antonymic substitution, macaronic speech and correlation of humoremes in the various types of humor. Apart from Kreps, Manakov (MaHaKOB 1986, 61-79) also studied these problems. He also set the system of the basic types of humor. Manakov introduced the linguistic means of humor of some Russian writers: Gogol, Tchechov. The means that Manakov showed with detailed examples, are trope, epithet, comic comparison, comic metaphor, comic periphrasis, euphemism, pun, zeugma, comic toponym, comic onomatopoeia, mania of foreign vocabulary, folk etymology, dialect etc. But these studies don't explain why these means make the works humorous. An, B.p tried to answer this question (안병팔 1997 a; b). An B.p. explains contexts of humor through the Release theory, the Superiority theory and the Incongruity theory. An, B.p. explained the process of deviation from the grammatical norms through morpho-syntactic and lexical means. But in these studies the humor was not analyzed by the semantic criteria. In order to linguistically evaluate various means of humor formation, it is necessary to elicit its deep structure, which makes it possible to research the formation and interpretation of humor. For this purpose this article, being based on the Incongruity theory, defined the structure of humor as negation of presupposition. Of course the former traditional studies also well shared the concept of 'contradiction' and 'contrast' of humor structure, but they didn't explain the structure by semantic differential features. This study, analyzing the works of' Zadornov, M., tried to note that through the negation of semantic presupposition the structure of contradiction is formed with semantic differential features on the semantic, syntactic or lexical dimensions.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.11
• /
• pp.537-542
• /
• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.3
• /
• pp.187-194
• /
• 2017

The present study introduces a method to parallelize k-means clustering algorithm for fast unsupervised classification of large satellite imagery. Known as a representative algorithm for unsupervised classification, k-means clustering is usually applied to a preprocessing step before supervised classification, but can show the evident advantages of parallel processing due to its high computational intensity and less human intervention. Parallel processing codes are developed by using multi-threading based on OpenMP. In experiments, a PC of 8 multi-core integrated CPU is involved. A 7 band and 30m resolution image from LANDSAT 8 OLI and a 8 band and 10m resolution image from Sentinel-2A are tested. Parallel processing has shown 6 time faster speed than sequential processing when using 10 classes. To check the consistency of parallel and sequential processing, centers, numbers of classified pixels of classes, classified images are mutually compared, resulting in the same results. The present study is meaningful because it has proved that performance of large satellite processing can be significantly improved by using parallel processing. And it is also revealed that it easy to implement parallel processing by using multi-threading based on OpenMP but it should be carefully designed to control the occurrence of false sharing.

• The Journal of Korean Obstetrics and Gynecology
• /
• v.21 no.4
• /
• pp.183-193
• /
• 2008

Purpose: To research the changes of Skin Resistance Variability(SRV) over the period of ovarian cycle of healthy young women with normal and regular menstrual cycles using Oriental Medical Diagnose Autonomic system-3000 (OMD-3000). Methods: We measured SRV of 15 women who were working at O O Oriental Hospital from July to Oct. 2008. Each woman took the OMD-3000 test 8 times during 2 menstrual cycles. Each cycle consists of 4 phases-follicular phase, ovulation, luteal phase and menstruation. We analyse the data by SPSS 12.0 for windows. the one-way ANOVA by Repeated Measure(p<0.05). Results: 1. (1) The Factor AA means at zone 1 were 0.77$\pm$0.40, at zone 2 were 1.07$\pm$0.68, at zone 3 were 0.77$\pm$0.35, at zone 4 were 0.68$\pm$0.32, at zone 5 were 0.74$\pm$0.29, at zone 6 were 0.85$\pm$0.30, and at zone 7 were 0.74$\pm$0.29. The Factor AA means were lower than normal range at zone 1,3,4,5,6 and 7. The graph pattern of M shape was caused by the Factor AA means at zone 2 and zone 6 were higher than others. (2) The Factor AA means at zone 1 and zone 3 show abnormal regulation state. 2. During the menstruation. the Factor AA means were higher at zone 1,2 and 3 than other zones. During the ovulation, the Factor AA means were higher at zone 4,5,6 and 7 than other zones. Especially at the menstruation phase in zone 2 and at the ovulation phase in zone 6 tend to be highest means than other phases respectively. 3. However there were no significant difference of means during 4 ovarian phases in 7 zones except ovulation phase to luteal phase at zone 4(p = 0.013). Conclusion: The results suggest that changes of SRV during 4 menstrual cycles are not variables in reading 7-zone-diagnostic system. Further study will be needed.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2012.05a
• /
• pp.1073-1074
• /
• 2012

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.2 no.2
• /
• pp.57-65
• /
• 1998

An estimation of remainder for Simpson's quadrature formula for differentiable mappings whose derivatives belong to $L_p$-spaces and applications in theory of special means (logarithmic mean, identric mean etc...) are given.

• Proceedings of the Korea Society of Design Studies Conference
• /
• 2004.10a
• /
• pp.160-161
• /
• 2004

• Communications of the Korean Mathematical Society
• /
• v.12 no.4
• /
• pp.1039-1064
• /
• 1997

Existence and Uniqueness for Volterra equations (VE) with a weak regularity assumption on A, the relative closedness of A are investigaed by means of the Laplace transform theory. Also, (VE) are studied by means of the method of convoluted solution operator families.

• Journal of the Korean Statistical Society
• /
• v.25 no.4
• /
• pp.603-611
• /
• 1996

In this article we consider a sequential procedure for the fixed width interval estimation of the means of two mutually independent linear processes. It is shown that the proposed stopping rule is asymptotically efficient as in iid samples (cf. Robbins, Simons and Starr(l967)).