• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.41-47
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• 2024

With the advancement and proliferation of the Internet of Things (IoT), a wide range of location-based services are being offered, and various ranging methods are being researched to meet the objectives of the required services. Conventional ranging methods involve the direct exchange of signals between tags and anchors to estimate distance, presenting a limitation in efficiently utilizing communication resources in large-scale IoT environments. To overcome these limitations, active-passive ranging methods have been proposed. However, there is a lack of theoretical convergence guarantees against clock drift errors and a detailed analysis of the characteristics of ranging estimation techniques, making it challenging to derive precise positioning results. In this paper, an improved active-passive ranging method that accounts for clock drift errors is proposed for precise positioning in large-scale IoT environments. The simulation results confirmed that the proposed active-passive ranging method can enhance distance estimation performance by up to 94.4% and 14.4%, respectively, compared to the existing active-passive ranging methods.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.3
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• pp.170-184
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• 2024

With the advancement of autonomous navigation technology in maritime domain, there is an active research on swarming Unmanned Surface Vehicles (USVs) that can fulfill missions with low cost and high efficiency. In this study, we propose a formation control algorithm that maintains a certain shape when multiple unmanned surface vehicles operate in a swarm. In the case of swarming, individual USVs need to be able to accurately follow the target state and avoid collisions with obstacles or other vessels in the swarm. In order to generate guidance commands for swarm formation control, the potential field method has been a major focus of swarm control research, but the method using the potential field only uses the position information of obstacles or other ships, so it cannot effectively respond to moving targets and obstacles. In situations such as the formation change of a swarm of ships, the formation control is performed in a dense environment, so the position and velocity information of the target and nearby obstacles must be considered to effectively change the formation. In order to overcome these limitations, this paper applies a method that considers relative velocity to the potential field-based guidance law to improve target following and collision avoidance performance. Considering the relative velocity of the moving target, the potential field for nearby obstacles is newly defined by utilizing the concept of Velocity Obstacle (VO), and the effectiveness and efficiency of the proposed method is verified through swarm control simulation, and swarm control experiments using a small scaled unmanned surface vehicle platform.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.417-426
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• 2024

Shield tunneling method is widely used to build tunnels in complex geological environment. Stability control of tunnel face is the key to the safety of projects. To improve the excavation efficiency or perform equipment maintenance, the excavation chamber sometimes is not fully filled with support medium, which can reduce the load and increase tunneling speed while easily lead to ground collapse. Due to the high risk of the face failure under non-fully support mode, the tunnel face stability should be carefully evaluated. Whether compressive air is required for compensation and how much air pressure should be provided need to be determined accurately. Based on the upper bound theorem of limit analysis, a non-fully support rotational failure model is developed in this study. The failure mechanism of the model is verified by numerical simulation. It shows that increasing the density of supporting medium could significantly improve the stability of tunnel face while the increase of tunnel diameter would be unfavorable for the face stability. The critical support ratio is used to evaluate the face failure under the nonfully support mode, which could be an important index to determine whether the specific unsupported height could be allowed during shield tunneling. To avoid of face failure under the non-fully support mode, several charts are provided for the assessment of compressed air pressure, which could help engineers to determine the required air pressure for face stability.

• Structural Engineering and Mechanics
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• v.91 no.2
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• pp.211-225
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• 2024

Soft bending actuators have gained significant interest in robotic applications due to their compliance and lightweight nature. Their compliance allows for safer and more natural interactions with humans or other objects, reducing the risk of injury or damage. However, the nonlinear behaviour of soft actuators presents challenges in accurately predicting their bending motion and force exertion. In this research, a new comprehensive study has been conducted by employing a developed 3D finite element model (FEM) to investigate the effect of geometrical and material parameters on the bending behaviour of a soft pneumatic actuator reinforced with Kevlar fibres. A series of experiments are designed to validate the FE model, and the FE model investigates the improvement of actuator performance. The material used for fabricating the actuator is RTV-2 silicone rubber. In this study, the Cauchy stress was expanded for hyperelastic models and the best model to express the stress-strain behaviour based on ASTM D412 Type C tensile test for this material has been obtained. The results show that the greatest bending angle was achieved for the semi-elliptical actuator made of RTV2 material with a pitch of 1.5 mm and second layer thickness of 1 mm. In comparison, the maximum response force was obtained for the semi-elliptical actuator made of RTV2 material with a pitch of 6 mm and a second layer thickness of 2 mm. Additionally, this research opens up new possibilities for development of safer and more efficient robotic systems that can interact seamlessly with humans and their environment.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.383-390
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• 2024

When measuring the radiated noise of an underwater vehicle, the range information between the vehicle and the receiver is an important factor, but since Global Positioning System (GPS) is not available in underwater, an alternative method is needed. As an alternative, the range is measured by estimating the arrival time, arrival time difference, and arrival frequency difference using a separate acoustic signal. However, errors occur due to the channel environment, and these outliers become obstacles in continuously measuring range. In this paper, we propose a method to reduce errors by curve fitting with a function in the form of a V-curve as a post-processing to remove outliers that occurred in the process of measuring range information. Simulation, lake and sea trials were conducted to verify the performance of the proposed method. In the results of the lake trial, the range estimation error was reduced by about 85 % from the Root Mean Square Error (RMSE) point of view.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.21-28
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• 2024

Nowadays, based on the 4th Industrial Revolution, by using the CHATGPT function and 3D virtual reality technology, anyone can easily open a virtual environment WEB-based, smart tourism OPEN source and travel destination without having to directly visit the travel location in the real world. Using the API function, it provides the convenience of virtual tourism. However, this function does not work if the travel transportation system is suddenly changed due to sudden bad weather, travel operation information cannot be checked in real time, and due to a lack of flight cancellation information and passenger ship operation information, it cannot be used until the plane or ferry departs normally. A very inconvenient problem arises where you have to wait a long time in the waiting room. Therefore, in this paper, in order to solve this problem, automatic duty-free product information and automatic product payment functions were added even when passenger ship cancellations and operation information suddenly occur due to bad weather and multiple products are purchased during the trip. In addition, the computer simulation experiment was conducted on a WEB basis so that anyone can conveniently travel smartly.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.109-123
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• 2024

Research into vision-based end-to-end autonomous driving systems utilizing deep learning and reinforcement learning has been steadily increasing. These systems typically encode continuous and high-dimensional vehicle states, such as location, velocity, orientation, and sensor data, into latent features, which are then decoded into a vehicular control policy. The complexity of urban driving environments necessitates the use of state representation learning through networks like Variational Autoencoders (VAEs) or Convolutional Neural Networks (CNNs). This paper analyzes the impact of different image state encoding methods on reinforcement learning performance in autonomous driving. Experiments were conducted in the CARLA simulator using RGB images and semantically segmented images captured by the vehicle's front camera. These images were encoded using VAE and Vision Transformer (ViT) networks. The study examines how these networks influence the agents' learning outcomes and experimentally demonstrates the role of each state representation technique in enhancing the learning efficiency and decision- making capabilities of autonomous driving systems.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.109-115
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• 2024

This paper proposes an interference recognition-based resource allocation (IaRA) scheme for very high frequency (VHF) data exchange-terrestrial (VDE-TER) to ensure the reliability of automatic identification system (AIS) message transmission in a coastal environment with dense vessels. To this end, IaRA conducts channel selection and slot allocation based on scheduling information obtained through monitoring VHF data exchange system (VDES) channels. The simulation results show that IaRA achieved an 8.79% higher packet delivery ratio (PDR) and a 9.69% lower channel interference ratio (CIR) compared to the VDE-TER resource allocation scheme of the existing VDES standard.

• Journal of Microbiology and Biotechnology
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• v.34 no.9
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• pp.1836-1847
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• 2024

Polyethylene terephthalate (PET), one of the most widely used plastics in the world, causes serious environmental problems. Recently, scientists have been focused on the enzymatic degradation of PET, an environmentally friendly method that offers an attractive approach to the degradation and recycling of PET. In this work, PET hydrolase from Streptomyces sp. W2061 was biochemically characterized, and the biodegradation of PET was performed using the PET model substrate bis (2-hydroxyethyl terephthalate) (BHET). PET hydrolase has an isoelectric point of 5.84, and a molecular mass of about 50.31 kDa. The optimum pH and temperature were 7.0 and 40℃, respectively. LC-MS analysis of the enzymatic products showed that the PET hydrolase successfully degraded a single ester bond of BHET, leading to the formation of MHET. Furthermore, in silico characterization of the PET hydrolase protein sequence and its predicted three-dimensional structure was designed and compared with the well-characterized IsPETase from Ideonella sakaiensis. The structural analysis showed that the (Gly-x1-Ser-x2-Gly) serine hydrolase motif and the catalytic triad (Ser, Asp, and His) were conserved in all sequences. In addition, we integrated molecular dynamics (MD) simulations to analyze the variation in the structural stability of the PET hydrolase in the absence and presence of BHET. These simulations showed the formation of a stable complex between the PET hydrolase and BHET. To the best of our knowledge, this is the first study on Streptomyces sp. W2061 to investigate the BHET degradation activity of PET hydrolase, which has potential application in the biodegradation of plastics in the environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.1047-1057
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• 2024

Recently, deep neural network-based reinforcement learning models have been applied in various advanced industrial fields such as autonomous driving, smart factories, and home networks, but it has been shown to be vulnerable to malicious adversarial attack. In this paper, we applied deep reinforcement learning models, DQN and PPO, to the autonomous driving simulation environment HighwayEnv and conducted three adversarial attacks: FGSM(Fast Gradient Sign Method), BIM(Basic Iterative Method), PGD(Projected Gradient Descent) and CW(Carlini and Wagner). In order to respond to adversarial attack, we proposed a method for deep learning models based on reinforcement learning to operate normally by removing noise from adversarial images using a bilateral filter algorithm. Furthermore, we analyzed performance of adversarial attacks using two popular metrics such as average of episode duration and the average of the reward obtained by the agent. In our experiments on a model that removes noise of adversarial images using a bilateral filter, we confirmed that the performance is maintained as good as when no adversarial attack was performed.