The subway station passenger flow prediction model can forecast passenger volume in the future. This model helps to carry out safety warnings and evacuation of passenger flow in advance. Based on the data of the Shanghai traffic card, the passenger volume in all the time intervals is clustered into three different models for prediction. Taking the Nanjing East Road Station in Shanghai as an example, the time series of passenger volumes was combined with weather data to create several supervised sequences and was converted to supervised sequences according to different values of timestep. To accelerate convergence, two artificial features were added as input. The gated recurrent unit (GRU) network model achieves accurate rolling prediction from 15 minutes to 6 hours. Finally, comparing it with the long short-term memory (LSTM) network and the back-forward propagation network (BPN), it was confirmed that the GRU network with a timestep of 1.5 hours is the best model for the long-term (more than 3 hours) traffic flow rolling prediction, while GRU with a timestep of 45 minutes has the best result for short-term rolling prediction.

As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment.

Lithium iron phosphate batteries with plateau in the open circuit voltage, hysteresis, and path dependence dynamics due to phase transition during intercalation/de-intercalation are challenging to model and even more challenging to control. A core-shell electrochemical modeling approach is able to capture the phase transition behavior at the cost of using a fine-grained spatial grid to transform the governing Partial Differential Algebraic Equations into Ordinary Differential Algebraic Equations, resulting in a computationally expensive system intractable for the design of real-time battery management system algorithms. This paper presents a reduced-order modeling paradigm to transform the high-dimensional model into a low-dimensional yet accurate control oriented electrochemical model. The Proper Orthogonal Decomposition-Galerkin method is used to reduce the state variable vector from 169 to a meager 9 with negligible loss in fidelity. The reduced-order model is validated against both experimental data and the high-dimensional model for discharging-charging load profiles of different C-rates and real driving cycles. Promising results with one-third the computational burden and a voltage RMS error of less than 0.6% are achieved.

Beginning with an introduction of the concept, situation, and application of big data, the authors discuss current challenges and propose a data-objective spectrum-based technology. They also propose the idea of a model group along two dimensions: the transition from a qualitative concept and unstructured data to a quantitative value in one dimension, and the Pareto optimal frontier of generalization ability in another. In the end, the feasibility of the framework proposed is illustrated based on the example of peoples behavior pattern acquisition.