• Journal of Radiation Industry
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• v.17 no.4
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.113-125
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• 2017

In spite of bulk literature about the tuning of TMD, the effectiveness of TMD in reducing the seismic response of engineering structures is still in a row. This paper deals with the optimum tuning parameters of a passive TMD and simulated on MATLAB with a ten-story numerical shear building. A weighted multi-objective optimization method based on computer experiment consisting of coupled with central composite design(CCD) central composite design and response surface methodology(RSM) was applied to find out the optimum tuning parameters of TMD. After the optimization, the so-conceived TMD turns out to be optimal with respect to the specific seismic event, hence allowing for an optimum reduction in seismic response. The method was employed on above structure by assuming first the El Centro seismic input as a sort of benchmark excitation, and then additional recent strong-motion earthquakes. It is found that the RSM based weighted multi-objective optimized damper improves frequency responses and root mean square displacements of the structure without TMD by 31.6% and 82.3% under El Centro earthquake, respectively, and has an equal or higher performance than the conventionally designed dampers with respect to frequency responses and root mean square displacements and when applied to earthquakes.

• Korean Journal of Plant Tissue Culture
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• v.25 no.1
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• pp.21-25
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• 1998

The effects of sucrose concentration and some absorbents on growth and tropane alkaloid production in hairy root cultures of Scopolia parviflora were investigated. The maximum effect on growth and tropane alkaloid production in hairy root clone SP11 was obtained for 1/2 B5 medium containing 5% sucrose. The production pattern of tropane alkaloid in hairy roots was some different from that of rhizome of mother plant, particulary showing high littorine contents, which was not found in ordinary roots. Among absorbents examined, charcoal 0.01% and XAD-II 1% made a slight growth promotion effect, whereas the other concentration of charcoal, XAD-II and absorbents (amberlite and chitosan) showed inhibition or no significant effect. The addition of hydroxyapatite enhanced the production of tropane alkaloids significantly than control cultures.

• Journal of Ginseng Research
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• v.40 no.4
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• pp.453-461
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• 2016

Background: This study aims to describe the characterization of Paenibacillus polymyxa GBR-1 (GBR-1) with respect to its positive and negative effects on plants. Methods: The morphological characteristics of GBR-1 were identified with microscopy, and subjected to Biolog analysis for identification. Bacterial population and media optimization were determined by a growth curve. The potential for GBR-1 as a growth promoting agent, to have antagonistic activity, and to have hydrolytic activity at different temperatures was assessed. The coinoculation of GBR-1 with other microorganisms and its pathogenicity on various stored plants, including ginseng, were assessed. Results: Colony morphology, endospore-bearing cells, and cell division of GBR-1 were identified by microscopy; identification was performed by utilizing the Biolog system, gas chromatography of fatty acid methyl esters (GC-FAME). GBR-1 showed the strongest antagonistic activity against fungal and bacterial pathogens. GBR-1 cell numbers were relatively higher when the cells were cultured in brain heart infusion (BHI) medium when compared with other media. Furthermore, the starch-hydrolytic activity was influenced by GBR-1 at higher temperature compared to low temperatures. GBR-1 was pathogenic to some of the storage plants. Coinoculation of GBR-1 with other pathogens causes differences in rotting on ginseng roots. A significant growth promotion was observed in tobacco seedlings treated with GBR-1 suspensions under in vitro conditions, suggesting that its volatile organic compounds (VOCs) might play a role in growth promotion. Conclusion: The results of this study indicate that GBR-1 has both positive and negative effects on ginseng root and other stored plants as a potential biocontrol agent and eliciting in vitro growth promotion.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.91-103
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• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2107-2119
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• 2022

Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

• Journal of the Korean Society for Railway
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• v.20 no.3
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• pp.311-319
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• 2017

The primary suspension system of a railway vehicle restrains the wheelset and the bogie, which greatly affects the dynamic characteristics of the vehicle depending on the stiffness in each direction. In order to improve the dynamic characteristics, different stiffness in each direction is required. However, designing different stiffness in each direction is difficult in the case of a general suspension device. To address this, in this paper, an optimization technique is applied to design different stiffness in each direction by using a conical rubber spring. The optimization is performed by using target and analysis RMS values. Lastly, the final model is proposed by complementing the shape of the weak part of the model. An actual model is developed and the reliability of the optimization model is proved on the basis of a deviation average of about 7.7% compared to the target stiffness through a static load test. In addition, the stiffness value is applied to a multibody dynamics model to analyze the stability and curve performance. The critical speed of the improved model was 190km/h, which was faster than the maximum speed of 110km/h. In addition, the steering performance is improved by 34% compared with the conventional model.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.11
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• pp.1386-1396
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• 2017

The purpose of this study was to determine the optimum Platycodon grandiflorum root concentrate (PGRC, $65^{\circ}Brix$), fermented P. grandiflorum root extract by Lactobacillus plantarum (FPGRE, $2^{\circ}Brix$), and cactus Chounnyouncho extract (Cactus-E, $2^{\circ}Brix$) for preparation of PGRC stick product with FPGRE using response surface methodology (RSM). The experimental conditions were designed according to a central composite design with 20 experimental points, including three replicates for three independent variables such as amount of PGRC (8~12 g), FPGRE (0~20 g), and Cactus-E (0~20 g). The experimental data for the sensory evaluation and functional properties based on antioxidant activity and antimicrobial activity were fitted with the quadratic model, and accuracy of equations was analyzed by ANOVA. For the responses, sensory and functional properties showed significant correlation with contents of three independent variables. The results indicate that addition of PGRC contributed to increased bitterness and acridity based on the sensory test and antimicrobial activity, addition of FPGRE contributed to increased antioxidant activity and antimicrobial activity, and addition of Cactus-E contributed to increased fluidity based on the sensory test, antioxidant activity, and antimicrobial activity. Based on the results of RSM, the optimum formulation of PGRC stick product was calculated as PGRC 8.456 g, FPGRE 20.00 g, and Cactus-Ex 20.00 g with minimal bitterness and acridity, as well as optimized fluidity, antioxidant activity, and antimicrobial activity.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.6
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• pp.861-869
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• 2012

The chlorine's residual concentration prevents the regrowth of microorganism in water transport along the pipeline system. Precise prediction of chlorine concentration is important in determining disinfectant injection for the water distribution system. In this study, a pilot scale water distribution system was designed and fabricated to measure the temporal variation of chlorine concentration for three flow conditions (V = 0.88, 1.33, 1.95 m/s). Various kinetic models were applied to identify the relationship between hydraulic condition and chlorine decay. Genetic Algorithm (GA) was integrated into five kinetic models and time series of chlorine were used to calibrate parameters. Model fitness was compared by Root Mean Square Error (RMSE) between measurement and prediction. Limited first order model and Parallel first order showed good fitness for prediction of chlorine concentration.