Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have proved the high efficiency of LISTA for solving sparse coding problems. However, existing LISTA methods are all serial connection. To address this issue, we propose a novel extragradient based LISTA (ELISTA), which has a residual structure and theoretical guarantees. Moreover, most LISTA methods use the soft thresholding function, which has been found to cause a large estimation bias. Therefore, we propose a thresholding function for ELISTA instead of soft thresholding. From a theoretical perspective, we prove that our method attains linear convergence. Through ablation experiments, the improvements of our method on the network structure and the thresholding function are verified in practice. Extensive empirical results verify the advantages of our method.

Droplet impact on solid surface is a common natural phenomenon and industrial process which is also a complex polyphase conditions coupling process with gas, liquid and solid. And during the droplet’s deformation and movement, it is often affected by the wettability of solid wall. In this paper, a lagrange SPH numerical method, with the attraction of the van der Waals state equation added to simulate the surface tension of the droplet, is applicated to simulate the process of single droplet impact on wettable surface and investigate the influence mechanism of surface wettability to the droplet’s deformation and movement. Several improvements for traditional SPH methd are presented such as, a Lagrange wettable solid wall boundary condition of which solid wall pariticles’ hydrophilia and capillary action are unified supposed to be a adsorption force to the liquid particles of support domains. The force is deemed relevant to fluid pressure, saturation and solid wall’s hydrophilia. Then a stress correction method is proposed for the stress instability caused by the SPH kernel function. The method uses two different shapes of kernel functions to calculate the tensile and compressive stresses, respectively. After that, a SPH model compiled in Fortran language and based on the above two improved methods is established to simulate static and dynamic droplets’ deformation processes on different wettable wall. The following studies were carried out based on the simulation results. Firstly, the effect of stress correction method is investigated by the comparison between simulating results with the stress correction before and after used. Secondly, the validity of the wettable solid wall boundary condition is studied based on the simulation results of static droplet deformation. Finally, the influence of the wall wettability on the droplet movement process after the collision is analyzed. Researches show that, the stress correction method improves the stress instability problem in the traditional SPH method, even in the large deformation movement can still maintain a uniform particle distribution. In addition, the stress correction method can better simulate the surface tension of free surface flow, and can get a more smooth free surface. According to the change characteristics of droplet’s static contact angle, the mentioned wettable solid wall boundary condition can clearly reflect the wall’s wettability. Simulation of doplet impact on wettable wall shows good agreement with the experiment result. During rebound stage, a large enough wettability will induce droplet deforming into liquid column. The wettability have a little influence to droplet’s spreading stage, the wall friction force plays a major role. While during retraction and rebound stages, the influence of wettability is obvious. This study improves the theory of the interaction between free surface fluid and solid wall, and can provide a reference for further study of the effects between free surface fluids and wettable media or discrete particles.

This paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. Then, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. The proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms.