• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.53-59
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• 2024

This study presents the design and implementation of a camera-based 6DoF (6 Degrees of Freedom) tracking and pose estimation system. In particular, we propose a method for accurately estimating the positions and orientations of all fingers of a user utilizing a 6DoF robotic arm. The system is developed using the Python programming language, leveraging the Mediapipe and OpenCV libraries. Mediapipe is employed to extract keypoints of the fingers in real-time, allowing for precise recognition of the joint positions of each finger. OpenCV processes the image data collected from the camera to analyze the finger positions, thereby enabling pose estimation. This approach is designed to maintain high accuracy despite varying lighting conditions and changes in hand position. The proposed system's performance has been validated through experiments, evaluating the accuracy of hand gesture recognition and the control capabilities of the robotic arm. The experimental results demonstrate that the system can estimate finger positions in real-time, facilitating precise movements of the 6DoF robotic arm. This research is expected to make significant contributions to the fields of robotic control and human-robot interaction, opening up various possibilities for future applications. The findings of this study will aid in advancing robotic technology and promoting natural interactions between humans and robots.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.411-417
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• 2024

Renal ultrasound can detect kidney diseases by observing the size and shape of the kidneys, but its functional predictive value is relatively low. Kidney function can decrease by 20-30% without significant clinical changes or specific symptoms. This study aimed to confirm the limitations of renal ultrasound in functional aspects while acknowledging its usefulness in structural evaluation. It compared and analyzed the results of kidney function tests (serum creatinine, glomerular filtration rate, blood urea nitrogen, proteinuria, hematuria) according to age and gender in a normal group without structural abnormalities on renal ultrasound. In the comparison of kidney function tests by gender, differences were observed in BUN, Creatinine, GFR, and RBC, while no difference was found in Urine Protein, indicating functional differences between genders. Significant differences were observed in BUN and GFR across age groups, with GFR showing a decreasing trend with increasing age. Between genders, significant differences were found in BUN, creatinine, GFR, and RBC. Men had higher BUN and creatinine levels, while women had higher GFR. The prevalence of abnormalities in blood tests was 3.3%, and in urine tests was 6.1%. These limitations suggest that renal ultrasound alone may not be sufficient. It is essential to consider other diagnostic methods and conduct various tests in combination to more accurately evaluate kidney function and potentially detect problems early in asymptomatic adults.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.37-45
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• 2024

This study aims to collect and systematize indicators for each stage of digital transformation at the infrastructure level to accurately diagnose the current status of digital transformation in Korea and to serve as a reference for establishing a balanced digital strategy. In order to establish a framework for digital transformation of infrastructure, 19 indicators in three categories(tangible/intangible, data) were identified across three stages of digital transformation: computerization, digitization, and digital transformation, and 19 indicators in three categories were identified to study the changes in digital infrastructure. The main findings are: First, the digital transformation of infrastructure is at a high level, moving from digitization to digital transformation. Second, the scope of digital transformation policies is expanding as digital transformation is triggered, and additional policies on inclusion and social disparities should be prepared. It is also important to improve the regulatory environment, which is relatively undervalued. Third, as data becomes more important, it is important to develop indicators and measurements to strengthen digital competitiveness in terms of data infrastructure. This study is an exploratory study of the existing indicators, which can be used to conduct specialized research on the differences in the level of digital transformation by industry, sector, company size, age, gender, region, and group, and to study indicators for the expansion of digital transformation to social and industrial sectors. The expected effect is to deepen the process of understanding the interaction between each indicator, so that future digital transformation policies can be organized and promoted, and policy outcomes can be predicted and responded to in advance.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.541-549
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• 2024

The various structures of artificial neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been extensively studied and served as the backbone of numerous models. Among these, a transformer architecture has demonstrated its potential for natural language processing and become a subject of in-depth research. Currently, the techniques can be adapted for image processing through the modifications of its internal structure, leading to the development of Vision transformer (ViT) models. The ViTs have shown high accuracy and performance with large data-sets. This study aims to develop a ViT-based model for detecting pneumonia using chest X-ray images and quantitatively evaluate its performance. The various architectures of the ViT-based model were constructed by varying the number of encoder blocks, and different patch sizes were applied for network training. Also, the performance of the ViT-based model was compared to the CNN-based models, such as VGGNet, GoogLeNet, and ResNet. The results showed that the traninig efficiency and accuracy of the ViT-based model depended on the number of encoder blocks and the patch size, and the F1 scores of the ViT-based model ranged from 0.875 to 0.919. The training effeciency of the ViT-based model with a large patch size was superior to the CNN-based models, and the pneumonia detection accuracy of the ViT-based model was higher than that of the VGGNet. In conclusion, the ViT-based model can be potentially used for pneumonia detection using chest X-ray images, and the clinical availability of the ViT-based model would be improved by this study.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.439-445
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• 2024

This study evaluated adjusting collimation size for shoulder anteroposterior(AP) projection in digital radiography, analyzing its effects on radiation dose and exposure index for the thyroid and breast. It aims to identify the most suitable collimation size for this procedure. A skin dosimeter was used on a chest phantom to measure radiation at the thyroid and breast across four collimation sizes: 17"×17", 12"×10", 10"×8", and 8"×8". entrance surface dose(ESD), dose area product(DAP), entrance skin exposure(ESE), and exposure index(EI) were recorded and compared for each size. Significant reductions in ESD for the thyroid and breast were observed when collimation size was decreased from 17"×17" to 8"×8", with decreases of 94.30% and 99.00% respectively. DAP also desreased by 72.12%. A similar trend was seen when altering the size from the standard 12"×10" to 8"×8", resulting in a decrease in ESD and DAP, but ESE and EI remained largely unchanged. Adopting an 8"×8" collimation size for shoulder AP projection in digital radiography system could significantly reduce radiation exposure to sensitive organs like the thyroid and breast, making it a preferable choice in clinical practice.

• Korean Chemical Engineering Research
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• v.62 no.4
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• pp.355-363
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• 2024

Although copolymer aramid is a fiber with excellent flexural performance, there is no test method to evaluate flexural fatigue performance. Various studies are currently being conducted in korea to develop copolymer aramid, and in order to develop the reliability of aramid fibers to a global level, it is necessary to develop a test method to evaluate the flexural fatigue performance of aramid fibers. In this study, we developed an test equipment and test method that can evaluate the flexural fatigue performance of copolymer aramid and analyzed the flexural fatigue performance of aramid fiber. Flexing rollers are made of ceramic materials and rotating shapes to minimize friction. The diameter of the roller was set to 10 mm by calculating the minimum allowable curvature. The B₁₀ life was calculated through a flexural fatigue test, and the para-aramid was 125,770 cycles, the copolymer aramid was 598,150 cycles, and the aramid nano fiber(ANF) coated copolymer aramid was 589,073 cycles. Through the S-N diagram, the fatigue life relationship according to the load change was confirmed. copolymer aramid fibers exhibit better flexural fatigue performance than para-aramid fibers at high loads. The ANF coated copolymer aramid also exhibits excellent flexural fatigue performance.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.5
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• pp.157-171
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• 2024

Recently, empowering leadership, which leads to psychological motivation by giving members authority, responsibility, and autonomy for decision-making and instilling a sense of ownership in their work, as well as the suitability between individual members and their jobs, has been emphasized as determining the success of an organization in a rapidly changing environment. In addition, jobcrafting is attracting attention as an action that proactively readjusts the physical, cognitive, and relational scope of work by changing beliefs and attitudes toward a given task. Therefore, this study examines the structural relationship between empowering leadership, person-job fit, jobcrafting, knowledge-sharing behavior, and innovation behavior. A survey was conducted on members of travel agencies in Busan and Gyeongnam, and the results showed that first, empowering leadership had a positive (+) significant effect on jobcrafting, and person-job fit had a positive (+) significant effect on jobcrafting. Second, it was found that jobcrafting had a positive (+) significant effect on knowledge-sharing behavior and innovation behavior, and finally, knowledge-sharing behavior had a positive (+) significant effect on innovation behavior, so all hypotheses were adopted. This study shows that in order to induce knowledge-sharing behavior and innovation behavior of members, it is necessary to create an environment in which jobcrafting can take place, increasing the level of empowering leadership, person-job fit, and increasing jobcrafting.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.9-19
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• 2024

In this paper, a real-time public transportation monitoring system is proposed. The proposed system was implemented by developing a public transportation app and utilizing optical sensors, pressure sensors, and an object detection algorithm. Additionally, a bus model was created to verify the system's functionality. The proposed real-time public transportation monitoring system has three key features. First, the app can monitor congestion levels within public transportation by detecting seat occupancy and the total number of passengers based on changes in optical and pressure sensor readings. Second, to prevent errors in the optical sensor that can occur when multiple passengers board or disembark simultaneously, we explored the possibility of using the YOLO object detection algorithm to verify the number of passengers through CCTV footage. Third, convenience is enhanced by displaying occupied seats in different colors on a separate screen. The system also allows users to check their current location, available public transportation options, and remaining time until arrival. Therefore, the proposed system is expected to offer greater convenience to public transportation users.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.395-403
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• 2024

Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However, this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages. Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual) between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99% in detecting abnormal messages and demonstrated superior performance compared to other existing models.