• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.209-221
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• 2024

The purpose of this study is to develop technical education programs that integrate robotics and artificial intelligence for employees in the robotics industry. To achieve this, the current status of robotics education programs for employees at domestic robotics institutions was investigated, and a demand survey on integrated robotics and AI education was conducted for domestic robotics companies. Based on the results of the demand survey, a robotics and AI integration education program was developed, and the contents of the designed curriculum and the survey results were presented. The developed education program was applied to the actual industry, and through evaluations of participant satisfaction and learning effectiveness, it was confirmed that the program can be effectively utilized in the industry. Suggestions for further research were also made.

• Journal of Korean Library and Information Science Society
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• v.52 no.3
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• pp.313-334
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• 2021

This study aimed to analyze AI research trends related to elementary, middle, and high school education. To this end, the related literature was collected from the SCOPUS database and the publication period of the collected literature was from 1974 to March 2021, with 154 journal papers and 571 conference papers. Research trends were analyzed based on the co-occurrences analysis technique of 4,521 words of author keyword and index keyword included in these papers. As a result of the analysis, big data, data mining, data science and deep learning were found as the latest research trends with machine learning and there was a difference between elementary, middle and high school education. It can be seen that elementary school had a lot of robot-related research, middle school had a lot of game and data-related research, and high school had various and in-depth research. In discussion, we mapped the top 50 words common to elementary, middle, and high schools with the 'Artificial Intelligence Basics' curriculum of Korean Government and '5 Big Ideas' of the United States Government so that AI research can be viewed at a glance.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.665-672
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• 2023

Purpose: Underground utility tunnel is facility that is jointly house infrastructure such as electricity, water and gas in city, causing condensation problems due to lack of airflow. This paper aims to prevent electricity leakage fires caused by condensation by detecting whether the control panel door in the underground utility tunnel is open using a deep learning model. Method: YOLO, a deep learning object recognition model, is trained to recognize the opening and closing of the control panel door using video data taken by a robot patrolling the underground utility tunnel. To improve the recognition rate, image augmentation is used. Result: Among the image enhancement techniques, we compared the performance of the YOLO model trained using mosaic with that of the YOLO model without mosaic, and found that the mosaic technique performed better. The mAP for all classes were 0.994, which is high evaluation result. Conclusion: It was able to detect the control panel even when there were lights off or other objects in the underground cavity. This allows you to effectively manage the underground utility tunnel and prevent disasters.

• The Journal of Korean Association of Computer Education
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• v.17 no.2
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• pp.65-76
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• 2014

This paper aims at implementing experimental devices with which middle school students can explore scientific ideas using GoGo Board and NetLogo that connect real and simulated worlds. Related research literature was reviewed to design a simulation-based learning model using computer simulation and robot-related activities. In order to construct devices for exploratory experiments, GoGo Board was adopted for developing the interface of Micro-Based Laboratory(MBL) devices with several sensors while NetLogo was used for connecting MBL devices (real world) and simulated experiments (virtual world). The simulation contents were developed in the area of heat equilibrium for changing temperature and the conduct-current relationship appeared in the textbook of middle school science class. With the developed device and contents students can visualize the change of temperature cold and hot waters in terms of heat equilibrium. They also can measure the change of conductor representing the relationship between conductor and current. The formative evaluation of the contents carried out with several middle school students indicated the future direction for upgrading simulation contents and interface. The results might be beneficial for science educators who want to apply simulation contents with the use of computers.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.995-1000
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• 2017

The purpose of this study is to analyze the educational effect on creativity and solving-problem ability based on hackathon of elementary SW education. We divided into control and experimental groups with activities of physical computing or robots. The control groups conducted by traditional project based learning collaboratively and the experimental group in hackathon. The outcomes from students were evaluated to by elementary teachers with questionnaire and analyzed to the creativity and solving-problem ability of physical computing or robot productions. As a result, a group of hackathon showed significant positive results in the accuracy on creativity and solving-problem skills statistically. We concluded that the free atmosphere of hackathon for communication and consultation have been attributed to various ideas of students.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2179-2184
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• 2007

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. This paper described appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1317-1322
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• 2021

Software in the era of the Fourth Industrial Revolution is becoming a key foundation in an intelligent information society. Therefore, it is necessary to study the new direction of manpower training and education that can cope with the times. To this end, the Ministry of Education reorganized the curriculum and is implementing software education based on a logical problem-solving process based on computing thinking skills rather than acquiring general ICT knowledge. However, there is a lack of securing high-quality educational content for software education, and there is also a lack of teaching aids that can be taught in connection with advanced IT technologies. To overcome this, this paper proposes the development of external expansion devices to expand educational content and functions capable of convergent software education such as artificial intelligence using coding robots for software education. Through this, effective software education is possible by improving the curriculum of the existing simple problem-solving method and developing various learning materials.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.107-112
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• 2018

In this paper, optical flow based keypoint detection and tracking technique is proposed for the collaboration between flying drone with vision system and ground robots. There are many challenging problems in target detection research using moving vision system, so we combined the improved FAST algorithm and Lucas-Kanade method for adopting the better techniques in each feature detection and optical flow motion tracking, which results in 40% higher in processing speed than previous works. Also, proposed image binarization method which is appropriate for the given marker helped to improve the marker detection accuracy. We also studied how to optimize the embedded system which is operating complex computations for intelligent functions in a very limited resources while maintaining the drone's present weight and moving speed. In a future works, we are aiming to develop collaborating smarter robots by using the techniques of learning and recognizing targets even in a complex background.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.720-729
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• 2005

In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.