• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.188-194
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• 2015

This paper proposes a generalized regression neural network (GRNN) based algorithm for data interpolation and design optimization of brushless dc (BLDC) motor. The procedure makes use of magnet length, stator slot opening and air gap length as design variables. Cogging torque and average torque are treated as performance indices. The optimal design necessitates mitigating the cogging torque and maximizing the average torque by varying design variables. The data set for interpolation and ensuing design optimisation using GRNN is obtained by modeling a standard BLDC motor using finite element analysis (FEA) tool MagNet 7.1.1. The performance indices of the standard motor obtained using FEA are validated with an experimental model and an analytical method. The optimal design is authenticated using particle swarm optimization (PSO) algorithm and the performance indices of the optimal design obtained using GRNN is validated using FEA. The results indicate the suitability of GRNN as an interpolation and design optimization tool for a BLDC motor.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.273-276
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• 2004

Multidisciplinary Design Optimization (MDO) is an individual and parallel design framework applied in designing large and complex systems. for successful implementation of MDO framework it is essential to manage data in efficient and integrated manner. In this study, we present a case study to implement database to support designing super high temperature vacuum furnace with MDO technology. For that purpose we first extract required data based on the analysis of design process and then data flows between different programs are analyzed. Finally an E-R diagram is presented to design database schema.

• Fashion & Textile Research Journal
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• v.12 no.2
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• pp.172-178
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• 2010

The purpose of this study was to explore relationships among technology development strategy, marketing competence, knowledge management competence, and company performance of textile and clothing companies. Survey data collected from 187 employees in the textile and clothing companies were analyzed by descriptive statistics, factor analysis, reliability analysis, correlation analysis, and multiple regression analysis. In results, certain levels of correlations were found among technological development strategy, marketing competence, knowledge management competence, and company performance. Specifically, technological gap which was one of the technology development strategy factors was a variable significantly affecting innovation performance and financial performance of textile and clothing companies. Knowledge management competence affected innovation performance whereas marketing competence affected financial competence of textile and clothing companies.

• Journal of the Korea Convergence Society
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• v.5 no.3
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• pp.45-51
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• 2014

Computer science and computing technologies are applied into mathematical, science, medical, engineering and educational applications. The models are used to solve the issues in all the domains. Educational systems are used top down, bottom up, Gap Analysis model in the educational learning system. Educational learning process integrated with Lerner, content and the methodology. The Learners and content are same in the educational system or similar courses but the teaching methodologies are differing one with another. The determinations of teaching methodologies are based on the factors related to that particular model or subject. The learning model influencing determinations are made by the surveys, analysis and observation of data to maximize the learning outcome. This paper attempted to evaluate the SNE learners cognitive using NASA Scaling.

• The Research Journal of the Costume Culture
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• v.18 no.3
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• pp.569-587
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• 2010

In the field of fashion in this modern era, the tendency of diversification has created a variety of hybrid patterns and this tendency is being accelerated simultaneously by the hybrid trend. This research aims to analyze the aesthetic features of hybrid fashion in the modern "Sports Inspired Fashion(SIF)." We analyze fashion in the past 10 years from 2001 S/S to 2010 S/S. We focus on the four largest worldwide collections from New York, Paris, Milano, and London and concentrate on analyzing the contents from 38 volumes of Gap Press magazine. To accomplish our goal of study, we first define the concept of sports-inspired fashion and propose a framework of analysis to study hybrid patterns by reviewing the previous studies on hybrid patterns. Second, we analyze a wide range of sports-inspired fashion examples that have appeared in Gap Press magazines for the past 10 years(those which have been inspired by sports uniforms and training wears). Third, we analyze and classify the hybrid patterns of sports-inspired fashion. The results of our research are as follows. We have collected a total of 534 SIF works from the whole set of 61359 pictures and examined 23 sports fields. In terms of seasonal changes, the SIF works were found the most in the spring collections. Then, we have identified 25 hybrid patterns. The time hybrid pattern comprises more than half of the data(58.2%). The class-culture hybrid patterns comprise 18.8%, while the gender hybrid patterns comprise 18.2%. However, the intercultural hybrid patterns were rarely found, comprising merely 5% of the data. Our analysis confirms that sports and sports wear fashion are changing and developing in truly diverse ways in this modern era. This trend has continued to influence the high fashion in the modern age and is expected to exert a continuous impact on the change of fashion in the future.

• Health Policy and Management
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• v.33 no.3
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• pp.338-354
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• 2023

Background: The influence of social capital on the spread of coronavirus disease 2019 (COVID-19) and related behaviors and perceptions has been recognized during the COVID-19 pandemic. This study aims to analyze the trends in social capital using primary data from the Korean Community Health Survey, which is the only available source in Korea for local-level social capital analysis. It also investigates the relationship between various variables, including social capital, as factors influencing COVID-19-related behaviors and perceptions. Methods: The study analyzed the temporal trends of social capital using raw data from four community health surveys conducted in 2017, 2019, 2020, and 2021. A multilevel analysis was conducted to examine the relationship between social capital and COVID-19-related behaviors and perceptions following the onset of the COVID-19 pandemic in 2020. Results: Social capital consists of trust, bonding social capital, and bridging social capital. Within the trust sub-factor, trust in neighbors (Trust-1) declined after the COVID-19 pandemic, whereas trust in safety and general environment (Trust-2) and trust in medical services and public transportation (Trust-3) increased. Additionally, the gap between municipalities narrowed. COVID-19-related behaviors and perceptions, such as adherence to COVID-19 prevention measures, return to normal activities, and fear of COVID-19, showed improvement in 2021 compared to the previous year. Individual-level trust in neighbors was associated with reduced fear of COVID-19, while community-level trust in neighbors was associated with increased fear of COVID-19. Conclusion: Social capital plays a role in mitigating public health crises, and it is necessary to implement active policies that address the gap in social capital between metropolitan and rural areas. Strengthening risk communication regarding emerging infectious diseases such as COVID-19 is crucial.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.154-173
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• 2010

Database outsourcing is unavoidable in the near future. In the scenario of data stream outsourcing, the data owner continuously publishes the latest data and associated authentication information through a service provider. Clients may register queries to the service provider and verify the result's correctness, utilizing the additional authentication information. Research on On-line Stream Analytics (OLSA) is motivated by extending the data cube technology for higher multi-level abstraction on the low-level-abstracted data streams. Existing work on OLSA fails to consider the issue of database outsourcing, while previous work on stream authentication does not support OLSA. To close this gap and solve the problem of OLSA query authentication while outsourcing data streams, we propose MDAHRB and MDAHB, two multi-dimensional authentication approaches. They are based on the general data model for OLSA, the stream cube. First, we improve the data structure of the H-tree, which is used to store the stream cube. Then, we design and implement two authentication schemes based on the improved H-trees, the HRB- and HB-trees, in accordance with the main stream query authentication framework for database outsourcing. Along with a cost models analysis, consistent with state-of-the-art cost metrics, an experimental evaluation is performed on a real data set. It exhibits that both MDAHRB and MDAHB are feasible for authenticating OLSA queries, while MDAHRB is more scalable.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.109-121
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• 2023

This study aims to address the literature gap by examining the direct relationship between big data analytics capability, marketing innovation, and organizational innovations. Additionally, this study would examine big data analytics capability as the antecedent for both innovation types and how these relationships influence firm performance. The research model is developed based on the integration of resource-based view and knowledge-based view theories. The quantitative method is used as the research methodology for this study. Based on a purposive sampling method, a total of 115 questionnaires were obtained from managers in star-rated hotels located in Malaysia. Partial least square structural equation modeling (PLS-SEM) is utilized for the data analysis. The result shows that big data analytics capability positively affects marketing and organizational innovations. The findings show that big data analytics capability and organizational innovation positively influence firm performance. Nonetheless, the result revealed that marketing innovation is not positively related to firm performance. The findings also indicate to hotel managers the importance of big data analytic capability and the resources required to build and develop this capability. The contributions from this study enrich the literature on big data and innovation, which is particularly limited in the hospitality and tourism context.

• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• The Journal of Industrial Distribution & Business
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• v.9 no.2
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• pp.17-26
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• 2018

Purpose - The purpose of this study is to provide a formalized process of decision making for companies or organizations that need to make various decisions in the age of uncertainty. Therefore, this study aimed to proposes a strategic decision-making approach citing the relatively easily accessible using IPA(important-performance analysis) and SWOT/AHP analysis. Research design, data, and methodology - To be specific, the first step is to derive necessary attributes and conduct IPA. The second step is to subdivide the IPA results into internal strength and weakness factors and the external opportunity and threat factors, hierarchize those factors, and weight them accordingly. The third step is to build a causality model to propose a method of supporting a rational decision making. Results - The foregoing approach seems to facilitate the diversification of decision-making strategies by helping businesses or organizations to measure and analyze the attributes needed for certain decisions. Additionally, the perceived importance and satisfaction (or achievement) usage of those derived attributes can be used as the reference data for SWOT/AHP analysis. Conclusions - The proposed stepwise approach is applicable to businesses or organizations in need of making stepwise decisions in line with their retained competencies in comparison to conventional or intuitive decision-making practices.