Pattern recognition techniques for classifying aeroballistic flying vehicle paths

Goncharenko, V. I.; Yury, Mikhaylov; Natalya, Kartushina

doi:10.1007/s00521-021-06662-8

Cited by 40 publications

(7 citation statements)

References 39 publications

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“…In other words, this approach allows the machine to learn which features to use, giving the machine the ability to "learn" without humans caring about how it is extracted Figure 2. Describes the IR process based on multilayer perceptrons and NNs [11,12]. This IR method can not only reduce the difficulty of feature design, but also improve the accuracy of recognition, and because of the data-driven characteristics of deep learning, it can also show high performance in the face of large-scale image data [13,14 ].…”

Section: Figure 1 Workflow Of Traditional Ir Methodsmentioning

confidence: 99%

Fruit and Vegetable Recognition and Nutrition Analysis Based on Multilayer Perceptron and Neural Network

2020

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The current research on automatic recognition of vegetable and fruit images is more focused on a single fruit and vegetable image without background environment, and more features such as texture and color are extracted, and shallow learning technology is used to realize the recognition of vegetable and fruit images. The method cannot meet the classification and identification of various fruits and vegetables. The main purpose of this paper is to carry out research on fruit and vegetable identification and nutritional analysis based on the knowledge of multilayer perceptron and neural network (NN). In view of the above background, this research focuses on the research on IR of fruits and vegetables, using deep learning methods to improve the recognition rate of fruits and vegetables images, and constructing models through CNNs to achieve classification and recognition. Experiments show that the number of network layers has a greater impact on IR, and the deeper the network depth, the higher the IR rate. Considering that the increase in the number of network layers will affect the structural complexity of the network model and increase the amount of mathematical operations, reasonable network layer selection is particularly important in IR.

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Section: Figure 1 Workflow Of Traditional Ir Methodsmentioning

confidence: 99%

Fruit and Vegetable Recognition and Nutrition Analysis Based on Multilayer Perceptron and Neural Network

2020

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“…Samples of rolled steel sheets with a constant thickness of 0.7 mm and, depending on the batch, different lengths and widths without and with coating were considered [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. A EUROPOLVERI epoxy-polyester powder coating (RAL 9010, Italy) with a thickness of approximately 250 μm with a ±30 μm spread was applied electrostatically.…”

Section: Experimental Studies On Coating Samplesmentioning

confidence: 99%

Effect of elevated temperature on critical load in stability tests of the steel plate

Alexandrova

Suvorova

2023

E3S Web of Conf.

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This paper presents the results of a study on the effect of epoxypolyester based powder coatings on the critical strength of plate specimens made of rolled steel sheets. The results show that the model using planar elements gives more accurate results than the model using three-dimensional elements. The best agreement with experiment was obtained with Ansys results using Eigenvalue bucling, less than 1%. The lowest experimental agreement was with the results obtained in Ansys using solid elements, about 70%.

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“…Zhao et al [5] achieve accurate identification of typical target maneuver modes by establishing a corresponding relationship between target maneuver modes and state information. Vladimir et al [6] implement the task of classifying ballistic vehicle paths in the atmospheric phase using pattern recognition technology based on the minimum grade distance standard method and artificial neural networks. Dong et al [7] use wavelet analysis to identify changes in the mechanical energy of spatial targets submerged in observation noise, and quickly determine whether there is an orbital maneuver.…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Recognition Method for Maneuver Modes of Re-entry Gliding Vehicle

He,

Wang,

et al. 2023

J. Phys.: Conf. Ser.

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Aiming at the problem of maneuver mode recognition for re-entry gliding vehicles (RGV), this article proposes a new intelligent method for maneuver mode recognition of re-entry gliding vehicles. Based on the extracted feature parameters that fit the maneuver characteristics of the vehicle trajectory and the constructed RGV maneuver modes trajectory library, an LSTM deep learning neural network was built to train the extracted new feature parameters. Compared with other typical feature parameters in network training, the results show that our proposed feature parameters converge faster and more stably in LSTM maneuver mode recognition network training, and achieve high recognition accuracy.

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Pattern recognition techniques for classifying aeroballistic flying vehicle paths

Cited by 40 publications

References 39 publications

Fruit and Vegetable Recognition and Nutrition Analysis Based on Multilayer Perceptron and Neural Network

Fruit and Vegetable Recognition and Nutrition Analysis Based on Multilayer Perceptron and Neural Network

Effect of elevated temperature on critical load in stability tests of the steel plate

A New Intelligent Recognition Method for Maneuver Modes of Re-entry Gliding Vehicle

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