• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.11a
• /
• pp.382-385
• /
• 2002

We design LVQ network using SOM network for the LVQ's performance improvement. Reference vectors and the number of output neurons, they are the proposed LVQ network's initial parameters, are determined in SOM which is used for preprocessing LVQ. We simulate it to the grouping test of Fisher's Iris data. In this result, we confirm proposed LVQ network is better than existing LVQ in grouping test.

• Archives of Pharmacal Research
• /
• v.27 no.10
• /
• pp.1023-1028
• /
• 2004

A new 12a-dehydrorotenoid 1, 11-dihydroxy-9, 10-methylenedioxy-12a-dehydrorotenoid (1), together with a new isoflavonoid glycoside tectorigenin-7-O-${\beta}$-glucosyl-4'-O-${\beta}$-glucoside (3), were isolated and identified from the rhizomes of I. spuria (Zeal). In addition, 4 known compounds, tectorigenin (2) tectorigenin-7-O-${\beta}$-glucosyl $(1{\leftrightarrow}6)$ glucoside (4), tectoridin (a tectorigenin- 7-O-${\beta}$-glucoside) (5) and tectorigenin-4'-O-${\beta}$-glucoside (6) were isolated and identified for the first time from this plant. The structures of the isolated compounds were determined by spectroscopic methods (UV, IR, $^1H,\;^{13}C$NMR, DEPT, HMQC, NOESY, and HMBC experiments and MS spectrometry) and by comparison with literature data of known compounds. Compounds 2, 4, 5, and 6 are reported for the first time from this plant through the present study.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.11a
• /
• pp.180-184
• /
• 1997

This paper presents the fuzzy neural network which utilizes a fuzzified Kohonen learning uses a fuzzy membership value, a function of the iteration, and a intra-membership value instead of a learning rate. The IRIS data set if used to test the fuzzy neural network. The test result shows the performance of the fuzzy neural network depends on k and the vigilance parameter T.

• Journal of Environmental Impact Assessment
• /
• v.21 no.4
• /
• pp.593-607
• /
• 2012

The Ministry of Environment have started the 'National Ecosystem Survey' since 1986. It has been carried out nationwide every ten years as the largest survey project in Korea. The second one and the third one produced the GIS-based inventory of species. Three survey methods were different from each other. There were few studies for species distribution using national survey data in Korea. The purposes of this study are to test species distribution models for finding the most suitable modeling methods for the National Ecosystem Survey data and to investigate the modeling results according to survey methods and taxonominal group. Occurrence data of nine species were extracted from the National Ecosystem Survey by taxonomical group (plant, mammal, and bird). Plants are Korean winter hazel (Corylopsis coreana), Iris odaesanensis (Iris odaesanensis), and Berchemia (Berchemia berchemiaefolia). Mammals are Korean Goral (Nemorhaedus goral), Marten (Martes flavigula koreana), and Leopard cat (Felis bengalensis). Birds are Black Woodpecker (Dryocopus martius), Eagle Owl (Bubo Bubo), and Common Buzzard (Buteo buteo). Environmental variables consisted of climate, topography, soil and vegetation structure. Two modeling methods (GAM, Maxent) were tested across nine species, and predictive species maps of target species were produced. The results of this study were as follows. Firstly, Maxent showed similar 5 cross-validated AUC with GAM. Maxent is more useful model to develop than GAM because National Ecosystem Survey data has presence-only data. Therefore, Maxent is more useful species distribution model for National Ecosystem Survey data. Secondly, the modeling results between the second and third survey methods showed sometimes different because of each different surveying methods. Therefore, we need to combine two data for producing a reasonable result. Lastly, modeling result showed different predicted distribution pattern by taxonominal group. These results should be considered if we want to develop a species distribution model using the National Ecosystem Survey and apply it to a nationwide biodiversity research.

• Proceedings of the KSEG Conference
• /
• 2005.04a
• /
• pp.107-110
• /
• 2005

The seismological observation of Korea began in 1905, and has been run with continuous earthquake network of observation, expanding to the advanced country, but still has some problems in accuracy and speed for report. There are many problems to issue the early warning system for earthquakes and Tsunami in the East Sea because most events in the East Sea occur outside the seismic network. Therefore multi-waveform data conversion and composition from the surrounding countries such as Korea, Japan and Far East Russia are requested in order to more accurate determination of earthquake parameters. We used FESNET(Far East Seismic Network) technology to analyze 2004 May 29th Uljin Earthquake and 2005 March 20th Japanese Fukuoka Earthquake in this research, using the data of KMA, Japan stations and IRIS(4 station).

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.43 no.2 s.308
• /
• pp.33-39
• /
• 2006

This paper presents the modified LVQ method for performance improvement of pattern classification. The proposed method uses the skewness of probability distribution between the input vectors and the reference vectors. During training, the reference vectors are closest to the input vectors using the probabilistic distribution of the input vectors, and they are positioned to approximate the decision surfaces of the theoretical Bayes classifier. In order to verify the effectiveness of the proposed method, we performed experiments on the Gaussian distribution data set, and the Fisher's IRIS data set. The experimental results show that the proposed method considerably improves on the performance of the LVQ1, LVQ2, and GLVQ.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.2
• /
• pp.225-230
• /
• 2020

Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

• The KIPS Transactions:PartD
• /
• v.8D no.3
• /
• pp.203-210
• /
• 2001

These days data is being collected and accumulated in a wide variety of fields. Stored data itself is to be an information system which helps us to make decisions. An information system includes many kinds of necessary and unnecessary attribute. So many algorithms have been developed for finding useful patterns from the data and reasoning approximately new objects. We are interested in the simple and understandable rules that can represent useful patterns. In this paper we propose an algorithm which can reduce the information in the system to a minimum, based on a probabilistic rough set theory. The proposed algorithm uses a value that tolerates accuracy of classification. The tolerant value helps minimizing the necessary attribute which is needed to reason a new object by reducing conditional attributes. It has the advantage that it reduces the time of generalizing rules. We experiment a proposed algorithm with the IRIS data and Wisconsin Breast Cancer data. The experiment results show that this algorithm retrieves a small reduct, and minimizes the size of the rule under the tolerant classification rate.

• Journal of the Korean Professional Engineers Association
• /
• v.29 no.2
• /
• pp.71-79
• /
• 1996

This paper evaluates the clustering performance of a neural network and a statistical method. Algorithms which are used in this paper are the GLVQ(Generalized Loaming vector Quantization) for a neural method and the k -means algorithm for a statistical clustering method. For comparison of two methods, we calculate the Rand's c statistics. As a result, the mean of c value obtained with the GLVQ is higher than that obtained with the k -means algorithm, while standard deviation of c value is lower. Experimental data sets were the Fisher's IRIS data and patterns extracted from handwritten numerals.

• Journal of The Korean Astronomical Society
• /
• v.29 no.spc1
• /
• pp.347-350
• /
• 1996

Spectroscopic data between 7 and 15 microns obtained in 1979 by Voyager 1 and 2 Infrared Interferometer Spectrometer (IRIS) have been revisited. Using the spectral data, Jupit.er images have been constructed at the emission bands of hydrocarbons, such as methane, ethane, and acetylene. The resultant. images show differences in emission intensities in the polar regions, suggesting inhomogeneous distributions of the hydrocarbons over the auroral regions of Jupiter.