• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.87-95
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• 2008

The purpose of this study is to provide a decision support to select an appropriate rapid prototyping(RP) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model for molding, material property, build time and part cost that greatly affect the performance of RP machines. However, the selection of a RP 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 RP 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 RP 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 RP machines.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.26-39
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• 2021

This study investigated the characteristics of the watershed and pollutants in the Seonakdong River basin in the lower stream of the Nakdong River Water System, and evaluated the areas vulnerable to nonpoint pollution by subwatershed according to the TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution) method. The selection method consists of selection of evaluation factors, calculation of weights and selection of areas vulnerable to non-point pollution through evaluation factors and weights. The entropy method was used as the weight calculation method and TOPSIS, a multi-criteria decision making(MCDM) method was used as the evaluation method. Indicator data were collected as of 2018, and national pollution source survey data and national statistics were used. Most of the vulnerable watersheds were highly urbanized had a large number of residents and were evaluated as having a large land area among industrial facilities and site area rate. Through this study, it is necessary to approach a variety of weighting methodologies to assess the vulnerability of non-point pollution with high reliability, and scientific analysis of the factors that affect non-point pollution sources and consideration of the effects are necessary.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.4
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• pp.1-9
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• 2015

This paper proposes CAC method that is more efficient for RRM using game theory combined with Multiple Attribute Decision Making(MADM). Because users request services with different Quality of Service(QoS), the network preference values to alternative networks for each service are calculated by MADM methods such as Grey Relational Analysis(GRA), Simple Additive Weighting(SAW) and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS). According to a utility function representing preference value, non-cooperative game is played, and then network provider select the requested service that provide maximum payoff. The appropriate service is selected through Nash Equilibrium that is the solution of game and the game is played repeated. We analyze two overlaid networks among four Wireless LAN(WLAN) systems with different properties. Simulation results show that proposed MADM techniques have same outcomes for every game round.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.3
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• pp.51-63
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• 2011

The purpose of this study is to create a landscape image that considers the selection of techniques that can enhance landscape reproduction in streetscape evaluation using 3 dimensional simulations and to evaluate ways to verify similarities and the psychological changes on the part of users by season. In the comparison of technique, the Low(apply normal map) technique was selected for the natural representation of trees in a near and middle view and the Plane technique was selected for the distant view. As the result of the verification, all indicators of physical similarity were evaluated over 4.50 points and most indicators of psychological similarity were found to have no difference except for indicators of 'disordered orderly' and 'dirty - clean'. According to the results of analyzing the landscape simulation by season, images of 'bright', 'beautiful', and 'static', etc., were evaluated high for the spring streetscape. The images of 'open', 'refresh', and 'animate' appeared high in summer and images of 'warm' and 'dark' were found to be high in fall. On the other hand, all images were evaluated as low except for the 'orderly' image. In the preference of streetscape by season, summer and spring were highly preferred at 5.01 and 4.98 with winter as the lowest at 3.48. As the results of the analysis of preference factor, the spring streetscape was found to be a major influence in preference by 0.540 in 'aesthetics'. In the case of summer, 'order' was found to be high at 0.417 while influences in preference included 'variety' and 'aesthetics' in fall and 'variety', 'aesthetics', and 'order' in winter. A determination of suitable spatial planning using a comparative analysis of various city streets will be enabled through the methods of this study.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• Land and Housing Review
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• v.11 no.1
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• pp.109-116
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• 2020

This study aims to establish an approach to assess urban flood vulnerability by identifying social characteristics such as the road transportation and the vulnerable groups. Assessment procedures comprise three steps as: (1) composing the assessment criteria to reflect the urban characteristics; (2) calculating the weight; and (3) evaluating the vulnerability. The criteria were adopted by Delphi survey technique. Four criteria as land cover, residents, vulnerable areas, and disaster response were adopted in the current study. To determine the weight set of criteria, subjective and objective methods were combined. The weight set was determined using the combined method which reflects the Delphi method and Entropy analysis. In the process of data-based construction, GIS tools wwere used to extract administrative unit materials such as land cover, road status, and slope. Data on population and other social criteria were collected through the National Statistical Office and the Seoul Metropolitan statistical data. TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) technique, which uses materials from cell units in order to rank the closest distance to the best case and the farthest distance from the worst case by calculating the distances to the area of assessment, was applied to assess. The study area was the Dorimcheon basin, a flood special treatment area of Seoul city. The results from the current study indicates that the established urban flood vulnerability assessment approach is able to predict the inherent vulnerable factors in urban regions and to propose the area of priority control.

• Computers and Concrete
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• v.21 no.4
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• pp.441-449
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• 2018

FE models for complex or large-scaled structures that need detailed modeling of structural components are usually constructed using commercial analysis softwares. Updating of such FE model by conventional sensitivity-based methods is difficult since repeated computation for perturbed parameters and manual calculations are needed to obtain sensitivity matrix in each iteration. In this study, an FE model updating procedure avoiding such difficulties by using response surface (RS) method and a Pareto-based multiobjective optimization (MOO) was formulated and applied to FE models constructed with a commercial analysis package. The test building is a low-rise reinforced concrete building that has been seismically retrofitted. Dynamic properties of the building were extracted from vibration tests performed before and after the seismic retrofits, respectively. The elastic modulus of concrete and masonry, and spring constants for the expansion joint were updated. Two RS functions representing the errors in the natural frequencies and mode shape, respectively, were obtained and used as the objective functions for MOO. Among the Pareto solutions, the best compromise solution was determined using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) procedure. A similar task was performed for retrofitted building by taking the updating parameters as the stiffness of modified or added members. Obtained parameters of the existing building were reasonably comparable with the current code provisions. However, the stiffness of added concrete shear walls and steel section jacketed members were considerably lower than expectation. Such low values are seemingly because the bond between new and existing concrete was not as good as the monolithically casted members, even though they were connected by the anchoring bars.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.12
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• pp.116-123
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• 2001

An automated injection molding design methodology has been developed to optimize multiple quality attributes, which are usually in conflict with each other, in injection molded parts. For the optimization, commercial CAE simulation tools and optimization techniques are integrated into the methodology. To decal with the multiple objective problem the relative closeness computed in TOPSIS(Technique for Order Preference by Similarity to Ideal Solution) is used as a performance measurement index for optimization multiple part defects. To attain robustness against process variation, Taguchi's quadratic loss function is introduced in the TOPSIS. Also, the modified complex method is used as an optimization tool to optimize objective function. The verification of the developed design methodology was carried out on simulation software with an actual model. Applied to production this methodology will be useful to companies in reducing their product development time and enhancing their product quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.522-541
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• 2016

Network virtualization provides an effective way to overcome the Internet ossification problem. As one of the main challenges in network virtualization, virtual network embedding refers to mapping multiple virtual networks onto a shared substrate network. However, existing heuristic embedding algorithms evaluate the embedding potential of the nodes simply by the product of different resource attributes, which would result in an unbalanced embedding. Furthermore, ignoring the hops of substrate paths that the virtual links would be mapped onto may restrict the ability of the substrate network to accept additional virtual network requests, and lead to low utilization rate of resource. In this paper, we introduce and extend five node attributes that quantify the embedding potential of the nodes from both the local and global views, and adopt the technique for order preference by similarity ideal solution (TOPSIS) to rank the nodes, aiming at balancing different node attributes to increase the utilization rate of resource. Moreover, we propose a novel two-stage virtual network embedding algorithm, which maps the virtual nodes onto the substrate nodes according to the node ranks, and adopts a shortest path-based algorithm to map the virtual links. Simulation results show that the new algorithm significantly increases the long-term average revenue, the long-term revenue to cost ratio and the acceptance ratio.