• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.357-381
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• 2009

Although it is known that there are personality characteristic variances in the sensitivity to environmental feedback, the trait individual difference has scarcely been explored in the context of recognition memory decision. The present study investigated this issue by examining the relationship between the feedback-based adaptive flexibility of recognition criterion positioning and personality differences in general sensitivity to non-laboratory outcomes. Experiment 1 demonstrated that veridical feedback itself had little effect on the recognition decision criterion whereas Experiment 2 demonstrated that biased feedback manipulations selectively restricted to high confidence errors, induced shifts even in the overall Old/New category criterion. Critically, individual differences in stable personality characteristic linked to reward seeking(Behavioral Activation System-BAS) and anxiety avoidance (Behavioral Inhibition System-BIS) has been shown to predict the sensitivity of subjects to this form of feedback-induced criterion learning. This data further support the idea that incremental reinforcement-based learning mechanism not often considered important during explicit recognition decisions may play a key role in criterion setting.

• KIEAE Journal
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• v.13 no.6
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• pp.23-28
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• 2013

Green Standard for Energy and Environmental Design(G-SEED) has been used for environmental friendly building certification since 2002. The certification criteria initialed with multi-residential building and now it expands to 10 criteria for new and existing building types. The purpose of this study is to understand current recognition of G-SEED from the survey of multi-complex residences in newtown. From the general question, 75.2% of responders answered the period of living term between 1~3 years, 58.6% lived in $102.48{\sim}132.23m^2$ residential area and 65.2% owned their residences. The 43.2% of respondents recognized that their residences gained G-SEED certification by G-SEED emblem(31.6%). This is the significant meaning to understand public recognition of G-SEED and how to approach the strategy for raising the G-SEED recognition. The responders expected positive influence for economical value from G-SEED and also 75.3% of responders agreed with that G-SEED would be a decision make to buy and rent their residences. Second, residents responded that the consideration issue for green building is energy & prevention of environmental pollution(27.7%) which carries equal concern in G-SEED criteria category. The result of this survey verifies that the current level recognition of G-SEED of the responder's perspectives still is not well-known but it confirmed they have a positive expectation. Therefore, from this result, G-SEED needs to draw road map with detail plans for developing G-SEED with public participation.

• International Journal of Contents
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• v.17 no.4
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• pp.35-45
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• 2021

In this study, we analyzed the factors that affect public awareness campaigns on social media platforms and developed an integrated model for measuring the persuasiveness of environmental social media campaigns. A survey questionnaire was created and distributed on Facebook with the goal of reaching individuals in their 20s and 40s in Vietnam, and 395 valid replies were gathered. The findings showed that the STOPS was reconfirmed as a suitable theoretical framework for analyzing the public's behaviour intention to conduct information related to the issue of fine dust, especially on social media. Furthermore, it also showed that social media efficacy has a moderating effect on the relationship between public's situational recognition and informational behaviour intention. This suggested that through social media platforms, personal characteristics play a vital part in developing effective environmental campaigns. Implications for both theory and practice were discussed.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.270-275
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• 2023

The government has defined domestic violence as one of the four major social evils and tried to prepare laws and systems to prevent it. Nevertheless, domestic violence has emerged as a serious social issue, and compare to the number of domestic violence cases that are constantly increasing, the report rate of domestic violence is significantly lower. This suggests the need to improve citizens' perception of domestic violence as the issue to solve it together in society, not as a private matter at home. Most of existing contents for preventing domestic violence and improving awareness are simple viewing forms of video contents, which have little effect on recognition. We aim to confirm the possibility of improving social awareness about domestic violence by producing Virtual Reality (VR) contents that have great features of user's immersion and empathy. In this VR content, the user can interact with various objects in the virtual reality of domestic violence and directly or indirectly experience the victim's position. Users who view the violent environment from the victim's point of view can have a subjective empathy for the problem, which is expected to induce more active awareness about domestic violence.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.366-373
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• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.

• International Journal of Advanced Culture Technology
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• v.4 no.1
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• pp.28-30
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• 2016

Due to the high increment in usage and built-in advanced technology of smartphones, human activity recognition relying on smartphone sensor data has become a focused research area. In order to reduce noise of collected data, most of previous studies assume that smartphones are fixed at certain positions. This strategy is impractical for real life applications. To overcome this issue, we here investigate a framework that allows detecting the status of a traveller as idle or moving regardless the position and the direction of smartphones. The application of our work is to estimate the total energy consumption of a traveller during a trip. A number of experiments have been carried out to show the effectiveness of our framework when travellers are not only walking but also using primitive vehicles like motorbikes.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.3
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• pp.632-642
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• 2013

Legacy preferences has become the key issue of educational policy. The purpose of this study was to investigate the recognition of university students and parents about legacy preferences. The questionnaire which was made by researcher of this study was used as the method of this study. As a sampling, university students 360 and parents 312 were sampled. And SPSSWIN 20.0 was used as the method of data analysis. As a conclusion, this study indicates as follows. First, the ratio of disagreement of legacy preferences was higher than the ratio of agreement of legacy preferences. But higher-income class parents were tended to agree with legacy preferences. Second, many university students and parents regarded 'economic advantages for tuition payments and a scholarship', 'excellences for education by helping financial deficit of university' as agreement reasons of legacy preferences. And many university students and parents regarded 'reproduction of social class', 'promotion of one's sense of values of mammonism' as disagreement reasons of legacy preferences.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.262-266
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• 1998

In the speech recognition under the noisy environments, reducing the mismatch introduced between training and testing environments is an important issue, and spectral subtraction is widely used technique because of its simplicity and relatively good performance in noisy environments. In this paper, we introduced histogram method as a reliable noise estimationi approach for spectral subtraction. To deal with the problem of residual noise after spectral subtraction, we proposed a new ove-estimation technique based on distribution characteristics of histogram used for noise estimation. Since the proposed technique decides the degree of over-estimation adaptively according to the measured noise distribution, it can cope with the SNR variations effectively in compared with the conventional over-estimation technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.428-434
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• 2011

Recently, subspace analysis has raised its performance to a higher level through the adoption of kernel-based nonlinearity. Especially, the radial basis function, based on its nonparametric nature, has shown promising results in face recognition. However, due to the endemic small sample size problem of face data, the conventional kernel-based feature extraction methods have difficulty in data representation. In this paper, we introduce a novel variant of the RBF kernel to alleviate this problem. By adopting the concept of the nearest feature line classifier, we show both effectiveness and generalizability of the proposed method, particularly regarding the small sample size issue.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.161-166
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• 2004

Electronic Ink is a stored data in the form of the handwritten text or the script without converting it into ASCII by handwritten recognition on the pen-based computers and Personal Digital Assistants(PDA) for supporting natural and convenient data input. One of the most important issue is to search the electronic ink in order to use it. We proposed and implemented a script matching algorithm for the electronic ink. Proposed matching algorithm separated the input stroke into a set of primitive stroke using the curvature of the stroke curve. After determining the type of separated strokes, it produced a stroke feature vector. And then it calculated the distance between the stroke feature vector of input strokes and one of strokes in the database using the dynamic programming technique.