• Korean Medical Education Review
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• v.15 no.3
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• pp.151-158
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• 2013

Patient safety is achieved through systematic improvement based on the knowledge and willingness of medical professionals. A systematic longitudinal curriculum for patient safety is essential to prepare medical students and professionals. The purpose of this article is to introduce our experience with a 'workshop for developing a patient safety curriculum' and to compare the results with previous studies. The workshop comprising 15 medical professors and patient safety experts met for 2 days. The Consensus Workshop method was applied, collecting opinions from all of the members and reaching consensus through the following stages: context, brainstorm, cluster, name, and resolve. The patient safety curriculum was developed by this method, covering patient safety topics and issues, and teaching and assessment methods. A total of 7 topics were extracted, 'activities for patient safety, concepts of patient safety, leadership and teamwork, error disclosure, self-management, patient education, policies.' Issues, teaching methods, and assessment methods were developed for each topic. The patient safety curriculum developed from the workshop was similar to previous curricula developed by other institutions and medical schools. The Consensus Workshop method proved to be an effective approach to developing a patient safety curriculum.

• Korean Journal of Materials Research
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• v.12 no.4
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• pp.274-278
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• 2002

Discrete-variational(DV)-$X{\alpha}$ method was applied to investigate the electronic structures of spinel- type manganese oxide which is well known to the high performance adsorbent or cathode material for lithium ion. The results of DOS(density of states) and Mulliken population analysis showed that Li was nearly fully ionized and interactions between Mn and O were strong covalent bond. The effective charge of Li and Mn was +0.77 and +1.44 respectively and the overlap population between Mn and O was 0.252 in $LiMn_2O_4$. These results from DV-X$\alpha$ method were well coincided with the experimental result by XPS analysis and supported the feasibility of theoretical interpretation for the $LiMn_2O_4$ compound.

• Nuclear Engineering and Technology
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• v.46 no.4
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• pp.481-488
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• 2014

An analysis of the uncertainty quantification related to LBLOCA using the Monte-Carlo calculation has been performed and compared with the tolerance level determined by the Wilks' formula. The uncertainty range and distribution of each input parameter associated with the LOCA phenomena were determined based on previous PIRT results and documentation during the BEMUSE project. Calulations were conducted on 3,500 cases within a 2-week CPU time on a 14-PC cluster system. The Monte-Carlo exercise shows that the 95% upper limit PCT value can be obtained well, with a 95% confidence level using the Wilks' formula, although we have to endure a 5% risk of PCT under-prediction. The results also show that the statistical fluctuation of the limit value using Wilks' first-order is as large as the uncertainty value itself. It is therefore desirable to increase the order of the Wilks' formula to be higher than the second-order to estimate the reliable safety margin of the design features. It is also shown that, with its ever increasing computational capability, the Monte-Carlo method is accessible for a nuclear power plant safety analysis within a realistic time frame.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.490-496
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• 2005

In this paper, we propose a self-constructed clustering algorithm based on inference information of the fuzzy model. This method makes it possible to automatically detect and optimize the number of cluster and parameters by using input-output data. The propose method improves the performance of clustering by extended supervised learning technique. This technique uses the output information as well as input characteristics. For effect the similarity measure in clustering, we use the TSK fuzzy model to sent the information of output. In the conceptually, we design a learning method that use to feedback the information of output to the clustering since proposed algorithm perform to separate each classes in input data space. We show effectiveness of proposed method using simulation than previous ones

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.8
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• pp.491-498
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• 2014

Due to the ossification of current Internet, it is hard to accommodate new service requirements. One of the solutions to this problem is network virtualization. In this paper, we propose a heuristic virtual network embedding method for network virtualization. The proposed method checks whether the candidate substrate nodes in the substrate network have the possibility of satisfying virtual link requirements. It gives priority to the virtual nodes and the substrate nodes, and embeds the node with higher priority first. Also, the proposed method tries to cluster the mapped substrate nodes if possible. We evaluate the performance of the proposed method in terms of time complexity and virtual network acceptance rate.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.99-102
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• 2003

The electronic structure of ${\beta}-MnO_2$ having impurities in the site of Mn was theoretically investigated by $DV-X_{\alpha}$ (the discrete variation $X{\alpha}$) method, which is a sort of the first principle molecular orbital method using Hatre-Fock-Slater approximation. The used cluster model was $[Mn_{14}MO_{56}]^{-52}$ (M = transient metals). Madelung potential and spin polarization were considered for more exact calculations. As results of calculations, the energy levels of all electron included in the model were obtained. The energy band gap and positions of impurity levies were discussed in association with impurity 34 orbital that seriously affect electrical properties of $MnO_2$. It was shown that the energy band gap decreased with the increase of the atomic number of transient metal impurity.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.27-31
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• 2013

In this paper, we propose a new quantization method using fuzzy C-means clustering(FCM) for lumbar ultrasound image recognition. Unlike usual histogram based quantization, our method first classifies regions into 10 clusters and sorts them by the central value of each cluster. Those clusters are represented with different colors. This method is efficient to handle lumbar ultrasound image since in this part of human body, the brightness values are distributed to doubly skewed histogram in general thus the usual histogram based quantization is not strong to extract different areas. Experiment conducted with 15 real lumbar images verified the efficacy of proposed method.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.369-384
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• 2021

In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low- and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.986-1016
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• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.