• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.525-538
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• 2019

The purpose of this study was to analyze the habitat status and species diversity of benthic diatoms and estimate the applicability of TDI (Trophic Diatom Index) to obtain the basic data for the identification and management of created wetlands in the Nakdong River. We observed a total of 38 families and 173 species of benthic diatom during the survey period, and spring and autumn showed a similar number of species of 156 and 154, respectively. The result of the SOM (Self-Organizing Map) analysis showed that the distribution of benthic diatom was sensitive to environmental factors such as nutrient concentration and rainfall in each wetland. The cluster 1 was characterized by the survey sites of autumn mostly and consisted of points of high TDI, although the nutrients such as total phosphorus and total nitrogen were low, and the species number and abundance of diatoms were low. Conversely, cluster 4 was characterized by the survey sites of spring mostly and consisted of points of low TDI, even though total nitrogen was high. Considering that most of the created wetlands had the reduced inflow and outflow, the increased flow rate in the summer lowers nutrient values in autumn, and the species number and abundance of benthic diatom decreases due to the increase of turbidity, which reduces the light penetrations to the substrates. On the contrary, the TDI value is low in spring because the low water level causes insufficient substrate surface to the benthic diatoms, and it is too early for the establishment and development of saprophilous species. Although various studies have used TDI as an indicator for evaluating the habitat environment and water quality, it is not a good evaluation indicator in this study since the nutrient concentration in the wetlands mostly high as they have a low flow rate and are close to the stagnant area. Nevertheless, additional periodic surveys that comprehensively reflect the fact that the summer rainfall and inflow/outflow regulating function might affect the species diversity and distribution of benthic diatoms are necessary.