• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.233-246
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• 2021

The present study aimed to investigate whether there would be differences in the severity of PTSD symptoms, self and others concepts between trauma types. Among 166 university students, 61 (simple trauma's n = 31, complex trauma's n = 30) finally met the criteria and completed the Life Events Checklist, Impact of the Event Scale-Revised, and the emotional Stroop task. The results were as follows: firstly, PTSD symptoms were higher in complex trauma group than single trauma group. Secondly, response time in the complex trauma group was longer in the condition that the negative word related to 'self' was presented for 2 seconds compared to the single trauma group. These results suggest that the complex trauma group has different features at least in the severity of PTSD symptoms and the concept of the self, compared with the single trauma group. Finally, the therapeutic implications and limitations of the study were discussed.

• Journal of the Korean Geotechnical Society
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• v.38 no.8
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• pp.67-74
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• 2022

The setting of the target area for ground stability evaluation during ground excavation is categorized into theoretical and empirical estimation methods and numerical analysis methods. Generally, the applied theoretical and empirical estimation methods include those by Peck (1969), Caspe (1966), and Clough et al. (1990). The numerical analysis method comprehensively considered the current status of the task section (maximum excavation depth section, ground condition vulnerable section, etc.). It reflected the results of performing two and three-dimensional numerical analyses on the weakest section. Therefore, this study shows an example of setting the scope of influence when excavating the vertical and tunnel sections of a 000-line double-track private investment project through the above theoretical, empirical, and numerical analysis methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• Geomechanics and Engineering
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• v.35 no.5
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• pp.525-538
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• 2023

The development of a city is closely linked to the construction and operation of its subway system. However, constructing a new subway tunnel under an existing station is an extremely complex task, and the deformation characteristics and mechanical behavior of the new subway tunnel during the excavation process can greatly impact the normal operation of the existing station. Although the previous studies about the case of underpass engineering have been carried out, there is limited research on the condition of a newly-built subway tunnel that closely undercrossing an existing station with zero distance between them. Therefore, this study analyzes the deformation law and mechanical behavior characteristics of the preliminary lining of the underpass tunnel during the excavation process based on the real engineering case of Chengdu Metro Line 8. This study also makes an in-depth comparison of the influence of different excavation methods on this issue. Finally, the accuracy of numerical simulation is verified by comparing it with on-site result. The results indicate that the maximum bending moment mainly occurs at the floor slab of the preliminary lining, while that of the ceiling is small. The stress state at the ceiling position is less affected by the construction process of the pilot tunnel. Compared to the all-in-one excavation method, although the process of partial excavation method is more complicated, the deformation of preliminary lining caused by it is basically less than the upper limit value of the standard, while that of the all-in-one excavation method is beyond standard requirements.

• Industry Promotion Research
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• v.8 no.4
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• pp.229-239
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• 2023

This research as a literature study is to clarify solution method of hypochondriasis through focused distraction. Hypochondriasis is called by various terms such as somatization symptoms, health anxiety, illness phobia, illness anxiety disorder, hypochondriacal disorder, hypochondriacal neurosis, and hypochondria personality disorder. Hypochondria is basically a factor that causes worry, fear, anxiety, and even phobia, lowering the quality of life at home, work, and society. As the quality of life of individuals is decreasing, the field of counseling psychology and psychological counseling has the task of solving this problem and restoring psychological peace. Accordingly, this study is an attempt to explore the symptoms of hypochondria by using the distraction strategy among cognitive strategies as a way to resolve the condition. Focused distraction strategy can be said to be a kind of cognitive 'avoidance strategy'. Focused distraction strategy is a way to avoid being overly focused on one's health. In addition, it weakens health concerns by 'dispersing' or 'distracting' the 'attention' focused on health in another direction.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.21-26
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• 2024

The one of the famous deep learning models for object detection task is you only look once version 5 (YOLOv5) framework based on the one stage architecture. In addition, YOLOv5 model indicated high performance for accurate lesion detection using the bottleneck CSP layer and skip connection function. The purpose of this study was to evaluate the performance of YOLOv5 framework according to various hyperparameters in position emission tomogrpahy (PET) phantom images. The dataset was obtained from QIN PET segmentation challenge in 500 slices. We set the bounding box to generate ground truth dataset using labelImg software. The hyperparameters for network train were applied by changing optimization function (SDG, Adam, and AdamW), activation function (SiLU, LeakyRelu, Mish, and Hardwish), and YOLOv5 model size (nano, small, large, and xlarge). The intersection over union (IOU) method was used for performance evaluation. As a results, the condition of outstanding performance is to apply AdamW, Hardwish, and nano size for optimization function, activation function and model version, respectively. In conclusion, we confirmed the usefulness of YOLOv5 network for object detection performance in nuclear medicine images.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.1
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• pp.55-70
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• 2024

The loss of biodiversity poses a significant threat not only to business sustainability and investment risk but also to societal well-being. Nature serves as a crucial driver for long-term business viability and economic prosperity. The Task Force on Nature-related Financial Disclosures (TNFD), established in September 2023, mandates that companies assess and disclose their impacts on nature. Despite this, many businesses lack a full understanding of their reliance on and impact upon natural capital and ecosystem services, leading to insufficient disclosures. This study evaluates the applicability of TNFD's assessment methodologies and indicators within a domestic context, highlighting the condition of nature and ecosystem services, and exploring potential synergies with national biodiversity policies. Our analysis suggests that TNFD necessitates a unique approach to the spatial and temporal data and methodologies traditionally employed in environmental impact assessments. This includes assessing the reciprocal influences of corporate activities on natural capital and ecosystem services via the LEAP framework. Moreover, in industries where the choice of specific indicators depends on unique sectoral traits, developing a standardized strategy for data and assessment indicators-adapted to local conditions-is crucial due to the variability in the availability of assessment tools and data. The proactive engagement of the private sector in ecosystem restoration projects is particularly promising for contributing towards national biodiversity objectives. Although TNFD is in its nascent phase, its global adoption by numerous companies signifies its potential impact. Successful implementation of TNFD is anticipated to deepen businesses' and financial institutions' understanding of natural capital and ecosystem services, thereby reinforcing their commitment to sustainable development.

• Journal of Korean Society on Water Environment
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• v.40 no.2
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• pp.79-88
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• 2024

This study employed field measurements and biogeochemical analysis to examine the effects of seasonal conditions (e.g., temperature and precipitation) and human intervention (e.g., dam or weir construction) on the chemical composition of dissolved organic matter, flocculation kinetics of suspended particulate matter, and formation of the fluid mud layer on riverbeds. The results indicated that a water environment with a substantial amount of biopolymers offered favorable conditions for flocculation kinetics during an algal bloom period in summer; a thick fluid mud layer was found to be predominated with cohesive materials during this period. However, after high rainfall, a substantial influx of terrigenous humic substances led to enhanced stabilization of the particulate matter, thereby decreasing flocculation and deposition, and the reduced biopolymer composition served to weaken the erosion resistance of the fluid mud on the riverbed. Moreover, a high-turbulence condition disaggregated the flocs and the fluid mud layer and resuspended the suspended particulate matter in the water column. This study demonstrates the mutual relationship that exists between biogeochemistry, flocculation kinetics, and the formation of the fluid mud layer on the riverine area during different seasons and under varying hydrological conditions. These findings are expected to eventually help inform the more optimal management of water resources, which is an urgent task in the face of anthropogenic stressors and climate change.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.63-76
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• 2024

Weather forecasting has become a very popular topic nowadays among researchers because of its various effects on global lives. It is a technique to predict the future, what is going to happen in the atmosphere by analyzing various available datasets such as rain, snow, cloud cover, temperature, moisture in the air, and wind speed with the help of our gained scientific knowledge i.e., several approaches and set of rules or we can say them as algorithms that are being used to analyze and predict the weather. Weather analysis and prediction are required to prevent nature from natural losses before it happens by using a Deep Learning Approach. This analysis and prediction are the most challenging task because of having multidimensional and nonlinear data. Several Deep Learning Approaches are available: Numerical Weather Prediction (NWP), needs a highly calculative mathematical equation to gain the present condition of the weather. Quantitative precipitation nowcasting (QPN), is also used for weather prediction. In this article, we have implemented and analyzed the various distinct techniques that are being used in data mining for weather prediction.

• The Journal of Korean Physical Therapy
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• v.18 no.6
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• pp.1-11
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• 2006

Purpose: This study compared the effects of non-cold and cold conditions on the viscoelastic properties of tendon structures in vivo. Methods: Seven male subjects perfomed plantar flesion exercise with maximal isokinetic voluntary contraction, which consisted of muscle contraction for 6 see and relaxation for 60 secs, 10 times for 1 set, Totally 10 sets were repeated. Before and after each task, the elongation of the tendon and aponeurosis of the medial gastrocnemius muscle (MG) was directly measured by ultrasonography. (The relationship between the estimated tendon force and tendon elongation.) Tendon cross-sectional area and ankle joint moment arm were obtained from magnetic resonance imaging (MRI). The tendon force was calculated from the joint moments and the tendon moment arm and stress was obtained by dividing force by cross-sectional areas (CSA). The strain was measured from the displacements normalized to tendon length. Results: After cooling, the tendon force was larger in cold than non-cold. The value of the tendon stiffness of MVC were significantly higher under the cold condition than under the non-cold condition. The maximal strain and stress of $7.4{\pm}0.7%$ and $36.4{\pm}1.8$ MPa in non-cold and $7.8{\pm}8.5%,\;31.8{\pm}1.1$ MPa in cold (P<0.05). Conclusion: This study shows for the first time that the muscle endurance in cooling increases the stiffness and Young's modulus of human tendons. The improvement in muscle endurance with cooling was directly related to muscle and tendon.