• Korean Journal of Environment and Ecology
• /
• v.5 no.1
• /
• pp.42-53
• /
• 1991

The Hwaomsa valley forest and the Piagol valley rarest in Mt. Chiri were studied to investigate forest structure and succession. Thirty plots in the Hwaomsa valley forest and thirty-nine plots in the Piagol valley forest were set up, and vegetation analysis of TWINSPAN classification and DCA ordination was carried out. The size of each plot was 20m $\times$ 25m, and the trees above 2cm DBH in each plot were measured. The Hwaomsa valley forest and the Piagol valley forest were classified into four communities and three communities by the altitude, respectively. The successional trends of major tree species seem to be from Pinus densiflora and Quercus mangalica through Quercus serrata to Carpinus spp. in the Hwaomsa valley forest. and from Quercus mongalica through Quercus serrata to Carpinus Spp. in the Piagol valley forest. The Hwaomsa valley is assumed to be interfered by the man more, and develop into the climax less than the Piagol rarest.

• Korean Journal of Environment and Ecology
• /
• v.25 no.2
• /
• pp.175-188
• /
• 2011

The purpose of this study was to investigate the vegetation structure of Daphniphyllum macropodum community in the Geumseon Valley area of Naejangsan National Park. To do so, thirty-seven plots($100m^2$) were set up and surveyed. The surveyed plots were divided into four groups according to the analysis of classification by TWINSPAN; (I)Carpinus laxiflora community (II)Carpinus laxiflora community (III)Deciduous Broad-leaved community and (IV)Zelkova serrata community. The results of vegetation structure analysis were; Daphniphyllum macropodum did not appeared in the canopy later but in understory and shrub layer. Even though Daphniphyllum macropodum will not be dominant species in the canopy later, but it was expected that Daphniphyllum macropodum will be major species in understory and shrub layer. The expected age of forest of the Geumseon Valley where Carpinus laxiflora and Zelkova serrata were dominant trees in canopy layer, was about 50 years old while that of Daphniphyllum macropodum in understory layer was 20 years old.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.403-406
• /
• 2005

In order to evaluate the Capability of ETM+ remotely- sensed data to provide 'Forest-shrub land-Rangeland' cover type map in areas near the timberline of northern forests of Iran, the data were analyzed in a portion of nearly 790 ha located in Neka-Zalemroud region. First, ortho-rectification process was used to correct the geometric errors of the image, yielding 0/68 and 0/69 pixels of RMS. error in X and Y axis, respectively. The original and panchromatic bands were fused using PANSHARP Statistical module. The ground truth map was made using 1 ha field plots in a systematic-random sampling grid, and vegetative form of trees, shrubs and rangelands was recorded as a criteria to name the plots. A set of channels including original bands, NDVI and IR/R indices and first components of PCI from visible and infrared bands, was used for classification procedure. Pair-wise divergence through CHNSEL command was used, In order to evaluate the separability of classes and selection of optimal channels. Classification was performed using ML classifier, on both original and fused data sets. Showing the best results of $67\%$ of overall accuracy, and 0/43 of Kappa coefficient in original data set. Due to the results represented above, it's concluded that ETM+ data has an intermediate capability to fulfill the spectral variations of three form- based classes over the study area.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.5
• /
• pp.531-540
• /
• 2010

In this paper, we present a two-phase shallow semantic parsing method based on a maximum entropy model. The first phase is to recognize semantic arguments, i.e., argument identification. The second phase is to assign appropriate semantic roles to the recognized arguments, i.e., argument classification. Here, the performance of the first phase is crucial for the success of the entire system, because the second phase is performed on the regions recognized at the identification stage. In order to improve performances of the argument identification, we incorporate syntactic knowledge into its pre-processing step. More precisely, boundaries of the immediate clause and the upper clauses of a predicate obtained from clause identification are utilized for reducing the search space. Further, the distance on parse trees from the parent node of a predicate to the parent node of a parse constituent is exploited. Experimental results show that incorporation of syntactic knowledge and the separation of argument identification from the entire procedure enhance performances of the shallow semantic parsing system.

• Korean Journal of Microbiology
• /
• v.43 no.4
• /
• pp.285-291
• /
• 2007

According to the Baltimore Scheme, viruses are classified into 6 main classes based on their replication and coding strategies. Except for some small DNA viruses, most viruses code for their own polymerases: DNA-dependent DNA, RNA-dependent RNA and RNA-dependent DNA polymerases, all of which contain 4 common motifs. We undertook a phylogenetic study to establish the relationship between the Baltimore Scheme and viral polymerases. Amino acid sequence data sets of viral polymerases were taken from NCBI GenBank, and a multiple alignment was performed with CLUSTAL X program. Phylogenetic trees of viral polymerases constructed from the distance matrices were generally consistent with Baltimore Scheme with some minor exceptions. Interestingly, negative RNA viruses (Class V) could be further divided into 2 subgroups with segmented and non-segmented genomes. Thus, Baltimore Scheme for viral taxonomy could be supported by phylogenetic analysis based on the amino acid sequences of viral polymerases.

• Spatial Information Research
• /
• v.16 no.3
• /
• pp.373-383
• /
• 2008

LiDAR, unlike satellite imagery and aerial photographs, which provides irregularly distributed three-dimensional coordinates of ground surface, enables three-dimensional modeling. In this study, urban area was classified based on 3D information collected by LiDAR. Morphological and spatial properties are determined by the ratio of ground and non-ground point that are estimated with the number of ground reflected point data of LiDAR raw data. With this information, the residential and forest area could be classified in terms of height and density of trees. The intensity of the signal is distinguished by a statistical method, Jenk's Natural Break. Vegetative area (high or low density) and non-vegetative area (high or low density) are classified with reflective ratio of ground surface.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.5
• /
• pp.1401-1408
• /
• 2000

Critical prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development cost because the problems in early phases largely affected the quality of the late products. Real-time systems such as telecommunication system are so large that criticality prediction is more important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing cause of the prediction results and low extendability. In this paper, we propose a criticality prediction model using fuzzy rulebase constructed by genetic algorithm. This model makes it easy to analyze the cause of the result and also provides high extendability, high applicability, and no limit on the number of rules to be found.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.3
• /
• pp.317-327
• /
• 2016

The extraction of significant feature points from a video is directly associated with the suggested method's function. In particular, the occlusion regions in trees or people, or feature points extracted from the background and not from objects such as the sky or mountains are insignificant and can become the cause of undermined matching or recognition function. This paper classifies the feature points required for building recognition by using multi-frames in order to improve the recognition function(algorithm). First, through SIFT(scale invariant feature transform), the primary feature points are extracted and the mismatching feature points are removed. To categorize the feature points in occlusion regions, RANSAC(random sample consensus) is applied. Since the classified feature points were acquired through the matching method, for one feature point there are multiple descriptors and therefore a process that compiles all of them is also suggested. Experiments have verified that the suggested method is competent in its algorithm.

• International Journal of Fluid Machinery and Systems
• /
• v.8 no.4
• /
• pp.264-273
• /
• 2015

This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and two Francis model test turbines all at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that two to three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.

• Journal of Information Technology Services
• /
• v.11 no.4
• /
• pp.233-250
• /
• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.