• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.5-17
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• 2023

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

• Research in Community and Public Health Nursing
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• v.33 no.1
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• pp.128-138
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• 2022

Purpose: This study aimed to identify the factors influencing the wellness of call center employees. Methods: From December 2018 to October 2019, a cross-sectional study was conducted with 155 workers recruited from the call centers in Seoul, South Korea. Data were collected using self-administered questionnaires. The questionnaires were used to measure the following scales: Korean occupational stress scale, emotional labor scale, work-life balance scale and wellness scale. Using the SPSS 26.0 program, the descriptive statistics, independent t-test, ANOVA, correlation analysis, and multiple regression analysis were conducted. Results: The mean score of the wellness level of call center employees was 3.05 out of a maximum of 5.00. More wellness level of call center employees was associated with gender, psychiatric diagnosis, and call characteristics. A multiple regression analysis indicated that the total scores on the wellness scale were predicted by call characteristics, occupational stress and work-life balance, with an explanatory power of 42.2%. Conclusion: Study findings show that it is necessary to promote wellness in call center workers with differentiated strategies according to call characteristics, occupational stress and work-life balance. This implies that it is necessary to understand the call characteristics and patterns of workers and to provide an innovative wellness program tailored to individual characteristics for an effective management of the emotional labor and occupational stress.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1382-1399
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• 2023

Pressure relief valve (PRV) is one of the important control valves used in nuclear power plants, and its sealing performance is crucial to ensure the safety and function of the entire pressure system. For the sealing performance improving purpose, an explicit function that accounts for all design parameters and can accurately describe the relationship between the multi-design parameters and the seal performance is essential, which is also the challenge of the valve seal design and/or optimization work. On this basis, a surrogate model-based design optimization is carried out in this paper. To obtain the basic data required by the surrogate model, both the Finite Element Model (FEM) and the Computational Fluid Dynamics (CFD) based numerical models were successively established, and thereby both the contact stresses of valve static sealing and dynamic impact (between valve disk and nozzle) could be predicted. With these basic data, the polynomial chaos expansion (PCE) surrogate model which can not only be used for inputs-outputs relationship construction, but also produce the sensitivity of different design parameters were developed. Based on the PCE surrogate model, a new design scheme was obtained after optimization, in which the valve sealing stress is increased by 24.42% while keeping the maximum impact stress lower than 90% of the material allowable stress. The result confirms the ability and feasibility of the method proposed in this paper, and should also be suitable for performance design optimizations of control valves with similar structures.

• Steel and Composite Structures
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• v.47 no.5
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• pp.551-568
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• 2023

In this research, a new two-dimensional (2D) and quasi three-dimensional (quasi-3D) higher order shear deformation theory is devised to address the bending problem of functionally graded plates resting on an elastic foundation. The displacement field of the suggested theories takes into account a parabolic transverse shear deformation shape function and satisfies shear stress free boundary conditions on the plate surfaces. It is expressed as a combination of trigonometric and exponential shear shape functions. The Pasternak mathematical model is considered for the elastic foundation. The material properties vary constantly across the FG plate thickness using different distributions as power-law, exponential and Mori-Tanaka model. By using the virtual works principle and Navier's technique, the governing equations of FG plates exposed to sinusoidal and evenly distributed loads are developed. The effects of material composition, geometrical parameters, stretching effect and foundation parameters on deflection, axial displacements and stresses are discussed in detail in this work. The obtained results are compared with those reported in earlier works to show the precision and simplicity of the current formulations. A very good agreement is found between the predicted results and the available solutions of other higher order theories. Future mechanical analyses of three-dimensionally FG plate structures can use the study's findings as benchmarks.

• Journal of Aerospace System Engineering
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• v.17 no.2
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• pp.1-8
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• 2023

Power transmission shafts in rotary wing aircraft use a hollow shaft to reduce weight. We can apply linear elastic fracture mechanics to predict crack propagation behavior. This paper predicted crack growth life of a hollow shaft with a circumferential through-type crack by finite element analysis. A 2D finite element model was created by applying a torsion and forming elements considering cracks. We defined the initial crack length and performed the finite element analysis by increasing the crack length to derive stress intensity factor at crack tips. We defined the length just prior to the stress intensity factor exceeding the fracture toughness as the crack limit length. We calculated the crack limit length using a handbook and numerically integrated the crack growth rate equation to derive growth life of each crack. The growth life of each crack was compared to verify the proposed finite element analysis method.

• 한국노년학
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• v.34 no.2
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• pp.277-297
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• 2014

This study investigates the predictors of the relationship quality between grandparents and grandchildren in emerging adulthood. Participants were 501 grandchildren with at least one living maternal/paternal grandparents. According to the t-test, the frequency of contact with maternal grandparents, the similarity of value to paternal grandparents, and the affective solidarity with paternal grandparents were significantly different depending on the grandchildren's sex. Results of multiple regression analyses indicated that the relationships of father-grandparents and mother-grandparents, the frequency of contact, and the similarities of value significantly predicted the affective solidarity between grandparents and grandchildren. However, the relative predictive power of these variables was different by the lineage and the grandchildren's sex.

• Steel and Composite Structures
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• v.44 no.5
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• pp.721-740
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• 2022

In this paper, an accurate kinematic model has been developed to study the mechanical response of functionally graded (FG) sandwich beams, mainly covering the bending, buckling and free vibration problems. The studied structure with homogeneous hardcore and softcore is considered to be simply supported in the edges. The present model uses a new refined shear deformation beam theory (RSDBT) in which the displacement field is improved over the other existing high-order shear deformation beam theories (HSDBTs). The present model provides good accuracy and considers a nonlinear transverse shear deformation shape function, since it is constructed with only two unknown variables as the Euler-Bernoulli beam theory but complies with the shear stress-free boundary conditions on the upper and lower surfaces of the beam without employing shear correction factors. The sandwich beams are composed of two FG skins and a homogeneous core wherein the material properties of the skins are assumed to vary gradually and continuously in the thickness direction according to the power-law distribution of volume fraction of the constituents. The governing equations are drawn by implementing Hamilton's principle and solved by means of the Navier's technique. Numerical computations in the non-dimensional terms of transverse displacement, stresses, critical buckling load and natural frequencies obtained by using the proposed model are compared with those predicted by other beam theories to confirm the performance of the proposed theory and to verify the accuracy of the kinematic model.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.841-852
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• 2023

Gas turbines, which are used as generators for frequency regulation of the domestic power system, are increasing in use due to the carbon-neutral policy, quick startup and shutdown, and high thermal efficiency. Since the gas turbine rotates the turbine using high-temperature flame, the turbine inlet temperature is acting as a key factor determining the performance and lifespan of the device. However, since the inlet temperature cannot be directly measured, the temperature calculated by the manufacturer is used or the temperature predicted based on field experience is applied, which makes it difficult to operate and maintain the gas turbine in a stable manner. In this study, we present a model that can predict the inlet temperature of a reheat gas turbine based on Deep Neural Network (DNN), which is widely used in artificial neural networks, and verify the performance of the proposed DNN based on actual data.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.67-77
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• 2024

Increasing energy efficiency in factories is an activity aimed at optimizing resource allocation in manufacturing processes to establish production plans. However, this strategy may not apply effectively when night shifts are unavoidable. Additionally, continuous fluctuations in production requirements pose challenges for its implementation in the factory. Recently, with the rapid proliferation of electric vehicles (EVs), technology utilizing electric vehicle batteries as energy storage systems has gained attention. Technology using these batteries can be an alternative for factory energy management. In this paper, a factory energy management method using EV batteries is proposed. The proposed method is analyzed using PSCAD/EMTDC software, considering the state of charge of EV batteries and Time-of-Use (TOU) rates. The proposed method was compared with production scheduling established considering predicted power usage and TOU rates. As a result, production scheduling saved 4,152 KRW per day, while the proposed method saved 7,286 KRW in electricity costs. Through this paper, the possibility of utilizing EV batteries for factory energy management has been demonstrated.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.454-460
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• 2009

In this paper, we propose a residual echo and noise reduction scheme by using linear predictor for hands-free telephony applications. The proposed scheme whitens residual echo by the linear prediction during the non double-talk. But whitened residual echo signal still has speech characteristics. In this scheme, the whitened residual echo signal is more whitened by using the power of the linear prediction error signal and the linear predicted signal. After whitening process, near-end speech and ambient noise is present during double-talk but white noise will appear during non double-talk situation. By linearly predicting again the combined signal of the near-end speech and the whitened signal, the ambient noise is removed. Through computer simulation, it is shown that the proposed method performs well at the side of AIC (acoustic interference cancellation).