• Journal of Distribution Science
• /
• v.11 no.6
• /
• pp.21-30
• /
• 2013

Purpose - This paper examines product design management, the current design focus of which has shifted to the need to produce innovation applications that can effectively respond to the market's consumption changes in a timely manner. Research design, data, methodology - This study discusses several methodologies that are widely used in experimental processes, such as fuzzy theory, multi-criteria decision-making theory, and managing decision making. The designers will better understand their customers by applying these methodologies. This study examines how the current trend in product innovation design observes customer needs, controls innovation, and stimulates design ability. Results - This paper takes innovative telephone design as an experimental case to investigate how to create a product using market-oriented and customized management concepts and creative design abilities. Conclusions - If accompanied by an innovative product value chain, a product can further the development of enterprise management, now the main element of every developed country's social and economic development.

• Culinary science and hospitality research
• /
• v.23 no.5
• /
• pp.140-150
• /
• 2017

The purpose of the study was to provide useful implications for management of dessert cafe by discovering selection attributes for product satisfaction and long-term orientation. Using SPSS 22.0 and AMOS 220 Version, the collected data from customers of dessert cafe in Seoul and metropolitan areas were analyzed for frequency analysis, exploratory factor analysis, confirmatory factor analysis, reliability analysis, and covariance structure analysis. As results of hypothesis verification, firstly, service standard and product diversity had a significant effect on product satisfaction of dessert cafe. Secondly, tastes and nutrients influenced positively long-term orientation of dessert cafe. Meanwhile, service standard, mood, and product diversity did not have a significant effect on long-term orientation. Thirdly, product satisfaction affected positively long-term orientation. The results of the study provided useful implications for management of dessert cafe.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2003.06a
• /
• pp.877-880
• /
• 2003

In a competitive and globalized business environment, the need for the green products becomes stronger. To meet these trends, the environmental assessment besides delivery, cost and quality of products should be considered as an important factor in new product development phase. In this paper. a knowledge-based approximate life cycle assessment system (KALCAS) for the collaborative design environment is developed to assess the environmental impacts in context of product concept development. It aims at improving the environmental efficiency of the product using artificial neural networks consisting of high-level product attributes and LCA results. The overall framework of the collaborative environment including KALCAS is proposed. This architecture uses the CO environment to allow users on a wide variety of platforms to access the product data and other related information. It enables us to trade-off the evaluation results between the objectives of the product development including the approximate environmental assessment in the collaborative design environment.

• Journal of Information Technology Applications and Management
• /
• v.26 no.6
• /
• pp.89-101
• /
• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.12
• /
• pp.123-129
• /
• 2018

The purpose of this paper is to draw a conclusion on the impact of intermediate software on enterprise results. In this paper, product innovation and product reliability are especially used as analytical factors. An exploratory analytical study is conducted on the competitiveness of intermediate software, in the hope of gaining a new understanding of the competitiveness of intermediate software. Data are analyzed using such quantitative analytical tools as SPSS and AMOS. Using reliability analysis, validity analysis and structural equation model analysis, the final results are achieved. According to the analysis results, we can draw the following conclusions: the competitiveness of intermediate software has a positive impact on the innovation of software products. The competitiveness of intermediate software doesn't have a positive impact on the reliability of software products. Product innovation has a positive impact on enterprise results. Product reliability also has a positive impact on enterprise results. By analyzing the conclusions, we can make certain suggestions and draw implications on the competitiveness of China's software industry.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.463-473
• /
• 2021

Recently, United States Geological Survey (USGS) distributed Landsat 8 Collection 2 Level 2 Science Product (L2SP). This paper aims to derive land surface temperature from L2SP and to validate it. Validation is made by comparing the land surface temperature with the one calculated from Landsat 8 Collection 1 Level 1 Terrain Precision (L1TP) and the one from Automated Synoptic Observing System (ASOS). L2SP is calculated from Landsat 8 Collection 2 Level 1 data and it provides land surface temperature to users without processing surface reflectance data. Landsat 8 data from 2018 to 2020 is collected and ground sensor data from eight sites of ASOS are used to evaluate L2SP land surface temperature data. To compare ground sensor data with remotely sensed data, 3×3 grid area data near ASOS station is used. As a result of analysis with ASOS data, L2SP and L1TP land surface temperature shows Pearson correlation coefficient of 0.971 and 0.964, respectively. RMSE (Root Mean Square Error) of two results with ASOS data is 4.029℃, 5.247℃ respectively. This result suggests that L2SP data is more adequate to acquire land surface temperature than L1TP. If seasonal difference and geometric features such as slope are considered, the result would improve.

• Korean Journal of Computational Design and Engineering
• /
• v.16 no.5
• /
• pp.344-350
• /
• 2011

In injection molding industries, CAE analyses are generally used to find out problems predicted during the process of manufacturing. The results of CAE analyses consist of much in formation such as meshes and stress, so that the size of data is pretty large. To reduce the size of the data and to make it easy to share, the CAE result to JT translator is proposed in this paper. The translator consists of three modules to translate CAE result to JT format; Extracting module gets ASCII data of product shape and the result values of CAE analysis. Sorting module and mapping module make an element data set and JT file with the data extracted from Extracting module respectively. To the JT files, engineers are able to append product properties and their comments, so that they can share the whole history of the analysis process. In addition, our case study shows that the size of JT format is reduced by almost 90% of its original data format.

• Proceedings of the Korea Contents Association Conference
• /
• 2005.11a
• /
• pp.590-595
• /
• 2005

RFID is rising as a means which recognizes products efficiently and correctly. The use of RFID technology can reduce the expense of excess stocktaking, forgery, labor and improve supply network. RFID technology uses RF to recognize the products. EPC, a small tag, is attached to each product, which includes specific product information. Each EPC is only one and can recognize each product. If EPC tag moves to a specific location, the reader reads the tag, the data of which get to be stored in EPC information service, a data storage system. All services can approach EPC information service through web and whenever users demand services, they are provided with the data they want. This study shows an inventory control system is implemented by EPC information service.

• The KIPS Transactions:PartD
• /
• v.8D no.5
• /
• pp.573-580
• /
• 2001

The large amounts of high dimensional data contains a significant amount of noises by it own sparsity, which adds difficulties in high dimensional clustering. The CLIP is developed as a clustering algorithm to support characteristics of the high dimensional data. The CLIP is based on the incremental one dimensional projection on each axis and find product sets of the dimensional clusters. These product sets contain not only all high dimensional clusters but also they may contain noises. In this paper, we propose extended CLIP algorithm which refines the product sets that contain cluster. We remove high dimensional noises by applying two dimensional projections iteratively on the already found product sets by CLIP. To evaluate the performance of extended algorithm, we demonstrate its effectiveness through a series of experiments on synthetic data sets.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.4
• /
• pp.168-173
• /
• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.