• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3913-3934
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• 2021

Real-time prediction of Web service of quality (QoS) provides more convenience for web services in cloud environment, but real-time QoS prediction faces severe challenges, especially under the cold-start situation. Existing literatures of real-time QoS predicting ignore that the QoS of a user/service is related to the QoS of other users/services. For example, users/services belonging to the same group of category will have similar QoS values. All of the methods ignore the group relationship because of the complexity of the model. Based on this, we propose a real-time Matrix Factorization based Clustering model (MFC), which uses category information as a new regularization term of the loss function. Specifically, in order to meet the real-time characteristic of the real-time prediction model, and to minimize the complexity of the model, we first map the QoS values of a large number of users/services to a lower-dimensional space by the PCA method, and then use the K-means algorithm calculates user/service category information, and use the average result to obtain a stable final clustering result. Extensive experiments on real-word datasets demonstrate that MFC outperforms other state-of-the-art prediction algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1163-1185
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• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.113-120
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• 2005

3D reconstruction of a scene structure from un-calibrated image sequences has been long one of the central problems in computer vision. For 3D reconstruction in Euclidean space, projective reconstruction, which is classified into the merging method and the factorization, is needed as a preceding step. By calculating all camera projection matrices and structures at the same time, the factorization method suffers less from dia and error accumulation than the merging. However, the factorization is hard to analyze precisely long sequences because it is based on the assumption that all correspondences must remain in all views from the first frame to the last. This paper presents a new projective reconstruction method for recovery of 3D structure over long sequences. We break a full sequence into sub-sequences based on a quantitative measure considering the number of matching points between frames, the homography error, and the distribution of matching points on the frame. All of the projective reconstructions of sub-sequences are registered into the same coordinate frame for a complete description of the scene. no experimental results showed that the proposed method can recover more precise 3D structure than the merging method.

• Communications of Mathematical Education
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• v.29 no.1
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• pp.111-130
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• 2015

The purpose of the study was to investigate the differences between two groups of students according to information recognition styles such as visual learners and linguistic learners. Two instructional methods, algeblocks and factorization formula, were utilized to introduce the factorization. Four students were participated for the study, and two of them were visual learners and the other two were linguistic learners based on learning style test. Interviews and the diagnostic tests were implemented before the instructions which were lasted for 6 sessions. After the instructions all the participants were interviewed and the researchers also interviewed them 5 days later. The results of the study were the followings: 1. All the participants regardless of their learning style revealed that algeblocks were helpful in understanding the factorization. 2. Visual learners were more likely using algeblocks, while the linguistic learners were more enthusiastic and proficient in using formula to solve the problems. 3. Five days later, two types of learning style students revealed different tendencies. Visual learners mainly used algeblocks, and linguistic learners were not enthusiastic about using algeblocks and one of them did not use them at all. 4. Five days later, two visual learners could not remember the formula, but linguistic learners could remember the formula in somewhat different level.

• Kyungpook Mathematical Journal
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• v.31 no.1
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• pp.119-130
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• 1991

• East Asian mathematical journal
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• v.1
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• pp.43-46
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• 1985

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.24-24
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• 1991

• Kyungpook Mathematical Journal
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• v.29 no.1
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• pp.104-113
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• 1989

• Journal of the Korean Mathematical Society
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• v.18 no.2
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• pp.175-180
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• 1982

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.185-189
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• 2007

This paper deals with a fault detection system design for nonlinear systems with uncertain time varying parameters modelled as a T-S fuzzy system. A coprime factorization for T-S fuzzy systems is defined and a residual generator is designed using a left coprime factor. A fault detection criteria derived from the residual generator is also suggested. In order to demonstrate the efficacy of the suggested method, the fault defection method is applied to an inverted pendulum system and computer simulations are performed.