• Journal of Food Hygiene and Safety
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• v.38 no.5
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• pp.297-304
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• 2023

Domoic acid (DA), a neurotoxin produced naturally by diatoms, is responsible for incidents of amnesic shellfish poisoning. In this study, a modified analytical method was established to determine domoic acid in seafood using solid phase extraction cleanup and optimizing the amount of sample and extraction solvent to reduce interference effects. The modified method using high-performance liquid chromatography with ultraviolet detection was validated using three seafood matrices (mussel, red snow crab, and anchovy) at three concentrations (1, 2, and 4 mg/kg) and compared to the Food Code method. Compared to the Food Code method, the modified method showed better performance in terms of linearity (R²>0.999), detection limit (0.02-0.03 mg/kg), quantification limit (0.05-0.09 mg/kg), intra-/inter-day accuracy (86.2-100.4%), and intra-/inter-day precision (0.2-4.0%). Furthermore, the method was successfully applied for the analysis of 87 seafood samples marketed in Korea, and DA was detected at a low concentration of 140 ㎍/kg in one anchovy sample. These results suggest that the modified method can be used for routine determination of DA in seafood.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.547-560
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• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.120-129
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• 2023

Hanger cable tension is a major response that can determine the integrity and safety of suspension bridges. In general, the vibration method is used to estimate hanger cable tension on operational suspension bridges. It measures natural frequencies from hanger cables and indirectly estimates tension using the geometry conditions of the hanger cables. This study estimated the hanger cable tension of the Palyeong Bridge using a vision-based system. The vision-based system used digital camcorders and tripods considering the convenience and economic efficiency of measurement. Measuring the natural frequencies for high-order modes required for the vibration method is difficult because the hanger cable response measured using the vision-based system is displacement-based. Therefore, this study proposed a back analysis technique for estimating tension using the natural frequencies of low-order modes. Optimization for the back analysis technique was performed by defining the difference between the natural frequencies of hanger cables measured in the field and those calculated using finite element analysis as the objective function. The direct search method that does not require the partial derivatives of the objective function was applied as the optimization method. The reliability and accuracy of the back analysis technique were verified by comparing the tension calculated using the method with that estimated using the vibration method. Tension was accurately estimated using the natural frequencies of low-order modes by applying the back analysis technique.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.34-44
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• 2024

Recently, due to abnormal climate caused by climate change, natural disasters such as floods, landslides, and soil outflows are rapidly increasing. In Korea, more than 63% of the land is vulnerable to slope disasters due to the geographical characteristics of mountainous areas, and in particular, Quarry mines soil and rocks, so there is a high risk of landslides not only inside the workplace but also outside.Accordingly, this study built a DEM using UAV and aviation LiDAR for monitoring the quarry, conducted a time series change analysis, and proposed an optimal DEM construction method for monitoring the soil collection site. For DEM construction, UAV and LiDAR-based Point Cloud were built, and the ground was extracted using three algorithms: Aggressive Classification (AC), Conservative Classification (CC), and Standard Classification (SC). UAV and LiDAR-based DEM constructed according to the algorithm evaluated accuracy through comparison with digital map-based DEM.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.4
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• pp.273-279
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• 2007

In order to estimate the sedimentation rates of continental shelf and slope of Ulleung Basin in the Southwestern East Sea, $^{210}Pb,\;^{226}Ra\;and\;^{137}Cs$ were simultaneously measured by a well-type high purity germanium(HPGe) gamma detector. $^{137}Cs$ was used to determine whether the sediment were affected by bioturbation or not, and to judge the accuracy of estimated sedimentation rates. The estimated sedimentation rates decreased exponentially from slope to basin - 0.6 cm/yr in the continental shelf, $0.3{\sim}0.4$ cm/yr in the slope, and below 0.2 cm/yr in the margin of Ulleung basin. From our and other research results, we suggest followings about sediment transport of the study area. The sediment particles were transported by coastal current from south to north through the Korea Straight. And much of them were accumulated in the shelf area. And then, the rest of sediment particles were deposited in the lower slope and the southwest margin of the basin. Also the excess $^{210}Pb$ profiles indicate that the depositional processes in the study area may have been very complicate.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.111-118
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• 2024

An in-structure response spectrum (ISRS) is required to evaluate the seismic performance of a nuclear power plant (NPP). However, when a new ISRS is required because of the change in the unique spectrum of an NPP site, considerable costs such as seismic response re-analyses are incurred. This study provides several approaches to generate approximate methods for ISRS scaling, which do not require seismic response re-analyses. The ISRSs derived using these approaches are compared to the original ISRS. The effect of the ISRS of the approximate method on the seismic response and seismic performance of one of the main systems of an NPP is analyzed. The ISRS scaling approximation methods presented in this study produce ISRSs that are relatively similar at low frequencies; however, the similarity decreases at high frequencies. The effect of the ISRS scaling approximate method on the calculation accuracy of the seismic response/seismic performance of the system is determined according to the degree of similarity in the calculation of the system's essential mode responses for the method.

• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.450-463
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• 2023

The objective of this study is to derive the core habitat of the Kirengeshoma koreana Nakai utilizing Habitat Suitability Index (HSI) and Maximum Entropy (MaxEnt) models. Expert-based models have been criticized for their subjective criteria, while statistical models face difficulties in on-site validation and integration of expert opinions. To address these limitations, both models were employed, and their outcomes were overlaid to derive the core habitat. Five variables were identified through a comprehensive literature review and spatial analysis based on appearance coordinates. The environmental variables encompass vegetation zone, forest type, crown density, annual precipitation, and effective soil depth. Through surveys involving six experts, importance rankings and SI (Suitability Index) scores were established for each variable, subsequently facilitating the creation of an HSI map. Using the same variables, the MaxEnt model was also executed, resulting in a corresponding map, which was merged to construct the definitive core habitat map. Out of 16 observed locations of K. koreana, 15 were situated within the identified core habitat. Furthermore, an area historically known to host K. koreana but not verified in the present, Mt. Yeongchwi, was found to lack a core habitat. These findings suggest that the developed models exhibit a high degree of accuracy and effectively reflect the current ecological landscape.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.21-30
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• 2024

In this paper, we propose a method to build a sample dataset of the features of eight sensor-only facilities built as infrastructure for autonomous cooperative driving. The feature extracted from point cloud data acquired by LiDAR and build them into the sample dataset for recognizing the facilities. In order to build the dataset, eight sensor-only facilities with high-brightness reflector sheets and a sensor acquisition system were developed. To extract the features of facilities located within a certain measurement distance from the acquired point cloud data, a cylindrical projection method was applied to the extracted points after applying DBSCAN method for points and then a modified OTSU method for reflected intensity. Coordinates of 3D points, projected coordinates of 2D, and reflection intensity were set as the features of the facility, and the dataset was built along with labels. In order to check the effectiveness of the facility dataset built based on LiDAR data, a common CNN model was selected and tested after training, showing an accuracy of about 90% or more, confirming the possibility of facility recognition. Through continuous experiments, we will improve the feature extraction algorithm for building the proposed dataset and improve its performance, and develop a dedicated model for recognizing sensor-only facilities for autonomous cooperative driving.