Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks

Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.

Modified Entropy Estimator Using a Haar Matrix to Fit the Weibull Growth Model

It is well known that the Generalized Maximum Entropy method can be used to fit linear regression models, especially as they are not restricted by the conditions to be verified as are other classical methods. Therefore, in this paper, a new method for estimating the parameters of the four-parameter Weibull growth model was proposed using the Generalized Maximum Entropy function by fitting data based on the Haar matrix which was used in the wavelet method. The suggested and classical entropy estimators for Weibull growth model parameters were compared using simulation and the preference for the suggested method estimator was shown. The Modified Generalized Maximum Entropy estimator was applied to the real data representing annual Iraqi oil production for the period 2010–2017. Iraqi crude oil production for the year 2022 was predicted and appeared as 4.4 million bb/day.

A Novel Method of Smooth Fitting for a Class of the Spatial Convex Cavities in the Multiply Connected Domain

According to a class of closed surfaces fitting problem which cant be solved by using maximum entropy function under the rectangular coordinate system, a new method of smooth fitting for a class of the spatial convex cavities in the multiply connected domain by some planes: the envelope algorithm of minimum entropy function is promoted to the spherical coordinates system, for every closed areas by which the border of spatial convex cavities construct, separately the suitable control parameter is chosen, the minimum entropy function is used to smooth the spatial convex cavities in the multiply connected domain. The smooth fitting graph can be drawn based on the function. This method can be used in soma fields such as closed surface modeling, mold designing, mold manufacturing and reverse engineering.

A Bimaximum Entropy Method for Solving Nonlinear Constrained Continuous Minimax Problem

A numerical method is proposed for solving a sort of constrained continuous minimax problem, in which both the objective function and the constraint functions are continuously differentiable about superior decision variables and are continuous about lower decision variables .Besides,the constraint functions include only superior or lower decision variables．The problem is transformed into unconstrained differentiable problem with the idea of the discrete maximum entropy function and the continuous maximum entropy function and the penalty function method．The basic algorithm is established．The convergence is proofed．Numerical examples are given and show the efficiency and the reliability of the algorithm.