scholarly journals INFORMATION SUPPORT OF THE REMOTE NITROGEN MONITORING SYSTEM IN AGRICULTURAL CROPS

2018 ◽  
pp. 47-54
Author(s):  
Vitalii Lysenko ◽  
Oleksiy Opryshko ◽  
Dmytro Komarchuk ◽  
Natalia Pasichnyk ◽  
Natalya Zaets ◽  
...  
Keyword(s):  
Neural Network ◽  
Nitrogen Nutrition ◽  
Information Support ◽  
Statistical Characteristics ◽  
Intermediate Layers ◽  
The Neural Network ◽  
The Matrix ◽  
Data Volume ◽  
Linear Elements ◽  
Neural Network Input

The article addresses issues on application of unmanned aerial vehicles (UAV) to monitor nitrogen nutrition through the example of wheat plants. The optical spectral range can be used to monitor exploitation of the UAV. It is recommended to develop specialized spectral indices for such equipment. The article provides calibration curves for nitrogen nutrition monitoring. In the created neural networks, the linear model is represented as a network without intermediate layers, which in the output layer contains only linear elements, the weight corresponds to the elements of the matrix, and the thresholds are the components of the shear vector. During the operation, the neural network actually multiplies the vector of inputs into the matrix of scales, and then adds a vector of displacement to the resulting vector. Results of the research show how to create the specialized RPVI adapted to technological capabilities of UAVs. It has been experimentally proved that input parameters that describe the state of agricultural plantations are regularly distributed. The average statistical characteristics for additive color RGB model is advisable to be the neural network input instead of large sample data volume.

Entropy estimation of the fragments of chest X-ray images

2021 ◽  
pp. 20-26
Author(s):  
Aleksei Aleksandrovich Rumyantsev ◽  
Farkhad Mansurovich Bikmuratov ◽  
Nikolai Pavlovich Pashin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Binary Classification ◽  
Statistical Characteristics ◽  
Type I ◽  
Deep Convolutional Neural Networks ◽  
Preliminary Calculation ◽  
X Ray ◽  
The Matrix ◽  
Chest X Ray

The subject of this research is medical chest X-ray images. After fundamental pre-processing, the accumulated database of such images can be used for training deep convolutional neural networks that have become one of the most significant innovations in recent years. The trained network carries out preliminary binary classification of the incoming images and serve as an assistant to the radiotherapist. For this purpose, it is necessary to train the neural network to carefully minimize type I and type II errors. Possible approach towards improving the effectiveness of application of neural networks, by the criteria of reducing computational complexity and quality of image classification, is the auxiliary approaches: image pre-processing and preliminary calculation of entropy of the fragments. The article provides the algorithm for X-ray image pre-processing, its fragmentation, and calculation of the entropy of separate fragments. In the course of pre-processing, the region of lungs and spine is selected, which comprises approximately 30-40% of the entire image. Then the image is divided into the matrix of fragments, calculating the entropy of separate fragments in accordance with Shannon’s formula based pm the analysis of individual pixels. Determination of the rate of occurrence of each of the 255 colors allows calculating the total entropy. The use of entropy for detecting pathologies is based on the assumption that its values differ for separate fragments and overall picture of its distribution between the images with the norm and pathologies. The article analyzes the statistical values: standard deviation of error, dispersion. A fully connected neural network is used for determining the patterns in distribution of entropy and its statistical characteristics on various fragments of the chest X-ray image.

scholarly journals Remanent battery capacity estimation for autonomous ground industrial vehicles

2019 ◽  
Vol 290 ◽  
pp. 02009
Author(s):  
Tom Savu ◽  
Bogdan Alexandru Jugravu
Keyword(s):  
Neural Network ◽  
Industrial System ◽  
Ground Vehicles ◽  
Future Developments ◽  
Capacity Loss ◽  
The Neural Network ◽  
Proposed Model ◽  
Battery Capacity ◽  
Data Volume ◽  
Remaining Capacity

When travelling in an industrial system for completing their assigned tasks, autonomous ground vehicles must estimate the remanent capacity of their batteries and decide if they are able to assume the next task and afterward travel to the charging or replacement station. The amount of energy needed for moving on a certain distance depends on a set of parameters belonging to the vehicle, to the runway and to the vehicle’s trajectory. The paper proposes a model for estimating the remaining capacity of the batteries after a certain distance would be covered by a vehicle. Parameters values were obtained by simulation, capacity loss was computed using the proposed model and then a neural network was taught to perform the estimation. The neural network was further used to simulate the situation when a vehicle is estimating the needed capacity before accepting a task to be performed. The results proved that the model and the network, even developed using low data volume and processing time, are able to provide accurate enough estimations and are able to allow future developments.

Depth Image Compression and Error Concealment for 3D Video Communication

2015 ◽  
Vol 764-765 ◽  
pp. 863-867
Author(s):  
Yih Chuan Lin ◽  
Pu Jian Hsu
Keyword(s):  
Neural Network ◽  
Communication Channel ◽  
Error Concealment ◽  
3D Video ◽  
Depth Image ◽  
Noisy Channel ◽  
The Neural Network ◽  
Processing Ability ◽  
Data Volume

In this paper, an error concealment scheme for neural-network based compression of depth image in 3D videos is proposed. In the neural-network based compression, each depth image is represented by one or more neural networks. The advantage of neural-network based compression lies in the parallel processing ability of multiple neurons, which can handle the massive data volume of 3D videos. The similarity of neuron weights of neighboring nodes is exploited to recover the loss neuron weights when transmitting with an error-prone communication channel. With a simulated noisy channel, the quality of compressed 3D video, which is reconstructed undergoing the noisy channel, can be recovered well by the proposed error concealment scheme.

Application of Neural Network in Bioactive Prediction of Inhibitor of Thymidylic Acid-Based Synthetase

2014 ◽  
Vol 937 ◽  
pp. 308-312
Author(s):  
Xi Hua Du ◽  
Xiao Hui Wang
Keyword(s):  
Neural Network ◽  
Biological Activity ◽  
Molecular Design ◽  
Structural Parameters ◽  
Regression Method ◽  
New Drugs ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Electrical State ◽  
Neural Network Input

Based on the molecular topology information and adjacency matrix, the 38 electrical state indices of molecules of inhibitor of thymidylic acid-based synthetase as five-membered heterocyclic pyrimidine derivatives were calculated to provide theoretical basis for molecular design of new drugs. By using variable regression method, the best subset of structural parameters ofE1,E2,E7,E16andE31were optimized. When the five structural parameters were used as the BP neural network input neurons and the neural network structure of 5:3:1 was used, an ideal prediction model of biological activity was obtained. Its total correlation coefficientrand average relative error were 0.972 and 2.13%, respectively. The result showed that the biological activity andE1,E2,E7,E16andE31have a good non-linear relationship with the biological activity, and the results predicted by neural networks was better than that by multiple regression method. The test proved that the model had good robust and predictive capabilities. Our research would provide theoretical guidance for the development of new drugs of inhibitor of thymidylic acid-based synthetase with efficient and low toxicity.

scholarly journals A factorisation-aware Matrix element emulator

2021 ◽  
Vol 2021 (11) ◽  
Author(s):  
D. Maître ◽  
H. Truong
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Matrix Element ◽  
Percent Level ◽  
Matrix Elements ◽  
Leading Order ◽  
Jet Production ◽  
The Neural Network ◽  
The Matrix ◽  
The One

Abstract In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements. In doing so we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. We apply our model to the case of leading-order jet production in e+e− collisions with up to five jets. Our results show that this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, and a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage.

Gear Noise Diagnosis System for Automobile Transmission Using Artificial Intelligence (Effect of Number of Intermediate Layers on Judgement Performance)

2013 ◽  
Author(s):  
Morimasa Nakamura ◽  
Masahiko Nishiyama ◽  
Ichiro Moriwaki
Keyword(s):  
Neural Network ◽  
Moment Method ◽  
Network System ◽  
Noise Levels ◽  
Diagnosis System ◽  
Gear Noise ◽  
Intermediate Layers ◽  
The Neural Network ◽  
Neural Network System ◽  
The Moment

The present paper describes a digitizing method for the measured gear noise and a construction of a neural network system for gear noise diagnosis. Gear noise emitted from automobile transmissions should be evaluated by gear noise experts. Although quietness performance estimates from measured noise levels of the transmissions on some production lines, the estimation must be very difficult. There is not a certain relationship between the measured noise levels and the evaluations by the gear noise experts. Therefore, the estimation should be severe. As a result, such an automatic gear noise diagnosis system must yield transmissions with over-quality. The present study deals with a new gear noise diagnosis system to which an artificial intelligence, that is, a neural network system is applied. The previous evaluations by the new gear noise diagnosis system were good when the statistical property of the teacher signals from which the neural network system learned was similar to that of population. This fact means that many teacher signals are necessary on the practical use. Proposed digitizing method of gear noise levels provided good evaluations of neural network system even when the statistical properties of the teacher signals were not similar to that of the population. In addition, a new method, “Moment method” for determining the construction of the neural network system was introduced instead of “Back Propagation Method”. The Moment Method contributed to the improvement of the system judgments. The neural network system constructed using the Moment Method brought good performance. And the number of intermediate layers in the neural network system could be small enough to obtain good performance. It was found that the Moment method provided good learning because of connecting weights update function. When the Moment method was used for determining the connection weights between neurons in the neural network system, the developed gear noise diagnosis system achieved high and stable correct answer ratio. And the number of intermediate layers in the neural network system was only one enough for obtaining good performance of the system. Four intermediate layers, which was the maximum in this paper, did not provide much good performance.

scholarly journals Application Research on Fault Diagnosis of the Filter Unit Based on Intelligent Algorithm of GA and WNN

2015 ◽  
Vol 9 (1) ◽  
pp. 922-926 ◽  
Author(s):  
Zhao Xuejun ◽  
Wang Mingfang ◽  
Wang Jie ◽  
Tong Chuangming ◽  
Yuan Xiujiu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Speed ◽  
Analog Circuit ◽  
Optimal Solution ◽  
Intelligent Algorithm ◽  
The Neural Network ◽  
Filter Unit ◽  
Band Sequence ◽  
Neural Network Input

This paper focuses on the potential of GA algorithm for adaptive random global search, and WNN resolution as well as the ability of fault tolerance to build a multi intelligent algorithm based on the GA-WNN model using the filter unit of analog circuit for fault diagnosis. Construction of GA-WNN model was divided into two stages; in the first stage GA was used to optimize the initial weights, threshold, expansion factor and translation factor of WNN structure; while in the second stage, initially, based on WNN training and learning, global optimal solution was obtained. In the process of using analog output signal by using wavelet decomposition, the absolute value of coefficient of each frequency band sequence was obtained along with the energy characteristics of the cross joint, with a combination of feature vectors as the input of the neural network. Through the pretreatment method, in order to reduce the neural network input, neural grid size of neurons was reduced in each layer and the convergence speed of neural network was increased. The experimental results show that the method can diagnose single and multiple soft faults of the circuit, with high speed and high precision.

scholarly journals Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Dan Yang ◽  
Hailin Mu ◽  
Zengbing Xu ◽  
Zhigang Wang ◽  
Cancan Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Classification Performance ◽  
Statistical Characteristics ◽  
Neural Network Ensemble ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Ensemble Technique ◽  
The Neural Network ◽  
Rolling Element

This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu’s norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.

A Study and Design of Compressed Encoder Based on Neural Network for Remote Sensing Image

2014 ◽  
Vol 989-994 ◽  
pp. 4100-4103
Author(s):  
Ling Tao ◽  
Wen Long Li ◽  
Yan Yan Yu ◽  
Jian Kang ◽  
Jun Yang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Image Compression ◽  
Remote Sensing Image ◽  
The Neural Network ◽  
Data Volume ◽  
High Resolution Images ◽  
And Storage ◽  
Self Learning

The characteristics of remote sensing image are big data volume and high resolution. It is difficult to meet actual demands by using traditional techniques for transmission and storage. This paper studies and designs remote sensing image compression encoder with FPGA technology and three-layer feedforward BP neural network. The neural network can parallelly process large amounts of data, and has a structural characteristic of self-learning and self-organizing. The encoder has a simple structure, safety, fast speed, good reconfiguration. It overcomes shortcomings of conventional compression techniques which compress high-resolution images ineffectively. The study has some theoretical values in the field of image compression.

scholarly journals Prediction of Corrosion Inhibitor Efficiency of Some Aromatic Hydrazides and Schiff Bases Compounds by Using Artificial Neural Network

2009 ◽  
Vol 2 (1) ◽  
pp. 108-113
Author(s):  
Hanan A. Al-Hazam
Keyword(s):  
Neural Network ◽  
Corrosion Inhibitor ◽  
Molecular Orbital ◽  
Schiff Bases ◽  
Experimental Tests ◽  
Molecular Volume ◽  
Inhibitor Efficiency ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Input

Artificial neural networks are used for evaluating the corrosion inhibitor efficiency of some aromatic hydrazides and Schiff bases compounds. The nodes of neural network input layer represent the quantum parameters, total negative charge (TNC) on molecule, energy of highest occupied molecular orbital (E Homo), energy of lowest unoccupied molecular orbital (E Lomo), dipole moment (μ), total energy (TE), molecular volume (V), dipolar-polarizability factor (Π) and inhibitor  concentration (C). The neural network output is the corrosion inhibitor efficiency (E) for the mentioned compounds. The training and testing of the developed network are based on a database of 31 published experimental tests obtained by weight loss. The neural network predictions for corrosion inhibitor efficiency are more reliable than prediction using other conventional theoretical methods such as AM1, PM3, Mindo, and Mindo-3. Key words: Neural network; Corrosion inhibitor efficiency. © 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reservedDOI: 10.3329/jsr.v2i1.2757                 J. Sci. Res. 2 (1), 108-113  (2010) 

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