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Research on digital twins (DTs) in the power system field has mainly focused on implementing DTs for specific resources, while few studies on electric vehicle (EV)-based DT implementation have considered integration and interoperability between systems. This study introduces a DT-based EV system operation framework to address the aforementioned research gap. The framework implements individual EVs, charging stations, and charging station operators (CPOs) as DTs, enabling integrated operation with the power grid. The DT-based EV agent supports independent decision-making on power service participation by considering location information, distance, charging amount, spare time, and incentives. In addition, the CPO can establish an optimal incentive strategy to induce EV users to participate in grid power services. The proposed DT systems map information between EVs, charging stations, and the grid, enabling analysis and verification of the impact of participants on charging station operation, grid stability, and economic efficiency in an independent environment. The effectiveness and usability of the proposed framework were verified through a case study on an incentive-based demand response program.

Research on digital twins (DTs) in the power system field has mainly focused on implementing DTs for specific resources, while few studies on electric vehicle (EV)-based DT implementation have considered integration and interoperability between systems. This study introduces a DT-based EV system operation framework to address the aforementioned research gap. The framework implements individual EVs, charging stations, and charging station operators (CPOs) as DTs, enabling integrated operation with the power grid. The DT-based EV agent supports independent decision-making on power service participation by considering location information, distance, charging amount, spare time, and incentives. In addition, the CPO can establish an optimal incentive strategy to induce EV users to participate in grid power services. The proposed DT systems map information between EVs, charging stations, and the grid, enabling analysis and verification of the impact of participants on charging station operation, grid stability, and economic efficiency in an independent environment. The effectiveness and usability of the proposed framework were verified through a case study on an incentive-based demand response program.

The traffic sign identification and recognition system (TSIRS) is an essential component for autonomous vehicles to succeed. The TSIRS helps to collect and provide helpful information for autonomous driving systems. The information may include limits on speed, directions for driving, signs to stop or lower the speed, and many more essential things for safe driving. Recently, incidents have been reported regarding autonomous vehicle crashes due to traffic sign identification and recognition system failures. The TSIRS fails to recognize the traffic signs in challenging conditions such as skewed signboards, scratches on traffic symbols, discontinuous or damaged traffic symbols, etc. These challenging conditions are presented for various reasons, such as accidents, storms, artificial damage, etc. Such traffic signs contain an ample amount of noise, because of which traffic sign identification and recognition become a challenging task for automated TSIRS systems. The proposed method in this paper addresses these challenges. The sign edge is a helpful feature for the recognition of traffic signs. A novel traffic sign edge detection algorithm is introduced based on bilateral filtering with adaptive thresholding and varying aperture size that effectively detects the edges from such noisy images. The proposed edge detection algorithm and transfer learning is used to train the Convolutional Neural Network (CNN) models and recognize the traffic signs. The performance of the proposed method is evaluated and compared with existing edge detection methods. The results show that the proposed algorithm achieves optimal Mean Square Error (MSE) and Root Mean Square Error (RMSE) error rates and has a better Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) ratio than the traditional edge detection algorithms. Furthermore, the precision rate, recall rate, and F1 scores are evaluated for the CNN models. With the German Traffic Sign Benchmark database (GTSRB), the proposed algorithm and Inception V3 CNN model gives promising results when it receives the edge-detected images for training and testing.

The traffic sign identification and recognition system (TSIRS) is an essential component for autonomous vehicles to succeed. The TSIRS helps to collect and provide helpful information for autonomous driving systems. The information may include limits on speed, directions for driving, signs to stop or lower the speed, and many more essential things for safe driving. Recently, incidents have been reported regarding autonomous vehicle crashes due to traffic sign identification and recognition system failures. The TSIRS fails to recognize the traffic signs in challenging conditions such as skewed signboards, scratches on traffic symbols, discontinuous or damaged traffic symbols, etc. These challenging conditions are presented for various reasons, such as accidents, storms, artificial damage, etc. Such traffic signs contain an ample amount of noise, because of which traffic sign identification and recognition become a challenging task for automated TSIRS systems. The proposed method in this paper addresses these challenges. The sign edge is a helpful feature for the recognition of traffic signs. A novel traffic sign edge detection algorithm is introduced based on bilateral filtering with adaptive thresholding and varying aperture size that effectively detects the edges from such noisy images. The proposed edge detection algorithm and transfer learning is used to train the Convolutional Neural Network (CNN) models and recognize the traffic signs. The performance of the proposed method is evaluated and compared with existing edge detection methods. The results show that the proposed algorithm achieves optimal Mean Square Error (MSE) and Root Mean Square Error (RMSE) error rates and has a better Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) ratio than the traditional edge detection algorithms. Furthermore, the precision rate, recall rate, and F1 scores are evaluated for the CNN models. With the German Traffic Sign Benchmark database (GTSRB), the proposed algorithm and Inception V3 CNN model gives promising results when it receives the edge-detected images for training and testing.

This study covers the structural optimization of vertical axis wind turbines (VAWTs) that can operate reliably for long periods of time in marine environments, as well as simulation analysis to evaluate their fatigue and strain resistance. Due to the nature of the marine environment, strong wind speeds and constant wave loads are applied, and VAWTs are likely to suffer from fatigue build-up and deformation problems in the long term. In this study, detailed numerical simulations were performed using ANSYS software (2024 R2) to analyze the effects of different airfoil shapes, material choices, tip speed ratios (TSRs), and foundation types on the turbine’s stress distribution and fatigue resistance. The results showed that NACA 0030 airfoil, composite steel, and single-pile foundation performed best under TSR 1.8 conditions, with the potential to reduce strain by approximately 30% and fatigue damage by approximately 25% compared to conventional structures. With this optimized combination, it was found that maintenance costs could be significantly reduced while maintaining structural stability at sea. These results could make an important contribution to the economical and durable design of VAWTs in the future.

This study covers the structural optimization of vertical axis wind turbines (VAWTs) that can operate reliably for long periods of time in marine environments, as well as simulation analysis to evaluate their fatigue and strain resistance. Due to the nature of the marine environment, strong wind speeds and constant wave loads are applied, and VAWTs are likely to suffer from fatigue build-up and deformation problems in the long term. In this study, detailed numerical simulations were performed using ANSYS software (2024 R2) to analyze the effects of different airfoil shapes, material choices, tip speed ratios (TSRs), and foundation types on the turbine’s stress distribution and fatigue resistance. The results showed that NACA 0030 airfoil, composite steel, and single-pile foundation performed best under TSR 1.8 conditions, with the potential to reduce strain by approximately 30% and fatigue damage by approximately 25% compared to conventional structures. With this optimized combination, it was found that maintenance costs could be significantly reduced while maintaining structural stability at sea. These results could make an important contribution to the economical and durable design of VAWTs in the future.

The increasing burden on shipowners and shipping companies due to environmental regulations imposed by the International Maritime Organization (IMO) has led to the adoption of various compliance strategies, including the use of low-sulfur fuel, installation of scrubbers, and the use of liquefied natural gas (LNG) as an alternative fuel. LNG is particularly prevalent in dual-fuel propulsion ships, with the IMO Type C tank being the most commonly used storage facility. The structure of the IMO Type C tank comprises a pressure vessel and supporting saddles, which can be integrated or separate systems. Despite being manufactured within specified tolerances, welding-induced deformation of the tank and saddle is inevitable since the saddle is welded directly onto the hull. In integrated tank–saddle systems, this deformation can lead to cracks in the epoxy resin, which has lower strength and stiffness, as well as burn damage to the resin and wooden blocks from welding heat. In separate tank–saddle systems, installation difficulties can arise due to interference between the fuel tank system and adjacent structures, such as insulation or the fuel preparation room (FPR), resulting from saddle deformation caused by welding. This study analyzes the sensitivity of all weld lines involved in saddle installation using the direct inherent strain (DIS) method. Based on this analysis, the initial welding deformations are evaluated in relation to the welding direction and sequence. Finally, an optimized method for saddle installation is proposed to minimize deformation.

The increasing burden on shipowners and shipping companies due to environmental regulations imposed by the International Maritime Organization (IMO) has led to the adoption of various compliance strategies, including the use of low-sulfur fuel, installation of scrubbers, and the use of liquefied natural gas (LNG) as an alternative fuel. LNG is particularly prevalent in dual-fuel propulsion ships, with the IMO Type C tank being the most commonly used storage facility. The structure of the IMO Type C tank comprises a pressure vessel and supporting saddles, which can be integrated or separate systems. Despite being manufactured within specified tolerances, welding-induced deformation of the tank and saddle is inevitable since the saddle is welded directly onto the hull. In integrated tank–saddle systems, this deformation can lead to cracks in the epoxy resin, which has lower strength and stiffness, as well as burn damage to the resin and wooden blocks from welding heat. In separate tank–saddle systems, installation difficulties can arise due to interference between the fuel tank system and adjacent structures, such as insulation or the fuel preparation room (FPR), resulting from saddle deformation caused by welding. This study analyzes the sensitivity of all weld lines involved in saddle installation using the direct inherent strain (DIS) method. Based on this analysis, the initial welding deformations are evaluated in relation to the welding direction and sequence. Finally, an optimized method for saddle installation is proposed to minimize deformation.

In Southeast Asian cities, it is common for logistic companies to operate a heterogeneous fleet of delivery vehicles with motorcycles being the preferred vehicle to handle the final phase of delivery. In such scenarios, heterogeneous fleet vehicle routing problem (HFVRP) is generally applied to plan an optimal delivery. However, in many downtown cores of large and rapidly developing Southeast Asian cities, HFVRP is neither viable nor reliable because of road usage restrictions. The purpose of this article is to develop and test a different approach that accurately takes these restrictions into account and provides viable and more sustainable results. Restrictions in this paper refer to situations of urban areas in Vietnam where (i) certain vehicle types are prohibited in specified areas or where narrow alleyways limit the utilization of vehicles that exceed the road capacity and (ii) certain roads are exclusive to certain vehicle types. In networks, limited access and exclusive lanes are represented as links, or arcs, exclusive to one or another. Taking these limitations into consideration, we have developed a unique model, which we have termed Vehicle Routing Problem with Exclusive Links (VRP-EL). The model was validated and tested for its performance on scenarios with varying ratios of exclusive links. Scenarios up to 500 customers were tested on a meta-heuristic algorithm, simulated annealing. VRP-EL produces realistic outcomes. Limiting certain links to be selected according to vehicle types increases overall travel distance. However, this increase outweighs the cost of re-planning and rerouting had they not been constrained initially. The reduction in traveling distance leads to fossil fuel reduction for the overall system. The estimation of reduced carbon emissions through applying the proposed model is presented. Considering the severe traffic congestion and carbon emissions caused by motorcycles in Vietnam, the proposed model leads to a sustainable road environment.