• Journal of Korean Critical Care Nursing
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• v.6 no.2
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• pp.51-64
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• 2013

Purpose: This study was to analyze adherence to self-care behaviors and identify factors affecting the adherence behaviors among inpatients with heart failure. Methods: A total 94 hospitalized inpatients from three hospitals participated in a survey. Data were collected using structured self-reported questionnaire from November 28, 2011 to March 31, 2013 and analyzed using frequency, t-test, ANOVA, Pearson's correlation coefficients and stepwise multiple regression. Results: The score of adherence to self-care behaviors among inpatients with heart failure was $26.02({\pm}8.84)$. Factors related to the adherence to self-care behaviors were living with spouse (t=-2.47, p=.019), functional state (t=2.18, p=.034), heart failure knowledge (r=-.49, p<.001), social support (r=-.35, p<.001), self-control (r=-.25, p=.016), and self-care confidence (r=-.24, p=.019). The factors affecting adherence to self-care behaviors were heart failure knowledge, self-care confidence, and social support. These factors explained 32% of the variance in adherence to self-care behaviors. Conclusion: The adherence to self-care behaviors with heart failure can be improved if heart failure knowledge, self-care confidence, and social support are improved. Therefore, developing a nursing intervention program for patient with heart failure that is considered these factors leads to improve quality of life and prevent readmission.

• Structural Engineering and Mechanics
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• v.25 no.3
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• pp.347-363
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• 2007

A novel methodology, referred to as Auxiliary Domain Method (ADM), allowing for a very efficient solution of nonlinear reliability problems is presented. The target nonlinear failure domain is first populated by samples generated with the help of a Markov Chain. Based on these samples an auxiliary failure domain (AFD), corresponding to an auxiliary reliability problem, is introduced. The criteria for selecting the AFD are discussed. The emphasis in this paper is on the selection of the auxiliary linear failure domain in the case where the original nonlinear reliability problem involves multiple objectives rather than a single objective. Each reliability objective is assumed to correspond to a particular response quantity not exceeding a corresponding threshold. Once the AFD has been specified the method proceeds with a modified subset simulation procedure where the first step involves the direct simulation of samples in the AFD, rather than standard Monte Carlo simulation as required in standard subset simulation. While the method is applicable to general nonlinear reliability problems herein the focus is on the calculation of the probability of failure of nonlinear dynamical systems subjected to Gaussian random excitations. The method is demonstrated through such a numerical example involving two reliability objectives and a very large number of random variables. It is found that ADM is very efficient and offers drastic improvements over standard subset simulation, especially when one deals with low probability failure events.

• Applied Biological Chemistry
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• v.38 no.4
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• pp.325-329
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• 1995

Changes in the physical properties of soybean curd upon the processing conditions such as coagulant concentration, heating temperature and molding pressure were determined by using a failure stress and residual delay time of ultrasonic wave(5 MHz). Maximum failure stress of Tofu was obtained at the 0.3% $CaCl_2$ coagulant concentration, $95^{\circ}C$ heating temperature and greater molding pressure, respectively, whereas the delay time is inverse proportion to the failure stress value. The results of the multiple regression analysis with factorial design showed that the model equation consisted with delay time and processing conditions gave the good prediction of the Tofu failure stress.

• Journal of the Korean Society of Systems Engineering
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• v.10 no.1
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• pp.43-48
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• 2014

The door system for railway vehicles is the critical device directly influences on safety and satisfaction of passengers, Recently, electrical type of passenger door system is widely used for EMU type train instead of pneumatic type of passenger door system. The estimation of MTBF and failure rates for electrical type door system is essential. The manufacturor simply provides intrinsic reliability data for the railway operator. But actual reliability data based on operation and maintenance data is not complying with intrinsic reliability. In this study, operation and failure data associated with electrical door system were analyzed in order to determine actual MTBF and failure data. Intrinsic reliability data and service reliability data were studied to finallize much more practical and reliable actual reliability. Relax 2011 was used to predict intrinsic reliability and 217Plus model was also used to estimate of actual reliability data based on field data. Furthermore, it is necessary to keep studying on reliability prediction methodology and applying it in the field and doing research on improvement of reliability through feedback as well.

• Knowledge Management Research
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• v.16 no.2
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• pp.91-111
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• 2015

Social media marketing has gained attention from marketers due to the growing number of social media users. Marketers around the globe have a serious concern over how to utilize social media as a successful marketing tool. Many of them show a lack of understanding of the essential feature of social media and effective social media marketing strategies, which brings about a higher chance of social media marketing failures. Analyzing the failure cases of social media marketing is vital because it provides rich insights for marketing experts who have difficulty in developing effective social media marketing strategies. Therefore, this study conducted multiple case studies by selecting five failure cases of social media marketing which we defined as paradigmatic social media marketing failures which happened in the last five years in South Korea. From the case studies, we derived five successful social media marketing strategies. This study has a theoretical implication because it is the first to suggest effective social media marketing strategies based on the analysis on social media marketing failure cases. It also has a practical implication since it proposes specific social media marketing strategies which can help facilitate successful social media marketing.

• The Journal of Korean Academic Society of Nursing Education
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• v.22 no.2
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• pp.171-181
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• 2016

Purpose: This study examined the effects of nursing professionalism, academic failure tolerance and social self-efficacy on college life satisfaction among nursing students. Methods: Data were collected between September 1 and October 16, 2015 via a self-reported questionnaire from 170 nursing students using convenient sampling methods. The survey included questions about nursing professionalism, academic failure tolerance, social self-efficacy, and college life satisfaction. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and hierarchical multiple regression with IBM SPSS/WIN 20.0. Results: Establishment vision about nursing science (${\beta}=.27$, p=.006), academic failure tolerance (${\beta}=.17$, p=.031) and social self-efficacy (${\beta}=.19$, p=.012) of nursing students were identified as significant predictors of college life satisfaction, after adjusting for establishment vision about nursing science and satisfaction in nursing science. This model explained 21.0% of the college life satisfaction in nursing students (F=6.38, p<.001). Conclusion: These results suggest that academic failure tolerance and social self-efficacy were significant factors influencing the college life satisfaction of nursing students. Also, as a strategy for improving the college life satisfaction of nursing students, it is necessary to develop programs that can help to establish apparent vision and to improve satisfaction in nursing science.

• Korean Journal of Materials Research
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• v.30 no.3
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• pp.105-110
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• 2020

Cu/PET composite films are widely used in a variety of wearable electronics. Lifetime of the electronics is determined by adhesion between the Cu film and the PET substrate. The formation of an anisotropic nanostructure on the PET surface by surface modification can enhance Cu/PET interfacial adhesion. The shape and size of the anisotropic nanostructures of the PET surface can be controlled by varying the surface modification conditions. In this work, the effect of Cu/PET interface nanostructures on the failure mechanism of a Cu/PET flexible composite film is studied. From observation of the morphologies of the anisotropic nanostructures on plasma-treated PET surfaces, and cross-sections and surfaces of the fractured specimens, the Cu/PET interface area and nanostructure width are analyzed and the failure mechanism of the Cu/PET film is investigated. It is found that the failure mechanism of the Cu/PET flexible composite film depends on the shape and size of the plasmatreated PET surface nanostructures. Cu/PET interface nanostructures with maximal peel strength exhibit multiple craze-crack propagation behavior, while smaller or larger interface nanostructures exhibit single-path craze-crack propagation behavior.

• Geomechanics and Engineering
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• v.11 no.6
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• pp.787-803
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• 2016

DDARF (Discontinuous Deformation Analysis for Rock Failure) is a numerical algorithm for simulating jointed rock masses' discontinuous deformation. While its reinforcement simulation is only limited to end-anchorage bolt, which is assumed to be a linear spring simply. Here, several new reinforcement modes in DDARF are proposed, including lining reinforcement, full-length anchorage bolt and equivalent reinforcement. In the numerical simulation, lining part is assigned higher mechanical strength than surrounding rock masses, it may include multiple virtual joints or not, depending on projects. There must be no embedding or stretching between lining blocks and surrounding blocks. To realize simulation of the full-length anchorage bolt, at every discontinuity passed through the bolt, a set of normal and tangential spring needs to be added along the bolt's axial and tangential direction. Thus, bolt's axial force, shearing force and full-length anchorage effect are all realized synchronously. And, failure criterions of anchorage effect are established for different failure modes. In the meantime, from the perspective of improving surrounding rock masses' overall strength, a new equivalent and tentative simulation method is proposed, it can save calculation storage and improve efficiency. Along the text, simulation algorithms and applications of these new reinforcement modes in DDARF are given.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.209-218
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• 2021

For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.