• Journal of the Korean earth science society
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• v.22 no.2
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• pp.138-146
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• 2001

The land cover over Korean peninsula was classified using a multi-temporal NOAA/AVHRR (Advanced Very High Resolution Radiometer) data. Four types of phenological data derived from the 10-day composited NDVI (Normalized Differences Vegetation Index), maximum and annual mean land surface temperature, and topographical data were used not only reducing the data volume but also increasing the accuracy of classification. Self organizing feature map (SOFM), a kind of neural network technique, was used for the clustering of satellite data. We used a decision tree for the classification of the clusters. When we compared the classification results with the time series of NDVI and some other available ground truth data, the urban, agricultural area, deciduous tree and evergreen tree were clearly classified.

• Journal of Korean Society of Forest Science
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• v.105 no.3
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• pp.351-359
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• 2016

This study was conducted to analyze the effect of climate change on the tree-ring growth of Pinus koraiensis in Korea. Annual tree-ring growth data of P. koraiensis collected by the $5^{th}$ National Forest Inventory were first organized to analyze yearly growth patterns of the species. When tree-ring growth data were analyzed through cluster analysis based on similarity of climatic conditions, five clusters were identified. Yearly growing degree days and standard precipitation index based on daily mean temperature and precipitation data from 1951 to 2010 were calculated by cluster. Using the information, yearly temperature effect index(TEI) and precipitation effect index(PEI) by cluster were estimated to analyze the effect of climatic conditions on the growth of the species. Tree-ring growth estimation equations by cluster were developed by using the product of yearly TEI and PEI as independent variable. The tree-ring growth estimation equations were applied to the climate change scenarios of RCP 4.5 and RCP 8.5 for predicting the changes in tree-ring growth by cluster of P. koraiensis from 2011 to 2100. The results of this study are expected to provide valuable information necessary for estimating local growth characteristics of P. koraiensis and for predicting changes in tree-ring growth patterns caused by climate change.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.77-85
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• 2006

In this paper, we propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet transformed images. We group the image classes into clusters which have similar texture features using Principal Component Analysis(PCA) and K-means. The hierarchical classifier has five layes which combine the clusters. The hierarchical classifier consists of 59 neural network classifiers learned with the back propagation algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 1000 training data and 1000 test data composed of 10 images from each of 100 classes shows classification rates of 81.5% and 75.1% correct, respectively.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.85-92
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• 2018

When using complicated image processing algorithms, we use superpixels to reduce computational complexity. Superpixel segmentation is a method of grouping pixels having similar characteristics into one group. Since superpixel is used as a preprocessing of image processing, it should be generated quickly, and the edge components of the image should be well preserved. In this paper, we propose a method of generating superpixels with a small amount of computation while preserving edge components well. In the proposed method, superpixels of an image are generated by using the existing k-mean method, and similar superpixels among the generated superpixels are merged to make final superpixels. When merging superpixels, the similarity is calculated only for superpixels. Therefore, the amount of computation is maintained small. It is shown by experimental results that the superpixel images produced by the proposed method are conserving edge information of the original image better than those produced by the existing method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.195-204
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• 2023

In this study, prompt engineering that improves prompts was explored to improve the performance of teaching and learning materials recommendations using generative artificial intelligence such as GPT and Stable Diffusion. Picture materials were used as the types of teaching and learning materials. To explore the impact of the prompt composition, a Zero-Shot prompt, a prompt containing learning target grade information, a prompt containing learning goals, and a prompt containing both learning target grades and learning goals were designed to collect responses. The collected responses were embedded using Sentence Transformers, dimensionalized to t-SNE, and visualized, and then the relationship between prompts and responses was explored. In addition, each response was clustered using the k-means clustering algorithm, then the adjacent value of the widest cluster was selected as a representative value, imaged using Stable Diffusion, and evaluated by 30 elementary school teachers according to the criteria for evaluating teaching and learning materials. Thirty teachers judged that three of the four picture materials recommended were of educational value, and two of them could be used for actual classes. The prompt that recommended the most valuable picture material appeared as a prompt containing both the target grade and the learning goal.

• Journal of Korean Society of Forest Science
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• v.105 no.1
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• pp.122-131
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• 2016

This study was conducted to analyze the effect of climatic variables on tree-ring growth of Larix leptolepis distributed in Korea by dendroclimatological method. For this, annual tree-ring growth data of Larix leptolepis collected by the $5^{th}$ National Forest Inventory were first organized to analyze yearly growth patterns of the species. To explain the relationship between tree-ring growth of Larix leptolepis and climatic variables, monthly temperature and precipitation data from 1950 to 2010 were compared with tree-ring growth data for each county. When tree-ring growth data were analyzed through cluster analysis based on similarity of climatic conditions, six clusters were identified. In addition, index chronology of Larix leptolepis for each cluster was produced through cross-dating and standardization procedures. The adequacy of index chronologies was tested using basic statistics such as mean sensitivity, auto correlation, signal to noise ratio, and expressed population signal of annual tree-ring growth. Response function analysis was finally conducted to reveal the relationship between tree-ring growth and climatic variables for each cluster. The results of this study are expected to provide valuable information necessary for estimating local growth characteristics of Larix leptolepis and for predicting changes in tree growth patterns caused by climate change.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.3
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• pp.221-228
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• 2007

Quantitative analysis of distribution characteristics of soils in Jeju Island was conducted by using geographic information system (GIS) technology. Soil series could be classified 5 groups after cluster analysis with proximity ratios among soil series which mean ratios of boundary lengths of other soils to total boundary length. Classification with proximity only was similar to conventional classification system at detailed soil map although conventional system was made from several criteria such as soil color, altitude and chemical characteristics of soils. Altitudinal sequence of soil series was also suggested from representative altitudes of them which could be found from areal distribution curve along altitudes. The sequence was brown forest soils - black soils - very dark brown soils - dark brown soils from the peak of Halla Mt. to the coast on all sides, which maybe related to pedogenesis process in Jeju Island.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.68-84
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• 2020

This study analyzed the relationship between spatial characteristics and heat waves in the distribution area of the elderly population in Changwon, Gyeongsangnam-do. For analysis, the Statistics Census data, the Ministry of Environment land cover, Landsat 8 surface temperature, and the Meteorological Agency's heat wave days data were used. The spatial characteristics of the distribution of the elderly population was classified into 5 types through K-mean cluster analysis considering the land use types. The characteristics of the elderly population by spatial type were higher in the urbanized type(cluster-3), but the proportion of the elderly population was higher in the agricultural and forest area types(cluster-1, cluster-2). In the characteristics of the surface temperature and the heat wave days, the surface temperature was the highest in the urban area, but heat wave days were the highest in the rural area. As a result of analyzing the heat wave characteristics according to the spatial type of the distribution area of elderly population, cluster-2 with the largest area in agricultural areas was highest at 15.95 days, and cluster-3 with a large area in urbanized types was the lowest at 9.41 days and 9.18 days. In other words, the elderly population living in rural areas is more exposed to heat waves than the elderly population living in urban areas, and the damage is expected to increase. The results of this study could be used as basic data to prepare various policy measures for effective management and prevention of vulnerable areas in summer.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.419-428
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• 2013

Real-time accessibility and agility are required in u-commerce under ubiquitous computing environment. Most of the existing recommendation techniques adopt the method of evaluation based on personal profile, which has been identified with difficulties in accurately analyzing the customers' level of interest and tendencies, as well as the problems of cost, consequently leaving customers unsatisfied. Researches have been conducted to improve the accuracy of information such as the level of interest and tendencies of the customers. However, the problem lies not in the preconstructed database, but in generating new and diverse profiles that are used for the evaluation of the existing data. Also it is difficult to use the unique recommendation method with hierarchy of each customer who has various characteristics in the existing recommendation techniques. Accordingly, this dissertation used the implicit method without onerous question and answer to the users based on the data from purchasing, unlike the other evaluation techniques. We applied FRAT technique which can analyze the tendency of the various personalization and the exact customer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.562-574
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• 2012

Simulation validation techniques which have been employed in most studies are statistical analysis, which validate a model with mean or variance of throughput and resource utilization as an evaluation object. However, these methods have not been able to ensure the reliability of individual elements of the model well. To overcome the problem, the weighted F-measure method was proposed, but this technique also had some limitations. First, it is difficult to apply the technique to complex system environment with numerous values of interarrival time because it assigns a class to an individual value of interarrival time. In addition, due to unbounded weights, the value of weighted F-measure has no lower bound, so it is difficult to determine its threshold. Therefore, this paper propose weighted F-measure technique with cluster analysis to solve these problems. The classes for the technique are defined by each cluster, which reduces considerable number of classes and enables to apply the technique to various systems. Moreover, we improved the validation technique in the way of assigning minimum bounded weights without any lack of objectivity.