The emergency evacuation route planning of cruise ships directly affects the safety of all crew members and passengers during emergencies. Research on the planning of emergency evacuation routes for cruise ships is a frontier subject of maritime safety. This study proposes an improved ant colony system (IACS) to solve the evacuation route planning of crowds on cruise ships. The IACS, which is different from common single-path ant colony system (ACS) evacuation algorithms, is used to solve the multipath planning problem of crowd evacuation from cruise ships by considering crowd density and speed in the model. An increasing flow method is introduced into the IACS to improve the efficiency of the proposed algorithm. Numerical experiments show that this method meets the requirements of evacuation analysis guidelines for new and existing passenger ships (MSC.1/Circ.1533)and can effectively and efficiently plan the emergency evacuation path for cruise ship crowd.

The adhesion coefficient of wheel-rail surface cannot be directly measured, so a cascaded sliding-mode observer is proposed to observe adhesion coefficient and its derivative. A kinetic model for running heavy-duty locomotive is also established. The state equation of wheel adhesion control system is derived from the equation of traction motor torque balance, and adhesion coefficient is proposed to be calculated by load torque. Then, the cascaded sliding-mode observer is designed, and its stability is justified by Lyapunov stability. Based on the equivalence control principle for sliding-mode variable structure, an algorithm to estimate adhesion coefficient and its derivative is established. The simulation and experimental results are used to verify the effectiveness of the observer with load variations or wheel-rail status changes.

In order to find the optimal path for emergency evacuation, this paper proposes a dynamic path optimization algorithm based on real-time information to search the optimal path and it takes fire accident as an example to introduce the algorithm principle. Before the accidents, it uses the Dijkstra algorithm to get the prior evacuation network which includes evacuation paths from each node to the exit port. When the accidents occur, the evacuees are unable to pass through the passage where the accident point and the blocking point are located, then the proposed method uses the breadth-first search strategy to solve the path optimization problem based on the prior evacuation network, and it dynamically updates the evacuation path according to the real-time information. Because the prior evacuation network includes global optimal evacuation paths from each node to the exit port, the breadth-first search algorithm only searches local optimal paths to avoid the blockage node or dangerous area. Because the online optimization solves a local pathfinding problem and the entire topology optimization is an offline calculation, the proposed method can find the optimal path in a short time when the accident situation changes. The simulation tests the performances of the proposed algorithm with different situations based on the topology of a building, and the results show that the proposed algorithm is effective to get the optimal path in a short time when it faces changes caused by the factors such as evacuee size, people distribution, blockage location, and accident points.

The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method.

Sea wind speed forecast is important for meteorological navigation system to keep ships in safe areas. The high volatility and uncertainty of wind make it difficult to accurately forecast multistep wind speed. This paper proposes a new decomposition-based model to forecast hourly sea wind speeds. Because mode mixing affects the accuracy of the empirical mode decomposition- (EMD-) based models, this model uses the variational mode decomposition (VMD) to alleviate this problem. To improve the accuracy of predicting subseries with high nonlinearity, this model uses stacked gate recurrent units (GRU) networks. To alleviate the degradation effect of stacked GRU, this model modifies them by adding residual connections to the deep layers. This model decomposes the nonlinear wind speed data into four subseries with different frequencies adaptively. Each stacked GRU predictor has four layers and the residual connections are added to the last two layers. The predictors have 24 inputs and 3 outputs, and the forecast is an ensemble of five predictors’ outputs. The proposed model can predict wind speed in the next 3 hours according to the past 24 hours’ wind speed data. The experiment results on three different sea areas show that the performance of this model surpasses those of a state-of-the-art model, several benchmarks, and decomposition-based models.