• Journal of Industrial Convergence
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• v.21 no.11
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• pp.29-41
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• 2023

In this study, assuming that the maritime manned-unmanned systems, which will be used as the main force of the ROK Navy in the future, conducts its sea line of communication(SLOC) protection operations, the combat effectiveness against major threats was measured, and through this, the development direction of the manned-unmanned systems was suggested. Multi-criteria decision-making techniques such as Delphi and AHP were used to measure combat effectiveness, and the AHP survey was conducted on 40 naval officers, including 25 senior officers who are well-understood in the combat effectiveness of the weapons system and MUM-T. As an evaluation index for measuring combat effectiveness, the OODA loop was set as the main attribute, followed by Observe(0.358), Orient(0.315), Act(0.217), and Decide(0.110). The combat effectiveness of each major threat in SLOC, the lowest alternative, was measured to be 1.68 times higher than the response to maritime conflicts in neighboring countries and 3.61 times higher than the response to transnational threats. These results are expected to support rational decision-making in determining the level of technology required for acquisition of marine manned-unmanned systems and establishing operational plans for naval forces.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Korean journal of applied entomology
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• v.62 no.4
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• pp.385-388
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• 2023

Gypsy moth (Lymantria dispar), a polyphagous insect pest belonging to the family Lymantriidae, is widely distributed in Korea, Japan, Siberia, Europe, and North America. They pose a threat to various host plants including pear trees, apple trees, and blueberries. Traditionally considered a forest pest, the increasing incursion of gypsy moths into agricultural land near forested areas has intensified damage to crops lacking effective control methods. This study aimed to investigate the temperature-dependent development of gypsy moths to enhance outbreak prediction and advance technology development. The effects of temperature on development of each life stage were investigated under constant temperature conditions of 18, 21, 24, 27, 30, and 33℃ (14L:10D, RH 60±5%) utilizing egg masses collected in Jeollanam-do Jangheung-gun in 2021. The results revealed that higher temperatures accelerated the development rate of the gypsy moth larvae with optimal development occurring at 30℃. However, the survival rate was lowest at 33℃. At the favorable temperature of 30℃, the total development period was 43.8 days for females and 42.5 days for males. The developmental threshold temperature were 13.1℃ for females and 12.5℃ for males, with effective accumulated temperature of 641.1 DD and 657.8 DD, respectively.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.283-290
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• 2023

With the recent steady development of IoT technology, it is widely used in medical systems and smart TV watches. 66% of software development is developed through language C, which is vulnerable to memory attacks, and acts as a threat to IoT devices using language C. A stack-smashing overflow attack inserts a value larger than the user-defined buffer size, overwriting the area where the return address is stored, preventing the program from operating normally. IoT devices with low memory capacity are vulnerable to stack smashing overflow attacks. In addition, if the existing vaccine program is applied as it is, the IoT device will not operate normally. In order to defend against stack smashing overflow attacks on IoT devices, we used canaries among several detection methods to set conditions with random values, checksum, and DSLR (random storage locations), respectively. Two canaries were placed within the buffer, one in front of the return address, which is the end of the buffer, and the other was stored in a random location in-buffer. This makes it difficult for an attacker to guess the location of a canary stored in a fixed location by storing the canary in a random location because it is easy for an attacker to predict its location. After executing the detection program, after a stack smashing overflow attack occurs, if each condition is satisfied, the program is terminated. The set conditions were combined to create a number of eight cases and tested. Through this, it was found that it is more efficient to use a detection method using DSLR than a detection method using multiple conditions for IoT devices.

• Journal of the Korean Institute of Gas
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• v.27 no.4
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• pp.43-49
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• 2023

Hydrofluoric acid is a less acidic substance than hydrochloric acid, nitric acid, and sulfuric acid, but it is one of the most dangerous substances for humans. In recent years, it has become an indispensable substance in industries such as chemical plants and the semiconductor industry, and although it is a threat to the human body, its use is increasing for various purposes, and the amount of use is constantly increasing due to the expansion and development of the industry. The dangers of hydrogen fluoride have been highlighted since the 2012 accident, which led to a more than fivefold increase in management standards for handling facilities. Hydrogen fluoride converts to hydrofluoric acid when exposed to the air, which can be fatal to humans. This study simulates the effects of a release of a toxic substance in the workplace, even though a protection layer has been provided to minimize the damage caused by the released toxic substance, and recommend ways to control the risk to workers in the event of a release in the workplace.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.163-172
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• 2022

This study investigates the IPI (Information Protection Intention) of DHS (Digital Healthcare Service) providers by introducing PMT (Protection Motivation Theory). This study examines the effects of protection motivation, such as threat appraisal and coping appraisal, on IPI, such as ICI(Induction Control Intention) and SDI(Self Defense Intention). The research model, based on the PMT, adopted severity, vulnerability, reaction efficacy and self-efficacy as independent variables. The research model was validated through quantitative research, a survey of 222 DHS providers in South Korea, using structural equation modeling. The results show that (1) a clear awareness of the consequences of security threats increases the understanding of DHS providers on the severity of closure of healthcare information, and thus may decreases abuse of DHS by providers; (2) user confidence and satisfaction on the security system may make them be confident that they can handle the closure of healthcare information by themselves; and (3) although DHS providers are realizing the consequences of closure of healthcare information, they think that they are unlikely to encounter such situations. As a result of this study, venture companies that provide DHS need to provide contents that can continuously increase providers' security level in order to increase providers' information protection intention. It suggests that IPI is important through trust of healthcare service providers.

• Journal of Ecology and Environment
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• v.47 no.4
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• pp.282-289
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• 2023

Background: As wildlife habitats are being destroyed and growth environments are changing, the survival of animals and plants is under threat. Epilobium hirsutum L., a species that inhabits wetlands, has held legally protected status since 2012. However, no specific measures are currently in place to protect its habitat, leading to a decline in remaining populations as a result of land use change and human activities. Results: The growth environment (including location, climate, land use, soil, and vegetation) of the five habitat sites (Samcheok, Taebaek1, Taebaek2, Cheongsong, Ulleung) of E. hirsutum L. was investigated and analyzed. These habitats were predominantly situated in flat areas with gentle south-facing slopes, at an average altitude of 452.7 m (8-726 m) above sea level in Gangwon-do and Gyeongsangbuk-do. The average annual temperature ranged 11.5℃ (9.2℃-12.9℃), whereas the average annual precipitation ranged 1,304.5 mm (1,062.7-1,590.7 mm). The surrounding land use status was mainly characterized by mountainous areas, and human interference, such as agricultural land and roads, was commonly found in proximity to these natural habitats. Soil physicochemical analysis revealed that the soil was predominantly sandy loam with a slightly high sand content. The average pH measured 7.64, indicating an alkaline environment, and electrical conductivity (EC) averaged 0.33 dS/m. Organic matter (OM) content averaged 66.44 g/kg, available phosphoric acid (P₂O₅) content averaged 115.73 mg/kg, and cation exchange capacity (CEC) averaged 23.43 cmolc/kg. The exchangeable cations ranged 0.09-0.43 cmol⁺/kg for potassium (K), 10.23-16.21 cmol⁺/kg for calcium (Ca), 0.67-4.94 cmol⁺/kg for magnesium (Mg), and 0.05-0.74 cmol⁺/kg for sodium (Na). The vegetation type was categorized as E. hirsutum community with high numbers of E. hirsutum L., Persicaria thunbergii (Siebold & Zucc.) H. Gross, Phragmites japonica Steud., Humulus japonicus (Siebold & Zucc.), and Bidens frondosa L.. An ecological flora analysis, including the proportion of lianas, naturalized plants, and annual herbaceous plants, revealed that the native habitat of E. hirsutum L. was ecologically unstable. Conclusions: Analysis of the habitat of E. hirsutum L., a class II endangered wildlife species, provided essential data for local conservation and restoration efforts.

• Steel and Composite Structures
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• v.50 no.3
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• pp.281-298
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• 2024

Stainless steel bolts (SSB) are increasingly utilized in bolted steel connections due to their good mechanical performance and excellent corrosion resistance. Fire accidents, which commonly occur in engineering scenarios, pose a significant threat to the safety of steel frames. The post-fire behavior of SSB has a significant influence on the structural integrity of steel frames, and neglecting the effect of temperature can lead to serious accidents in engineering. Therefore, it is important to evaluate the performance of SSB at elevated temperatures and their residual strength after a fire incident. To investigate the mechanical behavior of SSB after fire, 114 bolts with grades A4-70 and A4-80, manufactured from 316L austenitic stainless steel, were subjected to elevated temperatures ranging from 20℃ to 1200℃. Two different cooling methods commonly employed in engineering, namely cooling at ambient temperatures (air cooling) and cooling in water (water cooling), were used to cool the bolts. Tensile tests were performed to examine the influence of elevated temperatures and cooling methods on the mechanical behavior of SSB. The results indicate that the temperature does not significantly affect the Young's modulus and the ultimate strength of SSB. Up to 500℃, the yield strength increases with temperature, but this trend reverses when the temperature exceeds 500℃. In contrast, the ultimate strain shows the opposite trend. The strain hardening exponent is not significantly influenced by the temperature until it reaches 500℃. The cooling methods employed have an insignificant impact on the performance of SSB. When compared to high-strength bolts, 316L austenitic SSB demonstrate superior fire resistance. Design models for the post-fire mechanical behavior of 316L austenitic SSB, encompassing parameters such as the elasticity modulus, yield strength, ultimate strength, ultimate strain, and strain hardening exponent, are proposed, and a more precise stress-strain model is recommended to predict the mechanical behavior of 316L austenitic SSB after a fire incident.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.31-41
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• 2024

Deep learning models show excellent performance in tasks such as image classification and object detection in the field of computer vision, and are used in various ways in actual industrial sites. Recently, research on improving robustness has been actively conducted, along with pointing out that this deep learning model is vulnerable to hostile examples. A hostile example is an image in which small noise is added to induce misclassification, and can pose a significant threat when applying a deep learning model to a real environment. In this paper, we tried to confirm the robustness of the edge-learning classification model and the performance of the adversarial example detection model using it for adversarial examples of various algorithms. As a result of robustness experiments, the basic classification model showed about 17% accuracy for the FGSM algorithm, while the edge-learning models maintained accuracy in the 60-70% range, and the basic classification model showed accuracy in the 0-1% range for the PGD/DeepFool/CW algorithm, while the edge-learning models maintained accuracy in 80-90%. As a result of the adversarial example detection experiment, a high detection rate of 91-95% was confirmed for all algorithms of FGSM/PGD/DeepFool/CW. By presenting the possibility of defending against various hostile algorithms through this study, it is expected to improve the safety and reliability of deep learning models in various industries using computer vision.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.