• Journal of Soil and Groundwater Environment
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• v.22 no.1
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• pp.49-58
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• 2017

Biotic ligand model (BLM) and species sensitivity distribution (SSD) were used to determine the site-specific Cu threshold concentration (5% hazardous concentration; HC5) in soil pore water. Model parameters for Cu-BLM were collected for six plants, one collembola, and two earthworms from published literatures. Half maximal effective concentration ($EC_{50}\{Cu^{2+}\}$), expressed as $Cu^{2+}$ activity, was calculated based on activities of major cations and the collected Cu-BLM parameters. The $EC_{50}\{Cu^{2+}\}$ varied from 2 nM to $251{\mu}M$ according to the variation in environmental factors of soil pore water (pH, major cation/anion concentrations) and the type of species. Hazardous activity for 5% (HA5) and HC5 calculated from SSD varied from 0.076 to $0.4{\mu}g/L$ and 0.4 to $83.4{\mu}g/L$, respectively. HA5 and HC5 significantly decreased with the increase in pH in the region with pH less than 7 due to the decrease in competition with $H^+$ and $Cu^{2+}$. In the region with pH more than 7, HC5 increased with the increase in pH due to the formation of complexes of Cu with inorganic ligands. In the presence of dissolved organic carbon (DOC), Cu and DOC form a complex, which decreases $Cu^{2+}$ activity in soil pore water, resulting in up to 292-fold increase in HC5 from 0.48 to $140{\mu}g/L$.

• Progress in Medical Physics
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• v.31 no.4
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• pp.145-152
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• 2020

Purpose: In ionization-chamber dosimetry for high-dose-rate electron beams-above 20 mGy/pulse-the ion-recombination correction methods recommended by the International Atomic Energy Agency (IAEA) and the American Association of Physicists in Medicine (AAPM) are not appropriate, because they overestimate the correction factor. In this study, we suggest a practical ion-recombination correction method, based on Boag's improved model, and apply it to reference dosimetry for electron beams of about 100 mGy/pulse generated from an electron linear accelerator (LINAC). Methods: This study employed a theoretical model of the ion-collection efficiency developed by Boag and physical parameters used by Laitano et al. We recalculated the ion-recombination correction factors using two-voltage analysis and obtained an empirical fitting formula to represent the results. Next, we compared the calculated correction factors with published results for the same calculation conditions. Additionally, we performed dosimetry for electron beams from a 6 MeV electron LINAC using an Advanced Markus^® ionization chamber to determine the reference dose in water at the source-to-surface distance (SSD)=100 cm, using the correction factors obtained in this study. Results: The values of the correction factors obtained in this work are in good agreement with the published data. The measured dose-per-pulse for electron beams at the depth of maximum dose for SSD=100 cm was 115 mGy/pulse, with a standard uncertainty of 2.4%. In contrast, the k_s values determined using the IAEA and AAPM methods are, respectively, 8.9% and 8.2% higher than our results. Conclusions: The new method based on Boag's improved model provides a practical method of determining the ion-recombination correction factors for high dose-per-pulse radiation beams up to about 120 mGy/pulse. This method can be applied to electron beams with even higher dose-per-pulse, subject to independent verification.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.136-146
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• 2022

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.

• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.108-117
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• 2023

Objectives : Somatic symptom disorder (SSD) is characterized by the manifestation of a variety of physical symptoms, but little is known about differences in autonomic nervous system activity according to symptom severity, especially within patient groups. In this study, we examined differences in heart rate variability (HRV) across symptom severity in a group of SSD patients to analyze a representative marker of autonomic nervous system changes by symptoms severity. Methods : Medical records were retrospectively reviewed for patients who were diagnosed with SSD based on DSM-5 from September 18, 2020 to October 29, 2021. We applied inverse probability of treatment weighting (IPTW) methods to generate more homogeneous comparisons in HRV parameters by correcting for selection biases due to sociodemographic and clinical characteristic differences between groups. Results : There were statistically significant correlations between the somatic symptom severity and LF (nu), HF (nu), LF/HF, as well as SD1/SD2 and Alpha1/Alpha2. After IPTW estimation, the mild to moderate group was corrected to 27 (53.0%) and the severe group to 24 (47.0%), and homogeneity was achieved as the differences in demographic and clinical characteristics were not significant. The analysis of inverse probability weighted regression adjustment model showed that the severe group was associated with significantly lower RMSSD (β=-0.70, p=0.003) and pNN20 (β=-1.04, p=0.019) in the time domain and higher LF (nu) (β=0.29, p<0.001), lower HF (nu) (β=-0.29, p<0.001), higher LF/HF (β=1.41, p=0.001), and in the nonlinear domain, significant differences were tested for SampEn15 (β=-0.35, p=0.014), SD1/SD2 (β=-0.68, p<0.001), and Alpha1/Alpha2 (ß=0.43, p=0.001). Conclusions : These results suggest that differences in HRV parameters by SSD severity were showed in the time, frequency and nonlinear domains, specific parameters demonstrating significantly higher sympathetic nerve activity and reduced ability of the parasympathetic nervous system in SSD patients with severe symptoms.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.53-63
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• 2019

To provide a hand gesture interface through deep learning in mobile environments, research on the light-weighting of networks is essential for high recognition rates while at the same time preventing degradation of execution speed. This paper proposes a method of real-time recognition of written characters in the air using a finger on mobile devices through the light-weighting of deep-learning model. Based on the SSD (Single Shot Detector), which is an object detection model that utilizes MobileNet as a feature extractor, it detects index finger and generates a result text image by following fingertip path. Then, the image is sent to the server to recognize the characters based on the learned OCR model. To verify our method, 12 users tested 1,000 words using a GALAXY S10+ and recognized their finger with an average accuracy of 88.6%, indicating that recognized text was printed within 124 ms and could be used in real-time. Results of this research can be used to send simple text messages, memos, and air signatures using a finger in mobile environments.

• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.17-20
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• 2024

This paper presents a novel lightweight object detection model tailored for low-powered edge devices, addressing the limitations of traditional resource-intensive computer vision models. Our proposed detector, inspired by the Single Shot Detector (SSD), employs a compact yet robust network design. Crucially, it integrates an 'enhancer block' that significantly boosts its efficiency in detecting smaller objects. The model comprises two primary components: the Light_Block for efficient feature extraction using Depth-wise and Pointwise Convolution layers, and the Enhancer_Block for enhanced detection of tiny objects. Trained from scratch on the Udacity Annotated Dataset with image dimensions of 300x480, our model eschews the need for pre-trained classification weights. Weighing only 5.5MB with approximately 0.43M parameters, our detector achieved a mean average precision (mAP) of 27.7% and processed at 140 FPS, outperforming conventional models in both precision and efficiency. This research underscores the potential of lightweight designs in advancing object detection for edge devices without compromising accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.1-10
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• 2023

This study aimed to confirm the importance ratio of climate and management variables on production of orchardgrass in Korea (1982-2014). For the climate, the mean temperature in January (MTJ, ℃), lowest temperature in January (LTJ, ℃), growing days 0 to 5 (GD 1, day), growing days 5 to 25 (GD 2, day), Summer depression days (SSD, day), rainfall days (RD, day), accumulated rainfall (AR, mm), and sunshine duration (SD, hr) were considered. For the management, the establishment period (EP, 0-6 years) and number of cutting (NC, 2nd-5th) were measured. The importance ratio on production of orchardgrass was estimated using the neural network model with the perceptron method. It was performed by SPSS 26.0 (IBM Corp., Chicago). As a result, EP was the most important variable (100%), followed by RD (82.0%), AR (79.1%), NC (69.2%), LTJ (66.2%), GD 2 (63.3%), GD 1 (61.6%), SD (58.1%), SSD (50.8%) and MTJ (41.8%). It implies that EP, RD, AR, and NC were more important than others. Since the annual rainfall in Korea is exceed the required amount for the growth and development of orchardgrass, the damage caused by heavy rainfall exceeding the appropriate level could be reduced through drainage management. It means that, when cultivating orchardgrass, factors that can be controlled were relatively important. Although it is difficult to interpret the specific effect of climates on production due to neural networking modeling, in the future, this study is expected to be useful in production prediction and damage estimation by climate change by selecting major factors.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1596-1605
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• 2017

This study recognizes the narrative of the movie as a semiotic system and proposes a structured storytelling analysis model through theoretical basis and empirical analysis. It classifies as 'Narrative 17 Process' which considers the narrative of successful 11 animations as a continuous process of formal structure. It extract the paradigmatic sub-narrative units(NU) centered on the act of the character in each process. The structural pattern of the story types are extracted by comparing and analyzing with 5 NU analysis elements presented in this study. As a result, the 4 story types were consistently classified by the SSD distance value. Therefore, this study propose a storytelling analysis model that can be effectively applied to scenarios and narrative composition stages of movie production.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.140-146
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• 2020

Since the current crime prevention systems have a standard mechanism that victims request for help by themselves or ask for help from a third party nearby, it is difficult to obtain appropriate help in situations where a prompt response is not possible. In this study, we proposed and developed an automatic rescue request model and system using Deep Learning and OpenCV. This study is based on the prerequisite that immediate and precise threat detection is essential to ensure the user's safety. We validated and verified that the system identified by more than 99% of the object's accuracy to ensure the user's safety, and it took only three seconds to complete all necessary algorithms. We plan to collect various types of threats and a large amount of data to reinforce the system's capabilities so that the system can recognize and deal with all dangerous situations, including various threats and unpredictable cases.