• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1105-1112
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• 2002

Nowadays the information exchange based on XML such as B2B electronic commerce is spreading. Therefore a systematic and stable management mechanism for storing the exchanged information is needed. For this goal there are many research activities for concerning the connection between XML application and relational databases. But because XML data has hierarchical structure and relational databases can store only flat-structured data, we need to make a conversion rule which changes the hierarchical architecture to a 2-dimensional format. Accordingly the modeling methodology for storing such structured information in relational databases is needed. In order to build good quality application systems, modeling is an important first step. In 1997, the OMG adopted the UML as its standard modeling language. Since industry has warmly embraced UML, its popularity should become more important in the future. So a design methodology based on UML is needed to develop efficient XML applications. In this paper, we propose a unified design methodology for XML applications based on relational database using UML. To reach these goals, first we introduce a XML modeling methodology to design W3C XML schema using UML and second we propose data modeling methodology for relational database schema to store XML data efficiently in relational databases.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.85-100
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• 2003

Today's computing system has expanded its application to business trade and distributed work transactions using the Internet. As the demand for more flexible, adaptable, extensible, and robust web-based enterprise, these application development has been gradually expanded based on reusable, independent, and portable components. Component Based Development (CBD) works by developing and evolving software from selected reusable software components and then assembling them within appropriate software architecture. However, it requires an increase in cost to build new components as well as the necessary effort to develop of the business requirement these components. Standardized component models are required as well from the perspective of systems in order to support rapid and exact component information transmission on the web. In this paper, we describe the e-Business Component Development with agent for rapid application development on the web that correspond to the demands of users in the business domain. We design and implement the specifications of e-business components by combining these demands. In order to improve the agent register and retrieval, we propose the intelligent search and register agents, which can conduct more precise searching and specializing for components. The system enables the locating of user's frequently used components through an agent involving register and retrieval, as well as rapid procedures for registers The e-BCOS (e-Business Component System) is the agent system for the user to register distributed components and to search for components Information. The e-BCOS increases reusability through the e-business component development of distributed components in the business domain. For the share and delivery, specification with XML is acceptable to user's variable order e-BCOS Includes the effective investment, timeliness, reliability, efficiency, and maintenance effort by with agent.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.225-232
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• 2020

The underlaying communication scheme in 5G cellular network is a very promising resource sharing scheme, and it is an effective scheme for improving service performance of 5G and reducing communication load between a cellular link and a device to device (D2D) link. This paper proposes the algorithm to minimize the resource interference that occurs when performing 5G-based multi-class service on gNB(gNodeB) and the cooperative priority-based resource allocation scheduling scheme (CPRAS) to maximize 5G communication service according to the analyzed control conditions of interference. The proposed CPRAS optimizes communication resources for each device, and it optimizes resource allocation according to the service request required for 5G communication and the current state of the network. In addition, the proposed scheme provides a function to guarantee giga-class service by minimizing resource interference between a cellular link and a D2D link in gNB. The simulation results show that the proposed scheme is better system performance than the Pure cellular and Force cellular schemes. In particular, the higher the priority and the higher the cooperative relationship between UE(User Equipment), the proposed scheme shows the more effective control of the resource interference.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.457-466
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• 2011

The objectives of this study are to produce level 3 type LULC map and analysis of phenological features of North Korea, ISODATA clustering of the 88scenes of MVC of MODIS NDVI in 2008 and 8scenes in 2009 was carried out. Analysis of phenological phases based mapping method was conducted, In level 2 type map, the confusion matrix was summarized and Kappa coefficient was calculated. Total of 27 typical habitat types that represent the dominant species or vegetation density that cover land surface of North Korea in 2008 were made. The total of 27 classes includes the 17 forest biotopes, 7 different croplands, 2 built up types and one water body. Dormancy phase of winter (${\sigma}^2$ = 0.348) and green up phase in spring (${\sigma}^2$ = 0.347) displays phenological dynamics when much vegetation growth changes take place. Overall accuracy is (851/955) 85.85% and Kappa coefficient is 0.84. Phenological phase based mapping method was possible to minimize classification error when analyzing the inaccessible land of North Korea.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.177-182
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• 2010

The flow of a universal system-level design methodology consists of system specification, system-level hardware/software partitioning, co-design, co-verification using virtual or physical prototype, and system integration. In the developing process of a hardware component in system, the design phase has been regarded as a phase consuming lots of time and cost. However, the verification phase in which functionality of the designed component is verified has recently been considered as a much important phase. In this paper, the implementation of a verification environment which is based on SystemC infrastructure and verifies the functionality of a hardware component is described. The proposed verification system uses SystemC user-defined channel as communication interface between variables of SystemC module and registers of Verilog module. The functional verification of an UART is performed on the proposed verification system. SystemC provides class library for hardware modeling and has an advantage of being able to design a system consisting hardware and software in higher abstraction level than register transfer level. Source codes of SystemC modules are reusable with a minor adaptation on verifying functionality of another hardware component.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.3
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• pp.295-300
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• 2009

Statement of problem: As the number of elders is growing with the advancement of medicine, partially or fully edentulous patients have increased. Medically compromised conditions are common in the older population so that it should be taken into account in prosthetic treatment planning as well as their economic conditions. In the older patients, removable prosthesis has been preferred to implant prosthesis. However, cast metal based removable partial dentures also has several limitations. Purpose: In this report, we present several cases of Valplast$^{(R)}$ flexible denture which were fabricated in patients who had medically compromised conditions or whose remaining teeth showed a relatively poor prognosis. Results & Conclusion: This article describes an alternative treatment for a partially edentulous patient with mouth opening limitation, after cancer surgery, compromised general condition and questionable remaining teeth. In these patients, Valplast$^{(R)}$ flexible denture was used because of its unique characteristics and the results were all satisfactory. Patients had 1-2 check-up and there were no postoperative pain or fracture of denture up to now.

• Journal of the korean academy of Pediatric Dentistry
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• v.26 no.4
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• pp.636-643
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• 1999

Ectodermal dysplasia is a genetic birth defect in which at least abnormally develop two structures derived from the ectoderm. It is usually inherited in autosomal dominant or autosomal recessive pattern. Oral manifestations are oligodontia, anodontia, dysmorphic teeth(conical shape), decreased occlusal vertical dimension and alveolar bone. Extraoral signs may include decreased or absent sweat glands, sparse and fine hair, saddle nose, hearing loss and decreased production of body fluids including saliva. Most affected children require extensive dental treatment to restore their appearance and help the development of a positive self image. The patient's overclosed profile was due to a decreased vertical dimension. The use of overdenture is to preserve erupted teeth, to accomodate the newly constructed occlusal plane, to improve retention and stability of denture and to maintain the remaining alveolar bone. The restoration of vertical dimension improved the child's speech, swallowing, and eating. Growth continue until the age of approximately 18. As child grows, replacement dentures will have to be fabricated primarily to accomodate increasing vertical dimension and changing dentition. Implants may be indicated later if the alveolar bone is adequate. Periodic recall visits are advised, to monitor the dentures during periods of growth and development, and eruption of the permanent teeth.