With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson’s correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R²) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal.

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods.

Water is a critical resource globally, covering approximately 71% of the Earth’s surface. Employing analytical models to forecast water quality parameters based on historical data is a key strategy in the field of water quality monitoring and treatment. By using a forecasting model, potential changes in water quality can be understood over time. In this study, the gated recurrent unit (GRU) neural network was utilized to forecast dissolved oxygen levels following variational mode decomposition (VMD). The GRU neural network’s parameters were optimized using the grey wolf optimizer (GWO), leading to the development of a VMD–GWO–GRU model for forecasting water quality parameters. The results indicate that this model outperforms both the standalone GRU model and the GWO–GRU model in capturing key information related to water quality parameters. Additionally, it shows improved accuracy in forecasting medium to long-term water quality changes, resulting in reduced root mean square error (RMSE) and mean absolute percentage error (MAPE). The model demonstrates a significant improvement in the lag of forecasting water quality parameters, ultimately boosting forecasting accuracy. This approach can be applied effectively in both monitoring and forecasting water quality parameters, serving as a solid foundation for future water quality treatment strategies.

Prediction of water quality parameters is a significant aspect of contemporary green development and ecological restoration. However, the conventional water quality prediction models have limited accuracy and poor generalization capability. This study aims to develop a dependable prediction model for ammonia nitrogen concentration in water quality parameters. Based on the characteristics of the long-term dependence of water quality parameters, the unique memory ability of the Long Short-Term Memory (LSTM) neural network was utilized to predict water quality parameters. To improve the accuracy of the LSTM prediction model, the ammonia nitrogen data were decomposed using Empirical Modal Decomposition (EMD), and then the parameters of the LSTM model were optimized using the Improved Whale Optimization Algorithm (IWOA), and a combined prediction model based on EMD-IWOA-LSTM was proposed. The study outcomes demonstrate that EMD-IWOA-LSTM displays improved prediction accuracy with reduced RootMean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) in comparison to the LSTM and IWOA-LSTM approaches. These research findings better enable the monitoring and prediction of water quality parameters, offering a novel approach to preventing water pollution rather than merely treating it afterwards.

Driving intention, which can assist drivers to avoid dangerous emergence for the advanced driver assistant systems (ADAS), can be hardly described accurately for complex traffic environments. At present, driving intention can be mainly obtained by deep neural networks with neuromuscular dynamics and electromyography (EMG) signals of drivers. This method needs numerous drivers’ signals and neural networks with a complex structure. This paper proposes a driving intention direct inference method, namely direct inference from the road surface condition. A driving intention safety distance model based on a deep neural network with dropout regularization was built in an active collision avoidance control system of electric vehicles. Driving intention can be inferred by a deep neural network with dropout regularization from adhesion coefficients between the tire and road. Simulations using rapid control prototyping (RCP) and a hardware-in-the-loop (HIL) simulator were performed to demonstrate the effectiveness of the proposed driving intention safety distance model based on a deep neural network with dropout regularization. The proposed driving intention safety distance model can guarantee the safe driving of electric vehicles.

Purpose: The purpose of this paper is to propose a fractional order optimization method based on perturbation bound and gamma function of a DGM(r,1).Design/methodology/approach: By analyzing and minimizing perturbation bound, the sub-optimal solution on fractional order interval is obtained through offline solving without iterative calculation. By this method, an optimized fractional order non-equidistant ROGM (OFONEROGM) is applied in fitting and prediction water quality parameters for a surface water pollution monitoring system.Findings: This method can narrow fractional order interval in this work. In a surface water pollution monitoring system, the fitting and prediction performances of OFONEROGM are demonstrated comparing with integer order non-equidistant ROGM (IONEROGM).Originality/value: A method of offline solving the sub-optimal solution on fractional order interval is proposed. It can narrow the optimized fractional order range of NEROGM without iterative calculation. A large number of calculations are eliminated. Besides that, optimized fractional order interval is only related to the number of original data, and convenient for practical application. In this work, an OFONEROGM is modeled for predicting water quality trend for preventing water pollution or stealing sewage discharge. It will provide guiding significance in water quality parameter fitting and predicting for water environment management.