• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1763-1772
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• 1996

The conventional training set selection methods for the satellite image classification usually depend on the manual selection using data from the direct measurements of the ground or the ground map. However this task takes much time and cost, and some feature values vary in wide ranges even if they are in the same class. Such feature values can increase the robustness of the neural net but learning time becomes longer. In this paper,we propose anew training set selection algorithm using a rule-based method. By the technique proposed, the SPOT multispectral Imagery is classified in 3 bands, and the pixels which satisfy the rule are employed as the training sets for the neutralist classifier. The experimental results show faster initial convergence and almost the same or better classification accuracy. We also showed an improvement of the classification accuracy by using texture features and NDV1.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1415-1429
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• 2019

Nowadays object tracking process becoming one of the most challenging task in Computer Vision filed. A CSR-DCF (channel spatial reliability-discriminative correlation filter) tracking algorithm have been proposed on recent tracking benchmark that could achieve stat-of-the-art performance where channel spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process with only two simple standard features, HoGs and Color names. However, there are some cases where this method cannot track properly, like overlapping, occlusions, motion blur, changing appearance, environmental variations and so on. To overcome that kind of complications a new modified version of CSR-DCF algorithm has been proposed by integrating deep learning based object detection and CSRT tracker which implemented in OpenCV library. As an object detection model, according to the comparable result of object detection methods and by reason of high efficiency and celerity of Faster RCNN (Region-based Convolutional Neural Network) has been used, and combined with CSRT tracker, which demonstrated outstanding real-time detection and tracking performance. The results indicate that the trained object detection model integration with tracking algorithm gives better outcomes rather than using tracking algorithm or filter itself.

• Korean Journal of Human Ecology
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• v.18 no.1
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• pp.211-221
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• 2009

The study investigated medical treatment features of patients with type 2 diabetes mellitus, their further complications, levels of diet therapy education and the status of dietary compliance. The subjects were patients who came to oriental medicine clinics in Daejon. The percentage of male was 37.5% and female was 62.7% in the subject group's sex distribution. The outbreak of the disease was most common in the age of 50's. Also 40% of the subjects had been suffering for more than 5 years. 35.7% of men and 53.7% of women had family history. All the subjects had further complications, among them hypertension was the most common. Kidney, hepatic, vascular heart diseases were followed. 85.3% of the subjects answered they already had educations about diet therapy. Those educations were given in the public health center, general and private hospitals. 32% of subjects were not practicing diet therapy. The difficulties with which patients can be faced when they take dietary education were exchange(?exchanging?) food and calculating calories. Most of the subjects tend to overlook the importance of dietary habits and show low understanding to diet therapy, which leads to low participation rate of diabetes patients. Based on these results, therefore, we can conclude that diet therapy education to the patients should be more organized and easily practicable for them. To develop educational methods which can draw patients' attention and also be more effective is the most important task.

• International Journal of Contents
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• v.13 no.2
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• pp.29-34
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• 2017

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.33-46
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• 2016

The present study aims to investigate how teachers evaluate the Korean textbook they use for teaching primary students with multicultural background. Faced with the rich variety of materials available, it is often a challenging task for language teachers to select the most suitable coursebook. It is required that they carry out a detailed and comprehensive analysis of coursebooks and evaluate them with a set of criteria to guide the process. 32 Korean teachers took part in the primary KSL textbook evaluation that aimed to identify improvement areas. They used a pro-forma specified with 51 criteria. The teachers' responses to the questions in the pro-forma were statistically analyzed to draw implications for material evaluation. The findings of the analysis suggest that the KSL textbook for primary multicultural students need to be improved at the internal and external levels. Implications are discussed with focus on material evaluation and development, and KSL teacher professional development by helping them gain good and useful insights into the nature of the textbook, and sensitizing them to features to look for in teaching and learning materials.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.2
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• pp.60-67
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• 2009

Analyzing protein-protein interactions(PPI) is an important task in bioinformatics as it can help in new drugs' discovery process. However, due to vast amount of PPI data and their complexity, efficient visualization of the data is still remained as a challenging problem. We have developed efficient and effective visualization system that integrates Gene Ontology(GO) and PPI network to provide better insights to scientists. To provide efficient data visualization, we have employed dynamic interactive graph drawing methods and context-based browsing strategy. In addition, quick and flexible cross-reference system between GO and PPI; LCA(Least Common Ancestor) finding for GO; and etc are supported as special features. In terms of interface, our visualization system provides two separate graphical windows side-by-side for GO graphs and PPI network, and also provides cross-reference functions between them.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.351-363
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• 2015

Discourse analysis is useful for examining discourse characteristics of dementia patients and differentiating them from normal adults. However, inconsistent tasks, transcription methods and measures have been utilized, thus analyses were inevitably unsystematic. The purpose of this study was to investigate tasks, transcription methods and measures of discourse studies in dementia. Studies on dementia discourses written in English were collected via Scopus database containing abstracts and citations from 2005 to 2014, and were analyzed in detail. From the results, the most frequently used task was 'storytelling-topic'. It was observed that use of consistent transcription conventions rendered more reliable transcription data. Measures could be categorized into coherence, cohesion, productivity, propositional syntactic lexical analysis, and fluency, and each category belonged to discourse processing levels (i.e., processing of conceptual networks, propositions, and natural language units). In conclusion, future research warrants not only utilization of discourse tasks depending on dementia types and features, but also application of consistent transcription conventions. Additionally, measures based on the discourse processing theory would enable us to systematically interpret discourse studies.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.37-52
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• 2016

To injection mould polymers, designing mould is a key task involving several critical decisions with direct implications to yield quality, productivity and frugality. One prominent decision among them is specifying sprue-bush conduit expansion as it significantly influences overall injection moulding; abstruseness anguish in its design criteria deceives direct determination. Intuitively designers decide it wisely and then exasperate by optimising or manipulating processing parameters. To overwhelm that anomaly this research aims at proposing an ideal design criteria holistically for all polymeric materials also tend as a functional assessment metric towards perfection i.e., criteria to specify sprue conduit size before mould development. Accordingly, a priori analytical criterion was deduced quantitatively as expansion ratio from ubiquitous empirical relationships specifically a.k.a an exclusive expansion angle imperatively configured for injectant properties. Its computational intelligence advantage was leveraged to augment functionality of perfectly injecting into an impression gap, while synchronising both injector capacity and desired moulding features. For comprehensiveness, it was continuously sensitised over infinite scale as an explicit factor dependent on in-situ spatio-temporal injectant state perplexity with discrete slope and altitude for each polymeric character. In which congregant ranges of apparent viscosity and shear thinning index were conceived to characteristically assort most thermoplastics. Thereon results accorded aggressive conduit expansion widening for viscous incrust, while a very aggressive narrowing for shear thinning encrust; among them apparent viscosity had relative dominance. This important rationale would certainly form a priori design basis as well diagnose filling issues causing several defects. Like this the proposed generic design criteria, being simple would immensely benefit mould designers besides serve as an inexpensive preventive cliché to moulders. Its adaption ease to practice manifests a hope of injection moulding extremely alluring polymers. Therefore, we concluded that appreciating injectant's polymeric character to design exclusive sprue bush offers a definite a priori advantage.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• The Mathematical Education
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• v.44 no.2 s.109
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• pp.307-323
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• 2005

The purpose of this study was to develop a program to cultivate mathematical creativity based on open-ended problem and to investigate its effect. The major features of this innovative program are (a) breaking up fixations, (b) multiple answers, (c) various strategies, (d) problem posing, (e) exploring strategies, (f) selecting and estimating, (g) active exploration through open-ended problems. 20 units for 7th grade mathematics were developed. This study hypothesizes that experimental students may develop more divergent thinking abilities than their traditional counterparts. The participants were 7th grade students attending middle schools in Seoul. Instruments were pre and post tests to measure mainly divergent thinking skills through open-ended problems. The results indicated that the experimental students achieved better than the comparison students on overall and each component of fluency, flexibility, and originality of divergent thinking skills, when deleting the effect of covariance of the pretest. The developed program can be a useful resource for teachers to use in enhancing their students' creative thinking skills. Further this open-ended approach can be served as a model to implement in classes. This study suggests that further investigations are needed in order to examine effects on affective domains such as motivation and task perseverance which are also considered as important factors of creativity.