• 열처리공학회지
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• 제37권3호
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• pp.103-113
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• 2024

Martensite volume fraction significantly affects the mechanical properties of alloy steels. Martensite start temperature (M_s), transformation temperature for martensite 50 vol.% (M₅₀), and transformation temperature for martensite 90 vol.% (M₉₀) are important transformation temperatures to control the martensite phase fraction. Several researchers proposed empirical equations and machine learning models to predict the Ms temperature. These numerical approaches can easily predict the Ms temperature without additional experiment and cost. However, to control martensite phase fraction more precisely, we need to reduce prediction error of the Ms model and propose prediction models for other martensite transformation temperatures (M₅₀, M₉₀). In the present study, machine learning model was applied to suggest the predictive model for the Ms, M50, M90 temperatures. To explain prediction mechanisms and suggest feature importance on martensite transformation temperature of machine learning models, the explainable artificial intelligence (XAI) is employed. Random forest regression (RFR) showed the best performance for predicting the Ms, M50, M90 temperatures using different machine learning models. The feature importance was proposed and the prediction mechanisms were discussed by XAI.

• 대한원격탐사학회지
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• 제36권1호
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• pp.41-53
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• 2020

The production of near- and off-shore fisheries in South Korea is decreasing due to rapid changes in the fishing environment, particularly including higher sea temperature in recent years. To improve the competitiveness of the fisheries, it is necessary to provide fish catch information that changes spatiotemporally according to the sea state. In this study, artificial intelligence models that predict the CPUE (catch per unit effort) of mackerel, anchovies, and squid (Todarodes pacificus), which are three major fish species in the near- and off-shore areas of South Korea, on a 15-km grid and daily basis were developed. The models were trained and validated using the sea surface temperature, rainfall, relative humidity, pressure,sea surface wind velocity, significant wave height, and salinity as input data, and the fish catch statistics of Suhyup (National Federation of Fisheries Cooperatives) as observed data. The 10-fold blind test results showed that the developed artificial intelligence models exhibited accuracy with a corresponding correlation coefficient of 0.86. It is expected that the fish catch models can be actually operated with high accuracy under various sea conditions if high-quality large-volume data are available.

• 한국시뮬레이션학회논문지
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• 제33권2호
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• pp.27-35
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• 2024

본고에서는 인공지능 모델 학습에 사용하는 데이터셋에 내재한 편향성이 인공지능 예측 결과에 미치는 영향을 분석함으로써, 위의 경우가 사회적 격차를 고착화시키는 문제를 조명하고자 하였다. 따라서 데이터 편향성이 인공지능 모델에 끼치는 영향을 분석하기 위해, 성별 임금 격차에 관한 편향이 포함된 원본 데이터셋을 제작하였으며 해당 데이터셋을 비식별 처리한 데이터셋을 만들었다. 또한 의사결정트리 알고리즘을 통해 원본 데이터셋과 비식별화 된 데이터셋을 학습한 각각의 인공지능 모델 간의 산출물을 비교함으로써, 데이터 비식별화가 인공지능 모델이 산출한 결과의 편향에 어떠한 영향을 미치는지 분석하였다. 이를 통해 데이터 비식별화가 개인정보 보호뿐만 아니라, 데이터의 편향에도 중요한 역할을 할 수 있음을 도출하고자 하였다.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.13-26
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• 2023

Recently, the development of wireless network technologies has been advancing in several directions: increasing data transmission speed, enhancing user mobility, expanding the range of services offered, improving the utilization of the radio frequency spectrum, and enhancing the intelligence of network and subscriber equipment. In this research, a series of contradictions has emerged in the field of wireless network technologies, with the most acute being the contradiction between the growing demand for wireless communication services (on operational frequencies) and natural limitations of frequency resources, in addition to the contradiction between the expansions of the spectrum of services offered by wireless networks, increased quality requirements, and the use of traditional (outdated) management technologies. One effective method for resolving these contradictions is the application of artificial intelligence elements in wireless telecommunication systems. Thus, the development of technologies for building intelligent (cognitive) radio and cognitive wireless networks is a technological imperative of our time. The functions of artificial intelligence in prospective wireless systems and networks can be implemented in various ways. One of the modern approaches to implementing artificial intelligence functions in cognitive wireless network systems is the application of fuzzy logic and fuzzy processors. In this regard, the work focused on exploring the application of fuzzy logic in prospective cognitive wireless systems is considered relevant.

• 한국BIM학회 논문집
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• 제12권1호
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• pp.1-9
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• 2022

Concrete formwork is a crucial component for any construction project. Artificial intelligence offers great potential to automate formwork design by offering various design options and under different criteria depending on the requirements. This study applied image segmentation in 2D formwork drawings to extract sheathing, strut and pipe support formwork elements. The proposed artificial intelligence model can recognize, classify, and extract formwork elements from 2D CAD drawing image and training and test results confirmed the model performed very well at formwork element recognition with average precision and recall better than 80%. Recognition systems for each formwork element can be implemented later to generate 3D BIM models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4625-4647
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• 2020

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

• 전자통신동향분석
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• 제39권1호
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• pp.95-105
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• 2024

In natural language processing, large language models such as GPT-4 have recently been in the spotlight. The performance of natural language processing has advanced dramatically driven by an increase in the number of model parameters related to the number of acceptable input tokens and model size. Research on multimodal models that can simultaneously process natural language and image data is being actively conducted. Moreover, natural-language and image-based reasoning capabilities of large language models is being explored in robot artificial intelligence technology. We discuss research and related patent trends in robot task planning and code generation for robot control using large language models.

• Geomechanics and Engineering
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• 제19권2호
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.147-156
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• 2022

Ontologies are knowledge containers in which information about a specified domain can be shared and reused. An event happens within a specific time and place and in which some actors engage and show specific action features. The fact is that several ontology models are based on events called Event-Based Models, where the event is an individual entity or concept connected with other entities to describe the underlying ontology because the event can be composed of spatiotemporal extents. However, current event-based ontologies are inadequate to bridge the gap between spatiotemporal extents and participants to describe a specific domain event. This paper reviews, describes and compares the existing event-based ontologies. The paper compares various ways of representing the events and how they have been modelled, constructed, and integrated with the ontologies. The primary criterion for comparison is based on the events' ability to represent spatial and temporal extent and the participants in the event.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.