• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.1-18
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• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.633-640
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• 2023

With the recent development of static and dynamic modulated brachytherapy methods in brachytherapy, which use radiation shielding to modulate the dose distribution to deliver the dose, the amount of parameters and data required for dose calculation in inverse treatment planning and treatment plan optimization algorithms suitable for new directional beam intensity modulated brachytherapy is increasing. Although intensity-modulated brachytherapy enables accurate dose delivery of radiation, the increased amount of parameters and data increases the elapsed time required for dose calculation. In this study, a GPU-based CUDA-accelerated dose calculation algorithm was constructed to reduce the increase in dose calculation elapsed time. The acceleration of the calculation process was achieved by parallelizing the calculation of the system matrix of the volume of interest and the dose calculation. The developed algorithms were all performed in the same computing environment with an Intel (3.7 GHz, 6-core) CPU and a single NVIDIA GTX 1080ti graphics card, and the dose calculation time was evaluated by measuring only the dose calculation time, excluding the additional time required for loading data from disk and preprocessing operations. The results showed that the accelerated algorithm reduced the dose calculation time by about 30 times compared to the CPU-only calculation. The accelerated dose calculation algorithm can be expected to speed up treatment planning when new treatment plans need to be created to account for daily variations in applicator movement, such as in adaptive radiotherapy, or when dose calculation needs to account for changing parameters, such as in dynamically modulated brachytherapy.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.993-1000
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• 2023

Recently, enviromentally friendly natural substances derived from plants have been attracting attention as cosmetic materials, and research on various physiological activities of natural substances is being actively conducted. This study investigated the antioxidant, anti-inflammatory, moisturizing, and antibacterial effects of three types of extracts of mixtures containing different mixing ratios, Polygala tenuifolia, Angelica dahurica, and Elsholtzia splendens, known to have various physiological activities. The mixing ratio is 7 conditions (M1, 1:1:1; M2, 0.5:1.5:1; M3, 1.5:0.5:1; M4, 0.1:0.95:0.95; M5, 0.5:0.5:2; M6, 0.95 :1.95:0.1; M7, 1.45:0.1:1.45), and the optimal mixing ratio was confirmed for use as a cosmetic material. DPPH and ABTS radical scavenging activities showed scavenging abilities of 75.37% and 99.19%, respectively, at 1,000 ㎍/mL of M6. At a concentration of 200 ㎍/mL of M6, it showed 50% of nitric oxide production inhibition compared to the lipopolysaccharide-treated that induced an inflammatory response. It was confirmed that M3 and M6 produced hyaluronic acid 1.47 and 1.49 times higher than the control at a concentration of 50 ㎍/mL, respectively. Through the disc diffiusion test, the clear zone was 9.75 mm at 8 ㎍/mL of M6, confirming the inhibition of growth of staplylococcus aureus strain. Based on the above results, it is believed that the mixed extract of Polygala tenuifolia, Angelica dahurica, and Elsholtzia splendens can be used as a functional natural material for cosmetics.

• 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.