• Journal of Veterinary Science
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• v.23 no.5
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• pp.78.1-78.13
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• 2022

Background: Florfenicol might be ineffective for treating Staphylococcus aureus small colony variants (SCVs) mastitis. Objectives: In this study, florfenicol-loaded chitosan (CS)-sodium tripolyphosphate (TPP) composite nanogels were prepared to allow targeted delivery to SCV infected sites. Methods: The formulation screening, the characteristics, in vitro release, antibacterial activity, therapeutic efficacy, and biosafety of the florfenicol composite nanogels were studied. Results: The optimized formulation was obtained when the CS and TPP were 10 and 5 mg/mL, respectively. The encapsulation efficiency, loading capacity, size, polydispersity index, and zeta potential of the optimized florfenicol composite nanogels were 87.3% ± 2.7%, 5.8% ± 1.4%, 280.3 ± 1.5 nm, 0.15 ± 0.03, and 36.3 ± 1.4 mv, respectively. Optical and scanning electron microscopy showed that spherical particles with a relatively uniform distribution and drugs might be incorporated in cross-linked polymeric networks. The in vitro release study showed that the florfenicol composite nanogels exhibited a biphasic pattern with the sustained release of 72.2% ± 1.8% at 48 h in pH 5.5 phosphate-buffered saline. The minimal inhibitory concentrations of commercial florfenicol solution and florfenicol composite nanogels against SCVs were 1 and 0.25 ㎍/mL, respectively. The time-killing curves and live-dead bacterial staining showed that the florfenicol composite nanogels were concentration-dependent. Furthermore, the florfenicol composite nanogels displayed good therapeutic efficacy against SCVs mastitis. Biological safety studies showed that the florfenicol composite nanogels might be a biocompatible preparation because of their non-toxic effects on the renal tissue and liver. Conclusions: Florfenicol composite nanogels might improve the treatment of SCV infections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.125-144
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• 2022

When a variable message signs (VMS) system displays false information related to traffic safety caused by malicious attacks, it could pose a serious risk to drivers. If the normal message patterns displayed on the VMS system are learned, it would be possible to detect and respond to the anomalous messages quickly. This paper proposes a method for detecting anomalous messages by learning the normal patterns of messages using a bi-directional generative pre-trained transformer (GPT) network. In particular, the proposed method was trained using the normal messages and their system parameters to minimize the corresponding negative log-likelihood (NLL) values. After adequate training, the proposed method could detect an anomalous message when its NLL value was larger than a pre-specified threshold value. The experiment results showed that the proposed method could detect malicious messages and cases when the system error occurs.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.51-58
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• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.187-193
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• 2022

Domestic violence in our society is where the abuser and the abuser live in the same space. Problems are left unresolved in families where abuse is reproducing. Domestic violence can be viewed as a crime that violates and tramples human rights. They rely solely on family support networks for solutions to domestic violence. The physical, emotional, and psychological pain and wounds that victims of domestic violence must endure are too deep. In order to help victims of domestic violence, case management services that can provide long-term and attentive help in the neighborhood or community are needed. For this, prevention and treatment of domestic violence should be considered together. And the interest and professional role of the community must follow.

• Composites Research
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• v.36 no.1
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• pp.59-67
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• 2023

Damage caused by impact on a vehicle composed of CFRP(carbon fiber reinforced plastic) composite to reduce weight in the aerospace industries is related to the safety of passengers. Therefore, it is important to understand the damage behavior of materials that is invisible in impact situations, and research through the FEM(finite element model) is needed to simulate this. In this study, FEM suitable for predicting damage behavior was constructed for impact analysis of unidirectional laminated composite. The calibration parameters of the MAT_54 Enhanced Composite Damage material model in LS-DYNA were acquired by inverse estimation through ANN(artificial neural network) model. The reliability was verified by comparing the result of experiment with the results of the ANN model for the obtained parameter. It was confirmed that accuracy of FEM can be improved through optimization of calibration parameters.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1167-1175
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• 2022

In this study, management index method has been developed to estimate the level of deterioration, applied to Cheongju city, and compared with the previous estimation methods of deterioration level of water distribution system. From the results, distribution systems of Yullyang, Naedeok1 and Yongam2 are found to be seriously deteriorated. And it was also found that probability of pipe breakage was estimated as 3.21%, 4.64% which is highest level at the steel pipe of 200 mm and 300 mm diameter. It was found that risk degree was estimated as 0.2609, 0.2644 by using management index method in Naedeok1 which is the most dangerous distribution system in the city. It was also found that results of risk analysis by management index method have been similar with the results of safety analysis by reliability method and indirect estimation method of deterioration level. Therefore, newly developed management index method can be applied and may be useful to the estimation of deterioration level for the future maintenance and management of water distribution system.