• 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 Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.157-166
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• 2022

Exercise is necessary for a healthy life, but it is recommended that it be conducted in a non-face-to-face environment in the context of an epidemic such as COVID-19. However, in the existing non-face-to-face exercise content, it is possible to recognize exercise movements, but the process of interpreting and providing feedback information is not automated. Therefore, in this paper, to solve this problem, we propose a method of creating a formalized rule to track the contents of exercise and the motions that constitute it. To make such a rule, first make a rule for the overall exercise content, and then create a tracking rule for the motions that make up the exercise. A motion tracking rule can be created by dividing the motion into steps and defining a key frame pose that divides the steps, and creating a transition rule between states and states represented by the key frame poses. The rules created in this way are premised on the use of posture and motion recognition technology using motion capture equipment, and are used for logical development for automation of application of these technologies. By using the rules proposed in this paper, not only recognizing the motions appearing in the exercise process, but also automating the interpretation of the entire motion process, making it possible to produce more advanced contents such as an artificial intelligence training system. Accordingly, the quality of feedback on the exercise process can be improved.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.555-562
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• 2022

This study measured and analyzed the propagation characteristics at frequencies 6, 10, and 17 GHz to discover the new propagation demands in a semi-basement indoor corridor environment for meeting the 4th industrial revolution requirements. The measured indoor environment is a straight corridor consisting of three lecture rooms and glass windows on the outside. The measurement scenario development and measurement system were constructed to match this environment. The transmitting antenna was fixed, and the frequency domain and time domain propagation characteristics were measured and analyzed in the line-of-sight environment based on the distance of the receiving antenna location. In the frequency domain, reliability was determined by the parameters of the floating intercept (FI) path loss model and an R-squared value of 0.5 or more. In the time domain, the root mean square (RMS) delay spread and the cumulative probability of K-factor were used to determine that 6 GHz had high propagation power and 17 GHz had low propagation power. These research results will be effective in providing ultra-connection and ultra-delay artificial intelligence services for WIFI 6, 5G, and future systems in a semi-basement indoor corridor environment.

• Smart Media Journal
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• v.11 no.10
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• pp.89-96
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• 2022

An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.85-96
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• 2023

This study developed and evaluated a safe route guidance service for electric personal assistive mobility device used mainly by the mobility impaired people to improve their mobility. Thirteen underlying factors affecting the mobility of electric personal assistive mobility device have been derived through a survey with the mobility impaired people and employees in related organizations in Busan Metropolitan City. After assigning safety scores to individual factors and identifying the relevant factors along routes of interest with an object detection AI model, the safe route for electric personal assistive mobility device was provided through an optimal path-finding algorithm. As a result of comparing the general route of T-map and the recommended route of this study for the identical routes, the latter had relatively fewer obstacles and the gentler slope than the former, implicating that the recommended route is safer than the general one. As future works, it is necessary to enhance the function of a route guidance service based on the real-time location of users and to conduct spot investigations to evaluate and verify its social acceptability.

• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• Journal of Navigation and Port Research
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• v.47 no.1
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• pp.10-17
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• 2023

A series of studies on the development of autonomous surface ships have been promoted in domestic and foreign countries. One of the main technologies for the development of autonomous ships is path-following control, which is closely related to securing the safety of ships at sea. In this regard, the path-following performance of an autonomous ship should be first evaluated at the design stage. The main aim of this study was to develop a visual and quantitative evaluation method for the path-following control performance of an autonomous ship at the design stage. This evaluation technique was developed using a computational fluid dynamics (CFD)-based path-following control model together with a line-of-sight (LOS) guidance algorithm. CFD software was utilized to visualize waves around the ship, performing path-following control for visual evaluation. In addition, a quantitative evaluation was carried out using the difference between the desired and estimated yaw angles, as well as the distance difference between the planned and estimated trajectories. The results demonstrated that the ship experienced large deviations from the planned path near the waypoints while changing its course. It was also found that the fluid phenomena around the ship could be easily identified by visualizing the flow generated by the ship. It is expected that the evaluation method proposed in this study will contribute to the visual and quantitative evaluation of the path-following performance of autonomous ships at the design stage.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.341-352
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• 2022

The wave of a knowledge and information society led by AI, Big Data, and so on is having an all-round impact on our way of life. Therefore the Ministry of Education is in a hurry to strengthen Digital Literacy, including AI and SW Education, by improving the curriculum that can cultivate basic knowledge and capabilities to respond to changes in the future society. It can be seen that establishing a foundation for cultivating Digital Literacy through all subjects and improving basic and in-depth learning in new technology fields such as AI linked to the information curriculum is an essential part for future society. However, research on each content for cultivating Digital and AI literacy is relatively active, while research on teaching and learning strategies is insufficient. Therefore in this study, a CT-based Digital and AI teaching and learning strategy that can foster that was developed and Delphi expert verification was conducted, and the final teaching and learning strategy was completed after evaluating instructor usability and analyzing learner effectiveness.

• Knowledge Management Research
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• v.24 no.1
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• pp.31-49
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• 2023

TikTok, as a leading short video platform, has attracted many users, and the resulting attention generates immense business value as a platform to diffuse knowledge. As a qualitative and explorative approach, this study reviews the knowledge payment industry and discusses the influential factors of users' payment decisions regarding knowledge products on TikTok. By conducting in-depth interviews with ten participants and observing 95 knowledge providers' videos, we find that TikTok has significant business potential in the knowledge payment industry. By using the ATLAS. ti software to code the data collected from these interviews, this study finds that demander characteristics (personal needs), product characteristics (product quality), provider characteristics (the key opinion leader effect), and platform characteristics (platform management) are the four core categories that influence users' payment decisions regarding knowledge products on TikTok. A theoretical model consisting of the ten variables of emotional needs, professional needs, quality, price, helpfulness, value, charisma, user trust, service guarantee, and scarcity is proposed based on the grounded theory. The theoretical and practical implications of the study findings are also discussed.