• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.27 no.3
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• pp.245-256
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• 2017

Objectives: The present study is aimed at performing real-time measurement of fibrous materials using an F-1 fiber monitor, investigating the correlations between the measurements and environmental conditions, and assessing the feasibility of the use of the monitor in actual exposure assessments based on the accuracy and reliability of the device. Methods: Asbestos specimens with a fixed asbestos content were dispersed in a chamber and collected with a particle measuring test device. Measurements obtained by the existing PCM method, and with the F-1 fiber monitor were compared. In addition, concentrations of asbestos fibers obtained by the PCM method, the TEM method, and the F-1 fiber monitor were compared with that of specific ABS scenarios in NOA regions. Correlations of asbestos contents in soil and weather conditions with each method of measurement were analyzed. Results: Laboratory results showed that levels of asbestos fibers measured with each method increased as fiber contents in soil increased. In the accuracy and reproducibility assessment, no significant differences were found between the different methods of measurement. On-site assessment results showed positive correlations among the methods, and these correlations were less significant compared with what was shown by the laboratory results. Levels of asbestos fibers increased as asbestos contents in soil increased, and as temperature increased. Levels of asbestos fibers decreased as humidity increased, and wind speed did not significantly affect the extent to which asbestos fibers were scattered. Conclusions: While it would be premature to replace existing methods with the use of F-1 fiber monitors in real sites based on the results of this study, the monitor may be useful in the screening of the sites, which assesses hazard levels in different regions. Replacement of existing methods with the use of F-1 fiber monitors may be possible after the limitations identified in this study are overcome, and additional assessment data are obtained and reviewed under different conditions to confirm the reliability of the monitor in future research. Obtained assessment results may be used as basic data for the assessment of asbestos hazard in NOA regions.

• Journal of Distribution Science
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• v.11 no.3
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• pp.5-11
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• 2013

Purpose - Going concern is one of fundamental concepts in accounting and auditing and sometimes the assessment of a company's going concern status that is a tough process. Various going concern prediction models' based on statistical and data mining methods help auditors and stakeholders suggested in the previous literature. Research design - This paper employs a data mining approach to prediction of going concern status of Iranian firms listed in Tehran Stock Exchange using Particle Swarm Optimization. To reach this goal, at the first step, we used the stepwise discriminant analysis it is selected the final variables from among of 42 variables and in the second stage; we applied a grid-search technique using 10-fold cross-validation to find out the optimal model. Results - The empirical tests show that the particle swarm optimization (PSO) model reached 99.92% and 99.28% accuracy rates for training and holdout data. Conclusions - The authors conclude that PSO model is applicable for prediction going concern of Iranian listed companies.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.5
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• pp.552-560
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• 2013

In this paper, an optimization algorithm for the block assembly accuracy control assessment is proposed with consideration for the current block assembly process and accuracy control procedure used in the shipbuilding site. The objective function of the proposed algorithm consists of root mean square error of the distances between design and measured data of the other control points with respect to a specific point of the whole control points. The control points are divided into two groups: points on the control line and the other points. The grouped data are used as criteria for determining the combination of 6 degrees of freedom in the registration process when constituting constraints and calculating objective function. The optimization algorithm is developed by using combination of the sampling method and the point to point relation based modified ICP algorithm which has an allowable error check procedure that makes sure that error between design and measured point is under allowable error. According to the results from the application of the proposed algorithm with the design and measured data of two blocks data which are verified and validated by an expert in the shipbuilding site, it implies that the choice of whole control points as target points for the accuracy calculation shows better results than that of the control points on the control line as target points for the accuracy of the calculation and the best optimized result can be acquired from the accuracy calculation with a fixed point on the control line as the reference point of the registration.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1389-1403
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• 2021

Disaster management using VHR (very high resolution) satellite images supports rapid damage assessment and also offers detailed information of the damages. However, the acquisition of pre-event VHR satellite images is usually limited due to the long revisit time of VHR satellites. The absence of the pre-event data can reduce the accuracy of damage assessment since it is difficult to distinguish the changed region from the unchanged region with only post-event data. To address this limitation, in this study, we conducted the wildfire-induced change detection on national wildfire cases using post-fire VHR satellite images and GIS (Geographic Information System) data. For GIS data, a national land cover map was selected to simulate the pre-fire NIR (near-infrared) images using the spatial information of the pre-fire land cover. Then, the simulated pre-fire NIR images were used to analyze bi-temporal NDVI (Normalized Difference Vegetation Index) correlation for unsupervised change detection. The whole process of change detection was performed on a superpixel basis considering the advantages of superpixels being able to reduce the complexity of the image processing while preserving the details of the VHR images. The proposed method was validated on the 2019 Gangwon wildfire cases and showed a high overall accuracy over 98% and a high F1-score over 0.97 for both study sites.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.53-63
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• 2024

Biodiversity holds significant importance within the framework of environmental impact assessment, being utilized in site selection for development, understanding the surrounding environment, and assessing the impact on species due to disturbances. The field of environmental impact assessment has seen substantial research exploring new technologies and models to evaluate and predict biodiversity more accurately. While current assessments rely on data from fieldwork and literature surveys to gauge species richness indices, limitations in spatial and temporal coverage underscore the need for high-resolution biodiversity assessments through species richness mapping. In this study, leveraging data from the 4th National Ecosystem Survey and environmental variables, we developed a species distribution model using Random Forest. This model yielded mapping results of 24 mammalian species' distribution, utilizing the species richness index to generate a 100-meter resolution map of species richness. The research findings exhibited a notably high predictive accuracy, with the species distribution model demonstrating an average AUC value of 0.82. In addition, the comparison with National Ecosystem Survey data reveals that the species richness distribution in the high-resolution species richness mapping results conforms to a normal distribution. Hence, it stands as highly reliable foundational data for environmental impact assessment. Such research and analytical outcomes could serve as pivotal new reference materials for future urban development projects, offering insights for biodiversity assessment and habitat preservation endeavors.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Coupled systems mechanics
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• v.12 no.6
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• pp.539-555
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• 2023

Although some prediction models have successfully developed for ultra-high performance concrete (UHPC), they do not provide insights and explicit relations between all constituents and its consistency, and compressive strength. In the present study, based on the experimental results, several mathematical models have been evaluated to predict the consistency and the 28-day compressive strength of UHPC. The models used were Linear, Logarithmic, Inverse, Power, Compound, Quadratic, Cubic, Mixed, Sinusoidal and Cosine equations. The applicability and accuracy of these models were investigated using experimental data, which were collected from literature. The comparisons between the models and the experimental results confirm that the majority of models give acceptable prediction with a high accuracy and trivial error rates, except Linear, Mixed, Sinusoidal and Cosine equations. The assessment of the models using numerical methods revealed that the Quadratic and Inverse equations based models provide the highest predictability of the compressive strength at 28 days and consistency, respectively. Hence, they can be used as a reliable tool in mixture design of the UHPC.

• Journal of Environmental Impact Assessment
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• v.20 no.1
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• pp.71-78
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• 2011

This paper aims to assessing impact of reduction of non-point source pollution in the Bokha Stream watershed. The BASINS/HSPF model was calibrated and verified for water flow and water qualities using Total Maximum Daily Load 8days data from 2006 to 2007. Accuracy of the BASINS/HSPF models in simulating hydrology and water quality was compared and there were somewhat differences of statistical results, but water flow and water quality were simulated in good conditions over the study period. The applicability of models was tested to evaluate non-point source control scenarios to response hydrology and water quality in the Bokha stream using various measures which include BMPs approach and change of landuse. The evaluation of reduction of non-point source pollution was developed using load-duration curve. Despite strong reduction of non-point source, there are not satiated target quality at low flow season.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.5 no.2
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• pp.12-23
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• 1987

This paper deals with the analysis and adjustment computation for the Korean 1st order levelling data obtained by the National Geography Institute from 1974 to 1986. An evaluation of the accuracy of level lines using forward/back discrepancies and adjustment residuals has been carried out. A general assessment of the network in the light of current standards with other countries is discussed. The results of this study show that systematic errors are relatively high but the accuracy of the primary levelling is good enough within the limitation of precision levelling according to International Association of Geodesy(IAG) Resolution, and therefore it is concluded that this levelling will satisfy the present mapping and engineering survey.