• Smart Media Journal
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• v.13 no.2
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• pp.85-94
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• 2024

Artificial Intelligence of Things (AIoT), which combines AI and the Internet of Things (IoT), has recently gained popularity. Deep neural networks (DNNs) have achieved great success in many applications. Deploying complex AI models on embedded boards, nevertheless, may be challenging due to computational limitations or intelligent model complexity. This paper focuses on an AIoT-based system for smart sewing automation using edge devices. Our technique included developing a detection model and a decision tree for a sufficient testing scenario. YOLOv5 set the stage for our defective sewing stitches detection model, to detect anomalies and classify the sewing patterns. According to the experimental testing, the proposed approach achieved a perfect score with accuracy and F1score of 1.0, False Positive Rate (FPR), False Negative Rate (FNR) of 0, and a speed of 0.07 seconds with file size 2.43MB.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.833-849
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• 2021

Cloud accounting represents a new area of accounting information systems. Past research has often focused on accounting information systems and its antecedents, rather than factors that adopt cloud accounting system. The purpose of this paper is to explain the factors that influence the adoption of cloud accounting in the financial sectors. This paper applied the technology acceptance model (TAM), technology-organization-environment, and the De Lone and Mc Lean model, coupled with proposed factors relevant to cloud accounting. The proposed model was empirically evaluated using survey data from 187 managers (financial managers, IT department managers, audit managers, heads of accounting departments, and head of internal control departments) in Jordanian bank branches. Based on the SEM results, top management support, organizational competency, service quality, system quality, perceived usefulness, and perceived ease of use had a positive relationship with the intention of using cloud accounting. Cloud accounting adoption positively affected cloud accounting usage. This paper contributes to a theoretical understanding of factors that activate the adoption of cloud accounting. For financial firms in general the results enable them to better develop cloud accounting framework. The paper verifies the factors that affect the adoption of cloud accounting and the proposed cloud accounting model.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.455-462
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• 2024

In the construction industry, known for its dynamic and hazardous environments, there exists a crucial demand for effective safety incident prevention. Traditional approaches to monitoring on-site safety, despite their importance, suffer from being laborious and heavily reliant on subjective, paper-based reports, which results in inefficiencies and fragmented data. Additionally, the incorporation of computer vision technologies for automated safety monitoring encounters a significant obstacle due to the lack of suitable training datasets. This challenge is due to the rare availability of safety accident images or videos and concerns over security and privacy violations. Consequently, this paper explores an innovative method to address the shortage of safety-related datasets in the construction sector by employing generative artificial intelligence (AI), specifically focusing on the Stable Diffusion model. Utilizing real-world construction accident scenarios, this method aims to generate photorealistic images to enrich training datasets for safety surveillance applications using computer vision. By systematically generating accident prompts, employing static prompts in empirical experiments, and compiling datasets with Stable Diffusion, this research bypasses the constraints of conventional data collection techniques in construction safety. The diversity and realism of the produced images hold considerable promise for tasks such as object detection and action recognition, thus improving safety measures. This study proposes future avenues for broadening scenario coverage, refining the prompt generation process, and merging artificial datasets with machine learning models for superior safety monitoring.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.423-436
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• 2021

In the future, artificial intelligence (AI) technology is expected to become a general-purpose technology (GPT), and it is predicted that AI competency will become an essential competency. Several nations around the world are fostering experts in the field of AI to achieve technological proficiency while working to develop the necessary infrastructure and educational environment. In this study, we investigated the status of software education at the liberal arts level at 31 universities in Seoul, along with precedents from domestic and foreign AI education research. Based on this, we concluded that an AI literacy education model is needed to link software education at the liberal arts level with professional AI education. And we classified 20 AI-related lectures released in the KOCW according to the AI literacy competencies required; based on the results of this classification, we propose a model for AI literacy education in the liberal arts for undergraduate students. The proposed AI literacy education model may be considered as AI·SW convergence to experience AI along with literacy in the humanities, deviating from the existing theoretical and computer-science-based approach. We expect that our proposed AI literacy education model can contribute to the proliferation of AI.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.1-11
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• 2024

Bridges crack and become damaged due to age and external factors such as earthquakes, lack of maintenance, and weather conditions. With the number of aging bridge on the rise, lack of maintenance can lead to a decrease in safety, resulting in structural defects and collapse. To prevent these problems and reduce maintenance costs, a system that can monitor the condition of bridge and respond quickly is needed. To this end, existing research has proposed artificial intelligence model that use sensor data to identify the location and extent of cracks. However, existing research does not use data from actual bridge to determine the performance of the model, but rather creates the shape of the bridge through simulation to acquire data and use it for training, which does not reflect the actual bridge environment. In this paper, we propose a bridge safety determination edge AI model that detects bridge abnormalities based on artificial intelligence by utilizing acceleration data from bridge occurring in the field. To this end, we newly defined filtering rules for extracting valid data from acceleration data and constructed a model to apply them. We also evaluated the performance of the proposed bridge safety determination edge AI model based on data collected in the field. The results showed that the F1-Score was up to 0.9565, confirming that it is possible to determine safety using data from real bridge, and that rules that generate similar data patterns to real impact data perform better.

• The Journal of Bigdata
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• v.5 no.2
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• pp.77-84
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• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.771-778
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• 2004

Recently, various applications of robot system become more popular in accordance with rapid development of computer hardware/software, artificial intelligence, and automatic control technology. Formerly robots mainly have been used in industrial field, however, nowadays it is said that the robot will do an important role in the home service application. To make the robot more useful, we require further researches on implementation of natural communication method between the human and the robot system, and autonomous behavior generation. The gesture recognition technique is one of the most convenient methods for natural human-robot interaction, so it is to be solved for implementation of intelligent robot system. In this paper, we describe the state-of-the-art of advanced gesture recognition technologies for intelligent robots according to three methods; sensor based method, feature based method, appearance based method, and 3D model based method. And we also discuss some problems and real applications in the research field.

• Journal of Engineering Education Research
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• v.23 no.2
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• pp.24-30
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• 2020

This paper proposes the approaches to the evaluation of learning using concepts of artificial intelligence. Among various techniques, deep learning algorithm is employed to achieve quantitative results of evaluation. In particular, this paper focuses on the process-based evaluation instead of the result-based one using face expression. The expression is simply acquired by digital camera that records face expression when students solve sample test problems. Face expressions are trained using convolutional neural network (CNN) model followed by classification of expression data into three categories, i.e., easy, neutral, difficult. To substantiate the proposed approach, the simulation results show promising results, and this work is expected to open opportunities for intelligent evaluation system in the future.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1447-1456
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• 2019

As artificial intelligence technology is actively used in image processing, it is possible to generate high-quality fake images based on deep learning. Fake images generated using GAN(Generative Adversarial Network), one of unsupervised learning algorithms, have reached levels that are hard to discriminate from the naked eye. Detecting these fake images is required as they can be abused for crimes such as illegal content production, identity fraud and defamation. In this paper, we develop a deep-learning model based on CNN(Convolutional Neural Network) for the detection of StyleGAN fake images. StyleGAN is one of GAN algorithms and has an excellent performance in generating face images. We experiment with 48 number of experimental scenarios developed by combining parameters of the proposed model. We train and test each scenario with 300,000 number of real and fake face images in order to present a model parameter that improves performance in the detection of fake faces.