• KIPS Transactions on Computer and Communication Systems
• /
• v.7 no.9
• /
• pp.219-226
• /
• 2018

One of the systematic ways to generate the number of all cases is a combination to construct a combination tree, and its time complexity is O($2^n$). A combination tree is used for various purposes such as the graph homogeneity problem, the initial model for calculating frequent item sets, and so on. However, algorithms that must search the number of all cases of a combination are difficult to use realistically due to high time complexity. Nevertheless, as the amount of data becomes large and various studies are being carried out to utilize the data, the number of cases of searching all cases is increasing. Recently, as the GPU environment becomes popular and can be easily accessed, various attempts have been made to reduce time by parallelizing algorithms having high time complexity in a serial environment. Because the method of generating the number of all cases in combination is sequential and the size of sub-task is biased, it is not suitable for parallel implementation. The efficiency of parallel algorithms can be maximized when all threads have tasks with similar size. In this paper, we propose a method to efficiently collaborate between CPU and GPU to parallelize the problem of finding the number of all cases. In order to evaluate the performance of the proposed algorithm, we analyze the time complexity in the theoretical aspect, and compare the experimental time of the proposed algorithm with other algorithms in CPU and GPU environment. Experimental results show that the proposed CPU and GPU collaboration algorithm maintains a balance between the execution time of the CPU and GPU compared to the previous algorithms, and the execution time is improved remarkable as the number of elements increases.

• Journal of the Korean association of regional geographers
• /
• v.4 no.2
• /
• pp.79-94
• /
• 1998

This study begins with emphasis on the approach to public policy development and the extent to which a GIS framework can be used to evaluate projects objectively. This paper relates to public policy making and the use of GIS as a strategic management tool rather than the development of GIS technologies which has been the focus of attention since the advent of the first generation GIS systems in the 1960s. In order to consolidate a view towards public policy, the aim of this study is to show the advantages of using GIS to generate results which could be evaluated by cost and benefit analysis giving options of the alternative methods to estimate the feasibility of projects (both tangible and intangible) in a real public policy scenario. This study also reports that the tangible benefits associated with the GIS projects are better information processing, the easy analysis of data and the cost savings of map updates and printing, whilst the intangible benefits include quality decision making, and precise management of data through computing networks. In GIS context, the task of analysing and evaluating GIS projects is assumed in order to facilitate scientific and quantitative cost-benefit analysis. Previous methods of the cost-benefit analysis has not fully supported the evaluation of the intangible benefits and it has not been possible to make public policy realistic or scientifically understandable limiting decision makers in public domain. With the GIS decision makers are able to explore the potential of projects with this powerful decision supporting tool in practical application. On the basis of its potentials and limitations to cost-benefit analysis, therefore, it can be concluded that more flexible analysis and evaluation methodologies are needed to extend into the intangible benefits. In order to balance the evaluation of both quantitative and qualitative approaches on the cost-benefit analysis new or additional utilities will be required for the next GIS generation appraisal tool.

• The Journal of the Korea Contents Association
• /
• v.17 no.9
• /
• pp.498-509
• /
• 2017

Caregiving supports have been provided to decrease caregiving burdens of families of children with disabilities, but there are needs for improving caregiving services. The purpose of this study was to conceptualize content items and domains of caregiving supports from the perspectives of parents and service providers in order to help families of children with disabilities to decrease their caregiving burdens. This study used concept mapping that was useful for service-needs identification and program development. First, focus group interviews with two parent groups and one service provider group were conducted to generate a total of 31 items. Also, they sorted the items in terms of conceptual similarity and rated the items in terms of their needs. Next, the sorting data were analyzed using multidimensional scaling and cluster analysis and the rating data were calculated for mean. The results provided 31 content items in four domains: regular caregiving, activity-based caregiving, parent empowerment for caregving, and strengthening family relationships. All the domains were rated as important to decrease caregiving burdens. This study has practical implications for improving caregiving services and the related policies.

• Korean Journal of Remote Sensing
• /
• v.22 no.4
• /
• pp.265-274
• /
• 2006

Extracting ground points is the kernel of DTM generation being considered as one of the most popular LIDAR applications. The previous extraction approaches can be mostly characterized as a point based approach, which sequentially examines every individual point to determine whether it is measured from ground surfaces. The number of examinations to be performed is then equivalent to the number of points. Particularly in a large set, the heavy computational requirement associated with the examinations is obviously an obstacle to employing more sophisticated criteria for the examination. To reduce the number of entities to be examined and produce more robust results, we developed an approach based on features rather than points, where a feature indicates an entity constructed by grouping some points. In the proposed approach, we first generate a set of features by organizing points into surface patches and grouping the patches into surface clusters. Among these features, we then attempt to identify the ground features with the criteria based on the attributes of the features. The points grouped into these identified features are labeled ground points, being used for DTM generation afterward. The Proposed approach was applied to many real airborne LIDAR data sets. The analysis on the results strongly supports the prominent performance of the proposed approach in terms of not only the computational requirement but also the quality of the DTM.

• Korean Journal of Remote Sensing
• /
• v.22 no.2
• /
• pp.111-121
• /
• 2006

The classification using spectral angle is a new approach based on the fact that the spectra of the same type of surface objects in RS data are approximately linearly scaled variations of one another due to atmospheric and topographic effects. There are many researches on the unsupervised classification using spectral angle recently. Nevertheless, there are only a few which consider the characteristics of Hyperspectral data. On this study, we propose the ISOMUSAC(Iterative Self-Organizing Modified Unsupervised Spectral Angle Classification) which can supplement the defects of previous unsupervised spectral angle classification. ISOMUSAC uses the Angle Division for the selection of seed points and calculates the center of clusters using spectral angle. In addition, ISOMUSAC perform the iterative merging and splitting clusters. As a result, the proposed algorithm can reduce the time of processing and generate better classification result than previous unsupervised classification algorithms by visual and quantitative analysis. For the comparison with previous unsupervised spectral angle classification by quantitative analysis, we propose Validity Index using spectral angle.

• Korean Journal of Remote Sensing
• /
• v.15 no.2
• /
• pp.119-130
• /
• 1999

At present, many ground stations all over the world are using NORAD orbit element data in order to track and communicate with Earth orbiting satellites. The North American Aerospace Defense Command (NORAD) observes thousands of Earth orbiting objects on daily basis and provides their orbital information via internet. The orbital data provided by NORAD, which is also called two line element (TLE) sets, allows ground stations to predict the time-varying positions of satellites accurately enough to communicate with the satellites. In order to complete the mission of a high resolution remote sensing satellite which requires very high positional determination and control accuracy, however, a mission control and tracking ground station is dedicated for the observation and positional determination of the satellite rather than using NORAD orbital sets. In the case of KITSAT-3, NORAD orbital elements are currently used for image acquisition planning and for the processing of acquired images due to the absence of a dedicated KITSAT-3 tracking ground system. In this paper, we tested and analyzed the accuracy of NORAD orbital elements and the appropriate prediction model to determine how accurately a satellite acquisites an image of the location of interest and how accurately a ground processing system can generate the catalog of the images.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.5
• /
• pp.1007-1017
• /
• 2019

Currently, most biometrics system authenticates users by using single biometric information. This method has many problems such as noise problem, sensitivity to data, spoofing, a limitation of recognition rate. One method to solve this problems is to use multi biometric information. The multi biometric authentication system performs information fusion for each biometric information to generate new information, and then uses the new information to authenticate the user. Among information fusion methods, a score-level fusion method is widely used. However, there is a problem that a normalization operation is required, and even if data is same, the recognition rate varies depending on the normalization method. A rank-level fusion method that does not require normalization is proposed. However, a existing rank-level fusion methods have lower recognition rate than score-level fusion methods. To solve this problem, we propose a rank-level fusion method with higher recognition rate than a score-level fusion method using correlation coefficient. The experiment compares recognition rate of a existing rank-level fusion methods with the recognition rate of proposed method using iris information(CASIA V3) and face information(FERET V1). We also compare with score-level fusion methods. As a result, the recognition rate improve from about 0.3% to 3.3%.

• Smart Media Journal
• /
• v.10 no.3
• /
• pp.39-47
• /
• 2021

Enhanced index tracking is a problem of optimizing the objective function to generate returns above the index based on the index tracking that follows the market return. In order to avoid problems such as large transaction costs and illiquidity, we used a method of constructing a portfolio by selecting only some of the stocks included in the index. Commonly used enhanced index tracking methods tried to find the optimal portfolio with only one objective function in all tested periods, but it is almost impossible to find the ultimate strategy that always works well in the volatile financial market. In addition, it is important to improve generalization performance beyond optimizing the objective function for training data due to the nature of the financial market, where statistical characteristics change significantly over time, but existing methods have a limitation in that there is no direct discussion for this. In order to solve these problems, this paper proposes ensemble learning that composes a portfolio by combining several objective functions and a 3-stage portfolio selection algorithm that can select a portfolio by applying criteria other than the objective function to the training data. The proposed method in an experiment using the S&P500 index shows Sharpe ratio that is 27% higher than the index and the existing methods, showing that the 3-stage portfolio selection algorithm and ensemble learning are effective in selecting an enhanced index portfolio.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.6
• /
• pp.251-256
• /
• 2019

A capsule endoscopy is a medical device that can capture an entire digestive organ from the esophagus to the anus at one time. It produces a vast amount of images consisted of about 8~12 hours in length and more than 50,000 frames on a single examination. However, since the analysis of endoscopic images is performed manually by a medical imaging specialist, the automation requirements of the analysis are increasing to assist diagnosis of the disease in the image. Among them, this study focused on automatic detection of polyp images. A polyp is a protruding lesion that can be found in the gastrointestinal tract. In this paper, we propose a weighted-image generation method to enhance the polyp image learning by multi-scale analysis. It is a way to extract the suspicious region of the polyp through the multi-scale analysis and combine it with the original image to generate a weighted image, that can enhance the polyp image learning. We experimented with SVM and RF which is one of the machine learning methods for 452 pieces of collected data. The F1-score of detecting the polyp with only original images was 89.3%, but when combined with the weighted images generated by the proposed method, the F1-score was improved to about 93.1%.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_3
• /
• pp.1329-1350
• /
• 2018

There have been many applications to observe Earth using synthetic aperture radar (SAR) since it could acquire Earth observation data without reference to weathers or local times. However researches about digital map generation using SAR have hardly been performed due to complex raw data processing. In this study, we suggested feasibility of producing digital map using SAR stereo images. We collected two sets, which include an ascending and a descending orbit acquisitions respectively, of KOMPSAT-5 stereo dataset. In order to suggest the feasibility of digital map generation from SAR stereo images, we performed 1) rational polynomial coefficient transformation from radar geometry, 2) digital resititution using KOMPSAT-5 stereo images, and 3) validation using digital-map-derived reference points and check points. As the results of two models, root mean squared errors of XY and Z direction were less than 1m for each model. We discussed that KOMPSAT-5 stereo image could generated 1:25,000 digital map which meets a standard of the digital map. The proposed results would contribute to generate and update digital maps for inaccessible areas and wherever weather conditions are unstable such as North Korea or Polar region.