• Journal of KIISE:Computing Practices and Letters
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• v.6 no.1
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• pp.11-20
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• 2000

Many object query languages including OQL(the query language proposed by theOBMG) allow query block to be nested in any clause: select clause, from clause and where clause. The processing of nested queries can affect the performance of its query processor Therefore, an OQL query processor should have effective optimizing techniques for nested queries. This paper designs and implements a new framework of an unnesting front-end for an OQL query processor This unnesting module can minimize implementation overhead when developing a new OQL processor or extending an existing query processor to be equipped with an unnesting facility This is accomplished by separation between logical algebra operators used in an unnesting front-end and ones in a query optimizer.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.665-676
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• 2001

The most expensive spatial operation in patial database in a spatial join which computes a combined table of which tuple consists of two tuples of the two tables satisgying a spatial predicate. Although the execution time of sequential processing of a spatial join has been so far considerably improved the response time is not tolerable because of not meeting the requiremetns of interactive users. It is usually appropriate to use parallel processing to improve the performance of spatial join processing. in spatial database the fixed grids which consist of the regularly partitioned cells can be employed the previous works on the spatial joins have not studied the parallel processing of spatial joins using fixed grids. This paper has presented an analytical cost model that estimates the comparative performance of a parallel spatial join algorithm based on the fixed grids in terms of the number of MBR comparisons. disk accesses, and message passing, Several experiments on the synthetic and real datasets show that the proposed analytical model is very accurate. This most model is also expected to used for implementing a very important DBMS component, Called the query processing optimizer.

• Korean Journal of Plant Resources
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• v.14 no.3
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• pp.235-240
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• 2001

Polymerase chain reaction (PCR) is used in a wide array of researches in plant molecular genetics and breeding. However, considerable time and cost are still required for the preparation of DNA suitable for reliable PCR results, especially in plant species containing high amounts of polyphenols. To reduce time and effort for PCR-based analysis, a simplified but reliable method was developed by a combinational employment of a simple and fast DNA extraction procedure and BLOTTO (Bovine Lacto Transfer Technique Optimizer) in reaction mixture. Genomic DNAs prepared by one-step extraction method from recalcitrant plant species such as Rubus coreanus, apple, grape and lettuce were successfully amplified by random primers in the reaction mixture containing 2 to 4% BLOTTO. Successful amplification of ${\gamma}$-TMT transgene in lettuce transformants by the specific primers was also achieved in the same condition, making rapid screening of positive transformants possible. Our results suggest that use of a simple DNA extraction procedure and incorporation of BLOTTO in reaction mixture in combination can reduce time and effort required for the analyses of a large number of germplasms and transformants by PCR-based techniques.

• Environmental Engineering Research
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• v.23 no.4
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• pp.474-484
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• 2018

The cleaning efficiency of reverse osmosis (RO) membranes inevitably fouled by organic foulants depends upon both chemical (type of cleaning agent, concentration of cleaning solution) and physical (cleaning time, flowrate, temperature) parameters. In attempting to determine the optimal procedures for chemical cleaning organic-fouled RO membranes, the design of experiments concept was employed to evaluate key factors and to predict the flux recovery rate (FRR) after chemical cleaning. From experimental results and based on the predicted FRR of cleaning obtained using the Central Composite Design of Minitab 17, a modified regression model equation was established to explain the chemical cleaning efficiency; the resultant regression coefficient ($R^2$) and adjusted $R^2$ were 83.95% and 76.82%, respectively. Then, using the optimized conditions of chemical cleaning derived from the response optimizer tool (cleaning with 0.68 wt% disodium ethylenediaminetetraacetic acid for 20 min at $20^{\circ}C$ with a flowrate of 409 mL/min), a flux recovery of 86.6% was expected. Overall, the results obtained by these experiments confirmed that the equation was adequate for predicting the chemical cleaning efficiency with regards to organic membrane fouling.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.246-256
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• 2022

Recently, parents and teachers consider physical education as a minor subject for students in elementary and secondary schools. Physical education performance has become increasingly significant as parents and schools pay more attention to physical schooling. The sports mining with distribution analysis model considers different factors, including the games, comments, conversations, and connection made on numerous sports interests. Using different machine learning/deep learning approach, children's athletic and academic interests can be tracked over the course of their academic lives. There have been a number of studies that have focused on predicting the success of students in higher education. Sports interest prediction research at the secondary level is uncommon, but the secondary level is often used as a benchmark to describe students' educational development at higher levels. An Automated Student Interest Prediction on Sports Mining using DL Based Bi-directional Long Short-Term Memory model (BiLSTM) is presented in this article. Pre-processing of data, interest classification, and parameter tweaking are all the essential operations of the proposed model. Initially, data augmentation is used to expand the dataset's size. Secondly, a BiLSTM model is used to predict and classify user interests. Adagrad optimizer is employed for hyperparameter optimization. In order to test the model's performance, a dataset is used and the results are analysed using precision, recall, accuracy and F-measure. The proposed model achieved 95% accuracy on 400^th instances, where the existing techniques achieved 93.20% accuracy for the same. The proposed model achieved 95% of accuracy and precision for 60%-40% data, where the existing models achieved 93% for accuracy and precision.

• Composites Research
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• v.35 no.1
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• pp.31-37
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• 2022

In this study, an optimal structural design framework has been developed for the structural design of composite helicopter blades. The optimal design framework is constructed using PSGA (Particle Swarm assisted Genetic Algorithm), which combines the genetic algorithm and particle swarm optimizer. The optimization process consists of a finite element (FE) modeling over the blade section, two-dimensional (2D) cross-sectional FE analysis, and 1D rotating blade analysis. In the design process, the geometric curves and surfaces are formed using the B-spline scheme while discretizing the sections via a FE mesh generation program Gmsh. The blade cross-sections are created in accordance with the design variables when performing the blade structural analysis. The proposed optimization design framework is applied to a modernization of the HART II (Higher-harmonic Aeroacoustics Rotor Test II) blades. It is demonstrated that an improved blade design is reached through the current optimization framework with the satisfaction of all design requirements set for the study.

• Advances in nano research
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• v.12 no.1
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• pp.81-99
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• 2022

The aim of current work is to evaluate thermo-electrical characteristics of graphene nanoplatelets Reinforced Composite (GNPRC) coupled with magneto-electro-elastic (MEE) face sheet. In this regard, a cylindrical smart nanocomposite made of GNPRC with an external MEE layer is considered. The bonding between the layers are assumed to be perfect. Because of the layer nature of the structure, the material characteristics of the whole structure is regarded as graded. Both mechanical and thermal boundary conditions are applied to this structure. The main objective of this work is to determine critical temperature and critical voltage as a function of thermal condition, support type, GNP weight fraction, and MEE thickness. The governing equation of the multilayer nanocomposites cylindrical shell is derived. The generalized differential quadrature method (GDQM) is employed to numerically solve the differential equations. This method is integrated with Deep Learning Network (DNN) with ADADELTA optimizer to determine the critical conditions of the current sandwich structure. This the first time that effects of several conditions including surrounding temperature, MEE layer thickness, and pattern of the layers of the GNPRC is investigated on two main parameters critical temperature and critical voltage of the nanostructure. Furthermore, Maxwell equation is derived for modeling of the MEE. The outcome reveals that MEE layer, temperature change, GNP weight function, and GNP distribution patterns GNP weight function have significant influence on the critical temperature and voltage of cylindrical shell made from GNP nanocomposites core with MEE face sheet on outer of the shell.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.511-523
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• 2023

Research on the integration of unmanned aerial vehicles and deep learning for reinforced concrete damage detection is actively underway. Convolutional neural networks have a high impact on the performance of image classification, detection, and segmentation as backbones. The MobileNet, a pre-trained convolutional neural network, is efficient as a backbone for an unmanned aerial vehicle-based damage detection model because it can achieve sufficient accuracy with low computational complexity. Analyzing vanilla convolutional neural networks and MobileNet under various conditions, MobileNet was evaluated to have a verification accuracy 6.0~9.0% higher than vanilla convolutional neural networks with 15.9~22.9% lower computational complexity. MobileNetV2, MobileNetV3Large and MobileNetV3Small showed almost identical maximum verification accuracy, and the optimal conditions for MobileNet's reinforced concrete damage image feature extraction were analyzed to be the optimizer RMSprop, no dropout, and average pooling. The maximum validation accuracy of 75.49% for 7 types of damage detection based on MobilenetV2 derived in this study can be improved by image accumulation and continuous learning.