• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.1077-1080
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• 2003

본 연구는 룰들의 각 필드들을 index하여 곱한 cross-product 테이블을 이용한 packet classification 알고리즘에 대해 연구하고 그 것의 성능을 평가하고 분석한다. 현재 Packet Classification은 Packet Filtering, Policy Routing, Accounting & Billing, Traffic Rate Limiting, Traffic Shaping, 등등의 서비스를 위한 가장 핵심적인 작업이다. 그러나 이들을 빠르게 서비스하는 알고리즘은 아직 존재하지 않는다. 단지 하드웨어 TCAM 을 이용해서 작은 룰들에 대한 처리만이 어느 정도 가능한 실정이다. 이에 본 연구는 소프트웨어를 이용한 cross-product 알고리즘의 효용성을 가늠하고자 연구하고 이를 실제 구현해 평가하고자 한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.77-82
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• 2001

A study was conducted to develop a better classification method of Consumer Attributes that can enhance user-centered product design process. A modified QFD(Quality Function Deployment) survey form based upon Fuzzy set theory was proposed which contains 9 steps of importance level, and Certainty and Necessity function to improve the reliability of extracted consumer attributes. To verify the betterment and advantage of proposed classification method, a series of questionnaire survey was performed. Thirty male and 30 female university students were participated in the survey using a VCR as a target product. The result of the study showed that 80% of subjects were preferred the proposed classification over existing method. A cluster analysis was performed to further verify the betterment of the proposed method. The result also supported that the proposed classification method is more reliable and enhanced method in extracting consumer attributes and can be applied in the product design.

• Korean Journal of Construction Engineering and Management
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• v.9 no.1
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• pp.126-133
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• 2008

Current changing to the customer-oriented market naturally causes suppliers to meet an age of competition on the quality. In order to plan housing meeting this quality competitiveness era, I set up the type classification system of quality differentiation for the unit of apartment housing by executing differentiation cases of unit quality and type analysis of the object. The system is consist of 3 classification systems by quality element, user convenience element and product element as follows: First element is to classify quality element on the basis of plane and interior elements, architectural elements and second one is user convenience element relating facility to classify environment-oriented, safety, energy saving and convenience. The other one is the product element to classify furniture, installing product and convenient product. I believe that this classification system will be useful to determine any classification elements of product for product positioning and product planning in the stage of marketing planning of apartment housing in the future.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.107-126
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• 2021

In e-commerce, rapid and accurate automatic product classification according to product information is important. Recent developments in deep learning technology have been actively applied to automatic product classification. In order to develop a deep learning model with good performance, the quality of training data and data preprocessing suitable for the model are crucial. In this study, when categories are inferred based on text product data using a deep learning model, both effects of the data preprocessing and of the selection of training data are extensively compared and analyzed. We employ our CNN model as an example of deep learning model. In the experimental analysis, we use a real e-commerce data to ensure the verification of the study results. The empirical analysis and results shown in this study may be meaningful as a reference study for improving performance when developing a deep learning product classification model.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.128-135
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• 2018

Currently, the government selects products that are likely to cause harm to consumers as safety management targets and classifies them into three types: safety certification, safety confirmation, and supplier conformity verification. In addition, the government conducts safety surveys on products in circulation or accident products, and recalls products that are of great concern to consumer risks. In this paper, we have developed RAP (Risk Assessment method based on Probability), which is a probability based product risk assessment method, for the classification of safety management type of product and safety investigation, and have shown an application example. In this process, information is used for the CISS (Consumer Injury Surveillance System) of the Korean Consumer Agency. In addition, we apply the cluster analysis to classify the current supervised children products into three groups. Then, we confirm the effectiveness of RAP by comparing the result of RAP application, cluster analysis result and current safety management classification type. Also, we recognize the need to review the current safety management classification criteria for classifying products into three types.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.254-262
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• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.227-234
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• 2017

In this paper, we propose a system whereby one can automatically classifies categories based on image data of the products for a shopping mall platform. Many products sold within internet shopping malls are classified their category defined by the same use of product names and products. However, it is difficult to search by category classification when the classification of the product is uncertain and the product classified by the shopping mall seller judgment is different from the purchasing user judgment. We proposes classification and retrieval method by Deep Learning technique solely using product image. The system can categorize products by using their images and its speed and accuracy are quantified using test data. The performance is evaluated with the test data. In addition, its usability is tested with the participants.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.109-118
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• 2007

This paper proposed a method that can be simply analyzed instead of the basic general Fuzzy rule that its insufficient characters are cut out. Based on the proposed method. Rough sets are used to eliminate the incomplete attributes included in the rule and also for a classification more precise; the agreement of the membership function's output extracted the maximum attributes. Besides, the proposed method in the simulation shows that in order to verify the validity, compare the max-product result of fuzzy before and after reducing rule hosed on the rice taste data; then, we can see that both the max-product result of fuzzy before and after reducing rule are exactly the same; for a verification more objective, we compared the defuzzificated real number section.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.111-120
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• 2005

Software development creates many product that class components, Class Diagram, form, object, and design pattern. So this Paper suggests Index/XML Sequence Relation information system for product abstraction and classification, the system of design product Sequence Relation abstraction which can store, reuse design patterns in the meta modeling database with pattern Relation information. This is Index/XML Sequence Relation system which can easily change various relation information of product for product abstraction and classification. This system designed to extract and classify design pattern efficiently and then functional indexing, sequence base indexing for standard pattern, code indexing to change pattern into code and grouping by Index-ID code, and its role information can apply by structural extraction and design pattern indexing process. and it has managed various products, class item, diagram, forms, components and design pattern.

• Journal of Korea Technology Innovation Society
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• v.6 no.1
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• pp.110-124
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• 2003

In this study, we establish technology classification system for twelve material and component(MC) areas to sup-port systematic information services for MCT-20l0 which is supported by Korean government. We propose some design principles for MC technology classification system. The principles are suggested by considering of the characteristics of MC classification, regarding with scope, originality, hierarchy, relationship between technology classification and product classification, duplication and complex structure, use of information system, and life cycle of the classification system.