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The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

This study aims to investigate the fatigue crack propagation behavior of SiC particle-reinforced 2124 Al alloy composites under constant amplitude axial loading at a stress ratio of R = 0.1. For this purpose, it is performed experiments and comparatively analyze the results by producing 5, 10, 15 Vol.% SiCp-reinforced composites and unreinforced 2124 Al alloy billets with powder metallurgy (PM) production technique.

With the PM production technique, SiCp-reinforced composite and unreinforced 2124 Al alloy billets were produced at 5%, 10%, 15% volume ratios. After the produced billets were extruded and 5 mm thick plates were formed, tensile and fatigue crack propagation compact tensile (CT) samples were prepared. Optical microscope examinations were carried out to determine the microstructural properties of billet and samples. To determine the SiC particle–matrix interactions due to the composite microstructure, unlike the Al alloy, which affects the crack initiation life and crack propagation rate, detailed scanning electron microscopy (SEM) studies have been carried out.

Optical microscope examinations for the determination of the microstructural properties of billet and samples showed that although SiC particles were rarely clustered in the Al alloy matrix, they were generally homogeneously dispersed. Fatigue crack propagation rates were determined experimentally. While the highest crack initiation resistance was achieved at 5% SiC volume ratio, the slowest crack propagation rate in the stable crack propagation region was found in the unreinforced 2124 Al alloy. At volume ratios greater than 5%, the number of crack initiation cycles decreases and the propagation rate increases.

As a requirement of damage tolerance design, the fatigue crack propagation rate and fatigue behavior of materials to be used in high-tech vehicles such as aircraft structural parts should be well characterized. Therefore, safer use of these materials in critical structural parts becomes widespread. In this study, besides measuring fatigue crack propagation rates, the mechanisms causing crack acceleration or deceleration were determined by applying detailed SEM examinations.

The purpose of this paper is to solve the problem that mobile robots are still based on reactive collision avoidance in unknown dynamic environments leading to a lack of interaction with obstacles and limiting the comprehensive performance of mobile robots. A dynamic window approach with multiple interaction strategies (DWA-MIS) is proposed to solve this problem.

The algorithm firstly classifies the moving obstacle movement intention, based on which a rule function is designed to incorporate positive incentives to motivate the robot to make correct avoidance actions. Then, the evaluation mechanism is improved by considering the time cost and future information of the environment to increase the motion states. Finally, the optimal objective function is designed based on genetic algorithm to adapt to different environments with time-varying multiparameter optimization.

Faced with obstacles in different states, the mobile robot can choose a suitable interaction strategy, which solves the limitations of the original DWA evaluation function and avoids the defects of reactive collision avoidance. Simulation results show that the algorithm can efficiently adapt to unknown dynamic environments, has less path length and iterations and has a high comprehensive performance.

A DWA-MIS is proposed, which increases the interaction capability between mobile robots and obstacles by improving the evaluation function mechanism and broadens the navigation strategy of DWA at a lower computational cost. After real machine verification, the algorithm has a high comprehensive performance based on real environment and provides a new idea for local path planning methods.

This study reveals the green building development path and analyzes the optimal government subsidy equilibrium through evolutionary game theory and numerical simulation. This was done to explore the feasible measures and optimal incentives to achieve higher levels of green building in China.

First, the practice of green building in China was analyzed, and the specific influencing factors and incentive measures for green building development were extracted. Second, China-specific evolutionary game models were constructed between developers and homebuyers under the market regulation and government incentive mechanism scenarios, and the evolutionary paths were analyzed. Finally, real-case numerical simulations were conducted, subsidy impacts were mainly analyzed and optimal subsidy equilibriums were solved.

(1) Simultaneously subsidizing developers and homebuyers proved to be the most effective measure to promote the sustainability of green buildings. (2) The sensitivity of developers and homebuyers to subsidies varied across scenarios, and the optimal subsidy level diminished marginally as building greenness and public awareness increased. (3) The optimal subsidy level for developers was intricately tied to the building greenness benchmark. A higher benchmark intensified the developer’s responsiveness to losses, at which point increasing subsidies were justified. Conversely, a reduction in subsidy might have been appropriate when the benchmark was set at a lower level.

The expeditious advancement of green buildings holds paramount importance for the high-quality development of the construction industry. Nevertheless, the pace of green building expansion in China has experienced a recent deceleration. Drawing insights from the practices of green building in China, the exploration of viable strategies and the determination of optimal government subsidies stand as imperative initiatives. These endeavors aim to propel the acceleration of green building proliferation and materialize high-quality development at the earliest juncture possible.

The model is grounded in China’s green building practices, which makes the conclusions drawn more specific. Furthermore, research results provide practical references for governments to formulate green building incentive policies.

Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

This paper focuses on the application of a robotic technique for modeling a three-wheeled mobile robot (WMR), considering it as a multibody polyarticulated system. Then the dynamic behavior of the developed model is verified using a physical model obtained by Simscape Multibody.

Firstly, a geometric model is developed using the modified Denavit–Hartenberg method. Then the dynamic model is derived using the algorithm of Newton–Euler. The developed model is performed for a three-wheeled differentially driven robot, which incorporates the slippage of wheels by including the Kiencke tire model to take into account the interaction of wheels with the ground. For the physical model, the mobile robot is designed using Solidworks. Then it is exported to Matlab using Simscape Multibody. The control of the WMR for both models is realized using Matlab/Simulink and aims to ensure efficient tracking of the desired trajectory.

Simulation results show a good similarity between the two models and verify both longitudinal and lateral behaviors of the WMR. This demonstrates the effectiveness of the developed model using the robotic approach and proves that it is sufficiently precise for the design of control schemes.

The motivation to adopt this robotic approach compared to conventional methods is the fact that it makes it possible to obtain models with a reduced number of operations. Furthermore, it allows the facility of implementation by numerical or symbolical programming. This work serves as a reference link for extending this methodology to other types of mobile robots.

This paper aims to propose a simulation model integrated with an empirical regression analysis to provide a new mathematical formulation for automated storage and retrieval system (AS/RS) travel time estimation under class-based storage and different input/output (I/O) point vertical levels.

A simulation approach is adopted to compute the travel time under different warehouse scenarios. Simulation runs with several I/O point levels and multiple shape factor values.

The proposed model is extremely precise for both single command (SC) and dual command (DC) cycles and very well fitted for a reliable computation of travel times.

The proposed mathematical formulation for estimating the AS/RS travel time advances widely applied methodologies existing in literature. As well as, it provides a practical implication by supporting faster and more accurate travel time computations for both SC and DC cycles. However, the regression analysis is conducted based on simulated data and can be refined by numerical values coming from real warehouses.

This work provides a new simulation model and a refined mathematical equation to estimate AS/RS travel time.

The design goal for the tracking interval of high-speed railway trains in China is 3 min, but it is difficult to achieve, and it is widely believed that it is mainly limited by the tracking interval of train arrivals. If the train arrival tracking interval can be compressed, it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval. The goal of this article is to study how to compress the train arrival tracking interval.

By simulating the process of dense train groups arriving at the station and stopping, the headway between train arrivals at the station was calculated, and the pattern of train arrival headway was obtained, changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.

When the running speed of trains is high, the headway between trains is short, the length of the station approach throat area is considerable and frequent train arrivals at the station, the arrival headway for the first group or several groups of trains will exceed the headway, but the subsequent sets of trains will have a headway equal to the arrival headway. This convergence characteristic is obtained by appropriately increasing the running time.

According to this pattern, there is no need to overly emphasize the impact of train arrival headway on the headway. This plays an important role in compressing train headway and improving high-speed railway capacity.

In response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface, this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.

Based on the PLS-160 wheel-rail adhesion simulation test rig, the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip. Through statistical analysis of multiple sets of experimental data, the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained, and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed. The study analyzes the utilization of traction/braking adhesion, as well as adhesion redundancy, for different medium under small creepage and large slip conditions. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived.

When the third-body medium exists on the rail surface, the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance. Compared with the current adhesion control strategy of small creepage, adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization, thereby ensuring the traction/braking performance and operation safety of the train.

Most existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions, without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train. Therefore, there is a risk of traction overspeeding/braking skidding. This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.