• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.236-243
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• 2024

This paper introduces a dual manipulator system designed to automate the packaging process of industrial coils, which exhibit higher variability than other structured industrial fields due to diverse commercial requirements. The conventional solution involves the direct-teaching method, where an operator instructs the robot on a target configuration. However, this method has distinct limitations, such as low flexibility in dealing with varied sizes and safety concerns for the operators handling large products. In this sense, this paper proposes a two-step approach for coil packaging: motion planning and assembly execution. The motion planning includes a Rapidly-exploring Random Tree algorithm and a smoothing method, allowing the robot to reach the target configuration. In the assembly execution, the packaging is considered a peg-in-hole assembly. Unlike typical peg-in-hole assembly handling two workpieces, the packaging includes three workpieces (e.g., coil, inner ring, side plate). To address this assembly, the paper suggests a suitable strategy for dual manipulation. Finally, the validity of the proposed system is demonstrated through experiments with three different sizes of coils, replicating real-world packaging situations.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.411-416
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• 2023

The purpose of this study is to investigate the utilization of major learning tools among radiology science students and assess the accuracy of a conversational artificial intelligence service program, specifically a chatbot, in the context of the national radiologic technologist licensing exam. The survey revealed that 84.3% of radiology science students actively utilize electronic devices during their learning process. In addition, 104 out of 140 respondents said they use search engines as a top priority for efficient data collection while studying. When asked about their awareness of chatbots, 80% of participants responded affirmatively, and 22.9% reported having used chatbots for academic purposes at least once. From 2018 to 2022, exam questions from the first and second periods were presented to the chatbot for answers. The results showed that ChatGPT's accuracy in answering first period questions increased from 48.28% to 60%, while for second period questions, it increased from 50% to 62.22%. Bing's accuracy in answering first period questions improved from 55% to 64.55%, and for second period questions, it increased from 48% to 52.22%. The study confirmed the general trend of radiology science students utilizing electronic devices for learning and obtaining information through the internet. However, conversational artificial intelligence service programs in the field of radiation science face challenges related to accuracy and reliability, and providing perfect solutions remains difficult, highlighting the need for continuous development and improvement.

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.141-153
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• 2024

The 2022 revised science curriculum aims to develop the ability to solve scientific problems arising in daily life and society based on convergent thinking stimulated through participation in research activities using artificial intelligence (AI). Therefore, we developed a science-AI convergence education program that combines the science curriculum with artificial intelligence and employed it in convergence classes for high school students. The aim of the science-AI convergence class was for students to qualitatively understand the movement of a damped pendulum and build an AI model to predict the position of the pendulum using the block coding platform KNIME. Individual in-depth interviews were conducted to understand and interpret the learners' experiences. Based on Giorgi's phenomenological research methodology, we described the learners' learning processes and changes, challenges and limitations of the class. The students collected data and built the AI model. They expected to be able to predict the surrounding phenomena based on their experimental results and perceived the convergence class positively. On the other hand, they still perceived an with the unfamiliarity of platform, difficulty in understanding the principle of AI, and limitations of the teaching method that they had to follow, as well as limitations of the course content. Based on this, we discussed the strengths and limitations of the science-AI convergence class and made suggestions for science-AI convergence education. This study is expected to provide implications for developing science-AI convergence curricula and implementing them in the field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1143-1148
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• 2017

Currently, Artificial Intelligence and Deep Learning are rising as hot social issues, and these technologies are applied to various fields. A good method among the various algorithms in Artificial Intelligence is Convolutional Neural Networks. Convolutional Neural Network is a form that adds Convolution Layers to Multi Layer Neural Network. If you use Convolutional Neural Networks for small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning should take long time when the size of the learning data is large and the structure of layers is complicated. In these cases, GPU-based parallel processing is frequently needed. In this paper, we developed Convolutional Neural Networks using CUDA, and show that its learning is faster and more efficient than learning using some other frameworks or programs.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.1-10
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• 2019

The present research aims to identify the issues that occurred when uploading stock photos to the internet-based stock image agencies and to develop technical solutions based on web service technologies. We identify the issues by examination of previous studies and stock photo uploading systems of major three agencies currently in service. As such, we develop web service technology by focusing on the following matters. First, we apply an automatic tag system to ensure convenience. Second, to ensure safety, we apply a technology that easily enables prevention of portrait rights violations and trademark infringements. We also prepare for measures against possible harmfulness. Third, to ensure completeness, we apply a method which resolves upload failure issues that frequently occurred in the past. In particular, the present research is significant as it applies an automatic image analysis system based on Google Cloud Vision API as the artificial intelligence-based image processing technology. In addition, we develop a web service program which improves user access by using SNS-type screen composition.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.732-737
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• 2009

Due to various project delivery methods and the complexity of construction projects in the construction industry, developing the framework of construction management for critical, highly complex projects in the construction industry has become problematic. Currently, a set of construction manuals play a pivotal role in planning and managing construction projects as subcontractors try to complete their scope of work according to the instructions of a general contractor. It is challenging for general contractors to write a construction management procedure manual to cover various types of project delivery methods and construction projects. In construction, the construction procedure manuals describe specific actions to be taken through the project. In reality a few contactors own such manuals and their construction schedules include more construction operation activities. Thus, it is hard to estimate the workload and productivity of construction managers because the manual and the schedule do not present the amount of management efforts required to complete a project. This paper proposes a framework to present construction management tasks according to project delivery methods which can be applied to various construction projects. Actions for management tasks were mapped and were integrated with construction activities throughout the project life cycle. The framework can then be used to give specific instructions to project participants, collect management actions, and replicate management actions throughout the project life cycle. The framework can also be can used to visualize complete construction project to analyze and manage construction management activities in each phase of a project in order to enhance productivity and efficiency. The studies of existing construction manuals were carried out to identify construction managers' responsibilities. An artificial intelligence program, CLIPS (C-Language Integrated Production System) was used to search for appropriate actions for impending tasks from a set of predefined actions to be performed for a given situation. The framework would significantly help construction managers to understand interrelations among management tasks or actions within a project. Furthermore, the framework can be embedded into Building Information Modeling (BIM) or Facility Management Systems (FMS) so that designers and constructors would execute constructability review before construction begins.

• Industry Promotion Research
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• v.8 no.1
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• pp.55-68
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• 2023

The purpose of this study is to investigate the factors that influence the intention to continuously use the RPA program used in the financial industry for those working in the financial industry. In particular, the purpose of this study is to understand the will to accept and the perception of acceptance conflict by considering the characteristics of individuals in the relationship between work and information technology. As a result of the study, it can be confirmed that the RPA system based on intelligent process automation including artificial intelligence should be further strengthened in the transformation of a digitalized enterprise rather than the RPA based on simple task automation that is currently most used. In general, the phenomenon of cognitive dissonance was prominent for the adoption of new technology, but the phenomenon of cognitive dissonance did not appear for the continued use of RPA in the financial industry. Able to know. In the future in the financial industry, it is thought that the change in the labor organization will be accelerated as the suitability of repetitive tasks and technologies is increased.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.209-216
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• 2023

In this paper, artificial intelligence agents ChatGPT and Stable Diffusion were used to explore the possibility of educational use by implementing a program to recommend picture materials for teaching and learning according to the class topic entered by teachers. The average time spent recommending all picture materials is about 6 minutes. In general, pictures related to keywords were recommended, and the letters in the recommended pictures could only know the intention to represent the letters, and the letters could not be recognized and the meaning could not be known. However, further research seems to be needed on the fact that the type or content of the recommended picture depends entirely on the response of ChatGPT and that it is not possible to accurately recommend the picture for all keywords. In addition, it was concluded that it is true that the recommended picture is related to the keyword, but the evaluation of whether it has educational value is the subject of discussion that should be left to the judgment of human teachers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.329-332
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• 2022

This study aims to identify problems with existing e-learning content and non-face-to-face class methods, improve students' concentration, improve class achievement and educational effectiveness, and propose an artificial intelligence class system design using a web server. By using the function of face and eye tracking using OpenCV to identify attendance and concentration, and by inducing feedback through voice or message to questions asked by the instructor in the middle of class, learners relieve boredom caused by online classes and test by runner If the score is not reached, we propose an artificial intelligence education program system design that can bridge the academic gap and improve academic achievement by providing educational materials and videos for the wrong problem.