• Safety and Health at Work
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• v.13 no.3
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• pp.326-335
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• 2022

Background: One of the important actions for enhancing human reliability in any industry is assessing human error probability (HEP). The HEART technique is a robust tool for calculating HEP in various industries. The traditional HEART has some weaknesses due to expert judgment. For these reasons, a hybrid model is presented in this study to integrate HEART with Best-Worst Method. Materials Method: In this study, the blasting process in an iron ore mine was investigated as a case study. The proposed HEART-BWM was used to increase the sensitivity of APOA calculation. Then the HEP was calculated using conventional HEART formula. A consistency ratio was calculated using BWM. Finally, for verification of the HEART-BWM, HEP calculation was done by traditional HEART and HEART-BWM. Results: In the view of determined HEPs, the results showed that the mean of HEP in the blasting of the iron ore process was 2.57E-01. Checking the full blast of all the holes after the blasting sub-task was the most dangerous task due to the highest HEP value, and it was found 9.646E-01. On the other side, obtaining a permit to receive and transport materials was the most reliable task, and the HEP was 8.54E-04. Conclusion: The results showed a good consistency for the proposed technique. Comparing the two techniques confirmed that the BWM makes the traditional HEART faster and more reliable by performing the basic comparisons.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.385-396
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• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.390-398
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• 2022

Ultrasound examination using ultrasound equipment is an ultrasound device that images human organs using sound waves and is used in various areas such as diagnosis, follow-up, and treatment of diseases. However, if the quality of ultrasound equipment is not guaranteed, the possibility of misdiagnosis increases, and the diagnosis rate decreases. Accordingly, The Korean Society of Radiology and Korea society of Ultrasound in Medicine presented guidelines for quality management of ultrasound equipment using ATS-539 phantom. The DenseNet201 classification algorithm shows 99.25% accuracy and 5.17% loss in the Dead Zone, 97.52% loss in Axial/Lateral Resolution, 96.98% accuracy and 20.64% loss in Sensitivity, 93.44% accuracy and 22.07% loss in the Gray scale and Dynamic Range. As a result, it is the best and is judged to be an algorithm that can be used for quantitative evaluation. Through this study, it can be seen that if quantitative evaluation using artificial intelligence is conducted in the qualitative evaluation item of ultrasonic equipment, the reliability of ultrasonic equipment can be increased with high accuracy.

• Anxiety and mood
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• v.19 no.1
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• pp.10-18
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• 2023

Objective : The 18-item Obsessive-Compulsive Inventory-Revised (OCI-R) is widely employed to assess symptoms of obsessive-compulsive disorder (OCD). However, this instrument's factor structure does not align with contemporary dimensional models of OCD. Therefore, the objective of this study was to examine psychometric properties of the 12-item Korean OCI (OCI-12) on four obsessive-compulsive symptom dimensions, in patients with OCD. Methods : A total of 157 patients with OCD and 51 healthy controls completed psychological measures, including the OCI-R, Dimensional Obsessive-Compulsive Scale (DOCS), and scales evaluating anxiety and depressive symptoms. Pychometric characteristics of the OCI-12 with three neutralizing and three hoarding items eliminated from the OCI-R, were analyzed. Results : All OCI-12 items registered excellent internal consistency at 0.83. Confirmatory factor analysis revealed strong association between individual items and their proposed latent factors (i.e., subscales). Convergent validity was appropriate. A high correlation was particularly observed for the DOCS score (r=0.71, p<0 .001). Moreover, the OCI-12 was as sensitive as the OCI-R for determining effects of empirically supported treatment for OCD. Conclusion : The OCI-12 is a 12-item measure that adheres to the prevailing 4-factor model of OCD dimensions. Like OCI-R, it possesses good to excellent psychometric properties, including reliability, validity, and sensitivity to treatment.

• Korean Journal of Veterinary Research
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• v.63 no.2
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• pp.19.1-19.6
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• 2023

Rapid immunochromatography test (RICT) kits are commonly used for the diagnosis of canine parvovirus (CPV) because of their rapid turnaround time, simplicity, and ease of use. However, the potential for cross-reactivity and low sensitivity can yield false-positive or false-negative results. There are 4 genotypes of CPV. Therefore, evaluating the performance and reliability of RICT kits for CPV detection is essential to ensure accurate diagnosis for appropriate treatment. In this study, we evaluated the performance of commercial RICT kits in the diagnosis of all CPV genotypes. The cross-reactivity of 6 commercial RICT kits was evaluated using 8 dog-related viruses and 4 bacterial strains. The limit of detection (LOD) was measured for the 4 genotypes of CPV and feline panleukopenia virus. The tested kits showed no cross-reactivity with the 8 dog-related viruses or 4 bacteria. Most RICT kits showed strong positive results for CPV-2 variants (CPV-2a, CPV-2b, and CPV-2c). However, the 2 kits produced negative results for CPV-2 or CPV-2b at a titer of 10⁵ FAID₅₀/mL, which may result in inaccurate diagnoses. Therefore, some kits need to improve their LOD by increasing their binding efficiency to detect all CPV genotypes.

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.587-595
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• 2008

Many studies on bus scheduling optimization have been done from the 1960s to recent years for establishing rational bus schedule plan that can improve convenience of bus passengers and minimize unnecessary runs. After 2000, as part of the Intelligent Transport Systems (ITS), the importance of the schedule management and providing schedule information through bus schedule optimization has become a big issue, and much research is being done to develop optimization models that will increase bus passenger convenience and, on the side of bus management, minimize unnecessary bus operation. The purpose of this study is to calculate the optimal bus frequency and create a timetable for each bus stop by applying DTR or DTRC model that use data for each bus stop and route segment. Model verification process was implemented using data collected from bus management system (BMS) and integrated transit-fare card system for bus route of Seoul's No. 472 line. In order to evaluate the reliability and uncertainty of optimal solution, sensitivity analysis was implemented for the various parameters and assumptions used in the bus scheduling model.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.1
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• pp.17-29
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• 2024

This study was conducted to select the restoration priority of forest damage sites in Gangwon Province. We first identified the status of damaged areas. We then selected restoration evaluation indicators through a literature review. We then set weights for these indicators through expert surveys. We next acquired data that can represent these indicators and spatially mapped them. Finally, we prioritized the restoration target sites by taking the weights. The results of the study showed that disaster sensitivity and ecologicality are important criteria for selecting the restoration priority of damage sites. The analysis showed that damage sites in Doam, Jeongseon, Samcheok and Inje are in urgent need of restoration. The results of this study are significant in that they selected the restoration priority of damage sites in Gangwon Province based on the restoration priority evaluation criteria selected based on expert surveys. However, the priority restoration areas derived from the results of this study are not actually implementing restoration projects at present. Therefore, it is judged that it would be efficient in various aspects to establish the restoration priority area based on scientific analysis techniques and carry out the project for efficient implementation of the restoration project. In this study, it can be pointed out that the priority of restoration of damage sites was derived based on data from the past due to the limitation of data acquisition. However, the fact that the priority restoration area inferred based on past data has been restored over time has improved the reliability of the study by verifying the usefulness of the priority extraction technique. In the future, if the priority of damage sites is extracted by extracting the restoration target area boundary through the latest data based on the methodology applied in this study, it is considered that it will be available as a result that can be applied to the field.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4367-4381
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• 2023

Nuclear power plants are increasingly being equipped with digital I&C systems. Although some probabilistic safety assessment (PSA) models for the digital I&C of nuclear power plants have been constructed, there is currently no specific internationally agreed guidance for their modeling. This paper presents an initiative by the OECD Nuclear Energy Agency called "Digital I&C PSA - Comparative application of DIGital I&C Modelling Approaches for PSA (DIGMAP)", which aimed to advance the field towards practical and defendable modeling principles. The task, carried out in 2017-2021, used a simplified description of a plant focusing on the digital I&C systems important to safety, for which the participating organizations independently developed their own PSA models. Through comparison of the PSA models, sensitivity analyses as well as observations throughout the whole activity, both qualitative and quantitative lessons were learned. These include insights on failure behavior of digital I&C systems, experience from models with different levels of abstraction, benefits from benchmarking as well as major contributors to the core damage frequency and those with minor effect. The study also highlighted the challenges with modeling of large common cause component groups and the difficulties associated with estimation of key software and common cause failure parameters.