• The Korean Journal of Ecology
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• v.21 no.5_3
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• pp.713-721
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• 1998

The effects of artificial interference on the vegetation landscape in Mt. Nam of Seoul, Korea were clarified by analysing the distribution of vegetation landscape element and the number and size of patch depicted as a vegetation map in terms of landscape ecological principles. The effects of artificial interference on vegetation were also confirmed from the environmental gradient analysis on plant community extended from the lowland to the peak of that mountain. Vegetation landscape elements were divided into plantation and secondary forest in actual vegtation map. The ratio of plantation to secondary forest was higher in the lowland below mid-slope and the southern slope. Most afforested land were occupied by Robinia pseudoacacia and Populus tomentoglandulosa, Pinus rigida, P. koraiensis, Metasequoia glyptostroboides, Alnus hirsuta and so on are localy planted. In addition, projects to replace those afforested trees by P. densiflora as a kind of campaign for "Restoration of the one original feature of Mt. Nam" or to replace those tree species by planting young Abies holophylla or P. koraiensis under the mature afforested trees are also carried out in recent years. In cases of secondary forest, the southern slope was dominated by P. densiflora and the northern one by Q. mongolica. But the lowland of the northern slope is dominated by P. densiflora as the same as that in the southern slope. Vegetation landscape elements in Mt. Nam were much simplified comparing with that of suburban area around Seoul. The number of patches, which reflects the degree of diverse artificial interference was more in the lower area than in the upper area and more in the southern slope than in the northern one. On the other hand, the size of patch showed the antagonistic tendency to that of the number of patch. As a result of environmental gradient analysis, vegetation distribution in Mt. Nam was different from that in suburban area around Seoul. For example, Alnus japonica community, Zelkova serrata community, and Carpinus laxiflora community, which is established in mountain comparatively rare in artificial interference disappeared in Mt. Nam. As a result of analysis on vegetational succession in P. densiflora community and Q. mongolica community, both communities showed a tendency of retrogressive succession differently from that in control site located in suburban area around Seoul. In addition, species composition of P. densiflora and Q. mongolica communities in Mt. Nam were also different from those in Mt. Surak located around Seoul. It was interpreted that those results were originated from the environmental pollution and excessive arti ficial interferences.rferences.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.141-149
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• 2007

Associating the shoulder line with head location of the human body is useful in verifying, localizing and tracking persons in an image. Since the head line and the shoulder line, what we call ${\Omega}$-shape, move together in a consistent way within a limited range of deformation, we can build a statistical shape model using Active Shape Model (ASM). However, when the conventional ASM is applied to ${\Omega}$-shape fitting, it is very sensitive to background edges and clutter because it relies only on the local edge or gradient. Even though appearance is a good alternative feature for matching the target object to image, it is difficult to learn the appearance of the ${\Omega}$-shape because of the significant difference between people's skin, hair and clothes, and because appearance does not remain the same throughout the entire video. Therefore, instead of teaming appearance or updating appearance as it changes, we model the discriminative appearance where each pixel is classified into head, torso and background classes, and update the classifier to obtain the appropriate discriminative appearance in the current frame. Accordingly, we make use of two features in fitting ${\Omega}$-shape, edge gradient which is used for localization, and discriminative appearance which contributes to stability of the tracker. The simulation results show that the proposed method is very robust to pose change, occlusion, and illumination change in tracking the head and shoulder line of people. Another advantage is that the proposed method operates in real time.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.471-476
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• 2016

In this paper, recognition system for continuous human action is explained by using motion history image and histogram of oriented gradient with spotter model based on depth information, and the spotter model which performs action spotting is proposed to improve recognition performance in the recognition system. The steps of this system are composed of pre-processing, human action and spotter modeling and continuous human action recognition. In pre-processing process, Depth-MHI-HOG is used to extract space-time template-based features after image segmentation, and human action and spotter modeling generates sequence by using the extracted feature. Human action models which are appropriate for each of defined action and a proposed spotter model are created by using these generated sequences and the hidden markov model. Continuous human action recognition performs action spotting to segment meaningful action and meaningless action by the spotter model in continuous action sequence, and continuously recognizes human action comparing probability values of model for meaningful action sequence. Experimental results demonstrate that the proposed model efficiently improves recognition performance in continuous action recognition system.

• Journal of KIISE:Software and Applications
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• v.28 no.7
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• pp.501-510
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• 2001

Generally, it is essential to estimate exact disparity for the 3D modeling from stereo images. Because existing methods calculate disparities from a whole image, they require too much cimputational time and bring about the mismatching problem. In this article, using the characteristic that the disparity vectors in stereo images are distributed not equally in a whole image but only exist about the background and obhect, we do a wavelet transformation on stereo images and estimate coarse disparity fields from the reduced lowpass field using area-based method at first-step. From these coarse disparity vectors, we generate disparity histogram and then separate object from background area using it. Afterwards, we restore only object area to the original image and estimate dense and accurate disparity by our two-step pixel-based method which does not use pixel brightness but use second gradient. We also extract feature points from the separated object area and estimate depth information by applying disparity vectors and camera parameters. Finally, we generate 3D model using both feature points and their z coordinates. By using our proposed, we can considerably reduce the computation time and estimate the precise disparity through the additional pixel-based method using LOG filter. Furthermore, our proposed foreground/background method can solve the mismatching problem of existing Delaunay triangulation and generate accurate 3D model.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.5
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• pp.487-497
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• 2000

Spring time is a favorable season to be easily observed the Yellow Sand phenomenon in East Asia. In particular most of the phenomenon tend to occur in April. However, Yellow Sand phenomenon was observed from almost the whole country of Korea in winter of 1966, 1977 and 1999. The features of the synoptic weather pattern in the source regions, air stream flow between the source region and Korea, the measurement of TSP concentration, aerosol size distribution, and chemical composition of snow samples associated with Yellow Sand phenomenon were investigated. The result showed the characteristic evolutionary feature of the synoptic system associated with Yellow Sand phenomena, that is, a strong low level wind mobilized the dust within 2 or 3 days before Yellow Sand phenomenon being observed in Seoul. The wind was remarkably intensified in the source region on January 24, 1999 under the strong pressure gradient, A trajectory analysis showed that the Yellow Sand particle could be reached to Korea within 2 days from the source region, Gobi desert, through Loess plateau and Loess deposition region. The TSP concentration at the top of Kwanak mountain during the Yellow Sand phenomenon is abruptly increasing than the monthly mean concentration. The size resolved number concentration of aerosols ranging from 0.3 to 25${\mu}{\textrm}{m}$ was analyzed during Yellow Sand episode. It was evident that aerosols were distinguished by particles in the range of 2-3 ${\mu}{\textrm}{m}$ to result in the abrupt increase in January 1999, After Yellow Sand phenomenon, there was heavy snow in Seoul. By the analysis of snow collected during that time, it was observed that both the Ca(sup)2+ concentration and pH were increased abnormally compared to those in the other winter season.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Wind and Structures
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• v.19 no.5
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• pp.505-522
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• 2014

While effects of the atmospheric boundary layer flow on engineering infrastructure are more or less known, some local transient winds create difficulties for structures, traffic and human activities. Hence, further research is required to fully elucidate flow characteristics of some of those very unique local winds. In this study, important characteristics of observed vertical velocity profiles along the main wind direction for the gusty Bora wind blowing along the eastern Adriatic coast are presented. Commonly used empirical power-law and the logarithmic-law profiles are compared against unique 3-level high-frequency Bora measurements. The experimental data agree well with the power-law and logarithmic-law approximations. An interesting feature observed is a decrease in the power-law exponent and aerodynamic surface roughness length, and an increase in friction velocity with increasing Bora wind velocity. This indicates an urban-like velocity profile for smaller wind velocities and rural-like velocity profile for larger wind velocities, which is due to a stronger increase in absolute velocity at each of the heights observed as compared to the respective velocity gradient (difference in average velocity among two different heights). The trends observed are similar during both the day and night. The thermal stratification is near neutral due to a strong mechanical mixing. The differences in aerodynamic surface roughness length are negligible for different time averaging periods when using the median. For the friction velocity, the arithmetic mean proved to be independent of the time record length, while for the power-law exponent both the arithmetic mean and the median are not influenced by the time averaging period. Another issue is a large difference in aerodynamic surface roughness length when calculating using the arithmetic mean and the median. This indicates that the more robust median is a more suitable parameter to determine the aerodynamic surface roughness length than the arithmetic mean value. Variations in velocity profiles at the same site during different wind periods are interesting because, in the engineering community, it has been commonly accepted that the aerodynamic characteristics at a particular site remain the same during various wind regimes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.131-142
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• 1994

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terns, parameterized by the scalar factors ${\alpha}1,\;and\;{\alpha}2$. The analysis of concergence leads to eigenvalues of the transition matrix for the mean filter coefficient vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexity of the QFG algorithm is compared with those of the conventional LMS. sign, and LFG algorithm. The properties of convergence in the mean square error is derived and the neccessary condition for the CFG algorithm to be stable is attaned. In the computer simulation a channel equalization is utilized to demonstrate the performance feature of the QFG algorithm. The QFG algorithm has the more computational complexities but the faster convergence speed than LMS and LFG algorithm. Since the QFG algorithm has smoother convergence, it may be useful in case where error bursting is a problem.