• 통합자연과학논문집
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• 제14권2호
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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.

• 한국정밀공학회지
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• 제28권4호
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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.

• 대한안전경영과학회지
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• 제19권3호
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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.

• 한국콘텐츠학회논문지
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• 제21권6호
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• pp.81-93
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• 2021

본 연구는 화장품 선택속성 및 소비자의 구매의도와 추천의도 간의 영향 관계를 살펴보았다. 또한 소비자의 할인추구성향에 따른 조절효과를 검증하였다. 마지막으로 정보원천에 따라 연구 모형 경로에 대한 차이를 검증하기 위해 다중집단분석을 실시하였다. 본 연구를 통해서 화장품을 구매하는 소비자의 선택속성을 명확화하고, 이러한 선택속성이 할인추구성향에 따른 구매의도와 추천의도에 미치는 영향구조를 정보원천별로 규명함을 통해 보다 세부적이고 실효성 높은 전략적 통찰에 목말라 하는 산업업계의 필요와 요구에 부응하고자 한다. 이러한 필요성에 기인한 연구목적 달성을 위해 한국 여성 소비자 258명의 설문지를 수집하고 연구에 활용하였다. 분석결과는 제품 선택속성, 구매의도와, 추천의도 모두 긍정적인 영향관계에 있는 것으로 나타났다. 또한 소비자의 할인추구성향에 따른 조절효과 분석결과 화장품 선택속성과 구매의도 사이, 선택속성과 추천의도 사이, 구매의도와 추천의도 사이에서 조절효과를 나타내는 결과를 보였다. 마지막으로 모형의 개별 경로들이 정보원천에 따라 차이가 있는지를 검증하기 위해 다중집단분석을 실시한 결과 부분적으로 채택되었다.

• 한국공작기계학회논문집
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• 제17권1호
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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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• 제4권2호
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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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• 제4권5호
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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.

• 한국정보통신학회논문지
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• 제23권11호
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• pp.1321-1329
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• 2019

소셜 정보망의 발달로 마케팅의 방법도 다양하게 변화되고 있다. 기존의 유명인, 경제적 지원 기반의 성공적인 마케팅방법론과 달리, 최근 인플루언서 기반 유튜브 마케팅이 큰 대세를 이루고 있다. 본 논문 에서는 처음으로 유튜브 양적 정보 및 댓글분석 기반 다각도 질적 분석을 활용하여 54개 이상의 유튜브 채널에서 인플루언서 특징을 추출하고 대표적인 주제들을 모델링하여 개인 맞춤형 영상 만족도 극대화는 물론 기업체가 새로운 아이템을 마케팅 할 때 기존의 인플루언서 특징을 참고하여 새로운 아이템의 영상을 제작하고 배포함으로써 성공적인 홍보 효과를 누릴 수 있도록 보조 수단 제공을 목적으로 한다. 유튜브 채널 별 다양한 영상의 모든 댓글을 각 문서로 가정하고 TF-IDF 및 LDA알고리즘을 적용하여 성능 극대화 향상을 보였다.

• 비파괴검사학회지
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• 제28권3호
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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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• 제1권1호
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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.