• 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 Korean Institute of Industrial Engineers
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• v.16 no.1
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• pp.123-135
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• 1990

An extensive survey is carried out on the applications of AI (Artificial Intelligence) and ES (Expert System) in mathematical programming and simulation, which are the most frequently used tools in production planning. A scheduling field is also reviewed. The scheduling problem is one of the most attractive area for AI and ES researchers, since any practical algorithmic solution methods are not available. The current practice and difficulty of applying AI and ES to production planning are discussed and future research directions are identified.

• Electronics and Telecommunications Trends
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• v.35 no.7
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• pp.23-35
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• 2020

In this paper, we introduce ETRI AI Strategy #3, "Leading Future Technologies of Network, Media, and Content." Its first goal is "to innovate AI service technology to overcome the current limitations of AI technologies." Artificial intelligence (AI) services, such as self-driving cars and robots, are combinations of computing, network, AI algorithms, and other technologies. To develop AI services, we need to develop different types of network, media coding, and content creation technologies. Moreover, AI technologies are adopted in ICT technologies. Self-planning and self-managing networks and automatic content creation technologies using AI are being developed. This paper introduces the two directions of ETRI's ICT technology development plan for AI: ICT for AI and ICT by AI. The area of ICT for AI has only recently begun to develop. ETRI, the ICT leader, hopes to have opportunities for leadership in the second wave of AI services.

• Journal of the Korea Society for Simulation
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• v.15 no.2
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• pp.1-12
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• 2006

As the needs of intelligent systems increase, there have been diverse approaches that combine artificial intelligence (AI) and simulation in the last decade. RG-DEVS, which is the basis for this paper, embedded AI planning techniques into the simulation modeling methodology of DEVS, in order to specify dynamically a simulation model. However, a hierarchy concept, which is used for various types of problem solving systems. is not included in the planning of RG-DEVS. The hierarchy concept reduces the computational cost of planning by reducing the search space, and also makes it easy to apply the hierarchical process flow of a target system to planning. This paper proposes Hierarchical RG-DEVS (HRG-DEVS) in an attempt to insert hierarchical planning capability into RG-DEVS. For the verification of the proposed modeling methodology, HRG-DEVS is applied to model the block's world problem of ABSTRIPS, which is a classical planning problem.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.59-65
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• 2021

In this paper, we propose the AI certification standardization activities for systematic research and planning for the standardization of trustworthy artificial intelligence (AI). The activities will be in two-fold. In the stage 1, we investigate the scope and possibility of standardization through AI reliability technology research targeting international standards organizations. And we establish the AI reliability technology standard and AI reliability verification for the feasibility of the AI reliability technology/certification standards. In the stage 2, based on the standard technical specifications established in the previous stage, we establish AI reliability certification program for verification of products, systems and services. Along with the establishment of the AI reliability certification system, a global InterOp (Interoperability test) event, an AI reliability certification international standard meetings and seminars are to be held for the spread of AI reliability certification. Finally, TAIPP (Trustworthy AI Partnership Project) will be established through the participation of relevant standards organizations and industries to overall maintain and develop standards and certification programs to ensure the governance of AI reliability certification standards.

• Korean Journal of Remote Sensing
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• v.18 no.5
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• pp.263-271
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• 2002

Recently, spatial information technologies using remotely sensed imagery and functionality of GIS (Geographic Information Systems) have been widely utilized to various types of transportation-related applications. In this study, extraction programs of some practical indices, to be effectively used in transportation reference planning problem, were designed and implemented as prototyped extensions in GIS development environment: traffic flow estimation (TFL/TFB), urban rural index (URI), and accessibility index (AI). In TFL/TFB, user can obtain quantitative results on traffic flow estimation at link/block using high-resolution satellite imagery. Whereas, URI extension provides urban-rural characteristics related to road system, being considered one of important factors in transportation planning. Lastly, AI extension helps to obtain accessibility index between nodes of road segments and surrounding district areas touched or intersected with the road network system, and it also provides useful information for transportation planning problems. This approach is regarded as one of RS-T (Remote Sensing in Transportation), and it is expected to expand as new application of remotely sensed imagery.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.2
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• pp.188-196
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• 2023

In the future battlefield centered on the concept of mosaic warfare, the need for an unmanned combat system will increase to value human life. It is necessary for Multiple/Heterogeneous Unmanned Combat Systems to have suitable mission planning method in order to perform various mission. In this paper, we propose the MTSR model for mission planning of the unmanned combat system, and introduce a method of identifying a task by a combination of services using a request operator and a method of allocating resources to perform a task using the requested service. In order to verify the performance of the proposed task-resource matchmaking algorithm, simulation using occupation scenarios is performed and the results are analyzed.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.599-604
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• 2021

In an era where AI artificial intelligence converges into each field, design of AI-CARE function and UI UX design are in the spotlight. The application of shape design and the trend of color are important for visual effects to match new features. In this paper, we would like to use this to present an example of the arrangement and application of form trends and to present design directions. In the introduction, the latest design environmental factors were analyzed and studied in the direction of the new proposal. In the text, the design direction and consideration of the design in the planning were studied by separating the functional design form, and the direction of the design work was presented. At the stage of form and color, a plan was proposed to match the flow of minimalism, universal design, and affordance design. A study method based on case practice is a consideration of how to consider content on shapes and colors specialized in the partial design work, and proposed to enable graphic production of new areas of UI UX in content images to be feasible. Through this study, the design direction and planning of the derivative method of shape and color was reached by suggesting design directionality and validity for each AI CARE bed PART.

• The Korean Nurse
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• v.8 no.4 s.42
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• pp.31-40
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• 1969

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.64-70
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• 2021

The future society will be changed through an artificial intelligence (AI) based intelligent revolution. To prepare for the future and strengthen industrial competitiveness, countries around the world are implementing various policies and strategies to utilize AI in the manufacturing industry, which is the basis of the national economy. Manufacturing AI technology should ensure accuracy and reliability in industry and should be explainable, unlike general-purpose AI that targets human intelligence. This paper presents the future shape of the "autonomous factory" through the convergence of manufacturing and AI. In addition, it examines technological issues and research status to realize the autonomous factory during the stages of recognition, planning, execution, and control of manufacturing work.