• Education of Primary School Mathematics
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• v.27 no.1
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• pp.19-38
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• 2024

This study aims to analyze the characteristics of students' understanding of artificial intelligence and mathematical concepts by developing and applying an artificial intelligence education program for mathematics convergence using robots. To this end, we analyzed the content standards of elementary artificial intelligence education to extract conceptual elements of artificial intelligence and identified mathematics achievement standards that can effectively integrate them. In particular, a five-session (15 classes in total) program was developed by selecting the units 'angle' and 'quadrilateral' suitable for utilizing the robot's movement and reorganizing the lesson to integrate the mathematics achievement standard with the artificial intelligence content elements. As a result of applying this to 22 fourth grade elementary school students over five months and analyzing the students' understanding revealed by topic of artificial intelligence content, the artificial intelligence education program for mathematics convergence using robots was helpful in students' understanding artificial intelligence principles and mathematical concepts. In addition, the use of robots was confirmed to improve students' understanding of artificial intelligence and mathematics as well as their participation in class by making them visually check a series of performing procedures.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.225-234
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• 2021

Due to pandemic situations, the development of home robots that can make the house an optimized space for various activities is active. This study aims to confirm the effectiveness of approach or avoidance behavior for feedback positioning on the user experience, depending on the context in which the robot performs the task. Based on two types of the task contexts(Reactive vs. Proactive) and three types of robot feedback positioning(No move vs. Avoidance vs. Approach), six different scenarios were designed for experimental study. Likeability, perceived intelligence, rapport, negative attitude and predictability of behavior are measured for each conditions. The result showed the main effects of perceived intelligence, rapport, predictability in the context of tasks, and of likability, perceived intelligence, rapport in robot feedback positioning. The interaction effects were shown in likeability and perceived intelligence. In conclusion, approach-avoidance experiences can also be applied to robot behaviors as well, and the negative effects of avoidance have been significantly confirmed.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.375-382
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• 2016

This paper realizes control of the motion of a swimming robot fish in order to implement an underwater robot fish aquarium. And it implements positional control of a two-axis trajectory path of the robot fish in the aquarium. The performance of the robot was verified though certified field tests. It provided excellent performance in driving force, durability, and water resistance in experimental results. It can control robot motion, that is, it recognizes an object by using an optical flow object-detecting algorithm, which uses a video camera rather than image-detecting sensors inside the robot fish. It is possible to find the robot's position and control the motion of the robot fish using a radio frequency (RF) modem controlled via personal computer. This paper proposes realization of robot fish motion-tracking control using the optical flow object-detecting algorithm. It was verified via performance tests of lead-lag action control of robot fish in the aquarium.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.1-11
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• 2015

A concept of Deep Level Situation Understanding is proposed to realize human-like natural communication (called casual communication) among multi-agent (e.g., humans and robots/machines), where the deep level situation understanding consists of surface level understanding (such as gesture/posture understanding, facial expression understanding, speech/voice understanding), emotion understanding, intention understanding, and atmosphere understanding by applying customized knowledge of each agent and by taking considerations of thoughtfulness. The proposal aims to reduce burden of humans in humans-robots interaction, so as to realize harmonious communication by excluding unnecessary troubles or misunderstandings among agents, and finally helps to create a peaceful, happy, and prosperous humans-robots society. A simulated experiment is carried out to validate the deep level situation understanding system on a scenario where meeting-room reservation is done between a human employee and a secretary-robot. The proposed deep level situation understanding system aims to be applied in service robot systems for smoothing the communication and avoiding misunderstanding among agents.

• Journal of Information Technology Applications and Management
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• v.20 no.2
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• pp.147-160
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• 2013

Today, the management environments of intelligence firm are changing the way of production planning and logistics management, and are changing the process of supply chain management system. This paper shows the development of information system software for intelligence enterprises is used in supply chain management for robot engineering industry. Specifically, supply chain management system in this paper has been developed to analyze the impact of multi plant and multi distribution environment, showing the process analysis and system development of hierarchical assembly manufacturing industry. In this paper we consider a production planning and distribution management system of intelligence firm in the supply chain. We focus on a capacitated production resource and distribution volume allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using a genetic algorithm to solve it efficiently. This method makes it possible for the population to reach the feasible approximate solution easily. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solution converge to the feasible approximate solution quickly.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.11
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• pp.522-531
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• 2001

Recently conventional artificial intelligence(AI) approaches have been employed to build action selectors for the autonomous mobile robot(AMR). However, in these approaches, the decision making process to choose an action from multiple competence modules is still an open question. Many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we attempt to construct an action selector for an AMR based on the artificial immune network and internet. The information from vision sensors is used for antibody. We propose a learning method for artificial immune network using evolutionary algorithm to produce antibody automatically. The internet environment for an AMR action selector shows the usefulness of the proposed learning artificial immune network application.

• Electronics and Telecommunications Trends
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• v.39 no.1
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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.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.103-109
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• 2022

The purpose of this study is to ethical review on the development and service with care assistance robot. An integrative review concept analysis method was used. We analyzed the classification and role of service robots, the concept of the robot ethic and the care ethic. And there were derived the development and service about care assistance robot in ethical viewpoint. For improving current care problem, government had support to developing four types care assistance robots. But there were provided carefully care service due to the limitations of robot technology and lack of overall social awareness with care robot. In addition, in order to be successfully application in the field, care assistance robots were developed to provide high-quality care service that can consider to personal culture and living environment with the development of artificial intelligence and robot technology, as well as ethical care service.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.363-370
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• 2022

Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.