• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.585-596
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• 2022

The purpose of this study is to propose an assessment method to analyze clay collectively referred to as fine particles of clay components contained in fine aggregates, and to propose quality standards for clay use through correlation with the performance of concrete to verify the properties of clay measured according to the method. As a result, it is analyzed that it will be suitably utilized as a method to assess the fine particles of the clay component of fine aggregates through the component analysis of XRF. Regarding the related quality standards, considering the error rate of about 10% of KCS 14 20 10, the related quality standards were analyzed to be safe when Al₂O₃+Fe₂O₃+MgO is 23.5% or less and SiO₂+K₂OSiO₂+K2₂O is 66.5% or more. To build on this study, it is expected that a comprehensive review will be conducted through additional follow-up studies such as on clay of coarse aggregates and durability analysis to establish a system for quality control of the soil fraction of aggregates.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.2
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• pp.12-19
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• 2022

Propellant grains with embedded metal wires have been used for enhancement of burning rate while maintaining high loading density. For the performance design of a solid rocket motor using propellant grain with embedded metal wires, burn-back analysis is required according to number, location, arrangement angle of metal wires, and augmentation ratio of the propellant burning rate near a wire region. In this study, a numerical method to quickly calculate a burning surface area was developed in response to the design change of the propellant grain with embedded metal wires. The burning surface area derived from the developed method was compared with the results of a CAD program. Error rate decreased as the radial size of the grid decreased. Analysis for characteristics of burning surface area was performed according to the number and location of metal wires, the initial and final phases were shortened and the steady-state phase was increased when the number of metal wires increased. When arranging the metal wires at different radii, the burning surface area rapidly increased in the initial phase and sharply decreased in the final phase compared to the case where the metal wires were disposed in the same radius.

• Journal of Gastric Cancer
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• v.22 no.2
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• pp.120-134
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• 2022

Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1610-1610
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• 2001

Microbiological examination of silage is of little value in gauging the outcome of silage, and so chemical analysis is more reliable and meaningful indicator of quality. On the other hand chemical assessments of the principal fermentation products provide an unequivocal basis on which to judge quality. Livestock require energy, protein, minerals and vitamins from their food. While fresh forages provide these essential items, conserved forages on the other hand may be deficient in one or more of them. The aim of the conservation process is to preserve as many of the original nutrients as possible, particularly energy and protein components (Woolford, 1984). Silage fermentation is important to preservation of forage with respect of feeding value and animal performance. Chemical and bacteriological changes in the silo during the fermentation process can affect adversely nutrient yield and quality (Moe and Carr, 1984). Many of the important chemical components of silage must be assayed in fresh or by extraction of the fresh material, since drying either by heat or lyophilisation, volatilises components such as acids or nitrogenous components, or effects conversion to other compounds (Abrams et al., 1987). Maize silage dorms the basis of winter rations for the vast majority of dairy and beef cattle production in Uruguay. Since nutrient intake, particularly energy, from forages is influenced by both voluntary dry matter intake and digestibility; there is a need for a rapid technique for predicting these parameters in farm advisory systems. Near Infrared Reflectance Spectroscopy (NIRS) is increasingly used as a rapid, accurate method of evaluating chemical constituents in cereals and dried forages. For many years NIRS was applied to assess chemical composition in dry materials (Norris et al., 1976, Flinn et al., 1992; Murray, 1993, De Boever et al., 1996, De la Roza et al., 1998). The objectives of this study were (1) to determine the potential of NIRS to assess the chemical composition of dried maize samples and (2) to attempt calibrations on undried samples either for farm advisory systems or for animal nutrition research purposes in Uruguay. NIRS were used to assess the chemical composition of whole - plant maize silage samples (Zea mays, L). A representative population of samples (n = 350) covering a wide distribution in chemical characteristics were used. Samples were scanned at 2 nm intervals over the wavelength range 400-2500 nm in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance mode. Cross validation was used to avoid overfitting of the equations. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The calibration statistics were R$^2$ 0. 86 (SECV: 11.4), 0.90 (SECV: 5.7), 0.90 (SECV: 16.9) for dry matter (DM), crude protein (CP), acid detergent fiber (ADF) in g kg$\^$-1/ on dry matter, respectively for maize silage samples. This work demonstrates the potential of NIRS to analyse whole - maize silage in a wide range of chemical characteristics for both advisory farm and nutritive evaluation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.41-49
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• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.383-400
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• 2023

Air pollution-related diseases are escalating worldwide, with the World Health Organization (WHO) estimating approximately 7 million annual deaths in 2022. The rapid expansion of industrial facilities, increased emissions from various sources, and uncontrolled release of odorous substances have brought air pollution to the forefront of societal concerns. In South Korea, odor is categorized as an independent environmental pollutant, alongside air and water pollution, directly impacting the health of local residents by causing discomfort and aversion. However, the current odor management system in Korea remains inadequate, necessitating improvements. This study aims to enhance the odor management system by analyzing 1,010,749 data points collected from odor sensors located in Osong, Chungcheongbuk-do, using an Ensemble-Based Multi-Region Integrated Odor Concentration Prediction Model. The research results demonstrate that the model based on the XGBoost algorithm exhibited superior performance, with an RMSE of 0.0096, significantly outperforming the single-region model (0.0146) with a 51.9% reduction in mean error size. This underscores the potential for increasing data volume, improving accuracy, and enabling odor prediction in diverse regions using a unified model through the standardization of odor concentration data collected from various regions.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.1
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• pp.25-35
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• 2023

Considering the non-linear behavior of structure and soil when evaluating a nuclear power plant's seismic safety under a beyond-design basis earthquake is essential. In order to obtain the nonlinear response of a nuclear power plant structure, a time-domain SSI analysis method that considers the nonlinearity of soil and structure and the nonlinear Soil-Structure Interaction (SSI) effect is necessary. The Boundary Reaction Method (BRM) is a time-domain SSI analysis method. The BRM can be applied effectively with a Perfectly Matched Layer (PML), which is an effective energy absorbing boundary condition. The BRM has a characteristic that the magnitude of the response in far-field soil increases as the boundary interface of the effective seismic load moves outward. In addition, the PML has poor absorption performance of low-frequency waves. For this reason, the accuracy of the low-frequency response may be degraded when analyzing the combination of the BRM and the PML. In this study, the accuracy of the analysis response was improved by adjusting the PML input parameters to improve this problem. The accuracy of the response was evaluated by using the analysis response using KIESSI-3D, a frequency domain SSI analysis program, as a reference solution. As a result of the analysis applying the optimal PML parameter, the average error rate of the acceleration response spectrum for 9 degrees of freedom of the structure was 3.40%, which was highly similar to the reference result. In addition, time-domain nonlinear SSI analysis was performed with the soil's nonlinearity to show this study's applicability. As a result of nonlinear SSI analysis, plastic deformation was concentrated in the soil around the foundation. The analysis results found that the analysis method combining BRM and PML can be effectively applied to the seismic response analysis of nuclear power plant structures.

• Korean Journal of Optics and Photonics
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• v.34 no.3
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• pp.117-123
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• 2023

The size of an optical surface can significantly affect the performance of an optical system, and high spatial frequency errors have a greater impact. Therefore, it is crucial to measure the surface figure error with high frequency. To address this, a new method called rotational prism stitching interferometer (RPSI) is proposed in this study. The RPSI is a type of stitching interferometer that enhances spatial resolution, but it differs from conventional stitching interferometers in that it does not require the movement of either the mirror tested or the interferometer itself to obtain sub-aperture interferograms. Instead, the RPSI uses a beam expander and a rotating Dove prism to select particular sub-apertures from the entire aperture. These sub-apertures are then stitched together to obtain a full-aperture result proportional to the square of the beam expander's magnification. The RPSI's effectiveness was demonstrated by measuring a 40 mm diameter spherical mirror using a three-magnification beam expander and comparing the results with those obtained from a commercial interferometer. The RPSI achieved surface testing results with nine times higher sampling density than the interferometer alone, with a small difference of approximately 1 nm RMS.