• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3B
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• pp.315-321
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• 2008

Recently, in the light of environments and utilization, countermeasures to preserve beaches in coastal area are required without depending on such as jetties and breakwaters. The necessity of integrated sand management including not only coastal sediment but also sediment discharge from hinterland rivers is increased so as to establish long-term counterplan for sediment transport. In this regard, the following subjects are examined in this study; efficient ways for discharged sand to be transported from a river to the neighboring coast, the river terrace occurrence and its growth at the river delta, measures to improve storage efficiency of the discharged sand and measures to prevent the sand resources from being discharged into the deep sea during flooding. In recent, A jetty of 260 m length was constructed at Namdae River mouth in the year of 2005 as a countermeasure against the occurrence of sand-bar at river mouth and its close. In this study, a series of numerical experiments were carried out to investigate the characteristics of sediment transport and morphological change due to the construction of jetty at the entrance of Namdae River mouth. Firstly, The sand discharge from Namdae River is quantified by one-dimensional numerical analysis assuming the mixed sand of three different particle diameters. Then, in order to understand the transport behavior of the sand discharge from river and river mouth phenomena the numerical experiments were then conducted to examine the flow behaviors of river efflux and wind generated circulations in coastal area. And, after establishing the numerical model system, which predicts the sea bed changes obtained from the flux model combining with the wave propagation, wave-induced currents and sediment transport models, the sediment transport in the vicinity of Namdae River mouth is analyzed.

• International Area Studies Review
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• v.13 no.1
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• pp.55-79
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• 2009

This papers studies the causalities and effects on the relationship between international trade and economic growth in China for the period of 1950-2007, using the unit root test, the Granger causality test, the cointegration test, VAR model, and VECM. The results of this study are as follows: Firstly, in the unit root test, I found that each time series was unstable one that has unit root. Secondly, in the Granger Causality test, this papers shows that variable dlexp and dlinp influence on dlgdp and dlgdd, while bilateral causality relation between dlexp and dlgdp, dlexp and dlgdd for the whole period, for the whole period, pre-reform period and post-reform period. Thirdly, there is no cointegraion relation between lgdp(or dlgdp, lgdd, dlgdd) and lexp, linp for lgdd-limp in the whole period, and pre-reform period, while no cointegration relation for the post-reform period. Finally, in the impulse-response test, it was proved that lgdp represents (-) correlation with lexp for the whole period. Thorough the variance decomposition test, it was proved that linp(or dlinp) is the most affected variable of the each data and relation between linp(or dlinp) and lexp(or dlexp) has become bigger recently.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.315-323
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• 2023

Marine accidents due to loss of stability of small ships have continued to increase over the past decade. In particular, since sudden winds have been pointed out as main causes of most small ship accidents, safety measures have been established to prevent them. In this regard, to prevent accidents caused by sudden winds, a systematic analysis technique is required. The aim of the present study was to develop a probabilistic approach to estimate extreme value and evaluate effects of wind on motion characteristics of ships. The present study included studies of motion analysis, extraction of extreme values, and motion characteristics. A series analysis was conducted for three conditions: wave only, wave with uniform wind speed, and wave with the NPD wind model. Hysteresis filtering and Peak-Valley filtering techniques were applied to time-domain motion analysis results for extreme value extraction. Using extracted extreme values, the goodness of fit test was performed on four distribution functions to select the optimal distribution-function that best expressed extreme values. Motion characteristics of a fishing boat were evaluated for three periodic motion conditions (Heave, Roll, and Pitch) and results were compared. Numerical analysis was performed using a commercial solver, ANSYS-AQWA.

• Animal Bioscience
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• v.37 no.2
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• pp.274-283
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• 2024

Objective: The aim of the study was to evaluate the physicochemical properties (nutrient composition, pH, water content and activity, sorption properties) and mechanical properties (compression force and energy) of granulated feed mixtures with various inclusion levels of crude fibre concentrates ARBOCEL and VITACEL for broiler chickens, i.e. +0.0% (control group - group C), +0.3%, +0.8%, +1.0%, +1.2%. Methods: The feed mixtures were analyzed for their physicochemical properties (nutrient composition by near-infrared spectroscopy, pH with the use a CP-401 pH meter with an IJ-44C glass electrode, water content was determined with the drying method and activity was determined with the Aqua Lab Series 3, sorption properties was determined with the static method) and mechanical properties (compression force and energy with the use TA-HD plus texture analyzer). The Guggenheim-Anderson-de Boer (GAB) model applied in the study correctly described the sorption properties of the analyzed feed mixtures in terms of water activity. Results: The fibre concentrate type affected the specific surface area of the adsorbent and equilibrium water content in the GAB monolayer (p≤0.05) (significantly statistical). The type and dose of the fibre concentrate influenced the dimensionless C and k parameters of the GAB model related to the properties of the monolayer and multilayers, respectively (p≤0.05). They also affected the pH value of the analyzed feed mixtures (p≤0.05). In addition, crude fibre type influenced water activity (p≤0.05) as well as compression energy (J) and compression force (N) (p≤0.001) (highly significantly statistical) of the feed mixtures. Conclusion: The physicochemical analyses of feed mixtures with various inclusion levels (0.3%, 0.8%, 1.0%, 1.2%) of crude fiber concentrates ARBOCEL or VITACEL demonstrated that both crude fiber types may be used in the feed industry as a feedstuff material to produce starter type mixtures for broiler chickens.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.345-357
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• 2023

Nuclear forensics has been understood as a mendatory component in the international society for nuclear material control and non-proliferation verification. Radiochronometry of nuclear activities for nuclear forensics are decay series characteristics of nuclear materials and the Bateman equation to estimate when nuclear materials were purified and produced. Radiochronometry values have uncertainty of measurement due to the uncertainty factors in the estimation process. These uncertainties should be calculated using appropriate evaluation methods that are representative of the accuracy and reliability. The IAEA, US, and EU have been researched on radiochronometry and uncertainty of measurement, although the uncertainty calculation method using the Bateman equation is limited by the underestimation of the decay constant and the impossibility of estimating the age of more than one generation, so it is necessary to conduct uncertainty calculation research using computer simulation such as Monte Carlo method. This highlights the need for research using computational simulations, such as the Monte Carlo method, to overcome these limitations. In this study, we have analyzed mathematical models and the LHS (Latin Hypercube Sampling) methods to enhance the reliability of radiochronometry which is to develop an uncertainty algorithm for nuclear material radiochronometry using Bateman Equation. We analyzed the LHS method, which can obtain effective statistical results with a small number of samples, and applied it to algorithms that are Monte Carlo methods for uncertainty calculation by computer simulation. This was implemented through the MATLAB computational software. The uncertainty calculation model using mathematical models demonstrated characteristics based on the relationship between sensitivity coefficients and radiative equilibrium. Computational simulation random sampling showed characteristics dependent on random sampling methods, sampling iteration counts, and the probability distribution of uncertainty factors. For validation, we compared models from various international organizations, mathematical models, and the Monte Carlo method. The developed algorithm was found to perform calculations at an equivalent level of accuracy compared to overseas institutions and mathematical model-based methods. To enhance usability, future research and comparisons·validations need to incorporate more complex decay chains and non-homogeneous conditions. The results of this study can serve as foundational technology in the nuclear forensics field, providing tools for the identification of signature nuclides and aiding in the research, development, comparison, and validation of related technologies.

• Smart Structures and Systems
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• v.34 no.2
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• pp.97-116
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• 2024

The standard l₁-norm regularization is recently introduced for impact force identification, but generally underestimates the peak force. Compared to l₁-norm regularization, l_p-norm (0 ≤ p < 1) regularization, with a nonconvex penalty function, has some promising properties such as enforcing sparsity. In the framework of sparse regularization, if the desired solution is sparse in the time domain or other domains, the under-determined problem with fewer measurements than candidate excitations may obtain the unique solution, i.e., the sparsest solution. Considering the joint sparse structure of impact force in temporal and spatial domains, we propose a general l_p-norm (0 ≤ p < 1) regularization methodology for simultaneous identification of the impact location and force time-history from highly incomplete measurements. Firstly, a nonconvex optimization model based on l_p-norm penalty is developed for regularizing the highly under-determined problem of impact force identification. Secondly, an iteratively reweighed l₁-norm algorithm is introduced to solve such an under-determined and unconditioned regularization model through transforming it into a series of l₁-norm regularization problems. Finally, numerical simulation and experimental validation including single-source and two-source cases of impact force identification are conducted on plate structures to evaluate the performance of l_p-norm (0 ≤ p < 1) regularization. Both numerical and experimental results demonstrate that the proposed l_p-norm regularization method, merely using a single accelerometer, can locate the actual impacts from nine fixed candidate sources and simultaneously reconstruct the impact force time-history; compared to the state-of-the-art l₁-norm regularization, l_p-norm (0 ≤ p < 1) regularization procures sufficiently sparse and more accurate estimates; although the peak relative error of the identified impact force using l_p-norm regularization has a decreasing tendency as p is approaching 0, the results of l_p-norm regularization with 0 ≤ p ≤ 1/2 have no significant differences.

• Journal of Ginseng Research
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• v.48 no.5
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• pp.454-463
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• 2024

Background: Panax quinquefolius L, widely recognized for its valuable contributions to medicine, has aroused considerable attention globally. Different from the extensive research has been dedicated to the root of P. quinquefolius, its berry has received relatively scant focus. Given its promising medicinal properties, this study was focused on the structural characterizations and anti-inflammatory potential of acidic polysaccharides from the P. quinquefolius berry. Materials and methods: P. quinquefolius berry was extracted with hot water, precipitated by alcohol, separated by DEAE-52-cellulose column to give a series of fractions. One of these fractions was further purified via Sephadex G-200 column to give three fractions. Then, the main fraction named as AGBP-A3 was characterized by methylation analysis, NMR spectroscopy, etc. Its anti-inflammatory activity was assessed by RAW 264.7 cell model, zebrafish model and molecular docking. Results: The main chain comprised of α-L-Rhap, α-D-GalAp and β-D-Galp, while the branch consisted mainly of α-L-Araf, β-D-Glcp, α-D-GalAp, β-D-Galp. The RAW264.7 cell assay results showed that the inhibition rates against IL-6 and IL-1β secretion at the concentration of 625 ng/mL were 24.83 %, 11.84 %, while the inhibition rate against IL-10 secretion was 70.17 % at the concentration of 312 ng/mL. In the zebrafish assay, the migrating neutrophils were significantly reduced in number, and their migration to inflammatory tissues was inhibited. Molecular docking predictions correlated well with the results of the anti-inflammatory assay. Conclusion: The present study demonstrated the structure of acidic polysaccharides of P. quinquefolius berry and their effect on inflammation, providing a reference for screening anti-inflammatory drugs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.5
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• pp.261-273
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• 2024

Automobiles are one of the major sources of air pollution, and analyzing data on air pollutants, where vehicles are the primary pollutants, can help elucidate the correlation between factors like electric vehicles, traffic volume, and actual air pollution. Ensuring the reliability of air pollutant data is crucial for such analyses. This paper proposes a method for detecting sections of data exhibiting 'baseline anomalies' measured at air pollutant monitoring stations across the country by combining deep learning models with algorithms such as dynamic time warping and change point detection. While previous studies have focused on detecting data with unprecedented patterns and defined them as anomalies, this approach was not suitable for detecting baseline anomalies. In this study, we modify the U-Net model, typically used for image segmentation, to be more suitable for time-series data and apply dynamic time warping and change point detection algorithms to compare with nearby monitoring stations, thereby minimizing false detections.