• Korean Management Science Review
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• v.29 no.1
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• pp.153-168
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• 2012

Order allocation is one of the most important decision-making problems of firms having significant influences on performances of themselves and the whole supply chain. Existing researches about order allocation have mainly focused on evaluating capabilities of directly connected suppliers so that it is hard to consider effects and interactions from undirected connections over multiple lower-layers. To alleviate the limitation, this paper proposed a novel approach to order allocation using structural hole. By applying the concept of structural hole to the supply network, we could evaluate the structural supplying powers of firms with respect to both of direct and indirect connections. In the proposed approach, we derived a methodology to measure the potential supplying power of each firm by modifying the effective size as one of the measurements of structural hole and then, proposed its application, the structural hole based order allocation strategy. Furthermore, we conducted the agent based modeling of supply chain to perform the decision-making process of order allocation and simulated the proposed strategy. As a results, by coping with the variance of demand more stably, it could improve the performance of supply chain from the aspects of fill rate, inventory level and demand-supply balance.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.95-105
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• 2018

This study empirically test the roles of network closure and structural hole on radical innovation in technological knowledge exchange network in Gumi cluster. In doing so, we build 2,550 firm network, transforming association*firm(2-mode) to firm*firm(1-mode) network data. In addition, in order to investigate firms' attributes, we conduct survey for 101 firms in Gumi cluster using random sampling, and finally collect 86 firm samples. For analysis, we use ridge regression since network density and efficiency, indices of network closure and structural hole respectively, has a high level of multicollinearity. The findings show that structural hole has a significant and positive impact on radical innovation, but network closure has a significant and negative impact on radical innovation. This study contributes to present an empirical evidence of debate on network closure and structural hole based on past conceptual discussions and literature review and further goes a long way towards strategy formulation to establish social capital in accomplishing radical innovation. Further research is required that pays closer attention to features of technological knowledge, innovation types and interaction between network closure and structural hole, directing efforts to structural characteristics of various networks.

• Structural Engineering and Mechanics
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• v.63 no.3
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• pp.269-280
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• 2017

One major concern that has occupied the mind of the designers is a structural failure as result of stress concentration in the geometrical discontinuities. Understanding the effective parameters contribute to stress concentration and proper selection of these parameters enables the designer get to a reliable design. In the analysis of perforated isotropic and orthotropic plates, the effective parameters on stress distribution around holes include load angle, curvature radius of the corner of the hole, hole orientation and fiber angle for orthotropic materials. This present paper tries to examine the possible effects of these parameters on stress analysis of infinite perforated plates with central quasi-square hole applying grey wolf optimizer (GWO) inspired by the particular leadership hierarchy and hunting behavior of grey wolves in nature, and also the present study tries to introduce general optimum parameters in order to achieve the minimum amount of stress concentration around this type of hole on isotropic and orthotropic plates. The advantages of grey wolf optimizer are stout, flexible, simple, and easy to be enforced. The used analytical solution is the expansion of Lekhnitskii's solution method. Lekhnitskii applied this method for the stress analysis of anisotropic plates containing circular and elliptical holes. Finite element numerical solution is employed to examine the results of present analytical solution. Results represent that by selecting the aforementioned parameters properly, fewer amounts of stress could be achieved around the hole leading to an increase in load-bearing capacity of the structure.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.4
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• pp.353-361
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• 2012

All current shipyards are using the customized CAD/CAM programs in order to improve the design quality and increase the design efficiency. Even though the data structures for ship design and construction are almost completed, the implementation related to the ship design processes are still in progress so that it has been the main causes of the bottleneck and delay during the middle of design process. In this study, we thought that the hole-plan system would be a good example which is remained to be improved. The people of outfitting division who don't have direct authority to edit the structural panels, should request the hull design division to install the holes for the outfitting equipment. For acceptance, they should calculate the hole position, determine the hole type, and find the intersected contour of panel. After consideration of the hull people, the requested holes are manually installed on the hull structure. As the above, many processes are needed such as communication and discussion between the divisions, drawings for hole-plan, and the consideration for the structural or production compatibility. However this iterative process takes a lot of working time and requires mental pressure to the related people and cross-division conflict. This paper will handle the hole-plan system in detail to automate the series of process and minimize the human efforts and time-consumption.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.4
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• pp.115-124
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• 1987

Stop-hole method is one of the conventional methods for the temporary repair or reinforcement of the member in the case that the structural steel member has a fatigue crack. In this study, the stop-hole method. have been estimated quantitatively in terms of survival life time of the side edge cracked specimen. For this purpose, fatigue tests have been performed on the test specimens and the fatigue crack growth rates of the structural steel (SS41) members have been measured under load of constant amplitude. The results of this experiment show that it is desirable to use the stop-hole method before the crack reaches the region of elasto-plastic behaviour.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2475-2482
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• 2002

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is compensated using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could compensated the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for compensation of the error due to hole eccentricity in hole-drilling method.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.412-418
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• 2001

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is corrected using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could corrected the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for correction of the error due to hole eccentricity in hole-drilling method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.8 s.179
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• pp.2059-2064
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• 2000

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, one of the source of error is due to the misalignment between the drilling hole and strain gage center. This paper presents a finite element analysis of the influence of such misalignment for the uniaxial residual stress field. The stress error increases proportionally to hole eccentricity. The correction equations which easily obtain the residual stress taking account of the hole eccentricity are derived. The stress error due to the hole eccentricity decreases by approximately one percent using this equations.

• Structural Engineering and Mechanics
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• v.67 no.2
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• pp.131-142
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• 2018

The perfobond connector, composed of a steel plate with a number of holes, serves as a certain type of shear connector in steel and concrete composite structures. Depending on limits in hole distances and rib heights, various hole shapes including circular-hole and long-hole are alternatives for perfobond connectors. This study presented the results of tests performed on 72 push-out specimens with perfobond connectors. The purpose was to evaluate the shear strength of perfobond connectors with circular-hole and long-hole. The effects of various parameters were investigated, including the hole diameter, the hole length, the hole height, the concrete strength, the existence, diameter and strength of rebar in the hole, the thickness, height and distance of perfobond ribs, and the thickness of concrete slabs. On the basis of 132 push-out test results in references and in this study, an analytical model was proposed by regression analysis to predict the shear strength of perfobond connectors. The proposed equation agreed reasonably well with the experimental results of perfobond connectors with different hole shapes.