• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.15-25
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• 1999

We propose an extended relational data model which can represent the reliability of data. In this paper, the reliability of data is defined as the reliability of the source, from which the data originated. We represent the reliability of data at the level of attribute values, instead of tuples, then define the selection, product and join operators.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9A
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• pp.788-794
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• 2005

In this paper, we focus on the potential of dual polarized antennas in mobile system. thus, this paper designs exact dual polarized channel with Spatial Channel Model (SCM) and investigates the performance for certain environment. Using proposed the channel model; we know estimates of the channel capacity as a function of cross polarization discrimination (XPD) and spatial fading correlation. It is important that the MIMO channel matrix consists of Kronecker product dividable spatial and polarized channel. Through the channel characteristics, we propose an algorithm for the adaptation of transmit antenna configuration to time varying propagation environments. The optimal active transmit antenna subset is determined with equal power allocated to the active transmit antennas, assuming no feedback information on types of the selected antennas. We first consider a heuristic decision strategy in which the optimal active transmit antenna subset and its system capacity are determined such that the transmission data rate is maximized among all possible types. This paper then proposes singular values decision procedure consisting of Kronecker product with spatial and polarize channel. This method of singular value decision, which the first channel environments is determined using singular values of spatial channel part which is made of environment parameters and distance between antennas. level of correlation. Then we will select antenna which have various polarization type. After spatial channel structure is decided, we contact polarization types which have considerable cases It is note that the proposed algorithms and analysis of dual polarized channel using SCM (Spatial Channel Model) optimize channel capacity and reduce the number of transmit antenna selection compare to heuristic method which has considerable 100 cases.

• Knowledge Management Research
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• v.19 no.2
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.2
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• pp.213-223
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• 2013

This study presents the process of fashion item development with velvet through creative thinking methods. Creativity is one of the most important requirements for a successful job career and education enhancing creative thinking is needed in the area of fabrication, product design, and marketing strategy development. Velvet was selected as a research stimulus because it is a luxurious fabric with various differential properties such as a soft touch, unique luster, excellent drapability, and fine physical properties. The research methodology included creative thinking methods review, the selection of the tools, idea sourcing and listing, sequential idea evaluation and sample product making. After review of the various creative thinking methods, a combination method and forced connection method were employed as research tools to confirm the usefulness of creative thinking training because of their independence of use and application simplicity. A total of 12 university students participated as subjects in this research. After some training, each student derived ten ideas for velvet products that utilized a combination method and forced connection method. A total of 120 ideas were evaluated for novelty, technical possibility, practicality, and marketability; subsequently, 24 ideas were adopted and developed as sample products. The effectiveness of creativity education in fabrication and product design classes was verified through the whole process of product planning.

• Journal of Communications and Networks
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• v.13 no.3
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• pp.232-239
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• 2011

This paper investigates the user selection problem of successive zero-forcing precoded multiuser multiple-input multiple-output (MU-MIMO) downlink systems, in which the base station and mobile receivers are equipped with multiple antennas. Assuming full knowledge of the channel state information at the transmitter, dirty paper coding (DPC) is an optimal precoding strategy, but practical implementation is difficult because of its excessive complexity. As a suboptimal DPC solution, successive zero-forcing DPC (SZF-DPC) was recently proposed; it employs partial interference cancellation at the transmitter with dirty paper encoding. Because of a dimensionality constraint, the base station may select a subset of users to serve in order to maximize the total throughput. The exhaustive search algorithm is optimal; however, its computational complexity is prohibitive. In this paper, we develop two low-complexity user scheduling algorithms to maximize the sum rate capacity of MU-MIMO systems with SZF-DPC. Both algorithms add one user at a time. The first algorithm selects the user with the maximum product of the maximum column norm and maximum eigenvalue. The second algorithm selects the user with the maximum product of the minimum column norm and minimum eigenvalue. Simulation results demonstrate that the second algorithm achieves a performance similar to that of a previously proposed capacity-based selection algorithm at a high signal-to-noise (SNR), and the first algorithm achieves performance very similar to that of a capacity-based algorithm at a low SNR, but both do so with much lower complexity.

• The Journal of Society for e-Business Studies
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• v.4 no.2
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• pp.161-175
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• 1999

CALS is a strategy to share integrated product data through a set of standards to achieve efficiencies in business and operational mission areas. In this paper, we studied current status for CALS standard and then analyzed the case of US DoD. The results can be summarized as implementing for two major standard in defense CALS system. They are STEP and XML. Korea Defense can be used to set direction for CALS standard implementation and standard selection process based on this paper's recommendations.

• Journal of Information Technology Applications and Management
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• v.28 no.2
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• pp.35-41
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• 2021

This study sought to find importance factors for the quality of Mongolian consumers' evaluation for fresh food through online purchase. To compare the priorities of factors determining the choice of service quality of online purchase for fresh food, this study used a decision model using the appropriate Analytic Hierarchy Process (AHP). Through a prior study, the main factors of quality were classified as delivery quality, product quality, marketing, and system quality, respectively According to the results of AHP the quality of deliver information and deliver duration time under delivery quality are the main factor, followed by hygiene and freshness of product quality were the next highest. When consumers purchase fresh food through an online market. they considered deliver information, delivery duration time, hygiene, freshness, and deliver cost as important factors.

• The Korean Journal of Food & Health Convergence
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• v.9 no.3
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• pp.11-17
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• 2023

This study aims to investigate the effect of the selection attribute of Newtro (New + Retro) product marketing, which is a recent topic in the food industry, on consumer satisfaction. Newtro marketing, also known as Newtro-style marketing, is a marketing strategy that emerged in South Korea, particularly in the food industry. Newtro marketing aims to appeal to consumers' nostalgia for the past while incorporating contemporary elements. As a research method, a survey was conducted on the importance of selection attributes and repurchase intention of Newtro food for consumer groups of various age groups ranging from teenagers to those in their 40s or older. To analyze the demographic content of the sample, frequency analysis of the SPSS statistical package was performed, and structural equation modeling was performed using the AMOS program for confirmatory factor analysis and discriminant validity analysis. The analysis results are as follows. First, Package Design, Perceived Healthiness, and Emotional Taste, optional attributes of Newtro marketing, significantly affected satisfaction. Second, satisfaction was found to have a statistically significant effect on repurchase intention. However, Functional Flavor did not statistically affect satisfaction. This study empirically analyzed the importance of consumers' selection attributes for the recently popular food Newtro marketing and suggested implications.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.6
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• pp.967-978
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• 2016

This study examines consumers' multi-channel choices in the search phase and purchasing phase stage according to price sensitivity and product characteristics in order to propose a multichannel strategy. For the research, one-way ANOVA, t-test, clustering analysis, and crosstabs are used for the descriptive analysis of 317 surveys on men and women conducted in 2014. The findings are as follows. First, consumers that both experience goods and search goods rely on surrounding advice as well as a search channel regardless of price sensitivity. Second, channel selection differs by price sensitivity when it comes to purchasing phase. Consumers with high price sensitivity tend to purchase from online channels; however, consumers with low price sensitivity tend to purchase from off line channels in cases of search goods. Meanwhile, cases of experience goods have no meaningful result. Third, consumers are divided into 3 groups by the tendency of channel selection. In case of experience goods, search channel choice is aligned with purchasing channel; however, search channel choice is not aligned with purchasing channel in search goods. This study provides clear information on fashion consumers' behavior on multi-channel choices compared to ones for search goods consumers on strategic strategies for fashion companies.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.