• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.110-113
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• 2022

Nowadays, breaking through the conventional traffic signal control method based on mathematical optimization, artificial intelligence began to be used in the area. In response to this trend, many studies are ongoing to figure out how to utilize AI technology properly for traffic signal optimization. They just simply focus on which method will work well besides lots of machine learning techniques and abandon the reward function engineering. In many cases, the reward function consists of the average delay of the vehicles in the intersection. However, this may lead to AI's misunderstanding about the traffic signal control: what AI regards as a good situation may not be realistic. Even the reward function itself may not meet the service level. Therefore, this study analyzes the problems of previous reward functions and will suggest how to reward function can be enhanced.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.441-449
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• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

• BMB Reports
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• v.57 no.2
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• pp.71-78
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• 2024

Melanoma is one of the most aggressive skin tumors, and conventional treatment modalities are not effective in treating advanced melanoma. Although immunotherapy is an effective treatment for melanoma, it has disadvantages, such as a poor response rate and serious systemic immune-related toxic side effects. The main solution to this problem is the use of biological materials such as hydrogels to reduce these side effects and amplify the immune killing effect against tumor cells. Hydrogels have great advantages as local slow-release drug carriers, including the ability to deliver antitumor drugs directly to the tumor site, enhance the local drug concentration in tumor tissue, reduce systemic drug distribution and exhibit good degradability. Despite these advantages, there has been limited research on the application of hydrogels in melanoma treatment. Therefore, this article provides a comprehensive review of the potential application of hydrogels in melanoma immunotherapy. Hydrogels can serve as carriers for sustained drug delivery, enabling the targeted and localized delivery of drugs with minimal systemic side effects. This approach has the potential to improve the efficacy of immunotherapy for melanoma. Thus, the use of hydrogels as drug delivery vehicles for melanoma immunotherapy has great potential and warrants further exploration.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.230-245
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• 2023

The importance of evaluating the safety of Automated Vehicles (AV) is increasing with the advances in autonomous driving technology. Accordingly, an evaluation scenario that defines in advance the situations AV may face while driving is being used to conduct efficient stability evaluation. On the other hand, the single scenarios currently used in conventional evaluations address limited situations within short segments. As a result, there are limitations in evaluating continuous situations that occur on real roads. Therefore, this study developed a set of multiple scenarios that allow for continuous evaluation across entire sections of roads with diverse geometric structures to assess the safety of AV. In particular, the conditions for connecting individual scenarios were defined, and a methodology was proposed for developing concrete multiple scenarios based on the scenario evaluation procedure of the PEGASUS project. Furthermore, a simulation was performed to validate the practicality of these multiple scenarios.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.264-283
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• 2023

Unlike conventional vehicle traffic accidents, autonomous vehicles traffic accidents can be caused by various factors, including technical problems, the environment, and driver interaction. With the future advances in autonomous driving technology, new issues are expected to emerge in addition to the existing accident causes, and various scenario-based approaches are needed to respond to them. This study developed autonomous vehicle traffic accident scenarios by collecting autonomous driving accident reports, CA DMV collision reports, autonomous driving mode disengagement reports, and autonomous driving actual accident videos. The scenarios were derived based on the functional safety system failure modes of ISO 26262 and attempted to reflect the various issues of autonomous driving functions. The autonomous vehicle scenarios derived through this study are expected to play an essential role in preventing and preparing for various autonomous vehicle traffic accidents in the future and improving the safety of autonomous driving technology.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.99-107
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• 2013

Controller area network (CAN) was designed for multiplexing communication between electronic control units (ECUs) in vehicles and thus for decreasing the overall wire harness. The increasing number of ECUs causes the CAN bus overloaded and consequently the error probability of data transmission increases. Since the time duration for the data transmission is proportional to CAN frame length, it is desirable to reduce the frame length. In this paper, a CAN message compression method is proposed using Data Length Code (DLC) and compression area selection algorithm to reduce the CAN frame length and the error probability during the transmission of CAN messages. By the proposed method, it is not needed to predict the maximum value of the difference in successive CAN messages as opposed to other compression methods. Also, by the use of DLC, we can determine whether the received CAN message has been compressed or not without using two ID's as in conventional methods. By simulations using actual CAN data, it is shown that the CAN transmission data is reduced up to 52 % by the proposed method, compared with conventional methods. By using an embedded test board, it is shown that 64bit EMS CAN data compression can be performed within 0.16ms and consequently the proposed algorithm can be used in automobile applications without any problem.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.55-61
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• 2021

In this paper, we proposed a DeepLabv3+ based encoder-decoder model utilizing an attention mechanism for precise semantic segmentation. The DeepLabv3+ is a semantic segmentation method based on deep learning and is mainly used in applications such as autonomous vehicles, and infrared image analysis. In the conventional DeepLabv3+, there is little use of the encoder's intermediate feature map in the decoder part, resulting in loss in restoration process. Such restoration loss causes a problem of reducing segmentation accuracy. Therefore, the proposed method firstly minimized the restoration loss by additionally using one intermediate feature map. Furthermore, we fused hierarchically from small feature map in order to effectively utilize this. Finally, we applied an attention mechanism to the decoder to maximize the decoder's ability to converge intermediate feature maps. We evaluated the proposed method on the Cityscapes dataset, which is commonly used for street scene image segmentation research. Experiment results showed that our proposed method improved segmentation results compared to the conventional DeepLabv3+. The proposed method can be used in applications that require high accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.697-705
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• 2021

Currently, roadway operation agencies provide interior zones of tunnels with emergency information including crash, fire, and vehicles' stop, through state-of-the-art technologies such as variable message signs and radio-based broadcast systems. However, when coping with an emergency in tunnel interior zones, such information could be too late for drivers to access. A radio-based emergency alert system at the access zones of freeway tunnels, on the other hand,could be a good alternative for solving this problem. Therefore, the objective of this study is to assess user acceptability of such an alternative system. To carry out this study, an online survey was conducted on 762 drivers, and the survey results were analyzed using a structural equation modeling to identify factors affecting acceptability of the proposed system. As a result, driver characteristics such as age group, driving frequency, and driving career, utilization of conventional traffic information, and usefulness of conventional traffic information have a positive impact on acceptability. It is expected that the findings of the study will be a basis to effectively address and deploy a new emergency alert system at the access zones of freeway tunnels.

• Composites Research
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• v.21 no.6
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• pp.8-14
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• 2008

Lighting system of road is an essential structure used for the safety of pedestrians and vehicles. Most of the lighting pole is made with steel which is vulnerable under corrosive environment. To overcome such corrosion problems, stainless steel and iron steel are used, but they are usually manufactured by hand which is not efficient. Due to their high strength and stiffness, when there is car collision with the lighting pole structure the safety of driver may not be ensured. Hence, the development of new-type lighting pole system which is easy to adjust the right on the road, lengthen the service life, and reduce the maintenance, is necessary. Lighting pole made with aluminum alloy is high in strength per unit weight, is strong against corrosive environment, and is easy to construct due to flexibility and right weight. But, because the strength and stiffness of the material is lower than that of steel, the structural safety and serviceability of the system can be a problem. To mitigate the structural problem associated with conventional lighting pole system, experimental investigation is conducted on the conventional lighting pole and rib reinforced aluminum alloy lighting pole, respectively. By comparison of results, it was found that the rib reinforced Mg-Si aluminum alloy lighting pole is efficiently applicable to the lighting pole system of road.