• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.784-791
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• 2004

In this study, we deal with face recognition using fuzzy-based Fisherface method. The well-known Fisherface method is more insensitive to large variation in light direction, face pose, and facial expression than Principal Component Analysis method. Usually, the various methods of face recognition including Fisherface method give equal importance in determining the face to be recognized, regardless of typicalness. The main point here is that the proposed method assigns a feature vector transformed by PCA to fuzzy membership rather than assigning the vector to particular class. In this method, fuzzy membership degrees are obtained from FKNN(Fuzzy K-Nearest Neighbor) initialization. Experimental results show better recognition performance than other methods for ORL and Yale face databases.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.431-434
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• 2012

시간 흐름에 따라 TV 프로그램 스케줄은 변화하고 스케줄의 변화는 사용자 선호에 영향을 미친다. 이러한 스케줄 변화에 따른 토픽의 흐름이 사용자 선호도에 미치는 영향 외에도, 개성에 따른 선호도의 변화는 개인별 차이가 크다. 본 논문은 사용자 선호도 변화에 적응적으로 대응하면서 시간 변화에도 일정한 관심을 보이는 사용자의 선호도에는 가중치를 더한 모델을 목표로 한다. 따라서 제안 모델은 현재의 시청 데이터를 기준으로 한 사용자별 선호도의 선행 정보(prior)로 이전 시청선호를 두었고, 선호도 변화와 일관성을 고려하여 하나의 시청길이에 대한 선호도뿐만 아니라 여러 시청 길이의 선호도를 결합한 선호도를 구성할 수 있는 확장성 있는 모델을 제시한다. 선호도의 일관성에 대한 가중치 연산에 있어 전체 확률모델의 확률을 향상시키는 연산을 통해 정교성을 더한 모델을 제시한다. 실제 사용자들이 시청한 데이터인 2011 TNMS데이터를 기준으로 제안 모델의 성능을 확인한 결과, 기존의 LDA, MDTM모델 보다 나은 성능을 보임을 확인할 수 있었으며, 1주일 단위 추천결과, 5개 추천 시, 최대 67.9%의 추천 정확도를 확인할 수 있었다.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.46-58
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• 2021

Parks became essential to people after the introduction of modern parks in Korea. Following mayoral elections by popular vote, issues surrounding parks, such as the creation of parks, have arisen and have been publicized by the media, allowing for the formation of discourse. Accordingly, this study conducted a topic analysis by collecting news articles from major media outlets in Korea that addressed issues related to parks since 1995, after the introduction of mayoral elections by popular vote, and analyzed changes over time in the discourse on parks through semantic network analysis. As a result of a Latent Dirichlet allocation topic modeling analysis, the following five topics were classified: urban park expansion (Topic 1), historical and cultural parks (Topic 2), use programs (Topic 3), zoo event (Topic 4), and conflicts in the park creation process (Topic 5). The park-related discourse addressed by the media is as follows. First, the creation process and conflicts regarding the quantitative expansion of parks are treated as the central discourse. Second, the names of parks appear as keywords every time a new park is created, and they are mentioned continuously from then on, thereby playing an important role in the formation of discourse. Third, 'residents' form discourse about the public nature of the park as the principal agent in park-related media. This study has significance in that it examines how parks are interpreted and how discourse is formed and changed by the media. It is expected that discourse on parks will be addressed from various perspectives in further research focusing on other media, such as regional and specialized magazines.

• Information Systems Review
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• v.25 no.4
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• pp.89-112
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• 2023

In March 2020, as it was declared a COVID-19 pandemic, various quarantine measures were taken. Accordingly, many changes have occurred in the tourism and hospitality industries. In particular, quarantine guidelines, such as the introduction of non-face-to-face services and social distancing, were implemented in the restaurant industry. For decades, research on restaurant attributes has emphasized the importance of three attributes: atmosphere, service quality, and food quality. Nevertheless, to the best of our knowledge, research on restaurant attributes considering the COVID-19 situation is insufficient. To respond to this call, this study attempted an exploratory approach to classify new restaurant attributes based on understanding environmental changes. This study considered 31,115 online reviews registered in Naverplace as an analysis unit, with 475 general restaurants located in Euljiro, Seoul. Further, we attempted to classify restaurant attributes by clustering words within online reviews through TF-IDF and LDA topic modeling techniques. As a result of the analysis, the factors of "prevention of infectious diseases" were derived as new attributes of restaurants in the context of COVID-19 situations, along with the atmosphere, service quality, and food quality. This study is of academic significance by expanding the literature of existing restaurant attributes in that it categorized the three attributes presented by existing restaurant attributes and further presented new attributes. Moreover, the analysis results have led to the formulation of practical recommendations, considering both the operational aspects of restaurants and policy implications.

• Information Systems Review
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• v.25 no.2
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• pp.181-203
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• 2023

With the fierce competition of companies for the attraction of outstanding individuals, job satisfaction of employees has been of importance. In this circumstance, many companies try to invest in job satisfaction improvement by finding employees' everyday experiences and difficulties. However, due to a lack of understanding of the employee experience, their investments are not paying off. This study examined the relationship between employee experience and job satisfaction using employee reviews and company ratings from Glassdoor, one of the largest employee communities worldwide. We use text mining techniques such as K-means clustering and LDA topic-based sentiment analysis to extract key experience factors by job level, and DistilBERT sentiment analysis to measure the sentiment score of each employee experience factor. The drawn employee experience factors and each sentiment score were analyzed quantitatively, and thereby relations between each employee experience factor and job satisfaction were analyzed. As a result, this study found that there is a significant difference between the workplace experiences of managers and general employees. In addition, employee experiences that affect job satisfaction also differed between positions, such as customer relationship and autonomy, which did not affect the satisfaction of managers. This study used text mining and quantitative modeling method based on theory of work adjustment so as to find and verify main factors of employee experience, and thus expanded research literature. In addition, the results of this study are applicable to the personnel management strategy for improving employees' job satisfaction, and are expected to improve corporate productivity ultimately.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.7-12
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• 2011

In this papwer, we propose a new feature extraction method, named as Class-augmented Kernel Principal Component Analysis (CA-KPCA), which can extract nonlinear features for classification. Among the subspace method that was being widely used for feature extraction, Class-augmented Principal Component Analysis (CA-PCA) is a recently one that can extract features for a accurate classification without computational difficulties of other methods such as Linear Discriminant Analysis (LDA). However, the features extracted by CA-PCA is still restricted to be in a linear subspace of the original data space, which limites the use of this method for various problems requiring nonlinear features. To resolve this limitation, we apply a kernel trick to develop a new version of CA-PCA to extract nonlinear features, and evaluate its performance by experiments using data sets in the UCI Machine Learning Repository.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.101-110
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• 2020

Devices that add emotion-specific services or various functions are appearing in AI speakers and related devices. To this end, this study performed topic modeling analysis on the topics of post-purchase texts written by AI speaker users, and compared them with the data collected via survey questionnaires. Furthermore, data on the emotional intelligence of AI speakers and relationship quality were collected from 600 users and analyzed using structural equation modeling. The findings of the study are as follows: First, the analysis results of topic modeling showed that most of the articles mainly mention the functional aspects of AI speakers. Second, emotional intelligence of AI speaker perceived by consumer affected relationship quality, and relationship quality had a positive effect on customer satisfaction. Therefore, this study expands the area of AI research by integrating the concept of emotional intelligence and relationship quality to provide new theoretical and practical implications.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.1-9
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• 2020

This study aims to find new attributes by analyzing Korea and China duty free store online reviews and examine the influence of these attributes on star ratings(satisfaction)of duty free store. For study, we used Dazhong Dianping that largest online review site in China. Using R, we analyzed 5,659 reviews of Korea duty free store and 4,051 reviews of China duty free store. According to the analysis, Sale, Food and Membership attributes had a positive effect on star rating of Korea duty free store. Sale, Product, Airport, Food and Membership had a positive effect on star rating of China duty free store. This study has identified new factors such as food that showed the importance of providing space of restaurants while shopping at duty free store. This study has contributed to the existing literature by finding new attribute such as food. Practically, this finding will help to duty free industry workers better understand the impact of providing space of restaurants on duty free store.