• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.4
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• pp.189-199
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• 2002

This study is intended to propose a systematic procedure for the development of the conditional assessment based on the safety of structures and the cost effective performance criteria for designing and upgrading of bridge structures. As a result, a set of cost function models for a life cycle cost analysis of bridge structures is proposed and thus the expected total life cycle costs (ETLCC) including initial (design, testing and construction) costs and direct/indirect damage costs considering repair and replacement costs, human losses and property damage costs, road user costs, and indirect regional economic losses costs. Also, the optimum safety indices are presented based on the expected total cost minimization function using only three parameters of the failure cost to the initial cost (${\tau}$), the extent of increased initial cost by improvement of safety (${\nu}$) and the order of an initial cost function (n). Through the enough numerical invetigations, we can positively conclude that the proposed optimum design procedure for bridge structures based on the ETLCC will lead to more rational, economical and safer design.

• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.43-51
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• 2017

As the game industry is blooming, the generation of contents is far behind the consumption of contents. With this reason, it is necessary to afford the game contents considering level of game player's skill. In order to effectively solve this problem, Procedural Content Generation(PCG) using Artificial Intelligence(AI) is one of the plausible options. This paper proposes the procedural method to generate various monsters considering level of player's skill using genetic algorithm. One gene consists of the properties of a monster and one genome consists of genes for various monsters. A generated monster is evaluated by battle simulation with a player and then goes through selection and crossover steps. Using our proposed scheme, players adaptive monsters are generated procedurally based on genetic algorithm and the variety of monsters which are generated with different number of genome is compared.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1055-1060
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• 2018

In the wake of the 4th Industrial Revolution, the problem of career education in schools has become a big issue. While various studies are being conducted on services or technologies to effectively handle artificial intelligence and big data, in the field of education, data on students is simply processed. Therefore, in this paper, we are going to design and present career prediction programs for students using artificial intelligence and big data. Using observational data from students at the institute, the decision tree is constructed with the C4.5 algorithm known to be most intelligent and effective in the decision tree and is used to predict students' path of hope. As a result, the coefficient of kappa exceeded 0.7 and showed a fairly low average error of 0.1 degrees. As shown in this study, a number of studies and data will be deployed to help guide students in their consultation and to provide them with classroom attitudes and directions.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.337-346
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• 2021

In the fourth industrial revolution, fashion students are expected to work with various technologies to show creativity. This study aimed to conduct project-based learning(PBL) in collaboration with industry experts to design and operate artificial intelligence(AI) in the practice subject of fashion curation through the industrial academic teaching method. We first looked at teaching methods and strategies incorporating PBL in various academic fields. Next, we analyzed fashion projects and fashion curation services applying AI. Then through the question-and-answer method and by consulting with industry experts, we developed a curriculum for AI fashion curation, applying PBL(fashion market and trend analysis; new styles and time, place, and occasion planning; AI machine learning data set production; curation model development; and evaluation) suitable for the university's educational environment, information technology company conditions, and fashion students. As part of a close cooperation system with the industry, we conducted a 15-week Fashion Project II (Capstone Design) course and evaluated the outcomes and student satisfaction with the course. Students were able to develop new style, and time, place, and occasion categories and to utilize strategies for AI fashion curation services reflecting the unique needs of Millennials and Generation Z. Students showed high satisfaction with the curriculum. Further, it was confirmed that the study successfully applied PBL in class using AI technology in fashion education.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.85-92
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• 2022

Demand for big data analysis and AI developers is increasing, but there is a lack of an education base to supply them. In this paper, by developing a cloud-based artificial intelligence education platform, the goal was to establish an environment in which practical practical training can be efficiently learned at low cost at educational institutions and IT companies. The development of the education platform was carried out by planning scenarios for each user, architecture design, screen design, implementation of development functions, and hardware construction. This training platform consists of a containerized workload, service management platform, lecture and development platform for instructors and students, and secured cloud stability through real-time alarm system and age test, CI/CD development environment, and reliability through docker image distribution. The development of this education platform is expected to expand opportunities to enter new businesses in the education field and contribute to fostering working-level human resources in the AI and big data fields.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.367-375
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• 2021

This study developed and applied an artificial intelligence education program as an educational method for increasing computational thinking of elementary school students and verified its effectiveness. The educational program was designed based on the results of a demand analysis conducted using Google survey of 100 elementary school teachers in advance according to the ADDIE(Analysis-Design-Development-Implementation-Evaluation) model. Among Machine Learning for Kids, we use scratch for block-based programming and develop and apply textbooks to improve computational thinking in the programming process of learning the principles of artificial intelligence and solving problems directly by utilizing numerical data. The degree of change in computational thinking was analyzed through pre- and post-test results using beaver challenge, and the analysis showed that this study had a positive impact on improving computational thinking of elementary school students.

• Nutrition Research and Practice
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• v.16 no.6
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• pp.801-812
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• 2022

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications. MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human- and AI-generated diets in 2 steps. RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human- and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information). CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.29-36
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• 2022

In this paper, we propose an efficient agricultural products price prediction model based on dataset which provided in DACON. This model is XGBoost and CatBoost, and as an algorithm of the Gradient Boosting series, the average accuracy and execution time are superior to the existing Logistic Regression and Random Forest. Based on these advantages, we design a machine learning model that predicts prices 1 week, 2 weeks, and 4 weeks from the previous prices of agricultural products. The XGBoost model can derive the best performance by adjusting hyperparameters using the XGBoost Regressor library, which is a regression model. The implemented model is verified using the API provided by DACON, and performance evaluation is performed for each model. Because XGBoost conducts its own overfitting regulation, it derives excellent performance despite a small dataset, but it was found that the performance was lower than LGBM in terms of temporal performance such as learning time and prediction time.

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.375-387
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• 2023

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.