scholarly journals Early Mass Diagnosis of Fusarium Wilt in Banana Cultivations using an E-Nose Integrated Autonomous Rover System

2017 ◽  
Vol 5 (2) ◽  
pp. 261-266 ◽  
Author(s):  
M. Sanjay ◽  
B. Kalpana
Keyword(s):  
Neural Network ◽  
Fusarium Wilt ◽  
Infected Plant ◽  
Disease Outbreaks ◽  
Training Model ◽  
Early Warning Systems ◽  
Small Scale ◽  
The Neural Network ◽  
Similar Disease ◽  
And Control

Nucleic acid based diagnostics are the standard means for diagnosis of infected plant material. However, these methods are expensive and time-consuming, but they are accurate. On the contrary, disease prediction methods based on Volatile organic compound (VOC) emission from plants are less accurate but, allow for screening of large volumes of samples. This work reports the methodology for development of an inexpensive electronic nose for implementation as early warning systems intended to prevent plant disease outbreaks using VOC pattern analysis. It is proven that plants emit VOCs in response to pathogenic attacks. In this project, efforts were made to register the pattern of VOCs released by the diseased plants. The disease taken for this purpose was Fusarium wilt disease of banana. The E-Nose was successfully fabricated using five MOS sensors connected to a microcontroller, which along with a microSD card module was able to store the acquired VOC data. The VOC data analysis was done in MS-Excel, using NeuroXL Predictor, a neural networking add-in. A small scale banana field containing 35 plants, divided into disease, test and control groups, was established. The disease and test sets were subjected to similar disease induction protocols and VOC data was collected over a period of 40 days. NeuroXL Predictor was trained to recognize odours corresponding to diseases by feeding the neural network with the disease set VOC data. Finally, the training model was validated by providing the test set VOC data to the neural network and the results were found to be accurate. Efforts were made to automate the VOC data acquisition from the plants, as it will be impractical to carry around, a device, through several hectares of plantation. Therefore, a simple autonomous rover was fabricated using DC motors connected to a microcontroller. A DC motor placed on top was used to move the E-nose towards the plants in left and right of the rover. The microcontroller was programmed to stop, move forward and turn the E-nose towards left or right as per the measurements of the field.Int. J. Appl. Sci. Biotechnol. Vol 5(2): 261-266

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals A Nonlinear System Identification Method Based on Adaptive Neural Network

2021 ◽  
Vol 28 (2) ◽  
pp. 111-123
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Generic Model ◽  
Control Engineering ◽  
The Neural Network ◽  
Adaptive Pso ◽  
And Control ◽  
Scientific Engineering

Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.

scholarly journals Mind-Wave Controlled Robot: An Arduino Robot Simulating the Wheelchair for Paralyzed Patients

2018 ◽  
Vol 1 (1) ◽  
pp. 6
Author(s):  
Chi Hang Cheng ◽  
Shuai Li ◽  
Seifedine Kadry
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Eeg Signal ◽  
The Neural Network ◽  
Lead Electrode ◽  
Short Period ◽  
Specific Position ◽  
And Control

This project attempts to implement an Arduino robot to simulate a brainwave-controlled wheelchair for paralyzed patients with an improved controlling method. The robot should be able to move freely in anywhere under the control of the user and it is not required to predefine any map or path. An accurate and natural controlling method is provided, and the user can stop the robot any time immediately to avoid risks or danger. This project is using a low-cost and a brainwave-reading headset which has only a single lead electrode (Neurosky mind wave headset) to collect the EEG signal. BCI will be developed by sending the EEG signal to the Arduino Mega and control the movement of the robot. This project used the eye blinking as the robot controlling method as the eye blinking will cause a significant pulse in the EEG signal. By using the neural network to classify the blinking signal and the noise, the user can send the command to control the robot by blinking twice in a short period of time. The robot will be evaluated by driving in different places to test whether it can follow the expected path, avoid the obstacles, and stop in a specific position.

scholarly journals Neural network training for vehicle fuel level control

2021 ◽  
Vol 4 (135) ◽  
pp. 12-22
Author(s):  
Vladimir Gerasimov ◽  
Nadija Karpenko ◽  
Denys Druzhynin
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Confusion Matrix ◽  
Training Model ◽  
Neural Network Training ◽  
Raw Data ◽  
Network Training ◽  
The Neural Network ◽  
Fuel Level ◽  
Distorted Data

The goal of the paper is to create a training model based on real raw noisy data and train a neural network to determine the behavior of the fuel level, namely, to determine the time and volume of vehicle refueling, fuel consumption / excessive consumption / drainage.Various algorithms and data processing methods are used in fuel control and metering systems to get rid of noise. In some systems, primary filtering is used by excluding readings that are out of range, sharp jumps and deviations, and averaging over a sliding window. Research is being carried out on the use of more complex filters than simple averaging – by example, the Kalman filter for data processing.When measuring the fuel level using various fuel level sensor the data is influenced by many external factors that can interfere with the measurement and distort the real fuel level. Since these interferences are random and have a different structure, it is very difficult to completely remove them using classical noise suppression algorithms. Therefore, we use artificial intelligence, namely a neural network, to find patterns, detect noise and correct distorted data. To correct distorted data, you first need to determine which data is distorted, classify the data.In the course of the work, the raw data on the fuel level were transformed for use in the neural network training model. To describe the behavior of the fuel level, we use 4 possible classes: fuel consumption is observed, the vehicle is refueled, the fuel level does not change (the vehicle is idle), the data is distorted by noise. Also, in the process of work, additional tools of the DeepLearning4 library were used to load data training and training a neural network. A multilayer neural network model is used, namely a three-layer neural network, as well as used various training parameters provided by the DeepLearning4j library, which were obtained because of experiments.After training the neural network was used on test data, because of which the Confusion Matrix and Evaluation Metrics were obtained.In conclusion, finding a good model takes a lot of ideas and a lot of experimentation, also need to correctly process and transform the raw data to get the correct data for training. So far, a neural network has been trained to determine the state of the fuel level at a point in time and classify the behavior into four main labels (classes). Although we have not reduced the error in determining the behavior of the fuel level to zero, we have saved the states of the neural network, and in the future we will be able to retrain and evolve our neural network to obtain better results.

scholarly journals Neural Network Based Hurdle Avoidance System for Smart Vehicles

2019 ◽  
Vol 8 (04) ◽  
pp. 01-07
Author(s):  
Dr. B. Maruthi Shankar
Keyword(s):  
Neural Network ◽  
Small Scale ◽  
Direct Estimate ◽  
Feed Forward ◽  
Smart Vehicles ◽  
Ir Sensor ◽  
The Neural Network ◽  
Feed Forward Network ◽  
Positive Direct ◽  
Matlab Programming

The structure of a self-ruling vehicle dependent on neural sophisticated network for route in obscure condition is proposed. The vehicle is equipped with an IR sensor for obstacle separation estimation, a GPS collector for goal data and heading position, L298 H-connect for driving the engines which runs the wheels; all interfaced to a controller unit. The smaller scale controller forms the data gained from the sensor and GPS to produce robot movement through neural based network. The neural network running inside the small scale controller is a multi-layer feed-forward network with back-engendering blunder calculation. The network is prepared disconnected with tangent-sigmoid and positive direct estimate as enactment work for neurons and is executed progressively with piecewise straight guess of tangent-sigmoid capacity. The programming of the miniaturized scale controller is finished by PIC C Compiler and the neural network is actualized utilizing MATLAB programming. Results have shown that up to twenty neurons can be actualized in shrouded layer with this method. The vehicle is tried with differing goal places in open air situations containing fixed as well as moving obstructions and is found to arrive at the set targets effectively and its yield exactness is about equivalent to that of the normal precision.

scholarly journals An Islanding Detection and Control Strategy to Realize the Stable and Autonomous Operation of Microgrids Using the Virtual Synchronous Generator

2021 ◽  
Vol 9 (11) ◽  
pp. 1315-1333
Author(s):  
Illuru Sree Lakshmi
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Synchronous Generator ◽  
Islanding Detection ◽  
Neural Network Approach ◽  
Autonomous Operation ◽  
The Neural Network ◽  
Control Stability ◽  
And Control ◽  
Virtual Synchronous Generator

Abstract: An islanding detection and based control strategy is created in this exploration to accomplish the steady and independent activity of microgrids using the neural network based Virtual Synchronous Generator (VSG) idea during unplanned grid reconfigurations . Maybe of utilizing a design-orientedmethodology, this paper gives a rigorous and extensive hypothetical investigation and reaches a concise conclusion that is easy to execute and successful even in complex situations. Based on the results of the mutation sequence and voltage wavering, a neural network based islanding identification calculation is proposed, which requires less constraint strategy. The proposed neural network approach outperforms the thefrequency measured passive detection method in terms of detection speed and reliability. Broad recreations affirm the reasonableness of the proposed islanding location and control methodology. Additionally, think about the results of the reproductions for the PI regulator, fluffy organizations, and neural organizations. Keywords: Virtual Synchronous Generator, Islanding detection, Islanding operation, Droop control, Stability, Microgrids.

scholarly journals A Comprehensive Analysis of Metabolomics and Transcriptomics Reveals Novel Biomarkers and Mechanistic Insights on Lorlatinib Crosses the Blood-Brain Barrier

2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Chen ◽  
Chunyu Li ◽  
Yafei Shi ◽  
Yujun Zhang ◽  
Dujia Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Interaction Network ◽  
Gene Interaction ◽  
Accuracy Rate ◽  
Gene Interaction Network ◽  
The Neural Network ◽  
Novel Biomarkers ◽  
And Control ◽  
The Brain

Of late, lorlatinib has played an increasingly pivotal role in the treatment of brain metastasis from non-small cell lung cancer. However, its pharmacokinetics in the brain and the mechanism of entry are still controversial. The purpose of this study was to explore the mechanisms of brain penetration by lorlatinib and identify potential biomarkers for the prediction of lorlatinib concentration in the brain. Detection of lorlatinib in lorlatinib-administered mice and control mice was performed using liquid chromatography and mass spectrometry. Metabolomics and transcriptomics were combined to investigate the pathway and relationships between metabolites and genes. Multilayer perceptron was applied to construct an artificial neural network model for prediction of the distribution of lorlatinib in the brain. Nine biomarkers related to lorlatinib concentration in the brain were identified. A metabolite-reaction-enzyme-gene interaction network was built to reveal the mechanism of lorlatinib. A multilayer perceptron model based on the identified biomarkers provides a prediction accuracy rate of greater than 85%. The identified biomarkers and the neural network constructed with these metabolites will be valuable for predicting the concentration of drugs in the brain. The model provides a lorlatinib to treat tumor brain metastases in the clinic.

scholarly journals Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks

2019 ◽  
Vol 124 ◽  
pp. 05031 ◽  
Author(s):  
A.M. Sagdatullin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Oil And Gas ◽  
Gas Production ◽  
Operating Conditions ◽  
Identification System ◽  
Oil And Gas Production ◽  
The Neural Network ◽  
And Control

Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.

scholarly journals Neural-Network-Based Curve Fitting Using Totally Positive Rational Bases

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2197
Author(s):  
Rocio Gonzalez-Diaz ◽  
E. Mainar ◽  
Eduardo Paluzo-Hidalgo ◽  
B. Rubio
Keyword(s):  
Neural Network ◽  
Curve Fitting ◽  
Least Squares Method ◽  
Rational Bézier Curves ◽  
The Neural Network ◽  
Totally Positive ◽  
Gradient Based ◽  
Data Points ◽  
Satisfactory Approximation ◽  
And Control

This paper proposes a method for learning the process of curve fitting through a general class of totally positive rational bases. The approximation is achieved by finding suitable weights and control points to fit the given set of data points using a neural network and a training algorithm, called AdaMax algorithm, which is a first-order gradient-based stochastic optimization. The neural network presented in this paper is novel and based on a recent generalization of rational curves which inherit geometric properties and algorithms of the traditional rational Bézier curves. The neural network has been applied to different kinds of datasets and it has been compared with the traditional least-squares method to test its performance. The obtained results show that our method can generate a satisfactory approximation.

scholarly journals Nanoscale neural network using non-linear spin-wave interference

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ádám Papp ◽  
Wolfgang Porod ◽  
Gyorgy Csaba
Keyword(s):  
Neural Network ◽  
Spin Wave ◽  
Spin Waves ◽  
Field Pattern ◽  
Small Scale ◽  
Learning Framework ◽  
Wave Interference ◽  
The Neural Network ◽  
Neural Network Hardware ◽  
Signal Routing

AbstractWe demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic-field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.

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