• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

• ETRI Journal
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• v.39 no.5
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• pp.643-651
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• 2017

Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.425-430
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• 2022

As robots are considered one of the mainstream digital transformations, robots with machine vision becomes a main area of study providing the ability to check what robots watch and make decisions based on it. However, it is difficult to find a small object in the image mainly due to the flaw of the most of visual recognition networks. Because visual recognition networks are mostly convolution neural network which usually consider local features. So, we make a model considering not only local feature, but also global feature. In this paper, we propose a detection method of a small marker on the object using deep learning and an algorithm that considers global features by combining Transformer's self-attention technique with a convolutional neural network. We suggest a self-attention model with new definition of Query, Key and Value for model to learn global feature and simplified equation by getting rid of position vector and classification token which cause the model to be heavy and slow. Finally, we show that our model achieves higher mAP than state of the art model YOLOr.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.421-432
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• 2013

The purpose of this study is to develop contents and an instructional model that support children's dramatic play by integrating the robot and augmented reality technology. In order to support the dramatic play, the robot shows various facial expressions and actions, serves as a narrator and a sound manager, supports the simultaneous interaction by using the camera and recognizing the markers and children's motions, records children's activities as a photo and a video that can be used for further activities. The robot also uses a projector to allow children to directly interact with the video object. On the other hand, augmented reality offers a variety of character changes and props, and allows various effects of background and foreground. Also it allows natural interaction between the contents and children through the real-type interface, and provides the opportunities for the interaction between actors and audiences. Along with these, augmented reality provides an experience-based learning environment that induces a sensory immersion by allowing children to manipulate or choose the learning situation and experience the results. In addition, the instructional model supporting dramatic play consists of 4 stages(i.e., teachers' preparation, introducing and understanding a story, action plan and play, evaluation and wrapping up). At each stage, detailed activities to decide or proceed are suggested.

• Journal of Industrial Convergence
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• v.21 no.2
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• pp.103-109
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• 2023

In Korea, software education is implemented from elementary school. As a representative software education tool for elementary schools, various chess games reconstructed based on the rules of Western chess games are being used. On the other hand, Yutnori, one of our traditional games, also includes elements of software education, so research on this is needed. Therefore, in this study, a Yutnori program based on computational thinking using an educational programming language, Entry, and a turtle robot was developed and its validity verified. As a result of the validity verification, the CVR value was higher than 0.7 in the degree of agreement with the subject achievement standard (3 questions), the appropriateness of learning materials (4 questions), and the possibility of class application (3 questions). Therefore, it could be judged that the learning program developed in this study has a high level of agreement with the subject achievement standards, appropriate learning materials, and high possibility of being applied to classes. In order to generalize this content in the future, the effectiveness will need to be verified, and experimental research will be needed to understand this.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.369-376
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• 2021

With the development of artificial intelligence (AI) technology, discussions on the use of artificial intelligence are actively taking place in many fields, and various policies for nurturing artificial intelligence talents are being promoted in the field of education. In this study, we propose a robot programming framework using artificial intelligence technology, and based on this, we use Python, which is used frequently in the machine learning field, and an educational robot that is highly utilized in the field of education to provide artificial intelligence. (AI) education program was proposed. The level of autonomous driving (levels 0-5) suggested by the International Society of Automotive Engineers (SAE) is simplified to four levels, and based on this, the camera attached to the robot recognizes and detects lines (objects). The goal was to make a line detector that can move by itself. The developed program is not a standardized form of solving a given problem by simply using a specific programming language, but has the experience of defining complex and unstructured problems in life autonomously and solving them based on artificial intelligence (AI) technology. It is meaningful.

• Journal of The Korean Association of Information Education
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• v.13 no.1
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• pp.69-76
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• 2009

This paper proposes a teaching plan of programming language class for university of education students amusingly with LEGO Mindstorms NXT robot. The goal of class is not fragmentary knowledge acquirement but problem-solving of maze. This robot communicates with GUI named NXT-G installed in computer via USB. GUI is not text-based but icon-based programming tool. This paper designs a semester with 3 steps such as beginner, intermediate, high-rank. Beginner step is consists of learning of basic functions such as GUI usage and several sensors of robot. Intermediate step is consists of solving of maze problem with low complexity. High-rank step is consists of solving maze problem with medium and high complexity. All maze problem-solving have 3 process with algorithm, flowchart, and programming with stack.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.334-336
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• 2007

This paper presents a beat gesture recognition method to synchronize the tempo of a robot playing a piano with the desired tempo of the user. To detect an unstructured beat gesture expressed by any part of a body, we apply an optical flow method, and obtain the trajectories of the center of gravity and normalized central moments of moving objects in images. The period of a beat gesture is estimated from the results of the fast Fourier transform. In addition, we also apply a motion control method by which robotic fingers are trained to follow a set of trajectories, Since the ability to track the trajectories influences the sound a piano generates, we adopt an iterative learning control method to reduce the tracking error.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.527-529
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• 2021

This paper focuses on the implementation of machine learning model for Recycling bot, which is a platform service of recycling education. The recycling bot applied with a AI model using collected image set. The experiment confirms that classified by the model result are accurate.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.566-571
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• 1994

This paper presents in image-based visual servo control scheme for tracking a workpiece with a hand-eye coordinated robotic system using the fuzzy-neural-network. The goal is to control the relative position and orientation between the end-effector and a moving workpiece using a single camera mounted on the end-effector of robot manipulator. We developed a fuzzy-neural-network that consists of a network-model fuzzy system and supervised learning rules. Fuzzy-neural-network is applied to approximate the nonlinear mapping which transforms the features and theire change into the desired camera motion. In addition a control strategy for real-time relative motion control based on this approximation is presented. Computer simulation results are illustrated to show the effectiveness of the fuzzy-neural-network method for visual servoing of robot manipulator.