• 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.

• 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.

• Journal of Korean Society of Rural Planning
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• v.29 no.3
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• pp.79-89
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• 2023

As the demand for environmental-friendly agricultural products continues to rise due to increased concerns regarding food safety and ecosystem conservation, it is becoming important to identify regions and spatial locations where environmental-friendly should be intensively established for production integration. This study aims to analyze the spatial distribution of environmental-friendly certified farms in Jeollanam-do, South Korea. Spatial statistical analysis based on Local Moran's I and Getis-Ord Gi^* were used to identify spatial cluster characteristics and landscape indices were utilized to analyze spatial patterns of environmental-friendly certified farms. The results indicated that Haenam-gun, Gangjin-gun, Muan-gun, and Jindo-gun were identified as hotspots, while Muan-gun, Goheung-gun, and Jindo-gun exhibited high connectivity. This suggests that environmental-friendly certified farms in Muan-gun and Jindo-gun were clustered and closely connected to one another. Based on the results of the spatial distribution of environmental-friendly certified farms, areas belonging to the hotspot and with high connectivity should be managed as clustered districts to secure the foundation and system of environmental-friendly certified farms. Areas that belong to cold spots and have low connectivity should be preceded by measures to promote conversion to environmental-friendly agriculture. In addition, it is necessary to make it possible to create a large-scale cluster district through a long-term spatial planning strategy to expand the environmental-friendly certified farms. The findings of this study can provide quantitative data on policies and discussions for developing a model for rural spatial planning.

• The Journal of Pediatrics of Korean Medicine
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• v.37 no.3
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• pp.35-48
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• 2023

Objectives We aimed to analyze changes in growth indicators before and after Korean medicine treatment in patients treated at the pediatric department of a hospital. Methods We analyzed the medical records of children and adolescents under 18 years of age who underwent growth assessment between January 1, 2017 and December 31, 2022. Results A total of 21 patients were selected for this study. After treatment, there was a significant increase in the height percentile, whereas bone age-chronological age (BA-CA) and predicted adult height (PAH) did not show significant changes. No major adverse reactions were observed during the treatment. Growth reassessment was conducted twice for 10 participants. When comparing the growth indicators between the assessment sessions, the height percentile showed an increasing trend between the initial and the first growth reassessment. However, there were no significant differences between BA-CA and PAH across the different assessment periods. Conclusions There is a need to establish evidence for the efficacy and safety of continuous Korean medicine growth treatment through the long-term observation of growth indicators in patients undergoing treatment for two or more periods, as well as observational studies on liver and renal function indicators.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.707-716
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• 2023

The influence of waste technological waters of thermal and fast reactors of Beloyarsk NPP (Russia) on the accumulation of ⁶⁰Co, ⁹⁰Sr and ¹³⁷Cs in macrophytes and ichthyofauna of the cooling pond has been studied. Critical radionuclides, routes of their entry into the ecosystem and periods of maximum discharge of radioisotopes into the cooling pond have been determined. It is shown that the technology of electricity generation at the Beloyarsk NPP, based on fast reactors, has a much smaller effect on the release of artificial radionuclides into the environment. Therefore, during the entire period of monitoring studies (1976-2019), the decrease in the specific activity of radionuclides of NPP origin in macrophytes was 13-25800 times, in ichthyofauna 1.5-44.5 times. The maximum discharge of artificial radionuclides into the Beloyarsk reservoir was noted during the period of restoration and decontamination work aimed at eliminating the emergencies at the AMB reactors of NPP. The factors influencing the accumulation of artificial radionuclides in the components of the freshwater ecosystem of the Beloyarsk cooling pond have been determined, including: the physicochemical nature of radioisotopes, their concentration in surface water, the temperature of the aquatic environment, the trophicity of the reservoir, the species of hydrobionts.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.297-308
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• 2023

Displacement of structures is the most important parameter for safety and performance assessment and is measured to use for diagnosis and maintenance of bridges. Usually LVDT, Laser and GNSS are used for displacement measurement but these measurement instruments have problems in terms of field condition and cost. Therefore, in this study, displacements were evaluated using rotational angle measured by inclinometers and the proposed algorithm was experimentally verified. As the result, vertical displacements of cable supported bridges with traffic and temperature load were properly evaluated through the proposed algorithm. Therefore it is considered that the proposed algorithm can be used for displacement measurement by vehicle load test and long term displacement monitoring.

• Design Convergence Study
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• v.17 no.3
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• pp.55-70
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• 2018

This study was conducted as a part of long-term project on the development of a set of design guidelines for a crutch as mobility aids for the handicapped in the lower extremity and the suggestion a practical solution for a crutch design. The purpose of this study is to develop a design of a crutch and a set of prototypes that reflects the characteristics of crutch-gait and has a realistic possibility for mass production-based industry. TOGO, a axillary crutch as the result of this study, shows a number of characteristics distinguished from ordinary crutches. These are (1)Minimize the shock associated with planting of the crutch tips by improving the form and structure of crutch tip and axillary pad, (2)Ergonomically designed crutch in accordance with users' body movement while walking on crutches, (3)Easy length control to maximize mobility and maneuverability by changing the cross section of the crutch revolutionary, (4)Minimize possibilities of safety hazards, and (5)Attractive shape of the crutch to keep user self-esteem. The revolutionary crutch derived from this study results has been patented, and the company is seeking to mass-produce and find ways to commercialize it after reviewing the potential problems that may arise in the mass production environment.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• Steel and Composite Structures
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• v.48 no.1
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• pp.27-41
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• 2023

Under the long-term effect of corrosive environment, many cold-formed steel (CFS) structures have serious corrosion problems. Corrosion leads to the change of surface morphology and the loss of section thickness, which results in the change of instability mode and failure mechanism of CFS structure. This paper mainly investigates the elastic local buckling behavior of corroded CFS columns. The surface morphology scanning test was carried out for eight CFS columns accelerated corrosion by the outdoor periodic spray test. The thin shell finite element (FE) eigen-buckling analysis was also carried out to reveal the influence of corrosion surface characteristics, corrosion depth, corrosion location and corrosion area on the elastic local buckling behaviour of the plates with four simply supported edges. The accuracy of the proposed formulas for calculating the elastic local buckling stress of the corroded plates and columns was assessed through extensive parameter studies. The results indicated that for the plates considering corrosion surface characteristics, the maximum deformation area of local buckling was located at the plates with the minimum average section area. For the plates with localized corrosion, the main buckling shape of the plates changed from one half-wave to two half-wave with the increase in corrosion area length. The elastic local buckling stress decreased gradually with the increase in corrosion area width and length. In addition, the elastic local buckling stress decreased slowly when corrosion area thickness was relatively large, and then tends to accelerate with the reduction in corrosion area thickness. The distance from the corrosion area to the transverse and longitudinal centerline of the plate had little effect on the elastic local buckling stress. Finally, the calculation formula of the elastic local buckling stress of the corroded plates and CFS columns was proposed.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.550-554
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• 2023

Fossil energy sources are limited in their sustainable use and expansion due to global warming caused by carbon dioxide emissions. Hydrogen is considered as a promising alternative to traditional fossil fuels. To ensure the stable long-term storage, it is necessary to accurately predict its thermodynamic properties at cryogenic temperatures. Therefore, this study aimed to investigate thermodynamic properties, such as saturated vapor pressure and density, enthalpy, and entropy of liquid and gas, using cubic equations of state that demonstrate relatively simple relationships. Among the three types of equations of state (Redlich-Kwong (RK), Soave-Redlich-Kwong (SRK), and Peng-Robinson (PR)), the SRK model exhibited relatively accurate prediction results for various physical properties.