No abstract

ReliefF algorithm was used to analyze the weight of each water quality evaluation factor, and then based on the Relevance Vector Machine (RVM), Particle Swarm Optimization (PSO) was used to optimize the kernel width factor and hyperparameters of RVM to build a water quality evaluation model, and the experimental results of RVM, PSO-RVM, ReliefF-RVM and PSO-ReliefF-RVM were compared. The results show that ReliefF algorithm, combined with threshold value, selects 5 evaluation factors with significant weight from 8 evaluation factors, which reduces the amount of data used in the model, CSI index is used to calculate the separability of each evaluation factor combination. The results show that the overall separability of the combination is best when the evaluation factor with significant weight is reserved. When different water quality evaluation factors were included, the evaluation accuracy of PSO-ReliefF-RVM model reached 95.74%, 14.23% higher than that of RVM model, which verified the effectiveness of PSO algorithm and ReliefF algorithm, and had a higher guiding significance for the study of water quality grade evaluation. It has good practical application value.

Sleep staging has always been a hot topic in the field of sleep medicine, and it is the cornerstone of research on sleep problems. At present, sleep staging heavily relies on manual interpretation, which is a time-consuming and laborious task with subjective interpretation factors. In this paper, we propose an automatic sleep stage classification model based on the Bidirectional Recurrent Neural Network (BiRNN) with data bundling augmentation and label redirection for accurate sleep staging. Through extensive analysis, we discovered that the incorrect classification labels are primarily concentrated in the transition and nonrapid eye movement stage I (N1). Therefore, our model utilizes a sliding window input to enhance data bundling and an attention mechanism to improve feature enhancement after label redirection. This approach focuses on mining latent features during the N1 and transition periods, which can further improve the network model’s classification performance. We evaluated on multiple public datasets and achieved an overall accuracy rate of 87.3%, with the highest accuracy rate reaching 93.5%. Additionally, the network model’s macro F1 score reached 82.5%. Finally, we used the optimal network model to study the impact of different EEG channels on the accuracy of each sleep stage.

In order to solve the problems of slow convergence and chattering in formation control of multiple mobile robots, and to optimize the dynamic characteristics of formation system, a hybrid adaptive controller is proposed by analyzing the characteristics of different sliding mode reaching laws, which can accelerate the convergence speed and effectively suppress the chattering of the system. Firstly, the saturation function is introduced into the double power reaching law to form the saturation double power reaching law; Secondly, the hyperbolic tangent reaching law is used to improve the stability of the system when it approaches the sliding surface; Finally, fuzzy logic rules are used to adjust the coefficients of saturated double-power reaching law to optimize the overall dynamic parameters. Simulation results show that the designed controller can improve the convergence speed of the system, effectively suppress the chattering, and realize the fast and stable following of the follower robot to the pilot robot, which verifies the correctness and availability of the proposed controller.

Due to the influence of the environment on the water quality wireless sensor network, it is difficult to replace the node energy at any time, so it must make the most of the little energy available. This work provides a technique that combines cluster head selection, cluster structure creation, and data transmission into one optimal scheme. Firstly, we optimize the cluster head election probability threshold formula on the basis of LEACH and introduce overlap ratio in the competitive mechanism, to avoid excessive overlap between cluster heads. Subsequently, to alleviate the “hot spot” problem caused by multihop, the competitive mechanism of I-LEACH is optimized, which is based on the nonuniform competitive mechanism in the EEUC algorithm when electing the cluster head. Meanwhile, in the structural planning between cluster heads, the optimal path is searched based on the parallelism of genetic algorithm (variable path coding strategy); the combination of I-LEACH and EEUC-IGA, named Energy Balance Multihop Clustering Routing Protocol (BEBMCR), can avoid the emergence of “hot paths” and reduce running time. The simulation results show that BEBMCR still has a longer stable period and higher energy utilization rate under large-scale networks, and node energy consumption is more balanced.