processes | free full-text | cfd-based structural optimization of rotor cage for high-efficiency rotor classifier
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Mou, Xinliang, Fangchao Jia, Ying Fang, and Chuanwen Chen. 2021. "CFD-Based Structural Optimization of Rotor Cage for High-Efficiency Rotor Classifier" Processes 9, no. 7: 1148.
logistics automation control based on machine learning algorithm | springerlink
In order to improve the logistics problem, taking automated logistics system as research platform, a new optimization algorithm is proposed for the route planning of multi-goods picking operation of stacker in stereoscopic warehouse. First, the hardware composition of the automated logistics system is introduced, and then the characteristics of the picking operation of the stacker are deeply analyzed. According to these characteristics, a mathematical model for the time cost of the sorting operation is set up. Various algorithms for solving the problem are analyzed and compared. Aiming at the advantages and disadvantages of ant colony system and parthenogenetic algorithm, the two algorithms are properly improved and fused, and a new improved algorithmparthenogenetic ant colony algorithm is proposed. The validity is verified by the simulation experiment. The simulation is carried out in the Matlab environment, and the satisfactory optimization results are obtained. The simulation result shows that the algorithm is used to optimize the picking path of the stacker. Therefore, it is concluded that the parthenogenetic algorithm greatly reduces the time of the picking operation, and greatly improves the efficiency.
Fereidunian, A., Hosseini, M.M., Talabari, M.A.: Toward self-financed distribution automation development: time allocation of automatic switches installation in electricity distribution systems. IET Gener. Transm. Distrib. 11(13), 33503358 (2017)
Kisseleff, S., Akyildiz, I.F., Gerstacker, W.H.: magnetic induction-based simultaneous wireless information and power transfer for single information and multiple power receivers. IEEE Transact. Commun. 65(3), 13961410 (2017)
Zheng, F., Zecchin, A.C., Newman, J.P., et al.: An adaptive convergence-trajectory controlled ant colony optimization algorithm with application to water distribution system design problems. IEEE Transact. Evol. Comput. 21(5), 773791 (2017)
1. This study was supported by the National Natural Science Foundation of China (Nos. 61741203, 61163012 and 71462005); 2. Guangxi Natural Science Foundation (No. 2016GXNSFAA380243); 3. Guangxi innovation-driven development of special funds project (No. Gui Ke AA17204011); 4. Research Foundation of Guangxi Teachers Education University in 2017.
github - kk289/ml-logistic_regression-matlab: machine learning: logistics regression using matlab
Suppose that you are the administrator of a university department and you want to determine each applicants chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicants scores on two exams and the admissions decision.
We can use the model to predict whether a particular student will be admitted. For a student with an Exam 1 score of 45 and an Exam 2 score of 85, we should expect to see an admission probability of 0.776.
In this part, we will implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly.
Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. From these two tests, you would like to determine whether the microchips should be accepted or rejected. To help make the decision, we have a dataset of test results on past microchips, from which we can build a logistic regression model.
Figure shows that our dataset cannot be separated into positive and negative examples by a straight-line through the plot. Therefore, a straight-forward application of logistic regression will not perform well on this dataset since logistic regression will only be able to find a linear decision boundary.
One way to fit the data better is to create more features from each data point.
In the provided function mapFeature.m, we will map the features into all polynomial terms of x1 and x2 up to the sixth power.
As a result of this mapping, our vector of two features (the scores on two QA tests) has been transformed into a 28-dimensional vector. A logistic regression classifier trained on this higher-dimension feature vector will have a more complex decision boundary and will appear nonlinear when drawn in our 2-dimensional plot.
While the feature mapping allows us to build a more expressive classifier, it also more susceptible to overfitting.
In Octave/MALLAB, recall that indexing starts from 1, hence, we should not be regularizing the theta(1) parameter (which corresponds to 0_0) in the code. The gradient of the cost function is a vector where the jth element is defined as follows:
In plotDecisionBoundary.m, we plot the non-linear decision boundary by computing the classifiers predictions on an evenly spaced grid and then and drew a contour plot of where the predictions change from y = 0 to y = 1.
Notice the changes in the decision boundary as you vary . With a small , we should find that the classifier gets almost every training example correct, but draws a very complicated boundary, thus overfitting the data.
With a larger , we should see a plot that shows an simpler decision boundary which still separates the positives and negatives fairly well. However, if is set to too high a value, we will not get a good fit and the decision boundary will not follow the data so well, thus underfitting the data.