• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.135-147
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• 2003

This study used the multistage hierarchical clustering image classification to analyze the satellite images for the land-cover types of an area in the Korean peninsula. The multistage algorithm consists of two stages. The first stage performs region-growing segmentation by employing a hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous, and finally the whole image space is segmented into sub-regions where adjacent regions have different physical properties. Without spatial constraints for merging, the second stage clusters the segments resulting from the previous stage. The image classification of hierarchical clustering, which merges step-by step two small groups into one large one based on the hierarchical structure of digital imagery, generates a hierarchical tree of the relation between the classified regions. The experimental results show that the hierarchical tree has the detailed information on the hierarchical structure of land-use and more detailed spectral information is required for the correct analysis of land-cover types.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.427-433
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• 2010

Although tracking methods are efficient and popular for vessel segmentation, they require a seed to initiate an instance of tracking. In this paper, a new method to detect new seeds for tracking of arterial segments from CT angiography (CTA) and to construct a vascular structure is proposed. The proposed algorithm is based on shape analysis of connected components in a volume of interest around a vessel segment which was already extracted by tracking. The eigenvalues of the covariance matrix are used as the shape features for detection. The experimental results on actual clinical data showed that the results totally revealed the arterial tree not hindered by bone or veins. In visual comparison to a method which combines registration and subtraction of both pre-contrast and post-contrast CT volumes, the proposed method produced comparable results to the reference method and were confirmed of its feasibility for clinical use of reducing the cost and burden of patients.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.511-513
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• 2000

PaP Smear 테스트는 자궁 경부암 진단에 가장 효율적인 방법으로 알려져 있다. 그러나 이 방법은 높은 위 음성률(false negative error, 15~50%)을 나타내고 있다. 이런 큰 오류율은 주로 다량의 세포 검사에 기인하여, 자동화 시스템의 개발이 절실히 요구되고 있다. 본 논문은 자궁 경부암의 특징인 군집을 이루는 암세포를 인식할 수 있는 시스템을 제안한다. 시스템은 두 부분으로 나누어진다. 첫 단계에서는 저 배율(100배)에서 간단한 영상처리와 최소 근접 트리(Minimum Spanning Tree)를 통해 군집을 이루는 세포를 찾는다. 두 번째 단계서는 고 배율(400배)로 확대하여 군집 세포들로부터 여러 가지 특징을 추출한 후 KNN(k-Neighbor) 방법을 통해 인식하는 단계이다. 50개의 영상 (640X 480, RGB True Color 25 개의 100배 영상 , 25개의 400배 영상)이 실험에 사용되었다. 한 영상을 처리하는데 약 3초 (2.984초) 소요되었으며, 이는 region growing(20초)나 split and merge(58초) 방법 보다 덜 소요되었다. 100배 영상에서 정상과 비정상의 두 그룹으로 나누었을 경우에는 96%의 높은 인식율을 나타내었으나 비정상을 다시 5개의 그룹으로 나누었을 때는 45%로 나타내었다. 이는 영역 추출(segmentation) 단계에서 오류와 트레이닝 데이터의 비정확성에 기인한다. 400배 영상에서는 각각 92%와 30%로 나타내었다. 이는 영역추출 단계에서 사용한 Watershed 방법의 오류로 기인한 것으로 본다.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.83-101
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• 2005

Customer retention is one of the major issues in life insurance industry, in which competition is increasingly fierce. There are many things for the life insurers to do many things to retain the customers. One of those things is to make sure to keep in touch with all customers. When an insurance-planner resigned, his/her customers must be taken care of by some planner-assistants. This article outlines the design of Contact Scheduling System (CSS) that supports planner-assistants for contacting the customers. Planner-assistants are unable to share the resigned insurance-planner's experience and knowledge regarding the customer relationship management. The CSS developed by employing both Classification And Regression Tree (CART) technique and Sequential Pattern Mining (SPM) technique has a two-stage process. In the first stage, it segments the customers into eight groups by CART model. Then it generates contact scheduling information consisting of contact-purpose, contact-interval and contact-channel, according to the segment's typical contact pattern. Contact-purpose is derived by schedule-driven, event-driven, or business-rule-driven. Schedule-driven contact is determined by SPM model. In the operation of CSS in a realistic situation, it shows a practicality in supporting planner-assistants to keep in touch with the customers efficiently and effectively.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.195-208
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• 2023

This study aimed to use three-dimensional point cloud data (PCD) obtained from Terrestrial Laser Scanning (TLS) and Mobile Laser Scanning (MLS) to evaluate a deep learning-based species classification model for two tree species: Pinus koraiensis and Larix kaempferi. Sixteen models were constructed based on the three conditions: LiDAR platform (TLS and MLS), down-sampling intensity (1024, 2048, 4096, 8192), and deep learning model (PointNet, PointNet++). According to the classification accuracy evaluation, the highest kappa coefficients were 93.7% for TLS and 96.9% for MLS when applied to PCD data from the PointNet++ model, with down-sampling intensities of 8192 and 2048, respectively. Furthermore, PointNet++ was consistently more accurate than PointNet in all scenarios sharing the same platform and down-sampling intensity. Misclassification occurred among individuals of different species with structurally similar characteristics, among individual trees that exhibited eccentric growth due to their location on slopes or around trails, and among some individual trees in which the crown was vertically divided during tree segmentation.

• Korean Journal of Environment and Ecology
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• v.26 no.3
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• pp.463-471
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• 2012

Objective of this study is to provide basic information for developing more differentiated and targeted forest healing policy and Korean medical healing programs grounded on consumer preference for forest based Korean medical healing tourism products. The internet survey(CAWI) by percentage quota sampling with 400 Seoulite ages over 30 by the age, area, and gender was conducted, and 317 samples were used for a final analysis. 61.5% of the Seoulite associated 'forest bath/walking in the woods/tree' with an image of a forest based Korean medical healing tourism product, and preference for the product and the intention to use were positive at the percentages of 72.9% and 67.5%, respectively. Preferred areas were Seoul/Gyeonggi-do(53.5%) and Gangwon-do(38.8%). 'Stress solving and refreshment', 'taking a forest bath and a walk', and 'maintaining and promoting health' were the main purposes of the use. As for a therapy, 'walking therapy' was most preferred, and 'ergotherapy' was the next. First priority as for a use facility was 'healing trail', and 'professional medical facility' ranked second. Although important decision attributes were ' cost of use', 'food', and 'friendliness of medical staff', all the other sets of attributes related to use convenience, quality of medical service and tourism activities also recorded high, which forecasts higher consumer expectation for the product. As the result showing differences in consumer preference by the demographic segmentation, differentiated and segmented consumer needs should be considered when planing and managing a product. The scope of the study is limited to a demographic segmentation which is a basic stage of understanding consumer preference, therefore more detailed future researches on complicated and multi-dimensional consumer needs are required.

• Journal of Korean Society of Forest Science
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• v.96 no.1
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• pp.107-114
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• 2007

Establishments of the seedlings inside the natural forest from adjacent artificial forests would be an important factor in forest stand dynamics. This study was conducted to see the invasion of Korean pine (Pinus koraiensis) seedlings which is not native in this region, into the natural deciduous broad-leaved forest in Gwangneung, Korea. There is no mother tree at the I ha study site while the number of naturally regenerated P. koraiensis seedlings was 345 trees and 56% of them were clumped with more than two seedlings at each point. Applying the image segmentation method to IKONOS satellite image of January, 2003, the distance from the center of 1 ha study site to the nearest mother tree and plantation of Korean pine were 200 m and 270 m, respectively. The average height and root-collar diameter of the seedlings were 34 em and 7 mm, respectively and the age of 207 seedlings (60%) were below 5 years old. Most abundant range of soil moisture gradient and LAl (leaf area index) were from 16 to 20% and those of LAI were from 3.1 to 3.5. To understand the dynamics and seed dispersal pattern of Korean pine in the Gwangneung natural deciduous broad-leaved forests, additional studies not only long-term monitoring of growth and mortality of naturally regenerated Korean pine seedlings but also application of stable isotope analysis and molecular genetic techniques was recommended.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.83-91
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• 2012

Crown density, which is defined as the proportion of the forest floor concealed by tree crown, is important and useful information in various fields. Previous methods of measuring crown density have estimated crown density by interpreting aerial photographs or through a ground survey. These are time-consuming, labor-intensive, expensive and inconsistent approaches, as they involve a great deal of subjectivity and rely on the experience of the interpreter. In this study, the crown density of a forest in Korea was estimated using KOMPSAT-2 high-resolution satellite images. Using the image segmentation technique and stand information of the digital forest map, the forest area was divided into zones. The crown density for each segment was determined using the discriminant analysis method and the forest ratio method. The results showed that the accuracy of the discriminant analysis method was about 60%, while the accuracy of the forest ratio method was about 85%. The probability of extraction of candidate to update was verified by comparing the result with the digital forest map.

• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.335-346
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• 2015

The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.