• Journal of the Korea Society of Computer and Information
• /
• v.19 no.5
• /
• pp.89-98
• /
• 2014

This paper suggests heuristic algorithm for single-model simple assembly line balancing problem that is a kind of NP-hard problem. This problem primarily can be solved metaheuristic method. This heuristic algorithm set the main-path that has a most number of operations from start to end-product. Then the clustering algorithm can be assigns operations to each workstation within cycle time follow main-path. This algorithm decides minimum number of workstations and can be reduces the cycle time. This algorithm can be better performance then metaheuristic methods.

• The KIPS Transactions:PartB
• /
• v.15B no.1
• /
• pp.73-80
• /
• 2008

In this paper, an advertisement-keyword finding method using document clustering is proposed to solve problems by ambiguous words and incorrect identification of main keywords. News articles that have similar contents and the same advertisement-keywords are clustered to construct the contextual information of advertisement-keywords. In addition to news articles, the web page and summary of a product are also used to construct the contextual information. The given document is classified as one of the news article clusters, and then cluster-relevant advertisement-keywords are used to identify keywords in the document. We could achieve 21% precision improvement by our proposed method.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.305-316
• /
• 2021

The objective is to recognize the better opportunities from targeted reveal advertising, to show a banner ad to the consumer of online who is most expected to obtain a preferred action like signing up for a newsletter or buying a product. Discovering the most excellent commercial impression, it means the chance to exhibit an advertisement to a consumer needs the capability to calculate the probability that the consumer who perceives the advertisement on the users browser will acquire an accomplishment, that is the consumer will convert. On the other hand, conversion possibility assessment is a demanding process since there is tremendous data growth across different information dimensions and the adaptation event occurs infrequently. Retailers and manufacturers extensively employ the retail services from internet as part of a multichannel distribution and promotion strategy. The rate at which web site visitors transfer to consumers is low for online retail, out coming in high customer acquisition expenses. Approximately 96 percent of web site users concluded exclusive of no shopper purchase[1].This category of conversion rate is collected from the advertising of social media sites and pages that dataset must be estimating and assessing with the concept of big data clustering, which is used to group the particular age group of people along with their behavior. This makes to identify the proper consumer of the production which leads to improve the profitability of the concern.

• Proceedings of the ESK Conference
• /
• 1997.10a
• /
• pp.372-375
• /
• 1997

Classivication scheme of usability element for the evaluation of consumer electronic product was developed in this study. Using hierachical structuring and clustering methods, usability element of consumer products interface is developed both for the performance perspective and for the image/appeal perspective of a product. Perfoormance element included variables such as simplicity, directness, learn- ability, flexibility, user support and effectiveness. Image/Appeal element included variables such as sensibility, descriptive impression, evaluation of appeal, and attitude towards the product. The classifi- cation scheme developed in this study is found to be comprehensive and robust relative to other existing paradigms. They can be effectively used and applied for the usability evaluation of consumer electronic products.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.1
• /
• pp.35-42
• /
• 2016

The core of the machine vision that is frequently used at the pre/post-production stages is a marker alignment technology. In this paper, a method to detect the angle and position of a product at high speed by use of a unique pattern present in the marker stamped on the product, and calibrate them is proposed. In the proposed method, to determine the angle and position of a marker, the candidates of the marker are extracted by using a variation of the integral histogram, and then clustering is applied to reduce the candidates. The experimental results revealed about 5s 719ms improvement in processing time and better precision in detecting the rotation angle of a product.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.11a
• /
• pp.271-275
• /
• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this Purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between $0\∼1$. Second, RDB and SQL(Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS(Knowledge Management Systems)

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.4
• /
• pp.353-359
• /
• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).

• Journal of the Ergonomics Society of Korea
• /
• v.17 no.1
• /
• pp.47-54
• /
• 1998

Product design and development is very important process in enterprise activities. Reducing development time and reflecting consumer's needs is required to product design and development for increasing benefit and decreasing cost. Human sensibility ergonomics is one of the important technology of R&D in product development. However, the subjective method of human sensibility ergonomics has several problems to analyze and to Quantify experimental data and objective method of human sensibility ergonomics is still in process on study. In this research, new analyzing method is proposed for the subjective human sensibility ergonomics applied with fuzzy set theory. What is the useful theory for controlling uncertain type of information like human mind? This approach is more effective method for analyzing consumer's needs for product design and development process. At collecting needs, certainty scale is added for adapting hedge of fuzzy function. Using a kind of union operator, synthesize each item to analyze identification of each item with fuzzy hamming distance. Identification of analysis is classified with the relational weight using Relationship Chart Method, and is drawn the relationship diagram for clustering each item. A case study with sample test is conducted and demonstrated with this suggested method for more effective way.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.3
• /
• pp.331-343
• /
• 2002

This paper proposes a fuzzy rule-based model as a means to build usability models between emotional satisfaction and design variables of consumer products. Based on a subtractive clustering algorithm, this model obtains partially overlapping rules from existing data and builds multiple local models each of which has a form of a linear regression equation. The best subset procedure and cross validation technique are used to select appropriate input variables. The proposed technique was applied to the modeling of luxuriousness, balance, and attractiveness of office chairs. For comparison, regression models were built on the same data in two different ways; one using only potentially important variables selected by the design experts, and the other using all the design variables available. The results showed that the fuzzy rule-based model had a great benefit in terms of the number of variables included in the model. They also turned out to be adequate for predicting the usability of a new product. Better yet, the information on the product classes and their satisfaction levels can be obtained by interpreting the rules. The models, when combined with the information from the regression models, are expected to help the designers gain valuable insights in designing a new product.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.1
• /
• pp.139-148
• /
• 2015

In this paper, a new algorithm is proposed for detecting the product number of each thick plate and extracting each character of the product number from a image which contains several thick plates. In general, a image of thick plates contains several steal plates. To obtain the product number from the image, we first need to separate each plate. To do so, we use the line edges of thick plates and a clustering algorithm. After separating each plate, background parts are eliminated from the image of each plate. Background parts of an individual thick plate image consist of the dark part of steel and the white part of paint which is used for printing the product number. We propose a two-tiered method where dark background parts are first eliminated and then white parts are eliminated. Finally, each character is extracted from the product number image using the characteristics of product number. The results of the experiments on the various steal plates images emphasize that the proposed algorithm detects each thick plate and extracts the product number from a image effectively.