• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.374-380
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• 2022

This paper shows the results of a study that predicts changes in seawater conditions in sea farms using machine learning-based long short term memory (LSTM) algorithms. Hardware was implemented using dissolved oxygen, salinity, nitrogen ion concentration, and water temperature measurement sensors to collect seawater condition information from sea farms, and transferred to a cloud-based Firebase database using LoRa communication. Using the developed hardware, seawater condition information around fish farms in Tongyeong and Geoje was collected, and LSTM algorithms were applied to learning results using these actual datasets to obtain predictive results showing 87% accuracy. Flask and REST APIs were used to provide users with predictive results for each of the four parameters, including dissolved oxygen. These predictive results are expected to help fishermen reduce significant damage caused by fish group death by providing changes in sea conditions in advance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.394-405
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• 2022

This paper presents the numerical estimation results of oil outflows from damaged single-hull and double-hull ships by using computational fluid dynamics (CFD) simulations. A CFD method for multi-phase flow analysis was used, and the effects of numerical parameters on oil flows was investigated. Numerical simulations were conducted to predict the changes in oil outflows under various damage conditions owing to grounding or collision accidents and verified through available experimental results. The present numerical results showed a good agreement with the experimental results according to the geometrical characteristics of single and double hulls. In particular, the oil outflows from double hulls accompanying complex interactions between water and oil were reasonably predicted a shown in the experiment. This study established a reliable CFD technique necessary for estimating the oil outflows of damaged ships.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3745-3765
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• 2022

The concrete structures related to nuclear safety are threatened by accidental impact loadings, mainly including the low-velocity drop-weight impact (e.g., spent fuel cask and assembly, etc. with the velocity less than 20 m/s) and high-speed projectile impact (e.g., steel pipe, valve, turbine bucket, etc. with the velocity higher than 20 m/s), while the existing studies are still limited in the impact resistant design of nuclear power plant (NPP), especially the primary RC slab. This paper aims to propose the numerical simulation and theoretical approaches to assist the impact-resistant design of RC slab in NPP. Firstly, the continuous surface cap (CSC) model parameters for concrete with the compressive strength of 20-70 MPa are fully calibrated and verified, and the refined numerical simulation approach is proposed. Secondly, the two-degree freedom (TDOF) model with considering the mutual effect of flexural and shear resistance of RC slab are developed. Furthermore, based on the low-velocity drop hammer tests and high-speed soft/hard projectile impact tests on RC slabs, the adopted numerical simulation and TDOF model approaches are fully validated by the flexural and punching shear damage, deflection, and impact force time-histories of RC slabs. Finally, as for the two low-velocity impact scenarios, the design procedure of RC slab based on TDOF model is validated and recommended. Meanwhile, as for the four actual high-speed impact scenarios, the impact-resistant design specification in Chinese code NB/T 20012-2019 is evaluated, the over conservation of which is found, and the proposed numerical approach is recommended. The present work could beneficially guide the impact-resistant design and safety assessment of NPPs against the accidental impact loadings.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• Nutrition Research and Practice
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• v.16 no.4
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• pp.464-475
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• 2022

BACKGROUND/OBJECTIVES: Increased levels of uremic toxins and decreased antioxidant capacity have a significant impact on the progression of chronic kidney disease (CKD). However, it remains unclear whether they interact with each other to mediate the damage of kidney function. The purpose of this study was to investigate whether uremic toxins (i.e., homocysteine and indoxyl sulfate [IS]), as well as glutathione-dependent antioxidant enzyme activities are dependently or independently associated with kidney function during different stages of CKD patients. SUBJECTS/METHODS: One hundred thirty-two patients diagnosed with CKD at stages 1 to 5 participated in this cross-sectional study. RESULTS: Patients who had reached an advanced CKD stage experienced an increase in plasma uremic toxin levels, along with decreased glutathione peroxidase (GSH-Px) activity. Plasma homocysteine, cysteine, and IS concentrations were all positively associated with each other, but negatively correlated to GSH-Px activity levels after adjusting for potential confounders in all CKD patients. Although plasma homocysteine, cysteine, IS, and GSH-Px levels were significantly associated with kidney function, only plasma IS levels still had a significant association with kidney function after these parameters were simultaneously adjusted. In addition, plasma IS could interact with GSH-Px activity to be associated with kidney function. CONCLUSIONS: IS plays a more dominant role than homocysteine and GSH-Px activity in relation to kidney function.

• Wind and Structures
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• v.34 no.2
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• pp.185-197
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• 2022

This study aimed to analyze the wind-induced mechanical energy (WME) of a proposed super high-rise and long-span transmission tower-line system (SHLTTS), which, in 2021, is the tallest tower-line system with the longest span. Anew index - the WME, accounting for the wind-induced vibration behavior of the whole system rather than the local part, was first proposed. The occurrence of the maximum WME for a transmission tower, with or without conductors, under synoptic winds, was analyzed, and the corresponding formulae were derived based on stochastic vibration theory. Some calculation data, such as the drag coefficient, dynamic parameters, windshielding areas, mass, calculation point coordinates, mode shape and influence function, derived from wind tunnel testing on reducedscale models and finite element software were used in calculating the maximum WME of the transmission tower under three cases. Then, the influence of conductors, wind speed, gradient wind height and wind yaw angle on WME components and the energy transfer relationship between substructures (transmission tower and conductor) were analyzed. The study showed that the presence of conductors increases the WME of transmission towers and changes the proportion of the mean component (MC), background component (BC) and resonant component (RC) for WME; The RC of WME is more susceptible to the wind speed change. Affected by the gradient wind height, the WME components decrease. With the RC decreasing the fastest and the MC decreasing the slowest; The WME reaches the its maximum value at the wind yaw angle of 30°. Due to the influence of three factors, namely: the long span of the conductors, the gradient wind height and the complex geometrical profile, it is important that the tower-line coupling effect, the potential for fatigue damage and the most unfavorable wind yaw angle should be given particular attention in the wind-resistant design of SHLTTSs

• Korean Journal of Acupuncture
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• v.39 no.4
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• pp.132-141
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• 2022

Objectives : Some acupoints are commonly utilized to treat a variety of diseases. The acupoints appear to have a wide range of effects caused by several mechanisms. The purpose of this study is to investigate into the potential role of microRNAs (miRNAs) in the multipotent effects of individual acupoint stimulation. Methods : We examined the miRNA expressions in the dorsal root ganglia (DRG) of neuropathic or inflammatory pain rats following ST36 and GB34 electroacupuncture (EA) stimulation. Neuropathic pain was induced by L5 spinal nerve ligation. Inflammatory pain was induced by knee joint injection of Complete Freund's adjuvant (CFA). EA was given under gaseous anesthesia with the same parameters (1mA, 2Hz, 30 min) in 5 consecutive days. Pain behaviors and miRNA expressions were analyzed. Results : In rats with neuropathic and inflammatory pain, EA treatments significantly enhanced the paw withdrawal threshold and weight-bearing force. After nerve injury, 36 miRNAs were upregulated in the DRG of neuropathic rats, while EA downregulated 10 of them. Furthermore, 14 miRNAs were downregulated following nerve damage, while one was increased by EA. 15 miRNAs were increased in the DRG of inflammatory rats following CFA injection, while 5 were downregulated by EA. Furthermore, 17 miRNAs were downregulated following CFA injection, while 7 were increased by EA. The miRNAs rno-miR-335, rno-miR-381-5p, rno-miR-1306-3p, and rno-miR-1839-3p were regulated by EA in both models. Conclusions : In two pain models, EA applied to ST36 and GB34 regulated miRNA expression differently. There appeared to be both acupoint-specific and non-specific miRNAs, and miRNA regulation of differential protein expression may modulate a variety of EA mechanisms.

• Food Science of Animal Resources
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• v.43 no.5
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• pp.840-858
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• 2023

This study was designed to compare the quality changes in mackerel fillets stored under different conditions by using hyperspectral imaging (HSI) techniques. Fillets packaged in vacuum were stored for six days under five different conditions: refrigerated at 4℃ (R group); iced at 5±3℃ (I group); kept at an ambient of 17±2℃ (A group); frozen at -18℃ for 24 h and thawed in a refrigerator at 4℃ for 5 h on the sampling day (FTR group); FTR thawed in tap water instead of thawing in a refrigerator (FTW group). The FTR group had the lowest total bacterial count, drip loss, 2-thiobarbituric acid reactive substances, volatile basic nitrogen, and texture profile analysis values among groups during the entire storage period (p<0.05). Scanning electron microscopy revealed that the FTR group had less damage, while the other groups had shrunken muscle tissues. HIS integrated with the partial least squares model yielded reliable and efficient results, with high R²_cv values, for several quality parameters of the mackerel fillets. Overall, the FTR group, involving freezing and thawing in a refrigerator, appears to be the most favorable option for maintaining the quality of mackerel fillets, which could be practically implemented in the industry. HSI is a suitable and effective technique for determining the quality of mackerel fillets stored under different conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.491-493
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• 2022

Flood damage due to torrential rains and typhoons is occurring in many parts of the world. In this paper, we propose a water level prediction model using water level, precipitation, and humidity data, which are key parameters for flood prediction, as input data. Based on the LSTM and GRU models, which have already proven time-series data prediction performance in many research fields, different input datasets were constructed using the ASOS(Automated Synoptic Observing System) data and AWS(Automatic Weather System) data provided by the Korea Meteorological Administration, and performance comparison experiments were conducted. As a result, the best results were obtained when using ASOS data. Through this paper, a performance comparison experiment was conducted according to the input data, and as a future study, it is thought that it can be used as an initial study to develop a system that can make an evacuation decision in advance in connection with the flood risk determination model.