• Quality Improvement in Health Care
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• v.8 no.2
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• pp.186-207
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• 2001

Background : This study presented an analysis of healthcare quality indicators using data mining and a development of decision support system for quality improvement. Method : Specifically, important factors influencing the key quality indicators were identified using a decision tree method for data mining based on 8,405 patients who discharged from a medical center during the period between December 1, 2000 and January 31, 2001. In addition, a decision support system was developed to analyze and monitor trends of these quality indicators using a Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. Result : Among 12 selected quality indicators, decision tree analysis was performed for 3 indicators ; unscheduled readmission due to the same or related condition, unscheduled return to intensive care unit, and inpatient mortality which have a volume bigger than 100 cases during the period. The optimum range of target group in healthcare quality indicators were identified from the gain chart. Important influencing factors for these 3 indicators were: diagnosis, attribute of the disease, and age of the patient in unscheduled returns to ICU group ; and length of stay, diagnosis, and belonging department in inpatient mortality group. Conclusion : We developed a decision support system through analysis of healthcare quality indicators and data mining technique which can be effectively implemented for utilization review and quality management in a healthcare organization. In the future, further number of quality indicators should be developed to effectively support a hospital-wide Continuous Quality Improvement activity. Through these endevours, a decision support system can be developed and the newly developed decision support system should be well integrated with the hospital Order Communication System to support concurrent review, utilization review, quality and risk management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4537-4556
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• 2020

This research was based on the study conducted by Khodaei et al. (2012), namely, the least-significant-bit (LSB) substitution combined with the pixel-value differencing (PVD) steganography, and presented an improved irreversible image steganography method. Such a method was developed through integrating the improved LSB substitution with the modulus function-based PVD steganography to increase steganographic capacity of the original technique while maintaining the quality of images. It partitions the cover image into non-overlapped blocks, each of which consists of 3 consecutive pixels. The 2nd pixel represents the base, in which secret data are embedded by using the 3-bit LSB substitution. Each of the other 2 pixels is paired with the base respectively for embedding secret data by using an improved modulus PVD method. The experiment results showed that the method can greatly increase steganographic capacity in comparison with other PVD-based techniques (by a maximum amount of 135%), on the premise that the quality of images is maintained. Last but not least, 2 security analyses, the pixel difference histogram (PDH) and the content-selective residual (CSR) steganalysis were performed. The results indicated that the method is capable of preventing the detection of the 2 common techniques.

• Journal of Digital Contents Society
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• v.10 no.1
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• pp.147-157
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• 2009

Configuration and data management system to support processes for the weapon system should support the concurrent engineering and collaborative activities for various documents, drawings, part informations and structural informations, etc. which are produced through R&D processes for a long time. This thesis attempts to identify major functions in the configuration and data management system to support processes for the weapon system R&D into services based on a service oriented architecture. In order to identify major services in the configuration and data management system to support processes for the weapon system R&D, a configuration and data management system to support processes for the weapon system R&D was proposed with a service oriented architecture with four layers including a service consumer layer, a business service layer, an application service layer and a application layer, and major services were identified for each layer. In order to identify major services in four layers, this thesis adopted a bottom-up approach to identify the necessary business services from a well-defined domain implementation system rather than a top-down identification method in general. This thesis tried to identify the essential services in implementing the configuration and data management system to support processes for the weapon system R&D as a system based on the service oriented architecture using such a bottom-up service identification method while limiting those services to the general PDM system aspects and the business areas of the configuration and data management system to support processes for the weapon system R&D.

• Journal of the Korea Safety Management & Science
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• v.21 no.4
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• pp.59-65
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• 2019

The Location Based Service is growing rapidly nowadays due to the universalization of the use for smartphone, therefore the location determination technology has been placed in an important position. This study suggests a method that can provide the estimate of users' location by using PDR method and smartphone geomagnetic sensor data. This method assists the measure of enhancing the accuracy of indoor localization. Moreover, it is to study ways to provide the exact indoor layout for evacuating the workers in emergency such as fires and natural disasters.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.155-164
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• 2015

In this paper, we propose a new color interpolation method based on trilateral filter approach, which not only preserve the high-frequency components(image edge) while interpolating the missing raw data of color image(bayer data pattern), but also immune to the image noise components and better preserve the detail of the low-frequency components. The method is the trilateral filter approach applying a gradient to the low frequency components of the image signal in order to preserve the high-frequency components and the detail of the low-frequency components through the measure of the freedom of similarity among adjacent pixels. And also we perform Gaussian smoothing to the interpolated image data in order to robust to the noise. In this paper, we compare the conventional demosaicing algorithm and the proposed algorithm using 10 test images in terms of hue MAD, saturation MAD and CPSNR for the objective evaluation, and verify the performance of the proposed algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.248-252
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• 2002

Many sensors such as a structured grid pattern generator, a laser, and CCD camera to information have been used, but most of algorithms for a calibration are inefficient memory and experiment data are required. In this paper, the calibration algorithm of a structured grid pattern based on triang is introduced to calculate 3D information in the real world. The beams generated from str pattern generator established horizontally with the CCD camera are projected on the calibn CCD camera observes the intersection plane of a light and an object plane. The 3D infon calculated using observed and calibration data. This proposed method in this paper has advantages such as a memory saving and an experimental data since the 3D information are obtained simply triangulation method.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.2
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• pp.87-98
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• 2006

In many cases of cluster analysis we are forced to perform clustering without any prior knowledge on the number of clusters. But in some clustering methods such as k-means algorithm it is required to provide the number of clusters beforehand. In this study, we focus on the problem to determine the number of clusters in the given data. We follow the 2 stage approach of ASA clustering algorithm and mainly try to improve the performance of the first stage of the algorithm. We verify the usefulness of the method by applying it for various kinds of simulated data. Also, we apply the method for clustering two kinds of real life qualitative data.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.3
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• pp.33-46
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• 2007

This study deals with the adaptive multiuser OFDM (Orthogonal Frequency Division Multiplexing) system which adjusts the resource allocation according to the environmental changes in such as wireless and quality of service required by users. The resource allocation includes subcarrier assignment to users, modulation method and power used for subcarriers. We first develop a general optimization model which maximizes data throughput while satisfying data rates required by users and total power constraints. Based on the property that this problem has the 0 duality gap, we apply the subgradient dual optimization method which obtains the solution of the dual problem by iteration of simple calculations. Extensive experiments with realistic data have shown that the subgradient dual method is applicable to the real world system, and can be used as a dynamic resource allocation mechanism.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.85-90
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• 2016

In this paper, we study the domain-independent sensor data structuring method and the derivations about ' Filter Criteria' expansion factors of heterogeneous domain systems. For this purpose, we propose a 5W1H based information search and retrieval method without knowledges about the target domains. And we also suggest a resource management model that can support expanding search of information. By the suggested method, the oneM2M standard able to implement the use-cases like 'data exchanging between the heterogeneous domains'.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.211-219
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• 2013

The analysis method using Big Data has evolved based on the Big data Management Technology. There are quite a few researching institutions anticipating new era in data analysis using Big Data and IT vendors has been sided with them launching standardized technologies for Big Data management technologies. Big Data is also affected by improvements of IT gadgets IT environment. Foreran by social media, analyzing method of unstructured data is being developed focusing on diversity of analyzing method, anticipation and optimization. In the past, data analyzing methods were confined to the optimization of structured data through data mining, OLAP, statics analysis. This data analysis was solely used for decision making for Chief Officers. In the new era of data analysis, however, are evolutions in various aspects of technologies; the diversity in analyzing method using new paradigm and the new data analysis experts and so forth. In addition, new patterns of data analysis will be found with the development of high performance computing environment and Big Data management techniques. Accordingly, this paper is dedicated to define the possible analyzing method of social media using Big Data. this paper is proposed practical use analysis for social media analysis through data mining analysis methodology.