• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.167-170
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• 2000

This paper is concerned with development of a performance evaluation model for material handling system in cellular manufacturing system based on multi-attributes analysis method. We used the AHP(analytic hierarchy process) and fuzzy set ranking methodologies to overcome the special decision problems; those of multi -objective, multi-criterion, and multi-attributes. We proposed a 3-step approaches and we developed a systemic and practical computer program to solve the problems in the proposed methods. Computational experiments are then performed to cellular manufacturing system and show the effectiveness of the proposed model.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.44-49
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• 2001

This paper is concerned with development of a multi-attribute structured decision model. In this study, we used AHP(analytic hierarchy process) and fuzzy set ranking methodology to overcome the multi-attributes structured decision problems ; such as multi-objective, multi-criterion, and multi-attributes. We proposed a 2-step approach : 1) individual evaluation and 2) integration of individual evaluations. In the first step, we define the performance factors and construct ana]isis structure, and in the second step performance evaluation by individual evaluators, and in second step, the results of individual evaluations are integrate. Also we developed a systematic and practical computer program to solve the problems according to the proposed methods. The proposed approach was known to be effective through a set of sample problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.122-125
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• 2000

The Analytic Hierarchy Process (AHP), a decision making model, which is more applicable than other methods to R&D project selection, particularly when it is applied to intangibles. The objective of this paper is to develop an extended model of the AHP which Is linked to Cross Impact Analysis to assist in the ranking of a large number of technological alternatives. In this study, we developed a priority setting algorithm which considers the cross-impact of the future technology alternatives and thus developed an integrated cross-impact hierarchical decision-making model, which sets the priority by considering technological forecasting and technology dependency

• ETRI Journal
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• v.38 no.1
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• pp.52-61
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• 2016

A microblog is a service typically offered by online social networks, such as Twitter and Facebook. From the perspective of information dissemination, we define the concept behind a spreading matrix. A new WeiboRank algorithm for identification of key nodes in microblog networks is proposed, taking into account parameters such as a user's direct appeal, a user's influence region, and a user's global influence power. To investigate how measures for ranking influential users in a network correlate, we compare the relative influence ranks of the top 20 microblog users of a university network. The proposed algorithm is compared with other algorithms - PageRank, Betweeness Centrality, Closeness Centrality, Out-degree - using a new tweets propagation model - the Ignorants-Spreaders-Rejecters model. Comparison results show that key nodes obtained from the WeiboRank algorithm have a wider transmission range and better influence.

• International Journal of Contents
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• v.17 no.4
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• pp.79-90
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• 2021

With the deepening of population aging, pension has become an urgent problem in most countries. Community smart pension can effectively resolve the problem of traditional pension, as well as meet the personalized and multi-level needs of the elderly. To predict the pension intention of the elderly in the community more accurately, this paper uses the decision tree classification method to classify the pension data. After missing value processing, normalization, discretization and data specification, the discretized sample data set is obtained. Then, by comparing the information gain and information gain rate of sample data features, the feature ranking is determined, and the C4.5 decision tree model is established. The model performs well in accuracy, precision, recall, AUC and other indicators under the condition of 10-fold cross-validation, and the precision was 89.5%, which can provide the certain basis for government decision-making.

• Smart Structures and Systems
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• v.29 no.6
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• pp.791-797
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• 2022

A complex system is comprised of numerous entities containing physical components, devices and hardware, events or phenomena, and subsystems, there are intricate interactions among these entities. To reasonably identify the critical fault propagation paths, a system fault propagation model is essential based on the system failure mechanism and failure data. To establish an appropriate mathematical model for the complex system, these entities and their complicated relations must be represented objectively and reasonably based on the structure. Taking a command and control system as an example, this paper proposes a hierarchical fault propagation analysis method, analyzes and determines the edge betweenness ranking model and the importance degree of each sub-system.

• The Journal of Fisheries Business Administration
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• v.42 no.2
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• pp.15-30
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• 2011

This study is designed to measure the relative efficiency of regional fishery cooperatives based on Data Envelopment Analysis(DEA) methods. Selecting 40 regional fishery cooperatives in Busan as Decision Making Units (DMUs), the study uses their panel data from 2007 to 2008 to rank the relative efficiency of the DMUs. First, the efficiency score of the DMUs are calculated using CCR, SBM, and super-SMB model. Within the model, input variables are the number of employees and area of fishery cooperatives. Output variables are the amount of deposit money, loan and profit. Based on the efficiency scores calculated from super-SMB model, the efficiency ranking of the DMUs is determined. Second, the differences in average efficiency calculated from the three DEA models are tested using a pair-wise mean comparison test. The results based on the efficiency scores evaluated from super-SMB model show that seven out of the forty DMUs are efficient; among the efficient DMUs, the DMUs that can be benchmarked for inefficient DMUs through the frequency analysis of reference set being identified. Third, the differences in average efficiency of the three DEA models between 2007 and 2008 are tested using pair-wise mean comparison test and the study estimates the efficiency change of the DMUs between 2007 and 2008 using Malmquist productivity index(MPI). Finally, the paper suggests an improved composite DMU superior to the inefficient DMUs evaluated by Super-SBM model.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.197-206
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• 2010

For an emotion retrieval system implementation to support pedestrian navigation, coordinating the pedestrian emotion model with the system user's emotion is considered a key component. This study proposes a new method for capturing the user's model that corresponds to the pedestrian emotion model and examines the validity of the method. In the first phase, a database comprising a set of interior images that represent hypothetical destinations was developed. In the second phase, 10 subjects were recruited and asked to evaluate on navigation and satisfaction toward each interior image in five rounds of navigation experiments. In the last phase, the subjects' feedback data was used for of the pedestrian emotion model, which is called ‘learning' in this study. After evaluations by the subjects, the learning effect was analyzed by the following aspects: recall ratio, precision ratio, retrieval ranking, and satisfaction. Findings of the analysis verify that all four aspects significantly were improved after the learning. This study demonstrates the effectiveness of the learning algorithm for the proposed pedestrian emotion model. Furthermore, this study demonstrates the potential of such pedestrian emotion model to be well applicable in the development of various mobile contents service systems dealing with visual images such as commercial interiors in the future.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.659-668
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• 2010

Collaborative filtering systems grasp behaviors for a new user and need new information for the user in order to recommend interesting items to the user. For the purpose of acquiring the information the collaborative filtering systems learn behaviors for users based on the previous data and can obtain new information from the results. In this paper, we propose an inductive inference method to obtain new information for users and rank items by using the new information in the proposed method. The proposed method clusters users into groups by learning users through NMF among inductive machine learning methods and selects the group features from the groups by using chi-square. Then, the method classifies a new user into a group by using the bayesian probability model as one of inductive inference methods based on the rating values for the new user and the features of groups. Finally, the method decides the ranks of items by applying the Rocchio algorithm to items with the missing values.