• Journal of the Korean Society of Physical Medicine
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• v.8 no.2
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• pp.281-287
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• 2013

PURPOSE: The study was designed to investigate the effects of visual, auditory, and visuoauditory cues on simple Serial Task Performance in heaithy adults. METHODS: Sixty-three right-handed heaithy adults without history of neurological dysfunction were participated. A modified version of the Serial Reaction Time Task (SRTT) using five blocks of perceptual motor sequences was administered. The blocked paradigm consisted of the five blocks with randomly repeated 8 digit sequences with 5 repetition. Three types of sensory cue were employed: visual cue, auditory cue and visuoauditory cue. All subjects were assigned to press the matched botton as quickly and accurately as possible, when one of 8 stimulations was presented(one, two, three, four, five, six, seven, eight). The reaction time, accuracy, and P300 latency were measured during serial task performance. The mean reaction time(ms), accuracy(%), and P300 latency(ms) were compared between three types of cue using ANOVA. RESULTS: The reaction time to auditory cue was significantly longer than visual and visuoauditory cues(p<.001). And accuracy to auditory cue was significantly lower than visual and visuoauditory cues(p<.001). All P300 latency(at Fz, Cz, Pz) were significantly longer than to visual and visuoauditory cues(p<.05). CONCLUSION: It is suggested that type of cues influence in choice reaction. These data may helpful in designing not only effective motor learning training programs for healthy persons but also reeducation programs for patients with neurological dysfunction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4060-4079
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• 2020

Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.292-302
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• 2020

Machine learning algorithms have made immense contributions in various fields including sonar and radar applications. Recently developed Cycle-Consistency Generative Adversarial Network (CycleGAN), a variant of GAN has been successfully used for unpaired image-to-image translation. We present a modified CycleGAN for translation of underwater ship engine sounds with high perceptual quality. The proposed network is composed of an improved generator model trained to translate underwater audio from one vessel type to other, an improved discriminator to identify the data as real or fake and a modified cycle-consistency loss function. The quantitative and qualitative analysis of the proposed CycleGAN are performed on publicly available underwater dataset ShipsEar by evaluating and comparing Mel-cepstral distortion, pitch contour matching, nearest neighbor comparison and mean opinion score with existing algorithms. The analysis results of the proposed network demonstrate the effectiveness of the proposed network.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.103-112
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• 2018

"Ito2", an early childhood educational coding game that allows students to learn sequential, loop, and conditional statement concepts through games is introduced. The developed game is a two-stage process of mock and practical classes for children in actual nursing cares, and coding education is conducted for actual children to determine its effectiveness. The degree of change is observed by observing trends in childhood cognitive development performance in all six areas, including parts and the overall, space, observation, shape and measurement, classification, comparison, and listing, as the coding training is conducted. In this paper, the improvement of cognitive development and spatial perceptual abilities were achieved by children playing games with infant functional coding with fun elements plus learning factors.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.205-219
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• 2019

As the education and training is such a virtual environment is applied to various fields, its usability is endless. However, there is an underestimation of the depth of perception in the training environment. In order to solve this problem, we tried to solve the problem by applying the top-down correction method. However, it is difficult to classify the result as a learning effect or perception change. In this study, it was confirmed that the proportion of spatial components of urine had a significant effect on the depth perception, and it was confirmed that the size perception were corrected together. In this study, we propose a correction method using spatial component and depth perception to improve the accuracy of depth perception.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.76-84
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• 2022

In this paper, in order to compose the visual perception cognitive function training content that can be linked with the IPUZZLE image block, an interactive content device that utilizes photos and videos of smartphones. Four areas of visual memory, visual continuity, spatial relationship, and visual discrimination were derived and the content operation, application method, and scenario were written. It was intended to continuously give and induce children's desire to participate in training by designing the content image and developing the existing learning terrain visual and perceptual cognitive function training materials in the form of mobile mini-games. Experiential activities were conducted for general children and their guardians using the developed contents, and the results were found to be significant in terms of concentration, effect, and effect compared to basic puzzle toys. It is expected that this thesis will be a meaningful data for the study of cognitive function improvement activities based on digital toys and contents.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.579-587
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• 2015

The purpose of this study is to investigate how Korean learners perceive English vowels and further to test SLM which claims that new L2 vowel categories are more easily acquired than similar L2 vowel categories. Twenty Korean learners participated in English-to-Korean mapping test and English vowel identification test with target vowels /i, ɪ, u, ʊ, ɛ, æ/. The result revealed that Korean participants mapped the English pairs /i/-/ɪ/ and /u/-/ʊ/ onto single Korean vowel /i/ and /u/, respectively. in addition, both of English /ɛ/ and /æ/ were simultaneously mapped onto Korean /e/ and /ɛ/. This indicated that the Korean participants seemed to have perceptual difficulty for the pairs /i-ɪ/, /u-ʊ/, and /ɛ-æ/. The result of the forced-choice identification test showed that the accuracy of /ɪ, ʊ, æ/(ɪ: 81.3%, ʊ: 62.5%, æ: 60.0%) was significantly higher than that of /i, u, ɛ/(i: 28,8%, u: 28.8%, ɛ: 32.4%). Thus, the claim of SLM is confirmed given that /ɪ, ʊ, æ/ are new vowel categories whereas /i, u, ɛ/ are similar vowel categories. Further, the conspicuously low accuracy of the similar L2 vowel categories /i, u, ɛ/ was accounted for by over-generalization whereby the Korean participants excessively replaced L2 similar /i, u, ɛ/ with L2 new /ɪ, ʊ, æ/ as the participants were learning the L2 new vowel categories in the process of acquisition. Based on the findings this study, pedagogical suggestions are provided.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of The Korean Association For Science Education
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• v.33 no.2
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• pp.266-283
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• 2013

The goal of this study is to explore the characteristics of perceptual change among students majoring in arts on the nature of science and apply the results to science education. According to the study, it is important to consider the results of interaction between learners' aptitude and teaching method. Teaching the nature of science to first grade students explicitly, experimental inquiry strategy was applied to fine arts students, and teaching strategy of scientific history to music students. To find out which elements of the nature of science have come into view on modern philosophy of science, pre and post tests on the nature of science (VNOS-C) were conducted on the students. To find out specifically why views on the nature of science have changed, a case study was conducted focusing on students who showed changes in their views on the elements of the nature of science. In conclusion, this study suggests that by using experimental inquiry strategy and strategy of scientific history properly, it is possible to change students' viewpoints on the elements of the nature of science and on modern philosophy of science. Through explicit instruction, we were able to find some positive conceptual changes on the nature of science and the modern philosophy of science in terms of both quantity and quality. This shows that the students studying arts are experiencing a constructivist conceptual change on the nature of science, and that conceptual ecology and learning strategy are involved in this process. Therefore, it is thought that this study offers an important implication in organizing science education on the nature of science.

• Journal of The Korean Association For Science Education
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• v.33 no.5
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• pp.1055-1086
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• 2013

The purpose of this study is to identify the formation and evolution patterns of a trading zone and to explore the difficulties teachers experience in the trading zone and their perceptions of the experience. Seven teachers involved in the 'STEAM Teacher Community' in a middle school located in the southern part of South Korea participated in this study. Participant observation and in-depth interviews were carried out, and reflective essays were collected for analysis. The results show that teachers successfully formed a trading zone to share their expertise when they developed teaching materials for the convergence of different subject matters. Moreover, such a trading zone evolved in the order of pre-trading zone, trading zone under elite control, trading zone with boundary object, and trading zone of shared mental model. The difficulties teachers experienced in the trading zone were categorized under the difference of culture and opinion across subject matters, the lack of motivation for convergence, the hegemony of convergence and far-fetched factors for convergence, and difficulty of communication due to jargons. Also teachers in this study experienced perceptual changes in the trading zone. The trading zone model drawn from the results of this study bring forth implications for voluntary teachers' learning community activity for the convergence of different subject matters.