Applied Mechanics and Materials Vols. 644-650

Abstract: Based on the static mathematical model of Preisach hysteresis, this paper presents a modified method, combining Genetic Algorithm and Neural Network Method to get three conventional parameters of the Preisach hysteresis model under alternating magnetization. In the proposed method, considering the symmetry of the major hysteresis loop, a complete fitting hysteresis loop is obtained through the neural network training to find the optimization function and through the genetic algorithm to find the function extreme. The numerical fitting results obtained by this method are found to be in good agreement with the measurement data and verify the theory.

Abstract: According to the network public opinion’s characteristics of distributed, massive and heterogeneous, a new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop plateform is studied. The classification ability and execution efficiency of proposed scheme is verified and applied to the network public opinion documents classification test. The results show that the parallel K_NN algorithm can achieve rapid and accurate classification of network public opinion.

Abstract: This paper studies a multiple common due date assignment problem on a single machine. The job-dependent due dates are obtained based on common flow allowance criteria. We assume that the processing time of a job is controllable by the resource amount assigned to it. The objective is to find the optimal multiple common dues, the set of jobs assigned to each due date, the sequence of jobs and resource allocation scheme to minimize a total cost based on earliness and tardiness of jobs, the common dues and resource cost. We propose an optimal algorithm to solve the problem.

Abstract: This paper studies a multiple common due-window assignment problem on a single machine. The job-dependent due-windows are obtained based on common flow allowance criteria. We assume that the processing time of a job is controllable by the resource amount assigned to it. The objective is to find the optimal size and location of the multiple common due-windows, the set of jobs assigned to each due window, the sequence of jobs and resource allocation scheme to minimize a cost function based on the window size and location, earliness and tardiness of jobs and resource cost. We propose an optimal algorithm to solve the problem.

Abstract: This paper considers uniform parallel-machine scheduling with linear deterioration and rejection. The processing time of a job is a linear increasing function of its starting time and jobs can be rejected by paying penalties. The objective is to find a schedule which minimizes the time by which all jobs are delivered. We propose a fully polynomial-time approximation scheme to solve this problem.

Abstract: This paper studies a single machine scheduling with job rejection and multiple common due dates assignment. A job is either rejected, in which a rejection penalty has to be paid, or accepted and processed on the machine. There is a cost if the job is completed prior or after the due date. The objective is to determine the optimal due dates, the set of jobs assigned to each due date and the optimal sequence of jobs to minimize a total costs based on earliness, tardiness, multiple common due dates and rejection cost. We provide dynamic programming algorithms and show that the problem is solvable in polynomial time.

Abstract: In this paper, we investigate the boundary value problem of the nonlinear fractional differential equationwhere is a real number, is the Riemann-Liouville's fractional derivative, and is continuous. we obtain that the unique positive solution can be uniformly approximated by any iterative sequence, initiated by an arbitrary function that is nonnegative and continuous, and does not identically vanish on [0,1].

Abstract: The nodes in cloud environment are allowed to connect with each other through internet. Some malicious nodes in cloud will bring unsafe issues. The work used a trusted method to select a trusted node to connect. AHP method is used to compute the weight for each factor of the node, and constructed a model to evaluate the node’s trust value in a comprehensive way. The example shows that the method is a simple, but an effective way, which can be used in a cloud environment.

Abstract: precision agriculture, soil fertility evaluation is the foundation of variable rate fertilization, the initial clustering centers of K means algorithm soil fertility levels in the traditional evaluation methods generated randomly from the data set, the clustering result is not stable. This paper proposes an improved K-means algorithm density algorithm to optimize the initial clustering center selection algorithm based on K, the most far away to each other in high density region point as the initial cluster center. Experiments show that, the improved K-means algorithm can eliminate the dependence on the initial cluster center; the clustering result has been greatly improved.

Abstract: It is an inevitable result of the education informationization for middle school students to carry out network learning. In order to obtain good network study effect, media preference study of middle school students is very necessary. Media preference refers to students’ fond degree of media in learning network resources. From media preference, students’ personality characteristics of media can be better understood to help network system choose more suitable teaching method for high school students. This paper, by using Fuzzy C Means clustering algorithm, studied media preferences, and got reasonable results, thus provides personalized learning basis for network resources.With the development of information technology, network distance education is developing rapidly as a new means of education and way of education. However, most of the network resources learning rely mainly on students' autonomous learning, and did not provide good personalized learning style and environment. Media preference degree is an important basis of personalized teaching, so how to carry on reasonable analysis on media preference is very important. Students’ media preference behaviors are stored in network background. By using FCM (Fuzzy C Means clustering algorithm), reasonable analysis for media preference can be done. The analysis of the results can be used as the basis of personalized learning to better promote personalized online learning.