• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.36-41
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• 2020

This study is a descriptive study to investigate the presenteeism of nursing in long-term care hospital and general hospital. Data collection was conducted from October 01, 2019 to December 30, 2019. 74 nursing in long-term care hospital and 75 nursing in general hospital in this study. The collected data were analyzed using SPSS Win 21.0 program. Long-term care hospital nurses perceived higher health problems, job loss and perceiver productivity than general hospital nurses. This shows that there is no difference in work intensity according to the classification of patients in long-term care hospitals and general hospital nurses. In order to improve the quality of nursing care services in long-term care hospitals, it is necessary to manage the organizational aspects of long-term care hospital nurses' presenteeism.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.505-512
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• 2002

Rerouting mechanisms must be used by connections in order to provide QoS (Quality of service) characterization of services, which provides mean for reliable and efficient transfer of services under fault generating network. Also, user's services can classily according to their QoS characterizations. In this paper, we study classification of user services according to their characterization for providing differentiated services, and propose rerouting mechanisms under fault generating network. For this, we study various rerouting mechanisms including rerouting locus of start (Source Rerouting, Link Rerouting), rerouting timing of start (Immediate Rerouting, Random Rerouting) and their orthogonal relationship, eventually we propose new rerouting mechanisms such as DRIT, DRDT which show higher performance according to priority of services than others. Our simulation shows that rerouting mechanism (DRDT), applied differentiated mechanisms is better performance to provide differentiated service.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.1-6
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• 2018

With the increase of the elderly population, problem on social isolation and loneliness have grown in Korea. Elderly dwelling issues have also been gaining increased attention since dwelling environment is discussed in conjunction with elderly's health and life quality. In this study, the data was collected retrospectively of 350 elderlies who live as single households during 1 September, 2017 to 30 September to identify how to define and measure dwelling depression. The content validity and reliability were evaluated and the correlation between the depression and the dwelling satisfaction were compared. Lastly, a regression analysis on the classification of the dwelling depression was performed. The results show that depression appearing in elderly are more likely to experience a dwelling unsatisfaction. Our results contribute to an understanding and measurement of the dwelling depression which has not been sufficiently specified.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.15-22
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• 2019

Recent technological advances in three-dimensional (3D) sensing devices and machine learning such as deep leaning has enabled data-driven 3D applications. Research on artificial intelligence has developed for the past few years and 3D deep learning has been introduced. This is the result of the availability of high-quality big data, increases in computing power, and development of new algorithms; before the introduction of 3D deep leaning, the main targets for deep learning were one-dimensional (1D) audio files and two-dimensional (2D) images. The research field of deep leaning has extended from discriminative models such as classification/segmentation/reconstruction models to generative models such as those including style transfer and generation of non-existing data. Unlike 2D learning, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become increasingly popular owing to advances in 3D vision technology, the generation/acquisition of 3D data is still very difficult. Even if 3D data can be acquired, post-processing remains a significant problem. Moreover, it is not easy to directly apply existing network models such as convolution networks owing to the various ways in which 3D data is represented. In this paper, we summarize technological trends in AI-based 3D content generation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.675-681
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated in various fields, interest in video processing, which is the basis for automation such as object recognition and object classification, is increasing. Various studies have been conducted on noise removal in the video processing process, which has a significant impact on image quality and system accuracy and reliability, but there is a problem that it is difficult to restore images for areas with high impulse noise density. In this paper proposes a filter algorithm based on partial mask and Lagrange interpolation to restore the damaged area of impulse noise in the image. In the proposed algorithm, the filtering process was switched by comparing the filtering mask with the noise estimate and the purge weight was calculated based on the low frequency component and the high frequency component of the image to restore the image.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.1-11
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• 2015

In this study, an online seed potato sorting system using a visible and near infrared (40 1100 nm) transmittance spectral technique and statistical model was evaluated for the nondestructive determination of infected and sound seed potatoes. Seed potatoes that had been artificially infected with Pectobacterium atrosepticum, which is known to cause a soil borne disease infection, were prepared for the experiments. After acquiring transmittance spectra from sound and infected seed potatoes, a determination algorithm for detecting infected seed potatoes was developed using the partial least square discriminant analysis method. The coefficient of determination($R^2_p$) of the prediction model was 0.943, and the classification accuracy was above 99% (n = 80) for discriminating diseased seed potatoes from sound ones. This online sorting system has good potential for developing a technique to detect agricultural products that are infected and contaminated by pathogens.

• Journal of Korea Multimedia Society
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• v.2 no.1
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• pp.38-46
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• 1999

In this paper, the fractal image compression using the minimizing method of domain region is proposed. It is minimize to domain regions in the process of decoding. Since the conventional fractal decoding applies to IFS(iterative function system) for the total range blocks of the decoded image, its computational complexity is a vast amount. In order to improve this using the number of the referenced times to the domain blocks for the each range blocks, a classification method which divides necessary and unnecessary regions for IFS is suggested. If necessary regions for IFS are reduced, the computational complexity is reduced. The proposed method is to define the minimum domain region that a necessary region for IFS is minimized in the encoding algorithms. That is, a searched region of the domain is limited to the range regions that is similar with the domain regions. So, the domain region is more overlapped. Therefore, there is not influence on image quality or PSNR(peak signal-to-noise ratio). And it can be a fast decoding by reduce the computational complexity for IFS in fractal image decoding.

• Korean Journal of Construction Engineering and Management
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• v.11 no.6
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• pp.65-77
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• 2010

Our nation's housing construction is given much weight over 32% in 2007 and especially apartment is taking over 67%. If we put into construction environment consideration, we are having a trouble with price cap policy and the realestate recession due to the global economic crisis. So in order to get competitive power and supply of cheap apartment, the necessity of cost saving is increasing. This research collected the past constructed apartment project's cost saving examples which were influencing on the construction cost, quality and time. We analyzed collected cost saving datum and assorted these in compliance with classification system. By analysis of correlation among datum with exclusion and integration, we make a propose cost saving Checklist that will be a base data to give a chance to use in working level and other research.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.67-80
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• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.

• Journal of IKEEE
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• v.25 no.4
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• pp.710-715
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• 2021

Power line signal can show disturbances due to various causes. Typical anomalies are temporary sag/swell of the amplitude, flat topped signal, and harmonic distortions. The disturbances need to be detected and treated properly for the quality of the power signal. In this study, the power disturbances are detected using the inflection points (IP). The inflection points are defined as points where local maxima/minima or the slope changes occur. The power line signal has a fixed IP pattern since it is basically sinusoidal, and it may have additional inflection points if there is any disturbance. The disturbance is detected by comparing the IP patterns between the normal signal and distorted signal. In addition, by defining a cost function, the time instant where the disturbance happens can be decided. The computer simulation shows that the proposed method is useful for the detection of various disturbances. The simple sag or swell signal only shows the amplitude changes at the detected inflection points. However, the flat top signal and harmonically distorted signal produce additional inflection points and large values in the cost function. These results can be exploited for the further processing of disturbance classification.