• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Explosives and Blasting
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• v.39 no.2
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• pp.1-14
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• 2021

Recently, with the development of high-performance processing devices such as GPGPU, a three-dimensional dynamic analysis technique that can replace expensive rock material impact tests has been actively developed in the defense and aerospace fields. Experimentally observing or measuring fracture processes occurring in rocks subjected to high impact loads, such as blasting and earth penetration of small-diameter missiles, are difficult due to the inhomogeneity and opacity of rock materials. In this study, a three-dimensional dynamic fracture process analysis technique (3D-DFPA) was developed to simulate the fracture behavior of rocks due to impact. In order to improve the operation speed, an algorithm capable of GPGPU operation was developed for explicit analysis and contact element search. To verify the proposed dynamic fracture process analysis technique, the dynamic fracture toughness tests of the Straight Notched Disk Bending (SNDB) limestone samples were simulated and the propagation of the reflection and transmission of the stress waves at the rock-impact bar interfaces and the fracture process of the rock samples were compared. The dynamic load tests for the SNDB sample applied a Pulse Shape controlled Split Hopkinson presure bar (PS-SHPB) that can control the waveform of the incident stress wave, the stress state, and the fracture process of the rock models were analyzed with experimental results.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.34-41
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• 2021

The purpose of this research is to improve analysis methods of determining the contents of fatty acids in infant formulas and follow-up formulas. A gas chromatography (GC) method was performed on a GC system coupled to flame ionization detector, with a fused silica capillary column (SP2560, 100 m×0.25 mm, 0.20 ㎛). The method was validated using standard reference material (SRM, NIST 1849a). Performance parameters for method validation such as specificity, linearity, limits of detection (LOD) and quantification (LOQ), accuracy and precision were examined. The linearity of standard solution with correlation coefficient was higher than 0.999 in the range of 0.1-5 mg/mL. The LOD and LOQ were 0.01-0.06 mg/mL and 0.03-0.2 mg/mL, respectively. The recovery using standard reference material was confirmed and the precision was found to be between 0.8% and 2.9% relative standard deviation (RSD). Optimized methods were applied in sample analysis to verify the reliability. All the tested products had acceptable contents of fatty acids compared with component specification for nutrition labeling. The result of this research will provide efficient experimental information and strengthen the management of nutrients in infant formula and follow-up formula.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.512-519
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• 2023

Noise pollution poses significant challenges to human well-being and marine ecosystems. It is primarily caused by the flow around ships and marine installations, emphasizing the need for accurate noise evaluation of flow noise to ensure environmental safety. Existing flow noise analysis methods for underwater environments typically use a hybrid method combining computational fluid dynamics and Ffowcs Williams-Hawkings acoustic analogy. However, this approach has limitations, neglecting near-field effects such as reflection, scattering, and diffraction of sound waves. In this study, an alternative using direct method flow noise analysis via the lattice Boltzmann method (LBM) is incorporated. The LBM provides a more accurate representation of the underwater structural boundaries and acoustic wave effects. Despite challenges in underwater environments due to numerical instabilities, a novel DM-TS LBM collision operator has been developed for stable implementations for hydroacoustic applications. This expands the LBM's applicability to underwater structures. Validation through flow noise analysis in pipe orifice demonstrates the feasibility of near-field analysis, with experimental comparisons confirming the method's reliability in identifying main pressure peaks from flow noise. This supports the viability of near-field flow noise analysis using the LBM.

• Journal of Environmental Health Sciences
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• v.49 no.2
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• pp.89-98
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• 2023

Background: Organophosphorus flame retardants (OPFRs) are a group of chemical substances used in building materials and plastic products to suppress or mitigate the combustion of materials. Although OPFRs are generally used in mixed form, information on their mixture toxicity is quite scarce. Objectives: This study aims to elucidate the toxicity and determine the types of interaction (e.g., synergistic, additive, and antagonistic effect) of OPFRs mixtures. Methods: Nine organophosphorus flame retardants, including TEHP (tris(2-ethylhexyl) phosphate) and TDCPP (tris(1,3-dichloro-2-propyl) phosphate), were selected based on indoor dust measurement data in South Korea. Nine OPFRs were exposed to the luminescent bacteria Aliivibrio fischeri for 30 minutes and the human hepatocyte cell line HepG2 for 48 hours. Chemicals with significant toxicity were only used for mixture toxicity tests in HepG2. In addition, the observed EC_x values were compared with the predicted toxicity values in the CA (concentration addition) prediction model, and the MDR (model deviation ratio) was calculated to determine the type of interaction. Results: Only four chemicals showed significant toxicity in the luminescent bacteria assays. However, EC₅₀ values were derived for seven out of nine OPFRs in the HepG2 assays. In the HepG2 assays, the highest to lowest EC₅₀ were in the order of the molecular weight of the target chemicals. In the further mixture tests, most binary mixtures show additive interactions except for the two combinations that have TPhP (triphenyl phosphate), i.e., TPhP and TDCPP, and TPhP and TBOEP (tris(2-butoxyethyl) phosphate). Conclusions: Our data shows OPFR mixtures usually have additivity; however, more research is needed to find out the reason for the synergistic effect of TPhP. Also, the mixture experimental dataset can be used as a training and validation set for developing the mixture toxicity prediction model as a further step.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.4
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• pp.259-267
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• 2022

The existing method of deriving the fail-safe design requirements for the domestic developed rotary-wing aircraft system may miss the factors that cause critical system function failures, when being applied to the latest integrated avionics system. It is because the existing method analyzes the severity effect of the failures caused by a single item. To solve the issue, we present a systematic analysis procedure for deriving fail-safe design requirements of system architecture by utilizing functional hazard assessment and development assurance level analysis of SAE ARP4754A, international standard for complex system development. To demonstrate that our proposed procedure can be a solution for the aforementioned issue, we set up experimental environments that include common factors that can cause critical function failures of a system, and we conducted a cross-validation with the existing method. As a result, we showed that the proposed procedure can identify the potential critical common factors that the existing method have missed, and that the proposed procedure can derive fail-safe design requirements to control the common factors.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Analytical Science and Technology
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• v.19 no.5
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• pp.369-375
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• 2006

Calibration gas mixtures were prepared using dynamic volumetric method according to ISO 6145-5 and the uncertainty was evaluated. Ten identical capillaries with 0.25 mm in inner diameter and 50 cm in length were applied in this system. Dilution ratio of parent gas was determined by the number of capillaries that passes parent gas and that passes balance gas through. Capillaries were made of Teflon which had good chemical stability against adsorption of gaseous substances. Mechanical valves were introduced in this system in order to minimize the thermal effect of solenoid valves. Concentration of prepared gases were compared with master grade standard gases in cylinders made by RiGAS Co. and calibration of the instrument were completed using comparison method according to ISO 6143. Experimental results showed that the coefficient of variance of diluted oxygen standard gases showed less then 0.2% in most dilution range, that of diluted hydrogen sulfide standard gases showed less then 1.0%. Therefore, it is proven that the standard gases prepared by this system are appropriate to be used as a calibration standards in ambient monitoring, etc.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2A
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• pp.281-288
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• 2008

The Prestressed Concrete (hereinafter PSC) box girder bridges with corrugated steel webs have been drawing an attention as a new structure type of PSC bridge fully utilizing the feature of concrete and steel. However, the previous study focused on the shear buckling of the corrugated steel web and development of connection between concrete flange and steel web. Therefore, it needs to perform a study on the torsional behavior and develop the rational torsional analysis model for PSC box girder with corrugated steel web. In this study, torsional analysis model is developed using Rausch's equation based on space truss model, equilibrium equation considering softening effect of reinforced concrete element and compatibility equation. Validation studies are performed on developed model through the comparison with the experimental results of loading test for PSC box girder with corrugated steel webs. Parametric studies are also performed to investigate the effect of prestressing force and concrete strength in torsional behavior of PSC box girder with corrugated steel web. The modified correction factor is also derived for the torsional coefficient of PSC box girder with corrugated steel web through the parametric study using the proposed anlaytical model.