• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.329-332
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• 2000

Research interests on temporal data have been almost focused on data models. There has been relatively less research in the area of temporal data management. In this paper, we propose two data migration strategies based on time-segmented storage structure: the migration strategy by Time Granularity, the migration strategy by LST-GET. We describe the criterion for data migration and moving process. We simulated the performance of the migration strategy by Time Granularity in order to compare it with non-segmentation method. We compared and analyzed two data migration strategies for temporal data.

• Journal of Contemporary Eastern Asia
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• v.16 no.2
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• pp.110-144
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• 2017

Unlike most languages, written Chinese has no spaces between words. Word segmentation must be performed before semantic network analysis can be conducted. This paper describes how to perform Chinese word segmentation using the Stanford Natural Language Processing group's Stanford Word Segmenter v. 3.8.0, released in June 2017.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• Korea Journal of Hospital Management
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• v.17 no.2
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• pp.21-33
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• 2012

Purpose : This study aims to suggest application of patients DB to hospital marketing by performing market segmentation and selecting target market. Consequently help to establish suited strategy of marketing. Method : 14,072 patients hospitalized in a University Medical Center were recruited into this study. In order to classify the customer groups, cluster analysis was used with RFM(Recency, Frequency, Monetary) model, and 1-way ANOVA verified the differences among groups. And then, sociodemographical status, healthcare utilization and diagnosis(ICD-10) of each group were compared to draw a marketing strategy. Results : Four groups were classified through clustering analysis, and'high use and high profit' and'low use and high profit' groups were selected as a target market. The features of target market were as follows, the female proportion was high; used a private room; hospitalized through the emergency room; had operation; length of stay was long; had many comorbidity and cooperative treatment. There was difference in each feature of target market: as for the'high use and high profit' group, many patients were diagnosed with 'certain infectious and parasitic diseases'; and as for the'low use and high profit'group, the proportion of patients who purchased'industrial accident compensation insurance'and'auto insurance'was relatively high; many patients were diagnosed with'Injury, poisoning and certain other consequences of external causes'. Conclusion : It is needed to establish'positioning' strategy by monitoring and communicating with'high use and high profit' group. And for the case of'low use and high profit' group, it is necessary to make a follow-up management and lead them to have a medical check-up.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.515-523
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• 2012

Most companies make efforts to maximize their profitability by improving loyalty to existing customers through customer relationship management (CRM). According to the Wikipedia, CRM is a widely implemented strategy for managing a company's interactions with customers, clients and sales prospects. And RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail. In general, one considers recency, frequency, and monetary for customer segmentation in RFM method. In this paper, we apply RFMP method added to the purchase period of advertising items in the traditional RFM model for real estate customer segmentation. We will be able to establish the differentiated marketing strategy by RFMP method.

• Asia Marketing Journal
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• v.15 no.4
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• pp.187-200
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• 2014

The case study of OB shows dramatic market dynamics between leader brand vs. follower brand similar to Kirin vs. Asahi in Japan for two decades. Almost 20yrs ago, the brand status of OB was dramatically fallen because of the environmental pollution of subsidiary company and harsh competition of rivalry brand. But OB made a ground change in its brand strategy. OB departed from the pride in its past to bet on the new. OB decided to vitalize Cass brand through brand portfolio strategy. They deployed 3 phase articulated marketing plans; Phase I, Acquisition of Cass brand through M&A and strategic segmentation/targeting (1993-2005), Phase 2 - Mega Brand Strategy through Line Extension(2006-2009), Phase 3 - Experiential Marketing focused on Young Culture (2010- present). Finally, OB restored not only brand reputation of Cass and other brands but dominant market position in beer market. Now Cass has been growing rapidly in the last 20 years achieving 50% M/S. The three phases shows the typical successful process of brand management and revitalization adopting brand concept management and S-T-P strategy of manufacturing company.

• Proceedings of the KSRS Conference
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• v.1
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• pp.188-190
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• 2006

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The local segmentor of the first stage performs regiongrowing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. This stage uses a sliding window strategy with boundary blocking to alleviate a computational problem in computer memory for an enormous data. The global segmentor of the second stage has not spatial constraints for merging to classify the segments resulting from the previous stage. The experimental results show that the new approach proposed in this study efficiently performs the segmentation for the images of very large size and an extensive number of bands

• Korea Journal of Hospital Management
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• v.6 no.3
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• pp.90-108
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• 2001

This research examines the compensation package maximizing the utilities of internal customers by applying the market segmentation theory. Data were collected from four Korean hospitals in Seoul, Pusan and Kyunggi-do. The research is designed to seek the compensation package maximizing the utility of doctors and nurses by applying the market segmentation theory. The compensation package for doctors and nurses was classified into 5 attributes which are level of salary, payment method, education, promotion, reward method. The test results were as follows. First, the relative importance of each attribute in the compensation package is different. The level of salary is the most important, reward method is the next. Second, the utility of doctors increases by 8.7%, when they are segmented on the basis. of their preference for compensation attributes while that of nurses increases by 39.8%. The results of this study imply that the utility of doctors and nurses increases with differentiated compensation package for internal customer segmented by their preference.