• The Research Journal of the Costume Culture
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• v.31 no.1
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• pp.53-69
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• 2023

This study aims to analyze research trends regarding outdoor wear. For this purpose, the data-collection period was limited to January 2002-October 2022, and the collection consisted of titles of papers, academic names, abstracts, and publication years from the Research Information Sharing Service (RISS). Frequency analysis was conducted on 227 papers in total to check academic journals and annual trends, and LDA topic-modeling analysis was conducted using 20,964 tokens. Data pre-processing was performed prior to topic-modeling analysis; after that, topic-modeling analysis, core topic derivation, and visualization were performed using a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: experiential marketing and lifestyle, property and evaluation of outdoor wear, design and patterns of outdoor wear, outdoor-wear purchase behavior, color, designs and materials of outdoor wear, promotional strategies for outdoor wear, purchase intention and satisfaction depending on the brand image of outdoor wear, differences in outdoor wear preferences by consumer group. The results of topic-modeling analysis revealed that the topic, which includes a study on the design and material of outdoor wear and the pattern of jackets related to the overall shape, was the highest at 30.9% of the total topics. The next highest topic was also the design and color of outdoor wear, indicating that design-related research was the main research topic in outdoor wear research. It is hoped that analyzing outdoor wear research will help comprehend the research conducted thus far and reveal future directions.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.217-218
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• 2007

Face recognition, recognizing the human faces, is one of the most important techniques for making intelligent robot that provide commendable services to human. In this paper, we make a comparative study of Original PCA, 2DPCA, 2DPCA based algorithms and LDA in robot environment. Database is obtained through the robot's camera in a laboratory what is made like home environment for experiment.. We consider distance state what can be generated in home environment for database.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.173-178
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• 2014

In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.

• Journal of Information Science Theory and Practice
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• v.8 no.2
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• pp.6-17
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• 2020

Word similarity is often measured to enhance system performance in the information retrieval field and other related areas. This paper reports on an experimental comparison of values for word similarity measures that were computed based on 50 intentionally selected words from a Reuters corpus. There were three targets, including (1) co-occurrence-based similarity measures (for which a co-occurrence frequency is counted as the number of documents or sentences), (2) context-based distributional similarity measures obtained from a latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), and Word2Vec algorithm, and (3) similarity measures computed from the tf-idf weights of each word according to a vector space model (VSM). Here, a Pearson correlation coefficient for a pair of VSM-based similarity measures and co-occurrence-based similarity measures according to the number of documents was highest. Group-average agglomerative hierarchical clustering was also applied to similarity matrices computed by individual measures. An evaluation of the cluster sets according to an answer set revealed that VSM- and LDA-based similarity measures performed best.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.74-75
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• 2017

In this paper, we analyzed 3,668 papers of JKIICE from 2007 to 2016 using text mining methods for understanding research fields. We used web scraping programs of Python language for data collection, and utilized topic modeling methods based on LDA algorithm implemented by R language. In the results, we verified that representative research areas of JKIICE could be downsized to 9 areas only by the analysis though the submission areas were 19 areas by 2016.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.9-19
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• 2019

Service and users over the Internet are increasing rapidly. Cyber attacks are also increasing. As a result, information leakage and financial damage are occurring. Government, public agencies, and companies are using security systems that use signature-based detection rules to respond to known malicious codes. However, it takes a long time to generate and validate signature-based detection rules. In this paper, we propose and develop signature based detection rule generation and verification systems using the signature extraction scheme developed based on the LDA(latent Dirichlet allocation) algorithm and the traffic analysis technique. Experimental results show that detection rules are generated and verified much more quickly than before.

• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.189-198
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• 2009

This thesis studies two imputation methods, the MCMC method and the EM algorithm, that take care of the problem. The performance of the two methods for the linear (or quadratic) discriminant analysis are evaluated under various types of incomplete observations. Based on simulated experiments, the effect of the imputation using the EM algorithm and the MCMC method are evaluated and compared in terms of the probability of misclassification and the RMSE. This is done for the various cases of incomplete observations. The cases are differentiated by missing rates, sample sizes, and distances between two classification groups. The studies show that the probability of misclassification and the RMSE of the EM algorithm method is lower than the MCMC method. Therefore the imputation using the EM algorithm is more efficient than the MCMC method. And the probability of misclassification of the method that all vectors of observations with missing values are omitted from analysis is lower than the EM algorithm and the MCMC method when the samples size is small and the rate of missing values is extremely big.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.178-186
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• 2018

Subject classification of thesis units is essential to serve scholarly information deliverables. However, to date, there is a journal-based topic classification, and there are not many article-level subject classification services. In the case of academic papers among domestic works, subject classification can be a more important information because it can cover a larger area of service and can provide service by setting a range. However, the problem of classifying themes by field requires the hands of experts in various fields, and various methods of verification are needed to increase accuracy. In this paper, we try to classify topics using the unsupervised learning algorithm to find the correct answer in the unknown state and compare the results of the subject classification algorithms using the coherence and perplexity. The unsupervised learning algorithms are a well-known Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1168-1188
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• 2018

Social Network Aggregators are used to maintain and manage manifold accounts over multiple online social networks. Displaying the Activity feed for each social network on a common dashboard has been the status quo of social aggregators for long, however retrieving the desired data from various social networks is a major concern. A user inputs the query desiring the specific outcome from the social networks. Since the intention of the query is solely known by user, therefore the output of the query may not be as per user's expectation unless the system considers 'user-centric' factors. Moreover, the quality of solution depends on these user-centric factors, the user inclination and the nature of the network as well. Thus, there is a need for a system that understands the user's intent serving structured objects. Further, choosing the best execution and optimal ranking functions is also a high priority concern. The current work finds motivation from the above requirements and thus proposes the design of a query processing system to retrieve information from social network that extracts user's intent from various social networks. For further improvements in the research the machine learning techniques are incorporated such as Latent Dirichlet Algorithm (LDA) and Ranking Algorithm to improve the query results and fetch the information using data mining techniques.The proposed framework uniquely contributes a user-centric query retrieval model based on natural language and it is worth mentioning that the proposed framework is efficient when compared on temporal metrics. The proposed Query Processing System to Retrieve Information from Social Network (QPSSN) will increase the discoverability of the user, helps the businesses to collaboratively execute promotions, determine new networks and people. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a significant breakthrough scoring up to precision and recall respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.61-74
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• 2012

In this paper, we propose a illumination-robust face recognition system using MCS-LBP and 2D-PCA algorithm. A binary pattern transform which has been used in the field of the face recognition and facial expression, has a characteristic of robust to illumination. Thus, this paper propose MCS-LBP which is more robust to illumination than previous LBP, and face recognition system fusing 2D-PCA algorithm. The performance evaluation of proposed system was performed by using various binary pattern images and well-known face recognition features such as PCA, LDA, 2D-PCA and ULBP histogram of gabor images. In the process of performance evaluation, we used a YaleB face database, an extended YaleB face database, and a CMU-PIE face database that are constructed under varying lighting condition, and the proposed system which consists of MCS-LBP image and 2D-PCA feature show the best recognition accuracy.