• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.408-411
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• 2009

In this paper, conventional PI, fuzzy neural network(FNN) and adaptive teaming mechanism(ALM)-FNN for rotor field oriented controlled(RFOC) induction motor are studied comparatively. The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. Comparative study of PI, FNN and ALM-FNN are carried out from various aspects which is dynamic performance, steady-state accuracy, parameter robustness and complementation etc. To have a clear view of the three techniques, a RFOC system based on a three level neutral point clamped inverter-fed induction motor drive is established in this paper. Each of the three control technique: PI, FNN and ALM-FNN, are used in the outer loops for rotor speed. The merit and drawbacks of each method are summarized in the conclusion part, which may a guideline for industry application.

• Biomolecules & Therapeutics
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• v.20 no.4
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• pp.357-370
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• 2012

Radiation therapy, the most commonly used for the treatment of brain tumors, has been shown to be of major significance in tumor control and survival rate of brain tumor patients. About 200,000 patients with brain tumor are treated with either partial large field or whole brain radiation every year in the United States. The use of radiation therapy for treatment of brain tumors, however, may lead to devastating functional deficits in brain several months to years after treatment. In particular, whole brain radiation therapy results in a significant reduction in learning and memory in brain tumor patients as long-term consequences of treatment. Although a number of in vitro and in vivo studies have demonstrated the pathogenesis of radiation-mediated brain injury, the cellular and molecular mechanisms by which radiation induces damage to normal tissue in brain remain largely unknown. Therefore, this review focuses on the pathophysiological mechanisms of whole brain radiation-induced cognitive impairment and the identification of novel therapeutic targets. Specifically, we review the current knowledge about the effects of whole brain radiation on pro-oxidative and pro-inflammatory pathways, matrix metalloproteinases (MMPs)/tissue inhibitors of metalloproteinases (TIMPs) system and extracellular matrix (ECM), and physiological angiogenesis in brain. These studies may provide a foundation for defining a new cellular and molecular basis related to the etiology of cognitive impairment that occurs among patients in response to whole brain radiation therapy. It may also lead to new opportunities for therapeutic interventions for brain tumor patients who are undergoing whole brain radiation therapy.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.213-222
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• 2020

Recently, as awareness of the need for software education has spread worldwide, the government of Korea has led compulsory software education also. Basic software education in universities has been stabilized through various trials and efforts. However, due to software classes are mandatory, students not only could not have motivation for learning but also have treated programming course as a difficult subject. In this paper, two programming classes, which were designed and managed as a problem-oriented programming class for the purpose of cultivating computational thinking for the non-computer science students, are compared using the lecture assessment results. As a result, in the case of expanding the use of the problem as a grammatical explanation aid and expanding the ratio of major-friendly problems, the student's responses were concentrated on higher scores and the response average improved by about 7%. It means that the level of difficulty experienced by learners is lowered.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• Journal of The Korean Association For Science Education
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• v.39 no.3
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• pp.441-456
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• 2019

In this study, we investigated pre-service science teachers' design for lessons using analogies in the perspectives of PCK. Three pre-service science teachers at a college of education in Seoul participated in this study. After the workshop of instructional analogies in science education, they practiced lessons using analogies in teaching practices. We observed their lessons and collected all of the teaching-learning materials. Semi-structured interviews were also conducted. The analyses of the results reveal that they dealt with mapping and unshared attribute only when using main analogies in their lessons and these processes were teacher-centered. There were some cases where they failed to adequately deal with analogies including concepts beyond the curriculum. When dealing with unshared attributes, they did not tend to accept students' opinions although they thought that unshared attributes are strongly related to misconceptions. Their understanding of assessment using analogies was not high. Assessment was relatively well done when they use student-centered analogies such as physical analogies or role-playing analogies. On the bases of the results, we suggest some educational implications for pre-service science teacher education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.33-38
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• 2019

This paper proposes an effective recognition method of VPI patient's speech employing DNN-HMM-based speech recognition system, and evaluates the recognition performance compared to GMM-HMM-based system. The proposed method employs speaker adaptation technique to improve VPI speech recognition. This paper proposes to use simulated VPI speech for generating a prior model for speaker adaptation and selective learning of weight matrices of DNN, in order to effectively utilize the small size of VPI speech for model adaptation. We also apply Linear Input Network (LIN) based model adaptation technique for the DNN model. The proposed speaker adaptation method brings 2.35% improvement in average accuracy compared to GMM-HMM based ASR system. The experimental results demonstrate that the proposed DNN-HMM-based speech recognition system is effective for VPI speech with small-sized speech data, compared to conventional GMM-HMM system.

• Cross-Cultural Studies
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• v.40
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• pp.133-158
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• 2015

This study aims to analyse the conversation structure on Talking Culture("說漢語 淡文化") that is one of the conversation textbooks about Chinese culture and investigate Chinese textbooks published in Korea and Japan from a integration point of view among language, literature and culture for improving upon the problems on Talking Culture. For this purpose, this study, before analysing and investigating, considered the concept of language socialization on learning Chinese as a foreign language, and on the basis of it, analysed the conversation structure of Talking Culture. And then this study examined how we should organize the structures and contents when making conversations in Chinese textbook related with culture in compared with the Chinese textbooks published in Korea and Japan. In conclusion, this study argues that when composing a conversation textbook with culture, we not only need to pay attention not to have an inclination for conversation structure, but need to make use of the contents in Chinese literary and culture works for organizing conversations from the perspective of integration among language, literature and culture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3729-3749
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• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.139-150
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• 2021

Object orientation has become the predominant paradigm for conceptual modeling (e.g., UML), where the notions of class and object form the primitive building blocks of thought. Classes act as templates for objects that have attributes and methods (actions). The modeled systems are not even necessarily software systems: They can be human and artificial systems of many different kinds (e.g., teaching and learning systems). The UML class diagram is described as a central component of model-driven software development. It is the most common diagram in object-oriented models and used to model the static design view of a system. Objects both carry data and execute actions. According to some authorities in modeling, a certain degree of difficulty exists in understanding the semantics of these notions in UML class diagrams. Some researchers claim class diagrams have limited use for conceptual analysis and that they are best used for logical design. Performing conceptual analysis should not concern the ways facts are grouped into structures. Whether a fact will end up in the design as an attribute is not a conceptual issue. UML leads to drilling down into physical design details (e.g., private/public attributes, encapsulated operations, and navigating direction of an association). This paper is a venture to further the understanding of object-orientated concepts as exemplified in UML with the aim of developing a broad comprehension of conceptual modeling fundamentals. Thinging machine (TM) modeling is a new modeling language employed in such an undertaking. TM modeling interlaces structure (components) and actionality where actions infiltrate the attributes as much as the classes. Although space limitations affect some aspects of the class diagram, the concluding assessment of this study reveals the class description is a kind of shorthand for a richer sematic TM construct.

• The Research Journal of the Costume Culture
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• v.29 no.1
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• pp.134-151
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• 2021

The aim of this research is to develop a clothing and textiles studio course for preservice home economics teachers applying principles of Project-Based Learning (PBL) and maker education to equip future teachers with the ability to nurture creativity among adolescents. The studio course was developed in the following stages: analysis, design, development, implementation, and evaluation. We concluded that the resulting course met the following objectives extracted from the 2015 revised curriculum of home economics subjects: to promote creative and environmentally-friendly fashion design and styling abilities, gain the ability to use makerspace tools, understand flat pattern making and sewing processes, and develop creative thinking, aesthetic sense, and communication skills. Furthermore, the educational effects of PBL and maker education were confirmed through student comments on the course. Students mentioned the practicality of the material in their actual lives along with their enhanced integration of the subject material, self-directedness, aesthetic sense, ability to learn through trial and error, collaboration and communication, and sharing. Based on results from the implementation and evaluation stages, a clothing and textiles studio course should include the following modules: introduction of terms and tools, submission and sharing of clothing reformation and upcycling techniques, introduction to hand sewing, pouch making, heat-transfer printing, 3D printing, mask making, hat making, vest making, and the final team project on fashion styling. It is important for instructors to provide detailed guidelines on selecting personas for styling, looking for available materials, and selecting materials online.