• Journal of Digital Convergence
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• v.10 no.10
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• pp.215-220
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• 2012

This paper proposes a ontology of tree structure approach for adaptive object recognition in a situation-variant environment. In this paper, we introduce a new concept, ontology of tree structure ontology, for context sensitivity, as we found that many developed systems work in a context-invariant environment. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we have focused on designing such a context-variant system using ontology of tree structure. Ontology can be defined as an explicit specification of conceptualization of a domain typically captured in an abstract model of how people think about things in the domain. People produce ontologies to understand and explain underlying principles and environmental factors. In this research, we have proposed context ontology, context modeling, context adaptation, and context categorization to design ontology of tree structure based on illumination criteria. After selecting the proper light-ontology domain, we benefit from selecting a set of actions that produces better performance on that domain. We have carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, and we have achieved enormous success, which will enable us to proceed on our basic concepts.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.329-347
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• 2017

Early research into category-based feature inference reported various phenomena in human thinking including typicality, diversity, similarity effects, etc. Later research discovered that participants' prior knowledge has an extensive influence on these sorts of reasoning. The current research tested the effects of causal knowledge on feature inference and conducted modeling on the results. Participants performed feature inference for categories consisted of four features where the features were connected either in common cause or common effect structure. The results showed typicality effects along with violations of causal Markov condition in common cause structure and causal discounting in common effect structure. To model the results, it was assumed that participants perform feature inference based on the difference between the probabilities of an exemplar with the target feature and an exemplar without the target feature (that is, $p(E_{F(X)}{\mid}Cat)-p(E_{F({\sim}X)}{\mid}Cat)$). Exemplar probabilities were computed based on causal model theory (Rehder, 2003) and applied to inference for target features. The results showed that the model predicts not only typicality effects but also violations of causal Markov condition and causal discounting observed in participants' data.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.76-87
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• 2012

This study examined Korean and the U.S.A..'s television anti-smoking advertisements that were coined to discourage adults and teens smoking. A content analysis of 71 television advertisements listed in the "Smoking Guidance Program" operated by Korea Health Promotion Foundation. This study evaluated to detect whether the advertising content reflected core health communication theories such as health belief model, theory of reasoned action, and social cognitive theory used in the designing of anti-smoking ad message to change behavior and attitudes toward smoking unfavorably. The results showed that Korean anti-smoking ads mostly relied on social norm messages, followed by smoking attitude. The message of modeling and self-efficacy was least used; while, the U.S.A. ads focused more on modeling and self efficacy. This difference comes from the cultural difference. Namely, Korea focused more on collectivism rather than individualism. The anti-smoking ads of Korea and the U.S.A. most frequently adopted horror and humor rather than sadness, no appeal, and angry. The ads targeted more on adults rather than teens. The research identifies the types of advertisements that are most likely to utilized and underutilize in the Korea and U.S.A. anti-smoking ads and contribute to further understandings of anti-smoking ads theoretically.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.47-56
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• 2021

The purpose of this study is to use network analysis to identify trends in university personality-related studies and provide implications for future research directions. For the purpose of this study, 194 papers related to personality of university students published in Korean scholarly journals. First, research began to be published in 2004, slightly increased in 2012, continued an upward curve from 2015, peaked in 2017, and is confirmed to be a downward trend. Second, the main keywords with the centrality analysis were 'society' and 'cultivation'. Third, keywords on the cognitive side and individual dimension of personality in the first period (2004 - 2010), social dimension and emotional side of personality in the second period (2011-2015), and social level and cognitive, emotional, and behavioral aspects of personality in the third period (2016-2020). Fourth, Topic 2 consisted of keywords of ability, life, interpersonal, satisfaction, and adaptation, and Topic 1 consisted of competence, morality, citizens, society, and practice. Fifth, Topic 4 alone in the first period, in the order of Topic 1 and Topic 2 in the second period, and in the order of Topic 2 and Topic 1 in the third period.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.81-89
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• 2023

In modern society, blind people face difficulties in navigating common environments such as sidewalks, elevators, and crosswalks. Research has been conducted to alleviate these inconveniences for the visually impaired through the use of visual and audio aids. However, such research often encounters limitations when it comes to practical implementation due to the high cost of wearable devices, high-performance CCTV systems, and voice sensors. In this paper, we propose an artificial intelligence fusion algorithm that utilizes low-cost video sensors integrated into smartphones to help blind people safely navigate their surroundings during walking. The proposed algorithm combines motion capture and object detection algorithms to detect moving people and various obstacles encountered during walking. We employed the MediaPipe library for motion capture to model and detect surrounding pedestrians during motion. Additionally, we used object detection algorithms to model and detect various obstacles that can occur during walking on sidewalks. Through experimentation, we validated the performance of the artificial intelligence fusion algorithm, achieving accuracy of 0.92, precision of 0.91, recall of 0.99, and an F1 score of 0.95. This research can assist blind people in navigating through obstacles such as bollards, shared scooters, and vehicles encountered during walking, thereby enhancing their mobility and safety.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.331-336
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• 2015

The texture mapping used for the low-polygon models is one of the important workflows in the graphical representation of the 3D game. Only one hand painted texture is mapped on the surface of the 3D model and represents the color of the material and visual sense of touching by itself in that process. In the 3D game graphics, it is very important to visualize the textile sensation such as protruding and denting. It can be interpreted by the Gestalt Law to recognize a plane as a 3D sense of volume. Moreover, the concept of Affordance is necessary to recognize and perceive the textile sensation. It means visual recognizing of that relationship in the learning process. In this paper, The questionnaire survey targeting 3D game graphic designers is carried out. By analyzing the survey results, we suggest the important characteristic in the process of making hand painted texture.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.1-6
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• 2018

With the increase of the elderly population, demand for improvement of quality of life and measurement of mental state has increased. However, much of the self-reported diagnosis does not reflect cognitive impairment. This study aims to measure the dwelling depression by applying K-HTP and verify the validity. 301 persons over 65 years old who live as single and couple households in Daejeon and surrounding districts were surveyed during 22 January to 2 February, 2018. The correlations between the dwelling depression and K-HTP are clarified and the validity was evaluated. The correlations between the geriatric dwelling depression index(GDDI) and the GDDI based on K-HTP(GDDI-K) are clarified and the accuracy was analyzed. The results showed that the K-HTP can be utilized to measure the dwelling depression. We suggested a new measurement tool and provide further benefits for researches on diagnoses using the projective method.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.958-968
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• 2011

The purpose of the present study is to verify the structural relationship among motivation to participate, organizational support, satisfaction and learning outcome in lifelong education. The survey was conducted on the learners who enrolled in "A" lifelong education center in the first semester of 2011 and structural equation modeling analysis were used. A total of 151 respondents were analyzed for this study. The major findings of this research are as follow: First, motivation to participate factor has an influence on the satisfaction and learning outcomes. Second, organizational support factor has an influence on the satisfaction, but not on the learning outcomes. Third, learners' satisfaction have direct effects on the learning outcomes. Furthermore, learners' satisfaction mediated causal relationship between motivation to participate and learning outcomes. This study proposed implications and managing strategies that enhance satisfaction of female adult learners in lifelong education environment and learning outcomes.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.297-310
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• 2008

Since speech acts implies speakers' intentions, it is essential to determine speakers' speech acts if we want to implement an intelligent dialogue system. We propose a two-step model for effectively determining speakers' speech acts. In the first step, the proposed model returns speech act candidates by using a neural network model based on machine learning and a predictivity model based on statistics, respectively. In the second step, using speech act candidates which are returned by the predictivity model, the proposed model filters out speech act candidates which are returned by the neural network model. Then, the proposed model selects a speech act with maximum output value among the unremoved speech act candidates. In the experiment on a schedule management domain, the proposed two-step modeling method showed better precisions than the previous methods only using a machine learning model or a probability model.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.299-308
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• 2014

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.