• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.309-318
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• 2023

Recently, each country has been moving to introduce an LCA (Life Cycle Assessment) to regulate greenhouse gas emissions. The LCA is a mean of measuring and evaluating greenhouse gas emissions generated over the entire life cycle of a vehicle. Reliable data for each electric vehicle component is needed to increase the reliability of the LCA results. To this end, studies on life cycle evaluation models using blockchain technology have been conducted. However, in the existing model, key product information is exposed to other participants. And each time parts data information is updated, it must be recorded in the blockchain ledger in the form of a transaction, which is inefficient. In this paper, we proposed a DID(Decentralized Identity)-based data collection model for LCA to collect vehicle component data and verify its validity effectively. The proposed model increases the reliability of the LCA by ensuring the validity and integrity of the collected data and verifying the source of the data. The proposed model guarantees the validity and integrity of collected data. As only user authentication information is shared on the blockchain ledger, the model prevents indiscriminate exposure of data and efficiently verifies and updates the source of data.

• Journal of Soil and Groundwater Environment
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• v.28 no.6
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• pp.71-89
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• 2023

A groundwater potential map (GPM) was built for the Nakdonggang River Basin based on ten variables, including hydrogeologic unit, fault-line density, depth to groundwater, distance to surface water, lineament density, slope, stream drainage density, soil drainage, land cover, and annual rainfall. To integrate the thematic layers for GPM, the criteria were first weighted using the Analytic Hierarchical Process (AHP) and then overlaid using the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) model. Finally, the groundwater potential was categorized into five classes (very high (VH), high (H), moderate (M), low (L), very low (VL)) and verified by examining the specific capacity of individual wells on each class. The wells in the area categorized as VH showed the highest median specific capacity (5.2 m³/day/m), while the wells with specific capacity < 1.39 m³/day/m were distributed in the areas categorized as L or VL. The accuracy of GPM generated in the work looked acceptable, although the specific capacity data were not enough to verify GPM in the studied large watershed. To create GPMs for the determination of high-yield well locations, the resolution and reliability of thematic maps should be improved. Criterion values for groundwater potential should be established when machine learning or statistical models are used in the GPM evaluation process.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1195-1210
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• 2023

Coastal debris has emerged as a salient issue due to its adverse effects on coastal aesthetics, ecological systems, and human health. In pursuit of effective countermeasures, the present study delineated the construction of a specialized image dataset for coastal debris detection and embarked on a comparative analysis between two paramount real-time object detection algorithms, YOLOv8 and RT-DETR. Rigorous assessments of robustness under multifarious conditions were instituted, subjecting the models to assorted distortion paradigms. YOLOv8 manifested a detection accuracy with a mean Average Precision (mAP) value ranging from 0.927 to 0.945 and an operational speed between 65 and 135 Frames Per Second (FPS). Conversely, RT-DETR yielded an mAP value bracket of 0.917 to 0.918 with a detection velocity spanning 40 to 53 FPS. While RT-DETR exhibited enhanced robustness against color distortions, YOLOv8 surpassed resilience under other evaluative criteria. The implications derived from this investigation are poised to furnish pivotal directives for algorithmic selection in the practical deployment of marine debris monitoring systems.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.445-471
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• 2023

Long-term safety over millennia is the top priority consideration in the construction of disposal sites. However, ensuring the mechanical stability of deep geological repositories for spent fuel, a.k.a. radwaste, disposal during construction and operation is also crucial for safe operation of the repository. Imposing restrictions or limitations on tunnel support and lining materials such as shotcrete, concrete, grouting, which might compromise the sealing performance of backfill and buffer materials which are essential elements for the long-term safety of disposal sites, presents a highly challenging task for rock engineers and tunnelling experts. In this study, as part of an extensive exploration to aid in the proper selection of disposal sites, the anticipation of constructing a deep geological repository at a depth of 500 meters in an unknown state has been carried out. Through a review of 2D and 3D numerical analyses, the study aimed to explore the range of properties that ensure stability. Preliminary findings identified the potential range of rock properties that secure the stability of central and disposal tunnels, while the stability of the vertical tunnel network was confirmed through 3D analysis, outlining fundamental rock conditions necessary for the construction of disposal sites.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.163-171
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• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.

• The Korea Journal of Herbology
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• v.39 no.3
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• pp.57-67
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• 2024

Objectives : The aim of this study is to evaluate the potential biological activity of Veronica incana extracts (VIE) through in vitro, ex vivo, and in vivo experiments. Methods : In vitro, we conducted analyses on the total polyphenol (TP) and total flavonoid (TF) levels, alongside DPPHand ABTS radical scavenging activities. Ex vivo evaluations on adipose tissue measured glycerol release as a marker of lipolysis. In LPS-induced RAW 264.7 cells, we quantified nitric oxide (NO) production. Following H₂O₂ induction in U2OS cells, we performed mitochondrial assays such as MitoSox and MitoTracker. Moreover, Bodipy assays were conducted in 3T3-L1 cells. In vivo, we performed anti-osteoarthritis effect of VIE against monosodium iodoacetate (MIA)-induced osteoarthritis in rats. Results : The results presented encompass a myriad of models, from cell culture to animal experiments as well as ex vivo studies. VIE demonstrated high TP and TF contents, potent DPPH and ABTS scavenging activities, and regulated glycerol release. Moreover, the inhibition of NO production in LPS-induced inflammation was notably confirmed and the reduction of lipid droplets was distinctly shown. Furthermore, in H₂O₂-induced U2OS cells, MitoSox was effectively reduced while MitoTracker noticeably increased. In vivo assays confirmed a significant increase in hindpaw weight distribution (HWD) decreased by MIA after VIE treatment. Additionally, VIE inhibited serum inflammatory cytokines (TNF-𝛼, IL-6, and IL-1𝛽) and MDA levels in joint tissue. Conclusion : In conclusion, Veronica incana exhibited various pharmacological effects including antioxidant, anti-obesity, and anti-inflammatory properties.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4447-4464
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• 2023

This paper focuses on the development of a new computational model of the CNESTEN's TRIGA Mark II research reactor using the 3D continuous energy Monte-Carlo code TRIPOLI-4 (T4). This new model was developed to assess neutronic simulations and determine quantities of interest such as kinetic parameters of the reactor, control rods worth, power peaking factors and neutron flux distributions. This model is also a key tool used to accurately design new experiments in the TRIGA reactor, to analyze these experiments and to carry out sensitivity and uncertainty studies. The geometry and materials data, as part of the MCNP reference model, were used to build the T4 model. In this regard, the differences between the two models are mainly due to mathematical approaches of both codes. Indeed, the study presented in this article is divided into two parts: the first part deals with the development and the validation of the T4 model. The results obtained with the T4 model were compared to the existing MCNP reference model and to the experimental results from the Final Safety Analysis Report (FSAR). Different core configurations were investigated via simulations to test the computational model reliability in predicting the physical parameters of the reactor. As a fairly good agreement among the results was deduced, it seems reasonable to assume that the T4 model can accurately reproduce the MCNP calculated values. The second part of this study is devoted to the sensitivity and uncertainty (S/U) studies that were carried out to quantify the nuclear data uncertainty in the multiplication factor k_eff. For that purpose, the T4 model was used to calculate the sensitivity profiles of the k_eff to the nuclear data. The integrated-sensitivities were compared to the results obtained from the previous works that were carried out with MCNP and SCALE-6.2 simulation tools and differences of less than 5% were obtained for most of these quantities except for the C-graphite sensitivities. Moreover, the nuclear data uncertainties in the k_eff were derived using the COMAC-V2.1 covariance matrices library and the calculated sensitivities. The results have shown that the total nuclear data uncertainty in the k_eff is around 585 pcm using the COMAC-V2.1. This study also demonstrates that the contribution of zirconium isotopes to the nuclear data uncertainty in the k_eff is not negligible and should be taken into account when performing S/U analysis.

• The Mathematical Education
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• v.63 no.2
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• pp.187-207
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• 2024

In this study, we explored the feasibility of automatically scoring descriptive assessment items using GPT-4 based ChatGPT by comparing and analyzing the scoring results between teachers and GPT-4 based ChatGPT. For this purpose, three descriptive items from the permutation and combination unit for first-year high school students were selected from the KICE (Korea Institute for Curriculum and Evaluation) website. Items 1 and 2 had only one problem-solving strategy, while Item 3 had more than two strategies. Two teachers, each with over eight years of educational experience, graded answers from 204 students and compared these with the results from GPT-4 based ChatGPT. Various techniques such as Few-Shot-CoT, SC, structured, and Iteratively prompts were utilized to construct prompts for scoring, which were then inputted into GPT-4 based ChatGPT for scoring. The scoring results for Items 1 and 2 showed a strong correlation between the teachers' and GPT-4's scoring. For Item 3, which involved multiple problem-solving strategies, the student answers were first classified according to their strategies using prompts inputted into GPT-4 based ChatGPT. Following this classification, scoring prompts tailored to each type were applied and inputted into GPT-4 based ChatGPT for scoring, and these results also showed a strong correlation with the teachers' scoring. Through this, the potential for GPT-4 models utilizing prompt engineering to assist in teachers' scoring was confirmed, and the limitations of this study and directions for future research were presented.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.139-150
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• 2024

This study introduced a method of estimating water surface evaporation using the physical-based Penman combination equation (PCE) and the Penman wind function (PWF). A set of regression parameters in the PCE and PWF models were optimized by using the observed evaporation data for the period 2016-2017 in the Yongdam Dam watershed, and their effectiveness was explored. The estimated evaporation over the Deokyu Mountain flux tower demonstrated that the PWF method appears to have more improved results in terms of correlation, but both methods showed overestimation. Further, the PWF method was applied to the observed hydro-meteorological data on the surface of Yongdam Lake. The PWF method outperformed the PCE in the estimation of water surface evaporation in terms of goodness-of-fit measure and visual evaluation. Future studies will focus on a regionalization process which can be effective in estimating water surface evaporation for the ungauged area by linking hydrometeorological characteristics and regression parameters.