• Special Issue of the Society of Naval Architects of Korea
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• 2013.12a
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• pp.35-41
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• 2013

Nowadays, automated modeling system for steel margin based on interactive user interface has been developed and applied to the production design stage. The system could increase design efficiency and minimize human error owing to recent CAD technique. However, there has been no approach to the pre-nesting design stage at all in early modeling process especially where ship model should be handled at more than two design stages using AVEVA Marine. A designer of the design stage needs artificial intelligence system beyond modeling automation when 3D model must be prepared in early modeling process using AVEVA Marine because they have focused on 2D nesting traditionally. In addition, they have a hard time figuring out the model prepared in previous design stage and modifying the model for steel purchase size in early modeling process. In this paper, artificial intelligence modeling system for automated application of steel margin in early modeling process using AVEVA Marine is developed in order to apply to the pre-nesting design stage that can detect effective segments before a calculation to find if a segment locates near block butt boundaries by filtering noise segments among lines, curves and surface intersections based on IT big data analysis.

• The Mathematical Education
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• v.62 no.3
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• pp.401-416
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• 2023

This study systematically examined the influence of preservice teachers' self-efficacy and AI anxiety, on the intention to use AI programs 'knock-knock! mathematics expedition' in mathematics lessons based on a technology acceptance model. The research model was established with variables including self-efficacy, AI anxiety, perceived ease of use, perceived usefulness, and intention of use from 254 pre-service teachers. The structural relationships and direct and indirect effects between these variables were examined through structural equation modeling. The results indicated that self-efficacy significantly affected perceived ease of use, perceived usefulness, and intention to use. In contrast, AI anxiety did not significantly influence perceived ease of use and perceived usefulness. Perceived ease of use significantly affected perceived usefulness and intention to use and perceived usefulness significantly affected intention to use. The findings offer insights and strategies for encouraging the use of 'knock-knock! mathematics expedition' by preservice teachers in mathematics lessons.

• Convergence Security Journal
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• v.23 no.5
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• pp.3-8
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• 2023

The Fourth Industrial Revolution is bringing about great changes in many industrial fields, and among them, active research is being conducted on convergence technology using artificial intelligence. Among them, the demand is increasing day by day in the field of object recognition using artificial intelligence and digital transformation using recognition results. In this paper, we proposed an optimal learning model construction method to accurately recognize letters, symbols, and lines in images and save the recognition results as files in a standardized format so that they can be used in simulations. In order to recognize letters, symbols, and lines in images, the characteristics of each recognition target were analyzed and the optimal recognition technique was selected. Next, a method to build an optimal learning model was proposed to improve the recognition rate for each recognition target. The recognition results were confirmed by setting different order and weights for character, symbol, and line recognition, and a plan for recognition post-processing was also prepared. The final recognition results were saved in a standardized format that can be used for various processing such as simulation. The excellent performance of building the optimal learning model proposed in this paper was confirmed through experiments.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.267-273
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• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.

• Ocean Systems Engineering
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• v.5 no.1
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• pp.41-54
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• 2015

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.239-249
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• 2020

The purpose of this study was to verify whether the endorser effect similar to humans can be created in advertising campaigns based on the artificial intelligence endorser. In particular, considering the characteristics of artificial intelligence, a research model was presented by convergence of consumer innovativeness and anthropomorphism. The results of the online survey of 244 respondents showed that expertise of the artificial intelligence endorser has a positive effect on both brand attitude and purchase intention, but not for trustworthiness while it has a positive effect on brand attitude. Also, the effect of consumer innovativeness and anthropomorphism on brand attitude and purchase intention for artificial intelligence was found. The endorser effect was expanded to artificial intelligence, which is an intangible object, and the existing theory and research results were combined to re-verify it. Theoretical and practical implications for artificial intelligence-based products and services were presented.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.4
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• pp.283-292
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• 2020

Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.192-199
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• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.170-176
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• 2024

Since the World Health Organization (WHO) has declared COVID-19 as pandemic, many researchers have started working on developing vaccine and developing AI systems to detect COVID-19 patient using Chest X-ray images. The purpose of this work is to improve the performance of pre-trained Deep convolution neural nets (DCNNs) on Chest X-ray images dataset specially COVID-19 which is developed by collecting from different sources such as GitHub, Kaggle. To improve the performance of Deep CNNs, data augmentation is used in this study. The COVID-19 dataset collected from GitHub was containing 257 images while the other two classes normal and pneumonia were having more than 500 images each class. There were two issues whike training DCNN model on this dataset, one is unbalanced and second is the data is very less. In order to handle these both issues, we performed data augmentation such as rotation, flipping to increase and balance the dataset. After data augmentation each class contains 510 images. Results show that augmentation on Chest X-ray images helps in improving accuracy. The accuracy before and after augmentation produced by our proposed architecture is 96.8% and 98.4% respectively.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.97-108
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• 2024

Ethical and technical problems are becoming serious as the industrialization of artificial intelligence robots becomes active, research on risk is insufficient. In this situation, the researcher developed 52 verified indicators that can measure the body, rights, property, and social risk of artificial intelligence robots. In order to develop measurement indicators for each type of risk of artificial intelligence robots, 11 experts were interviewed in-depth after IRB deliberation. IIn addition, 328 workers in various fields where artificial intelligence robots can be introduced were surveyed to verify their fieldwork, and statistical verification such as exploratory factor analysis, reliability analysis, correlation analysis, and multiple regression analysis was verifyed to measure validity and reliability. It is expected that the measurement indicators presented in this paper will be widely used in the development, certification, education, and policies of standardized artificial intelligence robots, and become the cornerstone of the industrialization of artificial intelligence robots that are socially sympathetic and safe.