Automated Tool for Diagnosis of Sinus Analysis CT Scans
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A robot needs to localize an unknown object before grasping it. When the robot only has a monocular sensor, how can it get the object pose? In this work, we present a method of localizing the 6-DOF pose of a target object using a robotic arm and a hand-mounted monocular camera. The method includes an object recognition and a localization process. The recognition process uses point features on a surface of the target as a model of the object. The object localization process combines the robotic motion data and image data to calculate the 6-DOF pose of the object. This method can process objects containing textured planes. We verify the method in real tests.
Monocular vision
Monocular
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This paper made a detailed analysis of the objective factors that influencing our country's steel prices,and built a prediction model of steel prices based on BP neural network.After that,the weights of BP neural network were optimized by Levenberg-Marquardt algorithm.Then programmed in MATLAB language and obtained a result of the prediction by using data from 1990-2008.The results show that the predicted values are good agreement with the true values,and the established neural network model has a accurate precision in prediction.
Levenberg–Marquardt algorithm
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The paper describes the basic knowledge of artificial neural network and BP neural network,establishes the mathematical model of slope stability based on BP neural network,and slope stability can be forecasted by the neural network toolbox,and the slope cases of collection are trained.The results show that the forecasting result conforms to the realistic result using BP neural network and the requirements of engineering project can be satisfied,which indicates that the utilization of BP neural network in the slope stability prediction is feasible..
Toolbox
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Artificial neural networks have attracted considerable attention and have shown promise for modeling complex nonlinear relationships. This paper explores the use of artificial neural networks in predicting the confinement efficiency of concentrically loaded reinforced concrete (RC) columns with rectilinear transverse steel. Fifty-five experimental test results were collected from the literature of square columns tested under concentric loading. A multilayer-functional-link neural network was used for training and testing the experimental data. A comparison study between the neural network model and four parametric models is also carried out. It was found that the neural network model could reasonably capture the underlying behavior of confined RC columns. Moreover, compared with parametric models, the neural network approach provides better results. The close correlation between experimental and calculated values shows that neural network-based modeling is a practical method for predicting the confinement efficiency of RC columns with transverse steel because it provided instantaneous result once it is properly trained and tested.
Experimental data
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The prediction of hydrochemical types of salt lakes by probability artificial neural network model, which is one of the typical Radial basis function networks was studied. The good classing and predicting results were obtained. The average accuracy for predicting of hydrochemical types of salt lakes was 91.0%. Both the experimental results and structure analysis of neural networks indicated that the probability artificial neural network method is much better than the back-propagation (BP) neural network method . In fact, this study provides a new tool for chemical pattern recognition.
Salt lake
Backpropagation
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The four key factors include gravity, roughness, bolt pressure and contact area as the input parameters of neural network, the GA-BP neural network was established for predicting the assembly deformation. And the experimental data is trained for this GA-BP neural network. Finally, the test data shows that BP neural network optimized by GA algorithm can achieve higher accuracy than BP neural network.
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The artificial neural network has been widely used in various field of science and engineering. The artificial neural network has marvelous ability to gain knowledge. In this paper, according to principle of artificial neural network , Model of artificial neural network of rock bolt support of roadway of coal mine has been constructed,Learning system of BP artificial neural network has been trained,it is shown by engineering application that artificial neural network can handle imperfect or incomplete data and it can capture nonlinear and complex relationships among variables of a system. the artificial neural network is emerging as a powerful tool for modeling with the complex system. Method and parameters of rock bolt support of roadway of coal mine can be predicated accurately using artificial neural network, that is of significance and valuable to those subjects of investigation and design of mining engineering
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The multiple variable synthetical predication problems with error back propagation training artificial neural network(BP neural network) is researched.To study the farmer income of Shanghuang experimental area is the background,BP's models of multiple variable synthetical predication were established.The results of experimental prediction show that the accuracy of predication by the neural network models is very high. They open up a new way to predict farmer income.
Backpropagation
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Liptinite
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The impact of artificial neural network model output precision technology widespread attention. Quality sample study of neural network output accuracy is not much affected, most of the research is the structure (number of layers and the number of nodes), the impact of this paper to analyze samples of artificial neural network output for the neural network, to improve the output of neural network accuracy is important.
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