• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Journal of Platform Technology
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• v.11 no.4
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• pp.62-70
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• 2023

This research is one of a series of studies to develop a system to help agricultural producers and sellers determine when and how much to ship products to the wholesale market to maximize their profit. The purpose of this research is to incorporate the time-varying quality change effect, which was not used in the previous agricultural and marine product shipping model. The researchers developed four models to measure the quality change effect: quality declining steadily over time, quality declining rapidly at first and then slowly, quality declining first slowly and then rapidly, and quality rising over time and then decreasing again. According to the results of an empirical analysis of the effect of each model's quality change effect on shipments for apples traded in the Garak Wholesale Market from 2014 to 2021, statistical significance was found in the quality change effect of all four models. And there was no significant difference in explanatory power between the four models. Therefore, any of the four models should be introduced into the decision-making system for shipping time for apples.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.151-160
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• 2023

The Hadith is the second source of Islamic jurisprudence after Qur'an. Both sources are indispensable for muslims to practice Islam. All Ahadith are collected and are written. But most books of Hadith contain Ahadith that can be weak or rejected. So, quite a long time, scholars of Hadith have defined laws, rules and principles of Hadith to know the correct Hadith (Sahih) from the fair (Hassen) and weak (Dhaif). Unfortunately, the application of these rules, laws and principles is done manually by the specialists or students until now. The work presented in this paper is part of the automatic treatment of Hadith, and more specifically, it aims to automatically process the chain of narrators (Hadith Isnad) to find its different components and affect for each component its own tag using a statistical method: the Hidden Markov Models (HMM). This method is a power abstraction for times series data and a robust tool for representing probability distributions over sequences of observations. In this paper, we describe an important tool in the Hadith isnad processing: A chunker with HMM. The role of this tool is to decompose the chain of narrators (Isnad) and determine the tag of each part of Isnad (POI). First, we have compiled a tagset containing 13 tags. Then, we have used these tags to manually conceive a corpus of 100 chains of narrators from "Sahih Alboukhari" and we have extracted a lexicon from this corpus. This lexicon is a set of XML documents based on HPSG features and it contains the information of 134 narrators. After that, we have designed and implemented an analyzer based on HMM that permit to assign for each part of Isnad its proper tag and for each narrator its features. The system was tested on 2661 not duplicated Isnad from "Sahih Alboukhari". The obtained result achieved F-scores of 93%.

• Nuclear Engineering and Technology
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• v.26 no.4
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• pp.516-525
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• 1994

Since the TMI and Chernobyl accidents, many passive safety features are suggested in advanced reactors in order to enhance the safety in future nuclear power plants. In order to verify the effectiveness and provide the data for detailed design of passive cooling system, in the present work, the effects of air inlet position and external condition on the natural circulated air flow rate and the natural and forced convective heat transfer coefficient have been investigated for the one-side heated closed path such as the passive containment cooling system of the Westinghouse's AP-600. A series of experiments have been peformed with the 1/26th scaled segment type test facility of the AP-600 passive containment. Under natural and forced convection, the air velocities and temperatures are measured at several points of the air flow path. The experimental result are compared with a simple one-dimensional model and it shows a good agreement.

• Journal of the Korean Geotechnical Society
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• v.23 no.8
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• pp.69-76
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• 2007

Axial behavior of tapered piles is affected by taper angle, stress state of soils, soil frictional angle and pile-soil interface friction angle. In this paper, a series of model pile load tests were performed using a calibration chamber in order to investigate the effect of taper angle on the axial response of cast-in-place tapered piles in sand. According to results of the tests, as taper angle of piles increased, the shaft load capacity of piles increased but its base load capacity decreased. The unit base load capacity of piles increased with increasing taper angle for medium sand but decreased for dense sand. The ratio of shaft to total load capacity increased with increasing taper angle and with decreasing relative density of soils. The test results also showed that total load capacity per unit pile volume increased with increasing taper angle for medium sand, but it decreased for dense sand. Therefore, it can be stated that tapered piles are economically more beneficial for medium sand than for dense sand.

• Structural Engineering and Mechanics
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• v.81 no.2
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• pp.179-194
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• 2022

The bending, buckling and free vibration responses of functionally graded material (FGM) beams are investigated semi-analytically by the scaled boundary finite element method (SBFEM) in this paper. In the concepts of the SBFEM, the dimension of computational domain can be reduced by one, therefore only the axial dimension of the beam is discretized using the higher order spectral element, which reduces the amount of calculation and greatly improves the calculation efficiency. The governing equation of FGM beams is derived in detail by the means of the principle of virtual work. Compared with the higher-order beam theory, fewer parameters and simpler control equations are used. And the governing equation is transformed into a first-order ordinary differential equation by introducing intermediate variables. Analytical solutions of the governing equation can be obtained by pade series expansion in the direction of thickness. Numerical example are compared with the numerical solutions provided by the previous researchers to verify the accuracy and applicability of the proposed method. The results show that the proposed formulations can quickly converge to the reference solutions by increasing the order of higher order spectral elements, and high accuracy can be achieved by using a small number of the elements. In addition, the influence of the structural sizes, material properties and boundary conditions on the mechanical behaviors of FG beams subjected to different load types is discussed.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.939-946
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• 2023

Comprehensive Research Facility for Fusion Technology (CRAFT) is an integration of different demonstrating or testing facilities, which aim to develop the critical technology or composition system towards the fusion reactor. Due to the importance and challenge of the negative ion based neutral beam injection (NNBI), a NNBI test facility is included in the framework of CRAFT. The initial object of CRAFT NNBI test facility is to obtain a H⁰ beam power of 2 MW at the energy of 200-400 keV for the pulse duration of 100 s. Inside the negative ion accelerator of NNBI system, the interactions of the negative ions with the background gas and electrodes can generate abundant stray electrons. The stray electrons can be further accelerated and dumped on the electrodes or eject from the accelerator. The stray electrons, including the ejecting electrons, cause the unwanted particle and heat flux onto the electrodes and the inner components of beamline (especially the temperature sensitive cryopump). The suppression of the stray electrons from the CRAFT accelerator is carried out via a series of design and simulation works. The paper focuses the influence of different magnetic field configurations on the stray electrons and the character of the ejecting electrons.

• Smart Structures and Systems
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• v.31 no.3
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• pp.259-273
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• 2023

Real-time hybrid simulation (RTHS) is an effective experimental technique for structural dynamic assessment. However, time delay causes displacement de-synchronization at the interface between the numerical and physical substructures, negatively affecting the accuracy and stability of RTHS. To this end, the authors have proposed a model-based adaptive control strategy with a Kalman filter (MAC-KF). In the proposed method, the time delay is mainly mitigated by a parameterized feedforward controller, which is designed using the discrete inverse model of the control plant and adjusted using the KF based on the displacement command and measurement. A feedback controller is employed to improve the robustness of the controller. The objective of this study is to further validate the power of dealing with a nonlinear control plant and to investigate the potential challenges of the proposed method through actual experiments. In particular, the effect of the order of the feedforward controller on tracking performance was numerically investigated using a nonlinear control plant; a series of actual RTHS of a frame structure equipped with a magnetorheological damper was performed using the proposed method. The findings reveal significant improvement in tracking accuracy, demonstrating that the proposed method effectively suppresses the time delay in RTHS. In addition, the parameters of the control plant are timely updated, indicating that it is feasible to estimate the control plant parameter by KF. The order of the feedforward controller has a limited effect on the control performance of the MAC-KF method, and the feedback controller is beneficial to promote the accuracy of RTHS.

• Journal of Korean Academy of Nursing
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• v.52 no.1
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• pp.36-51
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• 2022

Purpose: The purpose of this study was to examine the effects of electroencephalogram (EEG) biofeedback training for emotion regulation and brain homeostasis on anxiety about COVID-19 infection, impulsivity, anger rumination, meta-mood, and self-regulation ability of late adolescents in the prolonged COVID-19 pandemic situation. Methods: A non-equivalent control group pretest-posttest design was used. The participants included 55 late adolescents in the experimental and control groups. The variables were evaluated using quantitative EEG at pre-post time points in the experimental group. The experimental groups received 10 sessions using the three-band protocol for five weeks. The collected data were analyzed using the Shapiro-Wilk test, Wilcoxon rank sum test, Wilcoxon signed-rank test, t-test and paired t-test using the SAS 9.3 program. The collected EEG data used a frequency series power spectrum analysis method through fast Fourier transform. Results: Significant differences in emotion regulation between the two groups were observed in the anxiety about COVID-19 infection (W = 585.50, p = .002), mood repair of meta-mood (W = 889.50, p = .024), self-regulation ability (t = - 5.02, p < .001), self-regulation mode (t = - 4.74, p < .001), and volitional inhibition mode (t = - 2.61, p = .012). Neurofeedback training for brain homeostasis was effected on enhanced sensory-motor rhythm (S = 177.00, p < .001) and inhibited theta (S = - 166.00, p < .001). Conclusion: The results demonstrate the potential of EEG biofeedback training as an independent nursing intervention that can markedly improve anxiety, mood-repair, and self-regulation ability for emotional distress during the COVID-19 pandemic.