Neural Network Application for Phasechronometric Measurement Information Processing

Boldasov, D. D.; Drozdova, J. V.; Komshin, A. S.; Syritskii, A. B.

doi:10.1007/s11018-021-01843-2

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Based on stock financial indicators and stock changing trends, it studies the quantitative stock selection problem with multiple influencing factors and proposes a stock trend identification algorithm to build a stock selection model. Reference [21] uses a deep neural network to determine one-dimensional fast ion velocity distribution function from ion cyclotron emission data. Reference [22] uses neural networks to process phase-time measurement information.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network

Niu²

2022

Journal of Sensors

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Language is the cornerstone of children’s learning knowledge and exploring the world. Language teaching is an indispensable part of early childhood education, which can help children improve their communication ability and help children communicate with classmates and teachers in school. The teaching form of preschool language education is too single, which does not conform to children’s own learning ability. Therefore, in teaching, teachers should actively optimize the language environment to make children willing to express themselves in language: introducing game activities to make children want to express themselves in language. With the help of situational display, children like to express themselves in language and carry out performance activities to give children the opportunity to express themselves in language. With the help of home force, children dare to express themselves in language. Children are in a dynamic stage of rapid growth. In order to enable children to learn language effectively, teachers should make clear the development center of children and their mastery of language. To guide language teaching in accordance with students’ aptitude for a long time, at the same time, we should update our rigid views on children at any time, boldly innovate the content and thinking mode of children’s language teaching activities, and mobilize children’s initiative and enthusiasm to participate in language learning, so that they can achieve the language learning goals of being able to speak, loving to speak and bravely speaking. This paper selects the learning situation of preschool children as the object, establishes an evaluation model by using the hybrid GA-BP neural network method, and makes an empirical analysis on the development of preschool education in Liaoning Province. The GA-BP neural network model is trained by MATLAB 7.0, and the simulation results show that the model can evaluate the development of preschool education scientifically, and it is scientific and practical. According to the results of this paper, the corresponding evaluation model of preschool education language teaching quality is put forward, which makes a good prediction preparation for children to learn language better in advance.

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Section: Introductionmentioning

confidence: 99%

Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network

Niu²

2022

Journal of Sensors

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“…The use of artificial neural networks can robustly provide high-precision fitting of functions. The design achieves static beamforming through RBF neural networks, which can be used to fit the nonlinear relationship between the received array response and waveform output [4]. Among them, the receiving array response is applied as the input layer data of the RBF neural network, and the waveform energy is generated at the output layer.…”

Section: Introductionmentioning

confidence: 99%

A Static Beamforming Method for Neural Networks with Continuous Spatial Coverage

Yang,

Jiang,

Liu

et al. 2023

Advances in Transdisciplinary Engineering

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The static beamforming algorithm obtains the beam response of the corresponding angle lattice within the range of scanning angles of interest by traversing different angles, resulting in the problem of discontinuous coverage in the spatial domain [1]. And the denser the airspace, the greater the beam storage resources required by the system. In response to the problem of large pointing angle estimation errors caused by discontinuous spatial coverage in traditional static digital beamforming algorithms, a beamforming algorithm based on RBF neural network is proposed. By utilizing the good generalization characteristics and nonlinear approximation ability of RBF neural network, the corresponding relationship between the array heading vector and beamforming weight is established to fit data on non lattice points, thus achieving continuous spatial coverage of static beamforming [2]-[3]. At the same time, after a large amount of precise network training, the use of neural networks can quickly obtain beamforming results in the same application scenario. Through theoretical analysis of the principle and ergodicity advantages of static beamforming algorithm based on RBF neural network, a simulation model was established, and the beamforming results of this algorithm were compared with traditional static beamforming methods. The simulation results show that the proposed static beamforming method based on RBF neural network has accurate beamforming ability and good robustness to amplitude and phase errors. The trained neural network can quickly obtain beamforming results. It has high practical application value.

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“…An increased level of vibration not only reduces the service life and the reliability of its elements but also leads to a decrease in the quality of processing and a decrease in productivity, the efficienc of machining decreases, which is especially important in the machining of materials prone to hardening, such as titanium alloys, stainless steels, etc. The known methods of vibration monitoring using neural networks [2,3,4,5,6,7,8,9] have their limitations due to the complexity of physical and mechanical processes in the MDTD (machine-device-tool-detail) system (here it is necessary to decipher) during machining. The considered methods in the sources [10,11,12] to reduce the level of vibrations do not fully cover the arsenal of tools and methods.…”

Section: Introductionmentioning

confidence: 99%

Methodology of ensuring the efficiency of mechanical processing due to the application of vibration monitoring and vibration protection means

Nikolaevich,

Ivanovich,

Grigorievich

et al. 2022

J Appl Eng Science

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The article describes the main causes and sources of impulse - vibration effects on the elements of the "machine tool - device - tool - detail" system. The paper deals with the development of methods for increasing the efficiency of machining due to the rational choice of technical solutions for vibration protection by design and technological methods with the use of vibration monitoring. A criterion for the efficiency of machining is given, which is determined by a matrix using scalar parameters, which are traditionally used for vibration diagnostics. A complex criterion for the quality of machining has been determined, which describes the indicators of the geometric accuracy of the machined surface. The block diagram of algorithms for selection and rational use of structural and technological methods of vibration protection of technological system elements is presented. Vibration and technological criteria for choosing solutions for vibration protection and monitoring are proposed. The implementation of the developed algorithms is considered on examples of machining and numerical estimates are given, confirming the high quality of the developed solutions.

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Neural Network Application for Phasechronometric Measurement Information Processing

Cited by 5 publications

References 6 publications

Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network

Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network

A Static Beamforming Method for Neural Networks with Continuous Spatial Coverage

Methodology of ensuring the efficiency of mechanical processing due to the application of vibration monitoring and vibration protection means

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