• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.131-137
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• 2016

In this paper, the peak finding algorithms corresponding to the Autocorrelation Function (ACF), which are widely exploited for detecting the pitch of voiced signal, are proposed. According to various researchers, it is well known fact that the estimation of fundamental frequency (F0) in speech signal is not only very important task but quite difficult mission. The proposed algorithms, presented in this paper, are implemented by using many characteristics - such as monotonic increasing function - of ACF function. Thus, the proposed algorithms may be able to estimate both reliable and correct the fundamental frequency as long as the autocorrelation function of speech signal is accurate. Since the proposed algorithms may reduce the computational complexity it can be applied to the real-time processing. The speech data, is composed of Korean emotion expressed words, is used for evaluation of their performance. The pitches are measured to compare the performance of proposed algorithms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.437-438
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• 2023

This paper takes the Pingjiang historical and cultural district of Suzhou city as an example, collects 1439 visitor review data from Ctrip.com with the help of Python technology, and uses web text analysis to conduct research on high-frequency words, semantic networks and emotional tendencies to comprehensively assess the tourist perception of the Grand Canal heritage. The study found that: natural and humanistic landscape, historical and cultural accumulation, and the style of Jiangnan Canal are fully reflected in the tourists' perception of Pingjiang historical and cultural district; tourists hold strong positive emotion towards Pingjiang Road, however, there is still more room for renovation and improvement of the historical and cultural district. Finally, countermeasure suggestions for improving the tourist perception of the Grand Canal heritage are given in terms of protection first, cultural integration and innovative utilization.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.141-146
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• 2016

대화 시스템에서 사용자가 나타내는 발화에 내재된 감정을 분류하는 것은, 시스템이 적절한 응답과 서비스를 제공하는데 있어 매우 중요하다. 본 연구에서는 대화 내 감정 분류를 하는데 있어 직접적, 간접적으로 드러나는 감정 자질을 자동으로 학습하고 감정이 지속되는 대화 문맥을 효과적으로 반영하기 위해 CNN-LSTM 방식의 딥 뉴럴 네트워크 구조를 제안한다. 그리고 대량의 구어체 코퍼스를 이용한 사전 학습으로 데이터 부족 문제를 완화하였다. 실험 결과 제안하는 방법이 기존의 SVM이나, 단순한 RNN, CNN 네트워크 구조에 비해 전반전인 성능 향상을 보였고, 특히 감정이 있는 경우 더 잘 분류하는 것을 확인할 수 있었다.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.301-306
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• 2017

This study is intended to analyze colors felt in TV commercials among video contents, to provide basic resources of color plan that can be applied to production of contents and to help acceptors to change their mood and to lower depression levels. Many studies have revealed the obvious correlation between depression and suicide, so the World Health Organization(WHO) recommends the importance of media by recognizing public that depression is a serious risk factor that leads to suicides and by asserting the necessity of establishing social environment for active treatment. Contents production companies have social and cultural responsibility to convey correct information and to make acceptors have positive emotions. If the result of colors that emotionally healthy people feel through this study is used for production of video contents, it will be helpful to lower the depression scale and to prevent and treat depression by providing visual comfort. In addition, it is expected to be used as an important basic resource for not only production of video contents but also color plan of industrial fields.

• Korean Institute of Interior Design Journal
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• v.21 no.3
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• pp.76-86
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• 2012

21st century has different characters from 20th century which was an era of machine and rationality based on the industrial revolution. With the advent of the digital revolution, it became an era of pluralism, culture, and emotion. The digital technology made it possible to connect the whole world together in real time and brought about the entirely new notion of time and space. It also dramatically altered the world view. Now we have a different set of social and cultural values from the past. This paper researched the influences of these social and cultural changes on public designs. The leading trends of public designs were analyzed and the successful cases were studied. The main research methodologies were the document review and the instrumental case study. The major social & cultural paradigms of a present era were classified as 'pluralism', 'digital & information revolution', 'human-centered value (against machine-centered value of 20th century)', and 'organic world view'. Each of the classified paradigms was analyzed more to find out the influences on the various trends of public designs. 'Pluralism' has influence on 'experiential public design', 'community art', and 'public design of local values'. 'Digital & information revolution' has influence on 'content-centered public design', 'smart public design', 'immaterial public design', and 'performance in public design'. 'Human-centered value' has influence on 'universal design approach in public design', and 'emotional public design'. 'Organic world view' has influence on 'sustainable public design', and 'ecological public design'.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2056-2069
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• 2014

In order to implement interactive and personalized Web services properly, it is necessary to understand the tangible and intangible responses of the users and to recognize their emotional states. Recently, some studies have attempted to build emotional state estimation models based on facial expressions. Most of these studies have applied multiple regression analysis (MRA), artificial neural network (ANN), and support vector regression (SVR) as the prediction algorithm, but the prediction accuracies have been relatively low. In order to improve the prediction performance of the emotion prediction model, we propose a novel SVR model that is optimized using a genetic algorithm (GA). Our proposed algorithm-GASVR-is designed to optimize the kernel parameters and the feature subsets of SVRs in order to predict the levels of two aspects-valence and arousal-of the emotions of the users. In order to validate the usefulness of GASVR, we collected a real-world data set of facial responses and emotional states via a survey. We applied GASVR and other algorithms including MRA, ANN, and conventional SVR to the data set. Finally, we found that GASVR outperformed all of the comparative algorithms in the prediction of the valence and arousal levels.

• Korean Institute of Interior Design Journal
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• v.20 no.2
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• pp.72-84
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• 2011

In accordance with an active development, we now enjoy web 2.0 generation centering bilateral participation through the web 1.0 environment mainly providing the offering information and take a step web 3.0 understanding information by itself. The same time of digital technic development, consumption market arrived at market3.0 generation, also passed by market 1.0 and went via market 2.0 based on bilateral participation and understanding. This change have the marketing implement index changed as well. Nowadays, 7C is generalized of making up sensibility, emotion, communication, relationship, customization and interactive built as a major strategy from the day of 3p focused on customer environment, service process and 4P focused on physical quality of product. It isn't that brand and merchandise create life and experience like as past. It is focused on embodiment of a way of life, culture and creative experience from consumers' own. This treatise, therefore, fixes the object as flagship store positively reflecting the change and examine the change of a marketing code element and social factor stimulating customer behavior change. As a condition of this study, It fixes Maslow Motivation Theory as the main index of customer behavior, and 7C as the marketing code. This treatise ultimately studies the index of flagship store discussion based on domestic outdoor brand cases.

• Journal of Korea Trade
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• v.24 no.8
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• pp.39-62
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• 2020

Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

• Journal of The Korean Association of Information Education
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• v.21 no.5
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• pp.527-535
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• 2017

In this study, we analyzed the image of artificial intelligence recognized by elementary students using semantic differential scale. First, we extracted 23 pairs of image adjectives related to perception of artificial intelligence. Adjectives were classified into three types related to recognition, emotion and ability and 827 elementary students were examined. Image factors were classified into four factors: convenience, technological progress, human-friendliness, and concern. As a result, they showed a clear image that artificial intelligence is clever, new, and complex but exciting. In comparison with variables, female students, coding experience and older students thought that artificial intelligence was more human-friendly and technological progressive.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2251-2270
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• 2015

More than 1 billion mobile apps (smartphone applications) have been released worldwide. Competition among developers of apps that are available in app stores has intensified because of increasing demand. App icons with an appealing design can instantly increase attention. Miryoku Engineering methods were used and combined with the Kansei interface model to examine the relationship between attractive icons and users. The evaluation grid method (EGM) is a qualitative method that was used to evaluate the icons, and Quantification Theory Type I is a quantitative method that was used to analyze the influence of design elements in icons. Eight attractive factors of app icons were determined using EGM, and six specific factors were identified using questionnaires. The quantitative results indicated that user cognition and emotion were influenced by the various design elements. The impact on the attractive factors of a single design element differed among users with diverse backgrounds. App icons were assessed on the basis of aesthetics to identify attractive factors and thereby assist designers in understanding and implementing design elements and improving the overall visual appeal of their apps. The result of this investigation is crucial to the presentation of app icons in online app stores.