• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.505-512
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• 2022

The purpose of this study is to synthesize effects of the living lab-based education through meta-analysis. Seven primary studies reporting the effect of living lab-based education were carefully selected for data analysis. Research questions are as follows. First, what is the overall effect size of the living lab-based education? The overall effect size refers to the effect on the cognitive and affective domains. Second, what is the effect size of the living lab-based education according to categorical variables? Categorical variables are outcome characteristics, study characteristics, and design characteristics. Results are summarized as follows. First, the overall effect size of living lab-based education was 0.347. Second, the effect size according to the cognitive domain was 1.244 for information process, 0.593 for communication, 0.261 for problem solving, and 0.26 for creativity. Third, the effect size according to subject area was shown in the order of electrical and electronic engineering 1.146, technology and home economics 0.489, artificial intelligence 0.379, and practical arts 0.168. Fourth, the effect size according to school level was 1.058 for high school, 0.312 for middle school, and 0.217 for elementary school. Fifth, the effect size by grade level was 0.295 when two or more grades were integrated and 0.294 for a single grade.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.563-568
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• 2023

The government's strengthening of the standards for mentally ill patients and expanding the scope of examinations to the entire nation reflects the changing times. According to the OECD's announcement (2021), the incidence of depression and anxiety has more than doubled since the prolonged COVID-19 pandemic in countries around the world, with Korea's prevalence ranking first. However, only 12.1% of those who have been diagnosed with mental disorders received counseling and treatment from experts. The difference between depression and simple depression is significant depending on whether it is medically treated or temporary, but it can be seen that the continuation of depression is depression. In order to reduce this depression, Kandinsky's work was visualized and created. In a study conducted by changing the playback speed of the produced Kandinsky image, beta and gamma values, which showed the largest deviation when compared to depressive patients and normal people, increased significantly when viewed at 90fps, which was most effective in relieving depression. Artistic creations are bound to be accepted differently depending on the individual's perspective, but it is hoped that research that can improve the phenomenon of individuals suffering from depression by integrating artificial intelligence and traditional mental health approaches will be further developed and widely used for treatment.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.381-389
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• 2023

One of the crucial components in building information modeling (BIM) is data. To systematically manage these data, various research studies have focused on the creation of object breakdown structures and property sets. Specifically, crucial data for managing programs and payments involves work breakdown structures (WBSs) and cost breakdown structures (CBSs), which are indispensable for mapping BIM objects. Achieving this requires disassembling CBS quantities based on 3D objects and WBS. However, this task is highly tedious owing to the large volume of CBS and divergent coding practices employed by different organizations. Manual processes, such as those based on Excel, become nearly impossible for such extensive tasks. In response to the challenge of computing quantities that are difficult to derive from BIM objects, this study presents methods for disassembling length-based quantities, incorporating significant portions of the bill of quantities (BOQs). The proposed approach recommends suitable CBS by leveraging the accumulated history of WBS-CBS mapping databases. Additionally, it establishes a unified CBS code, facilitating the effective operation of CBS databases.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.331-337
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• 2023

In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.91-98
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• 2023

Recently, according to development of artificial intelligence, a wide range of industry being automatic and optimized. Also we can find out some research of using supervised learning for deteceting defect of railway in domestic rail industry. However, there are structures other than rails on the track, and the fastener is a device that binds the rail to other structures, and periodic inspections are required to prevent safety accidents. In this paper, we present a method of reducing cost for labeling using semi-supervised and transfer model trained on rail fastener data. We use Resnet50 as the backbone network pretrained on ImageNet. At first we randomly take training data from unlabeled data and then labeled that data to train model. After predict unlabeled data by trained model, we adopted a method of adding the data with the highest probability for each class to the training data by a predetermined size. Futhermore, we also conducted some experiments to investigate the influence of the number of initially labeled data. As a result of the experiment, model reaches 92% accuracy which has a performance difference of around 5% compared to supervised learning. This is expected to improve the performance of the classifier by using relatively few labels without additional labeling processes through the proposed method.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.161-169
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• 2023

Due to the Fourth Industrial Revolution, innovation in various technologies such as artificial intelligence (AI), big data (Big Data), and cloud (Cloud) is accelerating, and data is considered an important asset. With the innovation of these technologies, various efforts are being made to lead technological innovation in the field of defense science and technology. In Korea, the government also announced the "Defense Innovation 4.0 Plan," which consists of five key points and 16 tasks to foster advanced science and technology forces in March 2023. The plan also includes the establishment of a Condition-Based Maintenance system (CBM+) to improve the operability and availability of weapons systems and reduce defense costs. Condition Based Maintenance (CBM) aims to secure the reliability and availability of the weapon system and analyze changes in equipment's state information to identify them as signs of failure and defects, and CBM+ is a concept that adds Remaining Useful Life prediction technology to the existing CBM concept [1]. In order to establish a CBM+ system for the weapon system, sensors are installed and sensor data are required to obtain condition information of the weapon system. In this paper, we propose a sensor data metadata schema to efficiently and effectively manage sensor data collected from sensors installed in various weapons systems.