• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.700-705
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• 2004

Clustering of incomplete data using the Autoencoder and the Fuzzy c-Means(PCM) is proposed in this paper. The Proposed algorithm, called Optimal Completion Autoencoder Fuzzy c-Means(OCAEFCM), utilizes the Autoencoder Neural Network (AENN) and the Gradiant-based FCM (GBFCM) for optimal completion of missing data and clustering of the reconstructed data. The proposed OCAEFCM is applied to the IRIS data and a data set from a financial institution to evaluate the performance. When compared with the existing Optimal Completion Strategy FCM (OCSFCM), the OCAEFCM shows 18％-20％ improvement of performance over OCSFCM.

• Journal of the Korean Mathematical Society
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• v.61 no.3
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• pp.427-444
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• 2024

The matrix completion problem is to predict missing entries of a data matrix using the low-rank approximation of the observed entries. Typical approaches to matrix completion problem often rely on thresholding the singular values of the data matrix. However, these approaches have some limitations. In particular, a discontinuity is present near the thresholding value, and the thresholding value must be manually selected. To overcome these difficulties, we propose a shrinkage and thresholding function that smoothly thresholds the singular values to obtain more accurate and robust estimation of the data matrix. Furthermore, the proposed function is differentiable so that the thresholding values can be adaptively calculated during the iterations using Stein unbiased risk estimate. The experimental results demonstrate that the proposed algorithm yields a more accurate estimation with a faster execution than other matrix completion algorithms in image inpainting problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1519-1534
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• 2016

In this paper, we study how to optimize the data shuffle phase by leveraging the flow relationship in datacenter networks (DCNs). In most of the clustering computer frameworks, the completion of a transfer (a group of flows that can enable a computation stage to start or complete) is determined by the flow completing last, so that limiting the rate of other flows (not the last one) appropriately can save bandwidth without impacting the performance of any transfer. Furthermore, for the flows enter network late, more bandwidth can be assigned to them to accelerate the completion of the entire transfer. Based on these characteristics, we propose the flow chasing algorithm (FCA) to optimize the completion of the entire transfer. We implement FCA on a real testbed. By evaluation, we find that FCA can not only reduce the completion time of data transfer by 6.24% on average, but also accelerate the completion of data shuffle phase and entire job.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• Kyungpook Mathematical Journal
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• v.54 no.2
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• pp.157-163
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• 2014

For a finite subset ${\Lambda}$ of $\mathbb{N}_0{\times}\mathbb{N}_0$, where $\mathbb{N}_0$ is the set of nonnegative integers, we say that a complex data ${\gamma}_{\Lambda}:=\{{\gamma}_{ij}\}_{(ij){\in}{\Lambda}}$ in the unit disc $\mathbf{D}$ of complex numbers has a cyclic subnormal completion if there exists a Hilbert space $\mathcal{H}$ and a cyclic subnormal operator S on $\mathcal{H}$ with a unit cyclic vector $x_0{\in}\mathcal{H}$ such that ${\langle}S^{*i}S^jx_0,x_0{\rangle}={\gamma}_{ij}$ for all $i,j{\in}\mathbb{N}_0$. In this note, we obtain some sufficient conditions for a cyclic subnormal completion of ${\gamma}_{\Lambda}$, where ${\Lambda}$ is a finite subset of $\mathbb{N}_0{\times}\mathbb{N}_0$.

• Korean Journal of Construction Engineering and Management
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• v.8 no.4
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• pp.203-213
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• 2007

In the past, several construction projects have exceeded their schedule resulting in financial losses to the owners; at present there are very few methods available to accurately forecast the completion date of a project. These nay be because of unforeseen outcomes that cannot be accounted for earlier and because of deficiency of proper tools to forecast completion date of said project. To overcome these difficulties, project managers may need a tool to predict the completion date at the early stage of project development. Bayesian Inference introduced in this paper is one such tool that can be employed to forecast project progress at all construction stages. Using this inference, project managers can combine an initially planned project progress (growth curve) with reported information from ongoing projects during the development, and in addition, dynamically revise this initial plan and quantify the uncertainty of completion date. This study introduces a theoretical model and proposes a mathematically information-based framework to forecast a project completion date that corresponds with the actual progress data and to monitor the modified uncertainties using Bayesian Inference.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.361-373
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• 2022

Purpose - This paper analyzes the student determinants of college non-completion and estimates the effects of each determinant on college non-completion. Design/methodology/approach - We use student panel data from a large Korean university from 2011 to 2021. Our results are from estimation of fixed-effects logit model. Findings - The results show that grade point average, participation in extracurricular activities, the number of counseling sessions with teachers, and financial aid are the main determinants of college non-completion. Academic probation, which is defined as any person who has a cumulative grade point average below a one point seven five, increases the non-completion rate by 2.6 percentage points and an one-point rise in extracurricular activities index reduces the rate by 0.1 percentage points. The effects of each determinant are heterogeneous across student sub-groups which are separated by gender, nationality, and academic discipline. Research implications or Originality - Tailored support programs for academically discouraged students that incorporate student characteristics and backgrounds are necessary to increase college completion rates and degree attainment.

• Korean Journal of Agricultural Science
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• v.37 no.3
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• pp.471-479
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• 2010

Forest roads of 16,424km have been constructed as infrastructure for efficient management of forest. The demand of forest road have been also increased steadily with SOC conception for forest management and wood production. But, accuracy verification by completion drawing of forest road needed aspects extration of geographic information to sound like forest road construction and completion drawing. However, verification for completion drawing has not ascertained. This study carried out the evaluation for position accuracy about constructed forest road in Chungcheongnam-do for evaluating horizontal position accuracy of completion drawing of forest road. In result, first of distance of completion drawing and real route designed completion drawing longer than the real route as Gongju 83m, Seosan 66m, Nonsan 27m and Dangjin 19m, respectively. Second, RMSE by point-correspondence was 11m~14.7m, buffering analysis appeared difference of 18~24m. Finally, index of shape was the similar completion and real route through 6.5~7.4 and data information of forest road corresponds to be perfect. For such reasons, the existing completion drawings have a problem that it cannot use graphic information for drawing digital map according to the regulation, and there is an urgent need for improvement to solve this problem in the process of design and construction.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.372-382
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• 2009

When the range of projection angles is limited, tomographic reconstruction suffers from artifacts caused by incomplete data. One can consider a data completion technique, which estimates projection data at unobserved angles using a prior knowledge or mathematical exploration, but the result is often not improved; the improvement by the data completion often undermined by the artifacts by inaccurate estimation, In this paper, we propose an iterative method, which computes projection data at unobserved angles by using the current estimate on the image, links the computed projection data to the observed ones by using the consistence condition of Radon transform, and reconstruct the next estimate on the image by filtered backprojection. The proposed method does not require a prior knowledge on the image, and has much faster approximation rate than the expectation maximization method. The performance of the proposed method was tested through several simulation studies.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.12-17
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• 2020

This study was a similar experimental study that analyzed the effect by applying the learning portfolio completion. The study period lasted from October 1, 2019 to November 20, 2019. A total of 47 people participated in the study, and the effectiveness of the program was analyzed with the SPSS 18.0 program for critical thinking disposition. The statistical analysis method was frequency analysis and paired t-test. As a result of the analysis, the critical thinking disposition increased significantly in the application of the learning portfolio completion (Truth-seeking MD= -0.05, p <0.01), Open-mindness MD= 0.11, p <0.001), Analyticity MD= 0.76, p <0.001), Systematicity MD= -.25, p <0.001), Self-confidence MD=-0.54, p <0.001), Inquisitiveness MD=0.29, p <0.001), Maturity MD=-.0.33, p <0.001). In conclusion, the teaching method applied with the learning portfolio completion actually helped nursing students learn nursing students learn based on critical thinking. Based on these result, further research using learning portfolio is to be done and more systematic and practical application of learning portfolio completion to nursing students. This study would be used as a basic data for the study guideline development for learners.