Fengying Ma

To effectively deal with the problem of high noise and low signal-to-noise ratio in the ultrasonic echo signal caused by mud pairs when the ultrasonic well logging instrument operates in the underground complex environment, this paper proposes a wavelet threshold denoising method based on NRBO-ICEEMDAN. This method employs the Newton-Raphson based optimizer (NRBO) algorithm to optimize the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), targeting the optimal combination of white noise amplitude weights (Nstd) and the number of noise additions (NE).Then with the help of correlation coefficient method to filter out the effective components from the intrinsic mode functions (IMFs) obtained from the decomposition, the improved wavelet threshold function is used to suppress the noise of the signal components, and finally the denoised components are reconstructed to constitute the denoised signal. The results indicate that the proposed method demonstrates superior performance over conventional signal denoising techniques. Compared with the ICEEMDAN algorithm, it achieves a 23.45% improvement in signal-to-noise ratio (SNR) and a 38.27% reduction in root mean square error (RMSE). This approach effectively enhances signal clarity, thereby substantially improving the reliability and measurement accuracy in ultrasonic logging applications.

Microbial fuel cells (MFCs) play a vital role in water quality monitoring, where stable power generation is essential for ensuring the accuracy of water-quality detection. However, the complex reactions occurring in MFCs make it challenging to maintain a stable output voltage under uncontrolled conditions. Thus, a fractional-order PID (FOPID) controller is proposed. The parameters of this controller are typically determined using the particle swarm optimization (PSO) algorithm. To address the limitations of traditional PSO algorithms, such as low precision and slow convergence, an improved PSO algorithm integrating chaotic mechanisms, reverse learning, golden sine algorithm, and elite Gaussian mutation is proposed. Simulation results demonstrate that faster and more accurate convergence of the improved PSO. The proposed FOPID controller achieves a setting time of 8.2965 s, outperforming others with times of 88.8889 s, 39.0680 s, and so on. The FOPID controller offers advanced technical support for the application of MFCs in water-quality monitoring.

The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable to wastewater treatment but can also be used for resource recovery from various organic wastes. The MFC usually requires an external controller that allows it to operate under controlled conditions to obtain a stable output voltage. Therefore, the application of a PID controller to the MFC is proposed in this paper. The design phase for this controller involves the identification of three parameters. Although the particle swarm optimization (PSO) algorithm is an advanced optimization algorithm based on swarm intelligence, it suffers from issues such as unreasonable population initialization and slow convergence speed. Therefore, this paper proposes an improved particle swarm algorithm based on the Golden Sine Strategy (GSCPSO). Using Circle chaotic mapping to make the distribution of the initial population more uniform, and then using the Golden Sine Strategy to improve the position update formula, not only improves the convergence speed of the population but also enhances convergence precision. The GSCPSO algorithm is applied to execute the described design process. The results of the simulation show that the designed control method exhibits smaller steady-state error, overshoot, and chattering compared with sliding-mode control (SMC), backstepping control, fuzzy SMC (FSMC), PSO-PID, and CPSO-PID.

The target following problem of mobile robot could be split into two dimensions: direction control and distance control. Therefore, this paper analyzes two aspects separately. The steering and forward and backward movement of the robot are controlled by dual fuzzy controller. First of all, this topic designs and realizes the steering function of mobile robot when following. Based on the existing autonomous robot, the Angle is changed by using the image information of the target, and the corresponding fuzzy rules are designed to realize the direction control to follow the target. Secondly, it also realizes the function of the robot to follow the objective forward and backward, and obtains the target distance through the sensor of the robot, and keeps the two at a certain distance through fuzzy control, so as to realize the range following of the robot. The designed controller is implemented on the Webots simulation platform, and contrasted with a PID regulator, the results are analyzed, and the effectiveness of the algorithm is proved.

When ground reconnaissance mobile robots perform tasks alone, they can only obtain ground view perception, which leads to the inability to globally optimize, resulting in local rather than global optimal solutions. Inspired by this, a servo control system for mobile robots based on UAV global vision is designed in this paper. Firstly, acquire global visual images and process them, selecting target points via mouse under global vision. Secondly, plan an obstacle avoidance path to reach the target point using artificial potential field method, and smooth the path using B-spline curves. Then, establish the kinematic model of the mobile robot and use the LQR algorithm to enable the robot to track the path. Finally, experimental tests were conducted via the Webots simulation platform to verify the feasibility of the system. Experimental results show that the system can ensure high accuracy and smooth path tracking, and has strong cooperative control, effectively addressing the single visual problem of ground mobile robots.

The collected data of a pumping unit contain environmental noise, which significantly reduces the precision of fault diagnosis. The previous fault detection approach depends on manual feature extraction, which is time-consuming and laborious, and it cannot cope with high-noise conditions. Therefore, we propose a dual-branch time–frequency fusion deep learning model for fault diagnosis of the pumping unit. One branch extracts time-domain information, while the other branch extracts frequency-domain information by employing the fast Fourier transform. The branch information of these two branches is concatenated, and the gate-controlled channel transfer unit module automatically learns the competitive and cooperative relationships between each branch, making the key features more prominent in information fusion. Consequently, an accurate fault diagnosis of the pumping unit can be achieved under high-noise conditions. The results demonstrate that the proposed model outperforms the traditional schemes in terms of noise, with different signal-to-noise ratios.