• Journal of Families and Better Life
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• v.25 no.3 s.87
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• pp.25-44
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• 2007

The purpose of this study was to classify the financial strategies and investigate major determinants of the financial performance in elderly households. This study used the data of 4,577households with all ages and 1,255 elderly households were from the Korean Labor and Income Panel Study(2000, 2003). The data were analyzed by various statistical methods such as frequency, mean-test, Duncan's multiple range test, k-mean cluster analysis and regression. The major findings were as follows: First, the classified household financial strategy types were Residual(44.3%), Financial Assets(24.0%), Informal Institutional(19.7%), Diversified Portfolio(7.6%), Real Estate(4.5%). Second, the criteria of classification of the financial strategies were relative, not absolute. Third, elderly households that employed a financial assets had the greatest financial performance (62,320,000 won net gain). Households with all ages that employed a diversified portfolio strategy had the greatest financial performance (98,360,000 won net gain). Forth, the determinants of the financial performance were significantly different according to the types of financial strategy.

• Journal of Korean Society for Quality Management
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• v.41 no.1
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• pp.109-118
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• 2013

Purpose: Given the several proper models for given mixture components-process variables experimental data, we propose a strategy to find the optimal condition in which the performance of the responses is well-behaved under those models. Methods: Given the mixture experimental data with process variables, first we choose the reasonable starting models among the class of admissible product models based on the model selection criteria and then, search for the candidate models that are the subset models of the starting model by the sequential variable selection method or all possible regressions procedure. Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. Results: We propose a strategy to find the optimal condition in which the performance of the responses is well-behaved under those good candidate models by adopting the optimization methods developed in multiple responses surface methodology. Conclusion: A strategy is proposed to find the optimal condition in which the performance of the responses is well-behaved under those proper combined models. This strategy to find the optimal condition is illustrated with the example in this paper.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1214-1219
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• 2011

Fiber reinforced shotcrete began to be used in tunnel constructions because it facilitates and expedites the construction process, and improves reinforcement properties. As one of the most widely used forms of shotcrete used in tunneling, steel fiber reinforced shotcrete offers excellent strength and ductility and allows quick reinforcement. However, steel fibers tend to lump together in cement matrix, and low levels of water and acid resistance cause corrosion in steel fiber, resulting in cracks and delamination. In particular, rebound and backlash of steel fiber is significantly increased during steel fiber reinforced shotcrete construction, compromising the flexural toughness and quality of shotcrete. In order to resolve the problems associated with steel fiber reinforced shotcrete and improve the application, durability, and cost-effectiveness of shotcrete, this paper proposes methods for manufacturing and constructing tunnels with multiple polyamide fiber reinforced shotcrete. We performed experiments to evaluate the performance of the proposed shotcrete, and the experimental results indicate that the multiple polyamide fiber reinforced shotcrete proposed in this paper offers outstanding performance that meets various construction design criteria.

• Ceramist
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• v.23 no.1
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• pp.54-88
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• 2020

Global effort has resulted in tremendous progress with energy harvesters that extract mechanical energy from ambient sources, convert it to electrical energy, and use it for systems such as wrist watches, mobile electronic devices, wireless sensor nodes, health monitoring, and biosensors. However, harvesting a single energy source only still pauses a great challenge in driving sustainable and maintenance-free monitoring and sensing devices. Over the last few years, research on high-performance mechanical energy harvesters at the micro and nanoscale has been directed toward the development of hybrid devices that either aim to harvest mechanical energy in addition to other types of energies simultaneously or to exploit multiple mechanisms to more effectively harvest mechanical energy. Herein, we appraise the rational designs for multiple energy harvesting, specifically state-of-the-art hybrid mechanical energy harvesters that employ multiple piezoelectric and triboelectric mechanisms to efficiently harvest mechanical energy. We identify the critical material parameters and device design criteria that lead to high-performance hybrid mechanical energy harvesters. Finally, we address the future perspectives and remaining challenges in the field.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.59-67
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• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.442-460
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• 2021

Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE⁺ are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.04a
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• pp.102-109
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• 1999

Two phase simulated annealing algorithm is presented as a structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. Univariate searching algorithm is adopted for automatic selection of initial values of design variables for SA algorithm. The proper values of cooling factors and reasonable stopping criteria for optimum design of space truss structures are proposed to enhance the performance of optimization process. Optimum weights and design solutions are presented for two well-blown example structures and compared with those reported in the literature.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.165-179
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• 2010

Recently, the processing of data streams such as stock quotes, buy-sell orders, and billing records becomes more important in e-Business environments. Especially, the use of skyline queries over data streams is rapidly increasing to support multiple criteria decision making. Given a set of multi-dimensional tuples, a skyline query retrieves a set of tuples which are not dominated by other tuples. Although there has been much work on processing skyline queries over static datasets, there has been relatively less work on processing multiple skyline queries over data streams. In this paper, we propose an efficient method for processing multiple continuous skyline queries over data streams. The proposed method efficiently identifies which tuple is a skyline tuple of which query, resulting in a lower cost of processing multiple skyline queries. Through performance evaluation, we show the performance advantage of the proposed method.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.277-280
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• 2006

Most real world design evaluation and risk-based decision support combine quantitative and qualitative (linguistic) variables. Decision-making based on conventional mathematics that combines qualitative and quantitative concepts always exhibit difficulty in modelling actual problems. The successful selection process for choosing a design/procurement proposal is based on a high degree of technical integrity, safety levels and low costs in construction, corrective measures, maintenance, operation, inspection and preventive measures. However, the objectives of maximising the degree of technical performance, maximising the safety levels and minimising the costs incurred are usually in conflict, and the evaluation of the technical performance, safety and costs is always associated with uncertainties, especially for a novel system at the initial concept design stage. In this paper, a design-decision support framework using a composite structure methodology grounded in approximate reasoning approach and evidential reasoning method is suggested for design evaluation of machinery space of a ship engine room at the initial stages. It is a Multiple Attribute Decision-Making (MADM) or Multiple Criteria Decision Making (MCDM) framework, which provides a juxtaposition of cost, safety and technical performance of a system during evaluation to assist decision makers in selecting the winning design/procurement proposal that best satisfies the requirement in hand. An illustrative example is used to demonstrate the application of the proposed framework.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.1
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• pp.57-73
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• 2015

While many previous studies investigated the effect of operations performance on financial performance, most studies considered only a few performance indicators and ignored the characteristics of industries. Therefore, this study intends to analyze the effect of operations performance on financial performance, by selecting a rather comprehensive operations performance indicators from firms' financial data. In doing so, we used operating efficiency and supply chain performance indicators for operations performance and a firm's profitability and future value indicators for financial performance. For the analysis, we collected 544 firms' operations and financial performance data belonging to eight key industries from the 'Forbes Global 2000'. We first analysed the differences in operations and financial performance among high, medium and low supply chain performance groups based on the quantitative criteria of Gartner's 'Supply Chain Top 25' ranking procedure. Then we analysed the effect of operations performance indicators on financial performance for both entire industry and individual industries, using multiple regression. Based on the results, we provided practical insights into key operations performance indicators to focus on and manage in order to improve financial performance.