• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.35-40
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• 2021

In this paper, we proposed a multi-subject-level sentiment analysis model for user reviews using the Latent Dirichlet Allocation (LDA) method targeting user-generated content (UGC). Data were collected from users' online reviews of hotels in major tourist cities in the world, and 30 hotel-related topics were extracted using the entire user reviews through the LDA technique. Six major hotel-related themes (Cleanliness, Location, Rooms, Service, Sleep Quality, and Value) were selected from the extracted themes, and emotions were evaluated for sentences corresponding to six themes in each user review in the proposed sentiment analysis model. Sentiment was analyzed using a dictionary. In addition, the performance of the proposed sentiment analysis model was evaluated by comparing the emotional values for each subject in the user reviews and the detailed scores evaluated by the user directly for each hotel attribute. As a result of analyzing the values of accuracy and recall of the proposed sentiment analysis model, it was analyzed that the efficiency was high.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.482-489
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• 2011

In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can't be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.

• Journal of the Korea Safety Management & Science
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• v.19 no.3
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• pp.137-150
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• 2017

Logistic enterprises want to be competitive enterprises in fierce logistic market and worry about the securement of discriminative competitiveness for it. The standards for the judgement of logistic industry's maintenance of competitiveness are not only economic feasibility of logistic costs but also the satisfaction of users because well-established service system for variety and enhancement of logistic needs. Some of the quality attributes sufficiently satisfy expectation of customers, but not guarantee high-quality satisfaction. Therefore, it's difficult to grasp quality attributes with the existing approach of perceived service quality. Quality attribute model suggested by Kano is widely used as the concept is accurate, there is high possibility to be used at the stage of product/service planning, and it can be easily applied. Kano model has a limitation that quality attributes are classified with mode and the differences between strong property of the quality attribute and week property in quality attributes were ignored. Therefore, Timko calculated customer satisfaction coefficient with the result of Kano's survey and effects of customer satisfaction and unsatisfaction through relations between satisfaction coefficient and unsatisfaction coefficient. The purposes of this study are to use ASC, the average of satisfaction coefficient and unsatisfaction, as the satisfaction of quality characteristics, decide the importance of quality characteristics with TOPSIS, a representative multi-standard decision-making method, and calculate strategy improvement propriety of logistic service quality.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.81-93
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• 2021

This study examined the effects of cosmetic selection attributes on consumers' purchase propensity and recommend intention. In addition, the moderating effect according to the consumer's deal proneness was verified. Finally, a multi-group analysis was conducted to verify the difference in the research model path according to the information source. Through this study, the selection attributes of consumers who purchase cosmetics were clarified. This study aims to meet the needs and demands of the related industry for more detailed and effective strategic insights by clarifying the structure of the influence of these selection attributes on purchase intention and recommendation intention according to the discount purchase intention. In order to achieve the research objectives due to this necessity, a questionnaire of 258 Korean female consumers was collected and used for research. The analysis results showed that product selection attributes, purchase intention, and recommendation intention all had a positive influence. As a result of analyzing the moderation effect according to the consumer's Deal Proneness, the results showed a moderating effect between the selection attribute and the purchase propensity, the selection attribute and the recommend intention, and the purchase propensity and the recommend intention. Finally, it was partially adopted as a result of conducting a multi-group analysis to verify whether individual paths of the model differ according to information sources.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Industrial Engineering and Management Systems
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• v.4 no.2
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• pp.129-135
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• 2005

Continuous quality improvement through process refinement is a must for survival of all industries in the contemporary market place. This is true for both manufacturing and service sectors. While manufacturing has spearheaded quality efforts, the service sector has lagged behind primarily because of inherent difficulties. Customer satisfaction is perhaps the most important performance measure for service quality. There are a number of quality dimensions in service quality, such as reliability, responsiveness, assurance, empathy, and tangibles. An issue of concern is ‘how can one have a unified measure of service quality across all the dimensions?' The intent of this paper is to determine if the Analytical Hierarchy Process (AHP) method could be used to derive a single quality index. AHP is a quantitative technique that structures a multi-attribute, multi-person and multi-period problem hierarchically so that solutions are facilitated. This paper presents the development of an AHP model and the derivation of a Quality Index through it. The model is used in a hypothetical case and a quality index was developed. The advantages of using such a technique are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.859-876
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• 2010

In this paper, we propose a hierarchical application traffic classification system as an alternative means to overcome the limitations of the port number and payload based methodologies, which are traditionally considered traffic classification methods. The proposed system is a new classification model that hierarchically combines a binary classifier SVM and Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset from the bi-directional traffic flows generated by our traffic analysis system (KU-MON) that enables real-time collection and analysis of campus traffic. The system is composed of three layers: The first layer is a binary classifier SVM that performs rapid classification between P2P and non-P2P traffic. The second layer classifies P2P traffic into file-sharing, messenger and TV, based on three SVDDs. The third layer performs specialized classification of all individual application traffic types. Since the proposed system enables both coarse- and fine-grained classification, it can guarantee efficient resource management, such as a stable network environment, seamless bandwidth guarantee and appropriate QoS. Moreover, even when a new application emerges, it can be easily adapted for incremental updating and scaling. Only additional training for the new part of the application traffic is needed instead of retraining the entire system. The performance of the proposed system is validated via experiments which confirm that its recall and precision measures are satisfactory.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1321-1329
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• 2019

With the development of social information networks, the marketing methods are also changing in various ways. Unlike successful marketing methods based on existing celebrities and financial support, Influencer-based marketing is a big trend and very famous. In this paper, we first extract influencer features from more than 54 YouTube channels using the multi-dimensional qualitative analysis based on the meta information and comment data analysis of YouTube, model representative themes to maximize a personalized video satisfaction. Plus, the purpose of this study is to provide supplementary means for the successful promotion and marketing by creating and distributing videos of new items by referring to the existing Influencer features. For that we assume all comments of various videos for each channel as each document, TF-IDF (Term Frequency and Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) algorithms are applied to maximize performance of the proposed scheme. Based on the performance evaluation, we proved the proposed scheme is better than other schemes.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.73-78
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• 2002

In this paper, we propose a constrained optimization model for conjoint analysis (a preference decomposition technique) to improve parameter estimation by restricting the relative importance of the attributes to an extent as decided by the respondents. Quite simply, respondents are asked to provide some pairwise attribute comparisons that are then incorporated as additional constraints in a linear programming model that estimates the partial preference values. This data collection method is typical in the analytic hierarchy process. Results of a simulation study show the new model can improve the predictive accuracy in partial value estimation by ordinal east squares (OLS) regression.