scholarly journals Use of an Artificial Neural Network to Assess the Degree of Training of an Operator of Selected Devices Used in Precision Agriculture

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6329
Author(s):  
Karolina Trzyniec ◽  
Adam Kowalewski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Precision Agriculture ◽  
Numerical Data ◽  
Neural Model ◽  
Operator Training ◽  
Training Time ◽  
Artificial Neural ◽  
The Moment ◽  
Parallel Strips

The article concerns the issue of automatic recognition of the moment of achieving the desired degree of training of an operator of devices used in precision agriculture. The aim of the research was to build a neural model that recognizes when an operator has acquired the skill of operating modern navigation on parallel strips used in precision agriculture. To conduct the test, a standard device to assist the operator in guiding the machine along given paths, eliminating overlaps, was selected. The thesis was proven that the moment of operator training (meaning driving along designated paths with an accuracy of up to eight centimeters) can be automatically recognized by a properly selected artificial neural network. This network was learned on the basis of data collected during the observation of the operator training process, using a criterion defined by experts. The data collected in the form of photos of the actual and designated route was converted into numerical data and entered into the network input. The output shows the binary evaluation of the trip. It has been shown that the developed neural model will allow the determining of the moment when operators acquire the skills to drive a vehicle along the indicated path and thus shorten the training time.

Tool Condition Monitoring Using Artificial Neural Network Models

2022 ◽  
pp. 400-426
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

scholarly journals Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
R. Manjula Devi ◽  
S. Kuppuswami ◽  
R. C. Suganthe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Model ◽  
Training Data ◽  
Experimental Result ◽  
Training Algorithms ◽  
Data Set ◽  
Training Time ◽  
Input Sample ◽  
Artificial Neural

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.

Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network

2020 ◽  
Vol 13 (7) ◽  
pp. 980-992 ◽  
Author(s):  
Muhammad F. Tahir ◽  
Chen Haoyong ◽  
Kashif Mehmood ◽  
Noman A. Larik ◽  
Asad Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Vital Role ◽  
Short Term ◽  
Training Time ◽  
Generalization Ability ◽  
Short Term Load Forecasting ◽  
Artificial Neural ◽  
Bagging Method

Background: Short Term Load Forecasting (STLF) can predict load from several minutes to week plays a vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. Methods: This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, make this algorithm unable to accurately predict future loads. Results: This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which help in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. Conclusion: This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperforms the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.

scholarly journals Artificial neural network inspired by quantum computing solutions using the movement model of the PR-02 robot

2018 ◽  
Vol 19 ◽  
pp. 01007
Author(s):  
Jerzy Tchórzewski ◽  
Dariusz Ruciński ◽  
Przemysław Domański
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantum Computing ◽  
Arm Movement ◽  
Neural Model ◽  
Movement Model ◽  
The Neural Network ◽  
Neural Network Toolbox ◽  
Artificial Neural ◽  
Matrix Quantum

The paper proposes a new method of quantum computing using control and systems theory as well as matrix-quantum computing. The algorithm developed on the basis of the PR-02 robot’s arm’s movement was implemented in using the Neural Network Toolbox. The application of the neural model instead of the analytic model allowed for obtaining the improvement of the trajectory of the PR-02 robot’s arm movement, while the application of the quantum artificial neural network for the assumed number of quasi-parallel computations equal 1000 did not result in the improvement of the model.

scholarly journals A hybrid artificial neural network - genetic algorithm for load shedding

2020 ◽  
Vol 10 (3) ◽  
pp. 2250 ◽  
Author(s):  
Le Trong Nghia ◽  
Quyen Huy Anh ◽  
Phung Trieu Tan ◽  
N Thai An
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Load Shedding ◽  
Software Systems ◽  
Training Time ◽  
Power Load ◽  
Artificial Neural ◽  
Input Variables

This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method.

Tool Condition Monitoring Using Artificial Neural Network Models

2020 ◽  
pp. 550-576
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

Damage Identification of Rock Mass with Artificial Neural Networks

2007 ◽  
Vol 353-358 ◽  
pp. 2325-2328
Author(s):  
Zi Chang Shangguan ◽  
Shou Ju Li ◽  
Mao Tian Luan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Damage Identification ◽  
Back Propagation ◽  
Training Time ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
High Degree

The inverse problem of rock damage detection is formulated as an optimization problem, which is then solved by using artificial neural networks. Convergence measurements of displacements at a few of positions are used to determine the location and magnitude of the damaged rock in the excavation disturbed zones. Unlike the classical optimum methods, ANN is able to globally converge. However, the most frequently used Back-Propagation neural networks have a set of problems: dependence on initial parameters, long training time, lack of problemindependent way to choose appropriate network topology and incomprehensive nature of ANNs. To identify the location and magnitude of the damaged rock using an artificial neural network is feasible and a well trained artificial neural network by Levenberg-Marquardt algorithm reveals an extremely fast convergence and a high degree of accuracy.

Artificial Neural Network for Non-Intrusive Electrical Energy Monitoring System

2017 ◽  
Vol 6 (1) ◽  
pp. 124
Author(s):  
Khairell Khazin Kaman ◽  
Mahdi Faramarzi ◽  
Sallehuddin Ibrahim ◽  
Mohd Amri Md Yunus
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Energy ◽  
Energy Monitoring ◽  
Training Time ◽  
Energy Meter ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons ◽  
Electrical Consumption

<p> This paper discusses non-intrusive electrical energy monitoring (NIEM) system in an effort to minimize electrical energy wastages. To realize the system, an energy meter is used to measure the electrical consumption by electrical appliances. The obtained data were analyzed using a method called multilayer perceptron (MLP) technique of artificial neural network (ANN). The event detection was implemented to identify the type of loads and the power consumption of the load which were identified as fan and lamp. The switching ON and OFF output events of the loads were inputted to MLP in order to test the capability of MLP in classifying the type of loads. The data were divided to 70% for training, 15% for testing, and 15% for validation. The output of the MLP is either ‘1’ for fan or ‘0’ for lamp. In conclusion, MLP with five hidden neurons results obtained the lowest average training time with 2.699 seconds, a small number of epochs with 62 iterations, a min square error of 7.3872×10-5, and a high regression coefficient of 0.99050.</p>

scholarly journals Artificial neural network based detection of neutral relay defects

2019 ◽  
Vol 294 ◽  
pp. 03001
Author(s):  
Volodymyr Havryliuk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Dependence ◽  
Transient Current ◽  
Practical Utilization ◽  
Training Time ◽  
Train Control ◽  
Artificial Neural ◽  
Set Up ◽  
Wavelet Energy Entropy

The problem considered in the work is concerned to the automatic detecting and identifying defects in a neutral relay. The special design of electromechanical neutral relays is responsible for the strong asymmetry of its output signal for all possible safety-critical influences, and therefore neutral relays have negligible values of dangerous failures rate. To ensure the safe operation of relay-based train control systems, electromechanical relays should be periodically subjected to routine maintenance, during which their main operating parameters are measured, and the relays are set up in accordance with technical regulations. These measurements are mainly done manually, so they take a lot of time (up to four hours per relay), are expensive, and the results are subjective. In recent years, fault diagnosis methods based on artificial neural networks (ANN) have received considerable attention. The ANN-based classification of relay defects using the time dependence of the transient current in the relay coil during its switching is very promising for practical utilization, but for efficient use of ANN a lot of data is required to train the artificial neural network. To reduce the ANN training time, a pre-processing of the time dependence of relay transient current was proposed using wavelet transform and wavelet energy entropy, which makes it possible to reveal the features of the main defects of the relay armature, contact springs, and magnetic system. The effectiveness of the proposed approach for automatic detecting and identifying of the neutral relays defects was confirmed during testing of the relays with various artificially created defects.

A LOCAL-PATCH BASED MULTI-STAGE ARTIFICIAL-NEURAL-NETWORK TRAINING PROCEDURE AND ITS APPLICATION TO MATERIAL CHARACTERIZATION

2007 ◽  
Vol 04 (03) ◽  
pp. 439-458 ◽  
Author(s):  
YUNHUA LUO ◽  
ARVIND SHAH
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Training Data ◽  
Training Procedure ◽  
Training Time ◽  
Multi Stage ◽  
Training Stage ◽  
Local Patch ◽  
Artificial Neural

A local-patch based multi-stage artificial-neural-network (ANN) training procedure is proposed in this paper, to improve the accuracy of an ANN trained by a backpropagation (BP) algorithm and, at the same time, to reduce the overall training time. In the proposed procedure a conventional one-stage training procedure is split into multiple stages: an initial training stage and subsequent re-training stage(s). In the initial stage the training data are so selected that the trained ANN has adequate ability of generalization, that is, if provided with a set of new input, the ANN can predict the right region where the output is located, but the accuracy of the solution is not necessarily high. In the following re-training stage(s), local patches of training data, either selected from an existing data pool or generated by numerical methods such as finite element method, are used to re-train the ANN to improve the accuracy. Several factors that may have significant effects on the proposed procedure were investigated based on function approximation. As an example of application, the procedure was then used to train an ANN with finite element data to characterize material parameters.

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