• YAKHAK HOEJI
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• v.20 no.1
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• pp.63-69
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• 1976

Prednisolone tablet product design problem was structured as constrained optimization problem and subsequently solved by multiple regression analysis and Lagrangian method of optimixation. Prednisolone was the drug chosen and anhydrous lactose and corn starch were the adjuvants. The effect of anhydrous lactose and corn starch concentrations on tablet hardness, volume, disintegration time and in vitro release rate was studied. The concentrations of anhydrous lactose and corn starch used in this experiment were 30-60 percent and 5-30 percent, respectively. A full second-order (quadratic) model with all possible two-factor interactions was employed. To obtain the values of anhydrous lactose and corn starch which miniumize the in vitro : release time (t$_{60%}$) subject to the constraint on tablet hardness, disintegration time and volume, we solved the Lagrange function. Multiple correlation coefficients for the regression models were correlated at less than 0.05 level and it was found that the optimum concentrations of anhydrous lactose and corn starch were 45 percent and 21 percent, respectively.

• The Journal of Society for e-Business Studies
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• v.3 no.1
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• pp.23-41
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• 1998

In a centralized database system, all system components reside at a single platform. In recent years there has been a rapid trend toward the integration of information systems over multiple sites that are interconnected via a communication network, and users' needs are changed to integration of multiple information sites. Multi database System is one of solutions for integrating distributed heterogeneous databases. However the problems in multi database system are restriction in distributed environment support, limitation in integrating heterogeneous media type data, static integration, and data-only of integration. In order to solve these problems, we propose a hybrid database integration model, HyDIM. HyDIM is used for the integrating legacy multimedia data, adopting CORBA, MDS, and mediator. We demonstrate a prototype system far PDM application domain.

• 한국연소학회:학술대회논문집
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• 2015.12a
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• pp.219-222
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• 2015

The kinetic analysis of energetic materials using Differential Scanning Calorimetry (DSC) is proposed. Friedman Isoconversional method is applied to DSC experiment data and AKTS software is used for analysis. The frequency factor and activation energy are extracted as a function of product mass fraction. The extracted kinetic scheme does not assume multiple chemical steps to describe the response of energetic materials; instead, multiple set of Arrhenius factors are used in describing a single global step. The proposed kinetic scheme has considerable advantage over the standard method based on One-Dimenaionl Time to Explosion (ODTX). Reaction rate and product mass fraction simulation are conducted to validate extracted kinetic scheme. Also a slow cook-off simulation is implemented for validating the applicability of the extracted kinetics scheme to a practical thermal experiment.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.331-341
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• 2011

This paper considers an inbound lot-sizing and outbound dispatching problem for a single product in a thirdparty logistics (3PL) distribution center. Demands are dynamic and finite over the discrete time horizon, and moreover, each demand has a delivery time window which is the time interval with the dates between the earliest and the latest delivery dates All the product amounts must be delivered to the customer in the time window. Ordered products are shipped by multiple vehicle types and the freight cost is proportional to the vehicle-types and the number of vehicles used. First, we formulate a mixed integer programming model. Since it is difficult to solve the model as the size of real problem being very large, we design a conventional genetic algorithm with a local search heuristic (HGA) and an improved genetic algorithm called adaptive genetic algorithm (AGA). AGA spontaneously adjusts crossover and mutation rate depending upon the status of current population. Finally, we conduct some computational experiments to evaluate the performance of AGA with HGA.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.137-145
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• 2020

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.577-585
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• 2004

Storing and estimating the high order probability distribution of classifiers and class labels is exponentially complex and unmanageable without an assumption or an approximation, so we rely on an approximation scheme using the dependency. In this paper, as an extended study of the second-order dependency-based approximation, the probability distribution is optimally approximated by the third-order dependency. The proposed third-order dependency-based approximation is applied to the combination of multiple classifiers recognizing handwritten numerals from Concordia University and the University of California, Irvine and its usefulness is demonstrated through the experiments.

• The Journal of Information Systems
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• v.32 no.3
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• pp.1-17
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• 2023

Purpose The proliferation of data on the internet has created a need for innovative methods to analyze user satisfaction data. Traditional survey methods are becoming inadequate in dealing with the increasing volume and diversity of data, and new methods using unstructured internet data are being explored. While numerous comment-based user satisfaction studies have been conducted, only a few have explored user satisfaction through video and audio data. Multimodal sentiment analysis, which integrates multiple modalities, has gained attention due to its high accuracy and broad applicability. Design/methodology/approach This study uses multimodal sentiment analysis to analyze user satisfaction of iPhone and Samsung products through online videos. The research reveals that the combination model integrating multiple data sources showed the most superior performance. Findings The findings also indicate that price is a crucial factor influencing user satisfaction, and users tend to exhibit more positive emotions when content with a product's price. The study highlights the importance of considering multiple factors when evaluating user satisfaction and provides valuable insights into the effectiveness of different data sources for sentiment analysis of product reviews.

• Asian Journal of Business Environment
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• v.4 no.4
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• pp.39-49
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• 2014

Purpose - This study classified consumers' value inclination to find out ways to enhance consumers' eco-friendly product purchase intention. Further, it verified the differences among eco-friendly product purchase intentions depending upon value inclination. Research design, data, and methodology - The structured model and hypotheses were established, and 202 copies of effective questionnaires were used. In order to verify the hypotheses, we used single regression analysis, multiple regression, 3-step mediating regression, and path analysis. Results - Individualism had a positive influence upon materialism, need for uniqueness, and face wants, and collectivism had a positive influence upon materialism only. Factors of self-expressive consumption inclination had a positive influence upon eco-friendly product purchase intention, and factors of value inclination also had a positive influence. Finally, self-expressive consumption inclination mediated between value inclination and eco-friendly product purchase intention. Conclusion - Consumers with individualism inclination felt the need to connect the ownership of an eco-friendly product with their extended self and, further, it was clear that not only the government but also enterprises should build up their public image regarding eco-friendly products.

• Journal of the Korean Home Economics Association
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• v.48 no.1
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• pp.27-40
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• 2010

The objectives of this study were to investigate differences in shopping style and consumption value of apparel product type, and to establish the effects of consumption value on shopping style. A questionnaire was used to collect data from 263 women aged between 20 to 49 in Gwangju on their shopping style and consumption value according to formal and casual product type. Collected data were subjected to frequency analysis, factor analysis, ANOVA, t-test and multiple regression analysis using statistical program SPSS(version 17.0). Results showed that shopping style could be influenced by six factors: fashion-recreational, quality-brand, impulsive, confused, brand-loyal, and price conscious consumer. Clothing consumption value was influenced by five factors: emotional, functional, epistemic, social, and situational value. Shopping style and clothing consumption value were significantly different between a formal product purchaser and a casual product purchaser. Consumption value had a significant influence on shopping style of the formal product purchaser and also the casual product purchaser.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.