• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.339-350
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• 2009

This study was carried out to investigate distributional condition of rare plants and useful plant resources, and to verify distributional patterns of understory vegetation associated with the upper layer's vegetation structure. Total 59 families, 160 genera, 218 kinds of vascular plants were identified at the study site including 6 rare plants designated by Korea Forest Service (Lloydia triflora Bak., Trillium kamtschaticum Pall., Lilium distichum Nakai, Anemone koraiensis Nakai, Iris odaesanensis Y.N. Lee, Viola diamantica Nakai). Twenty three species of useful plant resources were also identified at the site; 8 of them showed clustered distributions and the others were prone to scatter. Actual vegetation of this study area consisted of one natural community dominated by Quercus mongolica Fisch. and three disturbed communities of Larix kaempferi (Lamb.) Carriere, Abies holophylla Max. and/or a herbaceous vegetation resulting from forest removal and strong wind of mountain top. This classification was strongly supported by cluster analysis based on the surveyed plot data. Distributional patterns of understory vegetation within forest stand were somewhat related to overstory vegetation structure, but showed a different tendency according to site condition, species composition, and competitive pressure among understory vegetation. Therefore, in order to protect the important understory components as forest genetic resources, forest treatments such as density control of overstory should be implanted based on understanding of impact on understory's dynamics and growing condition.

• Journal of Korean Society of Forest Science
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• v.79 no.2
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• pp.173-186
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• 1990

To investigate the structure of the plant community of Soribong area in Kwangnung forest, forty-six plots were set up by the clumped sampling method. The classification by TWINSPAN and four kinds of multivariate ordination(PO, PCA, RA, DCA) were applied to the study area in order to classify them into several groups based on woody plants and environmental variables. The classification had been successfully overlayed on an ordination of the same data using DCA. The plots can be classified into four groups by TWINSPAN and DCA. The successional trends of tree species by both techniques seem to be from Pinus densiflora through Quercus mongolica, Q. serrata, Q. aliena, Carpinus laxiflora, Sorbus alnifolia to C. cordata, Fraxinus rhynchophylla, Cornus controversa in the canopy layer, and from Rhododendron mucronulatum, Rhus triohocarpa, Lespeoleza cyrtobotrya, Weigela subsessilis through Corylus sieboldiana, Lindera obtusiloba to Slaphylea bumalda, Callicarpa japonica, Lonicera maackii in the understory layer. As a result of the analysis for the relationship between the stand scores of DCA and environmental variables, they had a tendancy to increase significantly from the P. densiflora community to C. cordata community that was soil pH and the amount of humus, total nitrogen and exchangeable cations.

• Korean Journal of Child Education & Care
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• v.18 no.4
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• pp.65-89
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• 2018

Objective: The purpose of this study is to analyze the science concepts and teaching and learning methods presented in the science education-related activities of the Nuri Curriculum teacher guidebooks for ages 3-5. Methods: The research data included 772 activities related to science education in the teachers' guidebook. The analysis of science concepts was based on physical science (force and motion, physical structure, electricity and magnetism, light and shadow, sound properties), chemistry (material properties, material reaction), life science (organizational structure, growth and change, heredity and evolution, animal plant and human relationships), earth science (earth system interaction, earth system structure, and universe), engineering (designed world, engineering design, engineering, technology and society) and ecology (environment preservation). Teaching and learning methods were analyzed according to the types of small and large group activities and of free play activities. Results: Science concepts were mainly presented in the fields of engineering, chemistry, and life science commonly among children aged 3-5, whereas the concepts of physical science were lowly presented in all ages. Science concepts appeared mainly in the daily subjects of 'animal plant and nature', 'life tools', 'environment and life', and 'spring, summer, autumn and winter'. As the teaching and learning method, free paly activities (science area, free outdoor selection activity, math and manipulative activity) were mostly used for the ages of 3 and 4, and small and large group activities (cooking, story sharing, music activity) were for the age of 5. Conclusion/Implications: It is necessary to select the level of science area and concept that can be taught according to the age of children and the timing of the teaching.

• Korean Journal of Ecology and Environment
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• v.47 no.3
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• pp.176-185
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• 2014

There are no population ecological research on the natural monument (No. 191) Jeju Cymbidium kanran in South Korea. In this study, we analyzed the population structure and fine-scale habitat affinity of C. kanran in Sanghyo-dong, Jejudo Island from Oct. 2013 to Feb. 2014. We observed total of 1,237 individuals (4,341 pseudobulbs) of C. kanran (989.6 population $ha^{-1}$) within (1.25 ha) and only 17 (1.4%) individuals were inflorescent. In 60.9% of the entire populations, disease symptoms such as spots and blight leaves were observed. C. kanran populaton exhibited reverse-J shaped size distribution based on leaf area classes as individual size parameter. The three size related attributes of C. kanran (no. of pseudobulb $r_s$=-0.159, no. of leaves $r_s$=-0.148 and leaf arera $r_s$=-0.114) and soil temperature revealed a negative relationship (p<0.0001). Most of C. kanran (95.4%) were grown under Castamopsis cuspidata and spatially, C. kanran were strongly clumped at all distances. Population characteristics of C. kanran in the study area were likely originated from species habitat affinity and successional environment. Through this study, base line data for C. kanran's habitat monitoring was established and conservation measures based on population characteristics were discussed.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.4
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• pp.1-15
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• 2023

This research intended to reveal the special characteristics of the vegetation structure and the tendency of change of -landfill slope districts, which are reclaimed land, through an investigationsinto the presently existent vegetation and plant community structure of the World Cup Park landfill slope district. For the analysis of changes in vegetation, this study compared the results of field surveys in 1999, 2003, 2005, 2007, 2008, 2012, 2016, and 2021. For the investigation into the plant community structure, a field investigation was carried out in 2021 with six fixed investigation districts designated in 1999 as subjects. To analyze the change in the plant community structure, the past data on the population, the number of the species, and the species diversity by the layer in 2021 were compared and analyzed in the landfill slope district, which is reclaimed land. The changes of the vegetation distribution and the power had been affected by typhoons (Kompasu). Above the plantation foundation, which had been dry and poor, Salix koreensis, marsh woody plants that had formed the community, decreased greatly. The Robinia pseudoacacia community, after the typhoon in 2010, decreased in the number of species and population. Afterward, it showed a tendency to rebound. Regarding the Ailanthus altissima-Robinia pseudoacacia-Paulownia tomentosa community, the number of the species and the population had shown a change similar to the Robinia pseudoacacia community. The Paulownia tomentosa and the Ailanthus altissima have been culled. The slope was predicted as a Future Robinia pseudoacacia forest. The Salix pseudolasiogyne community has been transitioning to a Robinia pseudoacacia forest. Only some enumeration districts, the Robinia pseudoacacia forests and the Salix pseudolasiogyne, had been growing. However, most had been in been declining. It was predicted that this community will be maintained as a Robinia pseudoacacia forest in the future. As these vegetation communities are the representative vegetation of the landfill slope districts, which is reclaimed land, there is a need to understand the ecosystem changes of the community through continuous monitoring. The results of this research can be utilized as a basic material for the vegetation restoration of reclaimed land.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of Korean Society of Forest Science
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• v.107 no.2
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• pp.113-125
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• 2018

Subalpine zone is geographically vulnerable to climate change. Forest vegetation in this zone is one of the important basic indicator to observe the influence of climate change. This study was conducting phytosociological community classification and endemic-rare plants investigation based on vulnerable species to climate change at the subalpine zone, Mt. Jiri. Vegetation data were collected by 37 quadrate plots from March to October, 2015. In order to understand the species composition of plant sociological vegetation types and the ecological impacts of species, we analyzed the layer structure of vegetation type using important values. Vegetation type was classified into eight species groups and five vegetation units. The vegetation types can be suggested as an indicator on the change of species composition according to the future climate change. There were 9 taxa endemic plants and 17 taxa rare plants designated by KFS(Korea Forest Service) where 41.2% of them were the northern plant. Endemic-rare plants increased as the altitude of vegetation unit increase. Importance value analysis showed that the mean importance value of Abies koreana was highest of all vegetation units. Based on analysis of each layer, all units except vegetation unit 1 were considered to be in competition with the species such as Quercus mongolica and Acer pseudosieboldianum. The results of this study can be a basic data to understand the new patterns caused by climate change. In addition, it can be a basic indicator of long-term monitoring through vegetation science approach.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.96-100
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• 2014

Recently, the management system of wastewater treatment facility has magnified due to the stringent regulations for the protection of the environment, and a sewage treatment plant efficiency and research of the car development are activated in large facilities or industrial park. however, the existing sewerage disposal system and specific water quality monitoring network reliability for real-time transmission of this building is insufficient. In this paper, we proposed a local wireless network design for sludge multi meter data collection and control for measuring the concentration of the sludge efficiently. Also, the collected data over the local wireless network to transmitted to the central monitoring system and accumulate the data in real time to calculate statistics is possible to monitor the status of the sewage treatment facilities. The proposed system uses a short-range wireless networks of IEEE 802.15.4 and configures an IEEE 802.11 network which can monitor real-time status in central system. Also, we install a sludge multimeter and communication network in sewage treatment facilities and confirm the usefulness of the proposed technique by demonstrating its effectiveness.