• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.49.2-49
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• 2002

This paper describes the configuration of a multi-agent system that can recognize human intentions. This system constructs ontologies of human intentions and enables knowledge acquisition and sharing between intelligent agents operating in different environments. This is achieved by using a bi-directional associative memory network. The process of intention recognition is based on fuzzy association inferences. This paper shows the process of information sharing by using ontologies. The purpose of this research is to create human-centered systems that can provide a natural interface in their interaction with people.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.300-300
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• 2000

Reduced life-cycle and scale of electronic products make the conventional automated manufacturing system difficult to keep on competitiveness in these days. Reduced life-cycle requires an agile adaptation of manufacturing to new products and reduced scale requires enhanced precision as well as high speed. In this research, We propose a new concept called as "One-Cell Minifactory" in which various processes are combined to produce final modules or products and human interaction can be combined easily. We hope the proposed concept can guide new developments of automated manufacturing in electronics, optics and bio-engineering.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.69-75
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• 2024

This paper proposes an IoT-based indoor autonomous driving system that applies SLAM (Simultaneous Localization And Mapping) and Navigation techniques in a ROS (Robot Operating System) environment based on TurtleBot3. The proposed autonomous driving system can be applied to indoor autonomous wheelchairs and robots. In this study, the operation was verified by applying it to an indoor self-driving wheelchair. The proposed autonomous driving system provides two functions. First, indoor environment information is collected and stored, which allows the wheelchair to recognize obstacles. By performing navigation using the map created through this, the rider can move to the desired location through autonomous driving of the wheelchair. Second, it provides the ability to track and move a specific logo through image recognition using OpenCV. Through this, information services can be received from guides wearing uniforms with the organization's unique logo. The proposed system is expected to provide convenience to passengers by improving mobility, safety, and usability over existing wheelchairs.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.185-209
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• 2023

With the advent of the 4th industrial revolution, various technologies have received much attention. Technologies related to the 4th industry include the Internet of Things (IoT), big data, artificial intelligence, virtual reality (VR), 3D printers, and robotics, and these technologies are often converged. In particular, the robotics field is combined with technologies such as big data, artificial intelligence, VR, and digital twins. Accordingly, much research using robotics is being conducted, which is applied to distribution, airports, hotels, restaurants, and transportation fields. In the given situation, research on human-robot interaction is attracting attention, but it has not yet reached the level of user satisfaction. However, research on robots capable of perfect communication is steadily being conducted, and it is expected that it will be able to replace human emotional labor. Therefore, it is necessary to discuss whether the current human-robot interaction technology can be applied to business. To this end, this study first examines the trend of human-robot interaction technology. Second, we compare LDA (Latent Dirichlet Allocation) topic modeling and BERTopic topic modeling methods. As a result, we found that the concept of human-robot interaction and basic interaction was discussed in the studies from 1992 to 2002. From 2003 to 2012, many studies on social expression were conducted, and studies related to judgment such as face detection and recognition were conducted. In the studies from 2013 to 2022, service topics such as elderly nursing, education, and autism treatment appeared, and research on social expression continued. However, it seems that it has not yet reached the level that can be applied to business. As a result of comparing LDA (Latent Dirichlet Allocation) topic modeling and the BERTopic topic modeling method, it was confirmed that BERTopic is a superior method to LDA.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.585-596
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• 2020

The purpose of this study is to examine effects of maker education for high-school students on attitude toward software education, creative problem solving, and computational thinking. The program was designed to develop an artificial intelligence robot using mBlock and Arduino and implemented at a maker space. We analyzed 19 students among 20 who participated in the program, the result of paired t-test indicated significant increase in all variables. Also, we performed a multiple regression analysis to investigate predictors of perceived achievement and satisfaction. The finding demonstrated an initial attitude toward software education was found to be the significant predictor of perceived achievement and satisfaction. With the results, we confirmed maker education enhances attitude toward software education, creative problem solving, and computational thinking. Lastly, we discussed the implications and limitations and suggested the direction for future research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.52-54
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• 2021

With the advent of the era of artificial intelligence, robot education and teaching under its empowerment have been widely concerned and applied worldwide. The purpose of this study: systematically evaluate the application effect of AI educational robots in student education and teaching; the method of this study: use the computer to search for relevant education in the search tools such as "Web of Science", "CNKI", "ERIC", "IEEE" A comparative study of the effects of robot teaching and traditional teaching. The retrieval time is from January 2000 to January 2020. Comprehensive MetaAnalysis 2.0 was used for Meta analysis. The results of this study: A quantitative analysis of the 31 valid research literatures included, and an objective evaluation of the effect of the meta-analysis on AI educational robots. The analysis results show that the combined effect of AI educational robots on student learning effects is 0.465 This indicates that educational robots have a moderately positive effect on students 'learning effectiveness. The conclusion of this study: The application effect of AI educational robots in student education and teaching is better than traditional education methods, which can better promote student learning.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.385-391
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• 2023

In the elderly population, sarcopenia occurs due to physical aging, leading to movement restrictions and loss of function. This results in dependence on daily activities and limitations in participation, ultimately decreasing the overall quality of life. In this study, we propose an algorithm designed to enable patients with sarcopenia to perform sit-to-stand and stand-to-sit movements seamlessly in their daily lives. The algorithm incorporates a wearable robot for muscle support and includes algorithms for standing and seated muscle strength support. To validate the algorithm's performance, EMG sensors were attached to the Rectus Femoris and Biceps Femoris muscles. The participants underwent two scenarios: one without wearing the device and one with the device providing muscle strength support, performing sit-to-stand and stand-to-sit motions for one minute in each case. The results showed a 16% increase in the EMG peak value of the Rectus Femoris muscle during standing motion (p=0.009). On the right side, there was a roughly 20% decrease (p=0.018) during standing and a 21% decrease (p=0.014) during sitting motion. In the future, we aim to gather additional data to further refine the algorithm. Our goal is to develop an optimal muscle strength support algorithm based on this data, making it applicable for real-life use by patients with sarcopenia.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.4
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• pp.569-579
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• 2013

Robot control and image processing using a smartphone and Wi-Fi communication is introduced. The robot has a wheel and quadruped mechanism that is transformed according to the environment and is mainly used for drug bottle delivery. The captured image on the camera is transmitted to the smartphone in the form of stream data, and the image data is processed in the smartphone to enable the robot to identify an object and to control the robot itself. A network was constructed so that only image data from the stream data was used, and an image processing scheme to identify the drug bottle and deliver it to a person using a robot arm is also presented. In this study, image processing techniques and algorithms were purely implemented on a smartphone with considerable computational power and multiple functions rather than a computer, which contributes to the intelligence and miniaturization of the robot system.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.397-412
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• 2023

In order for social robots to take root in human life, it is important to consider the technical implementation of social robots and human psychology toward social robots. This study aimed to derive potential social robot clusters based on the emotions consumers feel about social robot appearance design, and to identify and compare important design characteristics and emotional differences of each cluster. In our study, we established a social robot emotion framework to measure and evaluate the emotions consumers feel about social robots, and evaluated the emotions of social robot designs based on the semantic differential method, an kansei engineering approach. We classified 30 social robots into 4 clusters by conducting a multidimensional scaling method and K-means cluster analysis based on the emotion evaluation results, confirmed the characteristics of design elements for each cluster, and conducted a comparative analysis on consumer emotions. We proposed a strategic direction for successful social robot design and development from a human-centered perspective based on the design characteristics and emotional differences derived for each cluster.