• Korean Journal of Construction Engineering and Management
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• v.24 no.3
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• pp.12-19
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• 2023

In the case of the construction industry, the relationship between process and cost should be appropriately distributed so that the finished product can be delivered at the minimum fee within the construction period. At that time, it should be considered the size of the bridge, the construction method, the environment and production capacity of the factory, and the transport distance. However, due to various reasons that occur during the construction period, problems such as construction delay, construction cost increase, and quality and reliability degradation occur. Therefore, a systematic and scientific construction technique and process management technology are needed to break away from the conventional method. The prefab(Pre-Fabrication) is a representative OSC (Off-Site Construction) method manufactured in a factory and constructed onsite. This study develops a resource and process plan optimization system for the process management of the Nodular girder, a prefab bridge girder. A simulation algorithm develops to automatically test various variables in the personnel equipment mobilization plan to derive the optimal value. And, the algorithm was applied to the Paju-Pocheon Expressway Construction (Section 3) Dohwa 4 Bridge under construction, and the results compare. Based on construction work standard product calculation, actual input manpower, equipment type, and quantity were applied to the Activity Card, and the amount of work by quantity counting, resource planning, and resource requirements was reflected. In the future, we plan to improve the accuracy of the program by applying forecasting techniques including various field data.

• Applied Chemistry for Engineering
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• v.34 no.3
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• pp.299-306
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• 2023

In this study, the O/W emulsification processes with the natural surfactants that were extracted from Medicago sativa L. and Sapindus saponaria L. as emulsifiers were optimized using the central composite design-response surface methodology (CCD-RSM). Herein, independent parameters were the amounts of mixed emulsifiers, the mixing ratio of natural emulsifiers (soapberry saponin/alfalfa saponin), and the emulsification time, whereas the reaction parameters were the emulsion stability index (ESI), mean droplet size (MDS), and antioxidant activity (DPPH radical scanvenging activity). Through basic experiments, the ranges of operation variables for the amount of mixed emulsifiers, the mixing ratio of natural emulsifiers, and the emulsification time were 12~14 wt%, 30~70%, and 20~30 min, respectively. The optimum operation variables deduced from CCD-RSM for the amount of mixed emulsifiers, the mixing ratio of natural emulsifiers, and the emulsification time were 13.2 wt%, 44.2%, and 25.8 min, respectively. Under these optimal conditions, the expected values of the ESI, MDS, and antioxidant activity were 88.7%, 815.5 nm, and 38.7%, respectively. And, the measured values of the ESI, MDS, and antioxidant activity were 90.6%, 830.2 nm, and 39.6%, respectively, and the average experimental error for validating the accuracy was about 2.1%. Therefore, it was possible to design an optimization process for evaluating the O/W emulsion process using CCD-RSM.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• Analytical Science and Technology
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• v.21 no.1
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• pp.58-63
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• 2008

Nano-sized ZnO crystals were successfully incorporated using ion exchange method in TMA-A zeolite synthesized by the hydrothermal method. The optimal composition for the synthesis of TMA-A zeolite was resulted in a solution of $Al(i-pro)_3$ : 2.2 TEOS : 2.4 TMAOH : 0.3 NaOH : 200 $H_2O$. 0.3 g of TMA-A zeolite and 5 mol of $ZnCl_2$ solution were employed for the preparation of ZnO incorporated TMA-A zeolite. The crystallization process of ZnO incorporated TMA-A zeolite was analyzed by X-ray diffraction (XRD). The incorporated nano-sized ZnO crystals and the crystallinity of TMA-A zeolite were evaluated by transmission electron microscopy (TEM) and high resolution transmission electron microscopy (HRTEM). The size of the incorporated nano-sized ZnO crystals was 3~5 nm, while the TMA-A zeolite was 60~100 nm. The bonding structure and absorption of the ZnO incorporated TMA-A zeolite were compared with the ZnO and TMA-A zeolite by the FT-IR analysis. Subsequentlly, the ZnO incorporated TMA-A zeolite showed the photoluminescent characteristics on the wavelengths of 330~260 nm and 260~230 nm by measurement of UV spectrophotometer.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.4
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• pp.29-34
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• 2022

A fabrication process of smart windows with controllable visible light transmittance by using retardation films is proposed. The 𝛌/4-phase retardation films that can convert a linearly polarized light into circularly polarized light are achieved through photo-alignment layers and reactive mesogen (RM) coating process. Two sheets of the fabricated retardation films with different orientation angles induced to light transmission mode (45°/-45°) and light blocking mode (45°/45°) for visible wavelength. We evaluated retardation characteristics according to the thickness of the birefringent RM material and found out the optimal condition for the film with 𝚫n·d of 𝛌/4-phase. The proposed structure of the smart window exhibited the light blocking ratio improved by more than 20% in the visible wavelength (380 nm to 780 nm). Finally, it was confirmed that the feasibility of the window structure by applying to a prototype for a smart window with a size of 150 × 150 mm².

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.751-759
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• 2023

Breast ultrasonography is difficult to image in fatty breasts and to find micro-calcification, but the discovery of micro-calcification is very important for breast cancer screening. Among the color Doppler artifact of ultrasound, twinkle artifact mainly occur on strong reflectors such as stones or calcification in images, and evaluation methods using them are clinically being used. In this study, we are conducting experiments on the color Doppler settings of ultrasound equipment, such as repetition frequency, ensemble, persist, wall filtering, smoothing, linear density, and dissociation value, by producing a breast simulation phantom using the largest amount of calcium phosphate among breast implants. The purpose of this study was to improve the contrast of twinkle artifact in breast ultrasound examinations and to maximize their use in clinical practice. As a result, the pulse repetition frequency occurred in the range of 3.6 kHz to 7.2 kHz, and did not occur above 10.5 kHz. For ensembles, twinkle artifact occurred in all sizes of calcification under low conditions, and in threshold settings, the twinkle artifact increased slightly only under 80 to 100 conditions, and did not occur in 1 mm size calcification. Persist, wall filter, smoothing, and line density settings did not have much meaning in the setting variable because conditions did not increase by condition, and pulse repetition frequency, ensemble, and thresholds had the greatest impact on the twinkling artifact image. This study is expected to help examiners select optimal conditions to effectively increase twinkle artifact by adjusting color Doppler settings.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.20-31
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• 2024

Purpose: Research and development of high-strength concrete enables high-rise buildings and reduces the self-weight of the structure by reducing the cross-section, thereby reducing the thickness of beams and slabs to build more floors. A large effective space can be secured and the amount of reinforcement and concrete used to designate the base surface can be reduced. Method: In terms of field construction and quality, the effect of reducing the occurrence of drying shrinkage can be confirmed by studying the combination of low water bonding ratio and minimizing bleeding on the concrete surface. Result: The ease of site construction was confirmed due to the high self-charging property due to the increased fluidity by using high-performance water reducing agents, and the advantage of shortening the time to remove the formwork by expressing the early strength of concrete was confirmed. These experimental results show that the field application of ultra-high-strength concrete with a design standard strength of 100 MPa or higher can be expanded in high-rise buildings. Through this study, we experimented and evaluated whether ultra-high-strength concrete with a strength of 130 MPa or higher, considering the applicability of high-rise buildings with more than 120 floors in Korea, could be applied in the field. Conclusion: This study found the optimal mixing ratio studied by various methods of indoor basic experiments to confirm the applicability of ultra-high strength, produced 130MPa ultra-high strength concrete at a ready-mixed concrete factory similar to the real size, and tested the applicability of concrete to the fluidity and strength expression and hydration heat.

• Korean Journal of Plant Resources
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• v.37 no.3
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• pp.263-269
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• 2024

Embryos of early-ripening peaches could not achieve physiological maturation or undergo abortion before harvest. Embryo rescue is an effective strategy to rescue embryos from early-ripening peaches. Thus, the current study was carried out to determine the appropriate developmental stage and optimal medium composition for embryo rescue in early-ripening peach. Development of open-pollinated 'Yumi' fruit was investigated from 20 to 90 days after full bloom (DAFB) to explore period occurring endocarp hardening. After endocarp hardening, embryo development was observed by light microscopes. Shoot and root meristems were observed at 65 DAFB and embryo size rapidly increased at 75 DAFB. Embryos collected at 75, 80, 85, and 90 DAFB were cultured on four media based on Driver and Kuniyuki (DKW) medium. Germination rate of embryos cultured on four media gradually increased from 75 to 90 DAFB and reached 100% at 90 DAFB. Notably, M3 medium (0.5 DKW supplemented with 6-benzylaminopurine (BAP) 1.0 ㎎/L) displayed the highest germination rate at 75 and 80 DAFB stages. Growth and development of shoot and root were pronounced in plantlet cultured at 90 DAFB stage. While delayed shoot growth was evident in plantlets cultured at 75, 80, and 85 DAFB stages, this retardation could be overcome through the application of growth regulators, particularly in M3 and M4 (0.5 DKW supplemented with BAP 1.0 ㎎/L and indole-3-butyric acid 0.5 ㎎/L) media. Remarkably, roots of plantlet grown in M4 medium exhibited limited elongation. In conclusion, germination rate of embryo and growth of embryo cultured plantlet can be enhanced by collecting seeds from early-ripening 'Yumi' at the 90 DAFB stage and conducting embryo culture using the M3 medium.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.