Past research on the process of extinguishing a fire typically used a traditional linear water jet falling point model and the results ignored external factors, such as environmental conditions and the status of the fire engine, even though the water jet falling point location prediction was often associated with these parameters and showed a nonlinear relationship. This paper constructed a BP (Back Propagation) neural network model. The fire gun nozzle characteristics were included as model inputs, and the water discharge point coordinates were the model outputs; thus, the model could precisely predict the water discharge point with small error and high precision to determine an accurate firing position and allow for the timely adjustment of the spray gun. To improve the slow convergence and local optimality problems of the BP neural network (BPNN), this paper further used a genetic algorithm to optimize the BPNN (GA-BPNN). The BPNN can be used to optimize the weights in the network to train them for global optimization. A genetic algorithm was introduced into the neural network approach, and the water jet landing prediction model was further improved. The simulation results showed that the prediction accuracy of the GA-BP model was better than that of the BPNN alone. The established model can accurately predict the location of the water jet, making the prediction results more useful for firefighters.
Information security is the foundation for building trust between the Internet of Things (IoT) and its users. Due to the sharp increase of information quantity and the limitation of hardware resources, it is difficult to maintain the high performance of hardware equipment, while also enhancing information security. To solve the problem of high consumption and low flexibility of multiple cryptographic algorithms hardware implementation, we have designed the Dynamically Reconfigurable Encryption and Decryption System, which is based on Field Programmable Gate Array. Considering the functional requirements, the cryptographic algorithm reconfigurable module files stored in External Memory could be configured dynamically into the assigned on-chip Reconfigurable Partition, supported by Core Controller and the Reconfiguration Control Platform. The experiment results show that, compared with the Static Encryption and Decryption System, our design reduces the logic resources by more than 30% and completes the algorithm swapping at the configuration speed of 15,759.51 Bytes/ms. It indicates that our design could reduce logic resources consumption and improve utilization efficiency and system flexibility.
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