Link scheduling is important for reliable data communication in wireless sensor networks. Previous works mainly focus on how to find the minimum scheduling length but ignore the impact of energy consumption. In this paper, we integrate them together and solve them by multiobjective genetic algorithms. As a contribution, by jointly modeling the route selection and interference-free link scheduling problem, we give a systematical analysis on the relationship between link scheduling and energy consumption. Considering the specific many-to-one communication nature of WSNs, we propose a novel link scheduling scheme based on NSGA-II (Non-dominated Sorting Genetic Algorithm II). Our approach aims to search the optimal routing tree which satisfies the minimum scheduling length and energy consumption for wireless sensor networks. To achieve this goal, the solution representation based on the routing tree, the genetic operations including tree based recombination and mutation, and the fitness evaluation based on heuristic link scheduling algorithm are well designed. Extensive simulations demonstrate that our algorithm can quickly converge to the Pareto optimal solution between the two performance metrics.
Human resources are the core resources of an enterprise, and the demand forecasting plays a vital role in the allocation and optimization of human resources. Starting from the basic concepts of human resource forecasting, this paper employs the backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) to analyze human resource needs and determine the key elements of the company’s human resource allocation through predictive models. With historical data as reference, the forecast value of current human resource demand is obtained through the two types of neural networks. Based on the prediction results, the company managers can carry out targeted human resource planning and allocation to improve the efficiency of enterprise operations. In the experiment, the actual human resource data of a certain company are used as the experimental basic samples to train and test the two types of machine learning tools. The experimental results show that the method proposed in this paper can effectively predict the number of personnel required and can support the planning and allocation of human resources.
Abstract-The screw con veyor with no overlap between two screw blades and the screw conveyor with large overlap between two screw blades were simulated by discrete element method and the distribution of particle axial velocity, angular velocity, forces around the shell and forces around screw blades was carried out. The results show that: the screw conveyor with no overlap between two screw blades has better performance, because its average axial velocity of particles and conveying capacity are larger, its mean angular velocity of particles and forces around screw blades and the shell are smaller and its power consumption is also less; Particles around the bottom of the shell and the edge of screw blades are more prone to be worn-out and break up; Forces around the bottom of the shell and the edge of screw blades are larger, so double screw conveyors will most probabl e be destroyed in these two positions. The above results can provide references for structures selection and optimal design of double screw conveyors.
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