• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.861-866
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• 2023

Individuals with visual impairments face difficulties in accessing accurate information about medical services and medications, making it challenging for them to ensure proper medication intake. While there are healthcare laws addressing this issue, there is a lack of standardized solutions, and not all over-the-counter medications are covered. Therefore, we have undertaken the design of a mobile application that utilizes image recognition technology, barcode scanning, and QR code recognition to provide guidance on how to take over-the-counter medications, filling the existing gaps in the knowledge of visually impaired individuals. Currently available applications for individuals with visual impairments allow them to access information about medications. However, they still require the user to remember which specific medication they are taking, posing a significant challenge. In this research, we are optimizing the camera capture environment, user interface (UI), and user experience (UX) screens for image recognition, ensuring greater accessibility and convenience for visually impaired individuals. By implementing the findings from our research into the application, we aim to assist visually impaired individuals in acquiring the correct methods for taking over-the-counter medications.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.5
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• pp.606-614
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• 2023

This study was conducted to optimize the size of olive flounder Paralichthys olivaceus (OF) as a material of sikhae and to investigate the quality characteristics. The results on the protease activity of OF meat, protein and ash contents of the bone, and yields and hardness of fish bone during fermentation time suggest that the suitable fish weight for sikhae was less than 250 g. The proximate compositions of the OF sikhae fermented under optimum condition (fermentation for 9 days at 15℃), were 73.0% moisture, 12.0% crude protein, 1.3% crude fat and 2.4% ash. The salinity, titration acidity and amino acid nitrogen contents per 100 g sikhae were 1.7 g, 2.46 g, and 311.3 mg, respectively. The lactic acid bacteria concentration in the sikhae were 8.84 log CFU/g, which were higher than those (5.78-6.62 log CFU/g) of 5 kind of commercial flounder sikhae. The functional properties, such as ACE inhibitory activity (69.0%), antioxidative activity (69.3%), α-glucosidase inhibitory activity (22.7%), xanthine oxidase inhibitory activity (88.2%), and nitrite scavenging activity (96.4%) of the sikhae were superior to those of 5 kind of commercial flounder sikhae.

• Journal of Marine Bioscience and Biotechnology
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• v.15 no.2
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• pp.41-48
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• 2023

The objective of the present study was to improve the biomass productivity of newly isolated freshwater green microalga Parachlorella sp. This was accomplished by culture conditions optimization, including CO₂ concentration, superficial gas velocity, and light intensity, in 0.5 L bubble column photobioreactors. The supplied CO₂ concentration and gas velocity varied from 0.032% (air) to 10% and 0.02 m/s - 0.11 m/s, respectively, to evaluate their effects on growth kinetics. Next, to maximize the production rate of Parachlorella sp., a lumostatic operation based on a specific light uptake rate (q_e) was applied. From these results, the optimal CO₂ concentration in the supplied gas and the gas velocity were determined to be 5% and 0.064 m/s, respectively. For the lumostatic operation at 10.2 µmol/g/s, biomass productivity and photon yield showed significant increases of 83% and 66%, respectively, relative to cultures under constant light intensity. These results indicate that the biomass productivity of Parachlorella sp. can be improved by optimizing gas properties and light control as cell concentrations vary over time.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.935-940
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• 2023

Digital-twin technology is emerging as an innovative solution for all industries, including manufacturing and production lines. Therefore, this paper optimizes all the energy used in a biomass plant based on unused resources. We will then implement a digital-twin prototype for biomass plants and evaluate its performance in order to improve the efficiency of plant operations. The proposed digital-twin prototype applies a standard communication platform between the framework and the gateway and is implemented to enable real-time collaboration. and, define the message sequence between the client server and the gateway. Therefore, an interface is implemented to enable communication with the host server. In order to verify the performance of the proposed prototype, we set up a virtual environment to collect data from the server and perform a data collection evaluation. As a result, it was confirmed that the proposed framework can contribute to energy optimization and improvement of operational efficiency when applied to biomass plants.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.6
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• pp.288-293
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• 2023

To optimize the process parameters of laser powder bed fusion process to fabricate the high density STS316L alloy, the effect of laser power, scanning speed and hatching distance on the relative density was studied. Tensile properties of additively manufactured STS316L alloy using optimized parameters was also evaluated according to the build direction. As a result of additive manufacturing process under the energy density of 55.6 J/mm³, 83.3 J/mm³ and 111.1 J/mm³, high density STS316L specimens was suitably fabricated when the energy density, power and scan speed were 83.3 J/mm³, 225 W and 1000 mm/s, respectively. The yield strength, ultimate tensile strength, and elongation of STS316L specimens in direction perpendicular to the build direction, show the most competitive values. Anisotropic shape of the pores and the lack of fusion defects probably caused strain localization which result in deterioration of tensile properties.

• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.412-419
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• 2023

Anion exchange membrane electrolysis is considered a promising next-generation hydrogen production technology that can produce low-cost, clean hydrogen. However, anion exchange membrane electrolysis technology is in its early stages of development and requires intensive research on electrodes, which are a key component of the catalyst-system interface. In this study, we optimized the pressure conditions of the hot-pressing process to manufacture cobalt oxide electrodes for the development of a high uniformity and high adhesion electrode production process for the oxygen evolution reaction. As the pressure increased, the reduction of pores within the electrode and increased densification of catalytic particles led to the formation of a uniform electrode surface. The cobalt oxide electrode optimized for pressure conditions exhibited improved catalytic activity and durability. The optimized electrode was used as the anode in an AEMWE single cell, exhibiting a current density of 1.53 A cm^-2 at a cell voltage of 1.85 V. In a durability test conducted for 100 h at a constant current density of 500 mA cm^-2, it demonstrated excellent durability with a low degradation rate of 15.9 mV kh^-1, maintaining 99% of its initial performance.

• Resources Recycling
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• v.33 no.1
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• pp.22-30
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• 2024

To synthesize the Ti₃AlC₂ MAX phase, a crucial precursor for generating the two-dimensional material MXene, the use of Ti scrap as an initial material is an economically feasible approach. This study aims to optimize the synthesis conditions for the phase fraction of the Ti₃AlC₂ MAX phase utilizing Ti scrap as the Ti source. The deoxidation of Ti powders, prepared through the hydrogenation-dehydrogenation process from Ti scrap, was effectively accomplished using the deoxidation in solid-state (DOSS) process. The optimal synthesis conditions were established by blending DOSS-Ti, Al, and graphite powders with particle sizes ranging from 25 ~ 32 ㎛ in a molar ratio of 3:1.1:2. The resulting phase fractions were as follows: Ti₃AlC₂ at 97.25 wt.%, TiC at 0.93 wt.%, and Al₃Ti at 1.82 wt.%. Furthermore, the oxygen content of the Ti₃AlC₂ MAX powder, spanning from 25 ~ 45 ㎛, was measured at 4,210 ppm.

• Information Systems Review
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• v.24 no.1
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• pp.39-57
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• 2022

This paper proposes the GMOPTBoost algorithm to improve the performance of the AdaBoost algorithm for bankruptcy prediction in which class imbalance problem is inherent. AdaBoost algorithm has the advantage of providing a robust learning opportunity for misclassified samples. However, there is a limitation in addressing class imbalance problem because the concept of arithmetic mean accuracy is embedded in AdaBoost algorithm. GMOPTBoost can optimize the geometric mean accuracy and effectively solve the category imbalance problem by applying Gaussian gradient descent. The samples are constructed according to the following two phases. First, five class imbalance datasets are constructed to verify the effect of the class imbalance problem on the performance of the prediction model and the performance improvement effect of GMOPTBoost. Second, class balanced data are constituted through data sampling techniques to verify the performance improvement effect of GMOPTBoost. The main results of 30 times of cross-validation analyzes are as follows. First, the class imbalance problem degrades the performance of ensembles. Second, GMOPTBoost contributes to performance improvements of AdaBoost ensembles trained on imbalanced datasets. Third, Data sampling techniques have a positive impact on performance improvement. Finally, GMOPTBoost contributes to significant performance improvement of AdaBoost ensembles trained on balanced datasets.