• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.60-67
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• 2018

This study was conducted to investigate the effects of DISC behavioral style on the knowledge and clinical performance of nursing students. The subjects of the study were nursing students in the fourth grade of university and the data collection period was from March 2015 to December 2016. The collected data were analyzed using SPSS 24.0. DISC behavioral style analysis showed that 10.6% were dominant, 33.8% were influence, and 48.5% were steadiness and 7.1% were conscientiousness. Knowledge score in accordance with the measured points of DISC behavioral style did not show differences in the first, but did show differences in the second and third. Conversely, clinical performance score in accordance with the measurement points of DISC behavioral style showed differences in the first, second and third.Knowledge and clinical performance scores revealed significant differences in the interactions between the groups, between measurement points and between groups and measurement points. As a result, DISC behavioral style of nursing college students vary, with each having merits and demerits. Therefore, it is necessary to provide an opportunity to understand these points and to develop merits in order to improve the learning outcomes of the curriculum.

• Korean Journal of Child Education & Care
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• v.19 no.2
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• pp.85-100
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• 2019

Objective: The purpose of this study was to find out the effect of gratitude enhancement program for freshman on pre-service early childhood teacher's gratitude disposition, empathic ability, and department satisfaction. Methods: 55 freshmen majoring in early childhood education were participated in this study. Twenty-eight of them were classified as an experimental group and made to go through gratitude enhancement program for freshman. Twenty-seven of them were classified as a control group. The gratitude enhancement program for freshman consisted of lectures on cognitive, affective and behavioral gratitude factors. To verify the effect of this program, the tests on gratitude disposition, empathic ability, and department satisfaction were carried out and the collected data were analyzed by ANCOVA. Results: The result of this study was that the gratitude enhancement program was effective for improving gratitude disposition, empathic ability, and department satisfaction of pre-service early childhood teachers. Conclusion/Implications: This study illustrated the need of gratitude enhancement program and its methodologies for pre-service early childhood teacher's college adjustment and persistence by improving gratitude disposition, empathic ability, and department satisfaction.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.39-48
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• 2021

According to International Associating for the Study of Pain (IASP) definition, neuropathic pain is a disorder characterized by dysfunction of the nervous system that, under normal conditions, mediates virulent information to the central nervous system (CNS). This pain can be divided into a disease with provable lesions in the peripheral or central nervous system and states with an incorporeal lesion of any nerves. Both conditions undergo long-term and chronic processes of change, which can eventually develop into chronic pain syndrome, that is, nervous system is inappropriately adapted and difficult to heal. However, the treatment of neuropathic pain itself is incurable from diagnosis to treatment process, and there is still a lack of notable solutions. Recently, several studies have observed the responses of CNS to harmful stimuli using image analysis technologies, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and optical imaging. These techniques have confirmed that the change in synaptic-plasticity was generated in brain regions which perceive and handle pain information. Furthermore, these techniques helped in understanding the interaction of learning mechanisms and chronic pain, including neuropathic pain. The study aims to describe recent findings that revealed the mechanisms of pathological pain and the structural and functional changes in the brain. Reflecting on the definition of chronic pain and inspecting the latest reports will help develop approaches to alleviate pain.

• Therapeutic Science for Rehabilitation
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• v.10 no.1
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• pp.37-52
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• 2021

Introduction : Oral motor function is basic function of sensory exploration, feeding, and communication, that develops from the fetal stage to childhood. Problems with oral motor function result in difficulty within handling food in the oral cavity, decreased swallowing and feeding skills, difficulty with communication, and problems with oral hygiene. To treat these symptoms, oral motor therapy is provided for normalizing sensory adaptation in the oral cavity, and increasing postural control, oral movement and oral motor function. Discussion : The oral motor facilitation technique (OMFT) was developed for increasing general and integrated oral motor function based on the following: 1) understanding orofacial muscular physiology; 2) a comprehensive approach to sensory·adaptation·behavior·cognition; 3) sensorimotor stimulation by a manual approach; 4) motor control and motor learning theory. The OMFT is a new evidence-based treatment protocol, for children and adults with neuromuscular and oral motor problems. Conclusion : The goal of this article is to provide a theoretical background for OMFT development and the basic concept for the clinical application of OMFT. We hope that this article will help oral motor therapy experts to provide effective therapy in a more professional way.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.944-953
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• 2020

Object detection plays a crucial role in a self-driving system. With the advances of image recognition based on deep convolutional neural networks, researches on object detection have been actively explored. In this paper, we proposed a lightweight model of the mask R-CNN, which has been most widely used for object detection, to efficiently predict location and shape of various objects on the road environment. Furthermore, feature maps are adaptively re-calibrated to improve the detection performance by applying an attention module to the neural network layer that plays different roles within the mask R-CNN. Various experimental results for real driving scenes demonstrate that the proposed method is able to maintain the high detection performance with significantly reduced network parameters.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.63-73
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• 2020

The purpose of this study was to investigate the relationship of academic resilience, academic burnout and major satisfaction and to identify the influencing factors on major satisfaction of nursing students. Data were collected using questionnaires from 215 students who were in 3rd and 4th year of the nursing college in G city, from May to June 2019. The collected data was analyzed using descriptive statistics, pearson's correlation coefficient and stepwise multiple regression with IBM SPSS 25.0 program. The study results showed that university students in nursing scored 3.71±.56 points for academic resilience, 2.96±.52 for academic burnout, 3.78±.54 for major satisfaction. The higher the academic Resilience, the lower the academic burnout and the higher the level of major satisfaction. The factors affection the relationship of academic resilience, academic burnout and major satisfaction are interpersonal relationships and admission motive which resulted in the major satisfaction level of 44.3%. In conclusion, to enhance major satisfaction for nursing students, efficient education program development is required considering the factors that explain the major satisfaction of nursing students. Also It should be considered that required the students' talent and aptitude. So it makes sure that this will require an advanced method of studying to students' capabilities.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.326-334
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• 2022

In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.

• Journal of Christian Education in Korea
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• v.70
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• pp.141-173
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• 2022

The purpose of this study is to illuminate depersonalization in the flow of technological revolution and to present a Christian SARAMDAUM education that aims for a new human image. It represents the Christian SARAMDAUM education that adapts to, mediates, and offers alternatives to the technological and human evolutionary flow of the machine age. The purpose of education for this purpose is to aim for 'Homo Creators', creative human beings presented as a new human image in the age of technological revolution. The educational goal is to nurture creative human beings through creative interpretation, creative integration between disciplines, and personal dialogue in the post-mechanical/ post-conventional paradigm. The content of the education is a conversation with the SARAMDAUM that consiliences the characteristics of post-machine and post-convention. The educational method utilizes Edu-Tech and AIED(Artificial Intelligence in Education) to realize systemic thinking and SARAMDAUM dialogue of technology. In addition, the composition of teachers and learners, educational environment and educational evaluation is presented. The significance of this study is that from the point of view of Christian education, the identity of human beings in the era of the technological revolution has been identified, and research on the creative image of the human being is newly attempted, and the direction of Christian SARAMDAUM education aimed at this is presented. This can be said to be a Christian education that emphasizes the essential characteristics of human beings while accommodating the era of technological revolution.