scholarly journals Colour recognition using colour histogram feature extraction and K-nearest neighbour classifier

2020 ◽  
pp. 08-14
Author(s):  
Rabia Bayraktar ◽  
Batur Alp Akgul ◽  
Kadir Sercan Bayram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Video Camera ◽  
Training Data ◽  
Machine Learning Classification ◽  
The Neural Network ◽  
Knn Classifier ◽  
Nearest Neighbour Classifier ◽  
Histogram Feature

K-nearest neighbours (KNN) is a widely used neural network and machine learning classification algorithm. Recently, it has been used in the neural network and digital image processing fields. In this study, the KNN classifier is used to distinguish 12 different colours. These colours are black, blue, brown, forest green, green, navy, orange, pink, red, violet, white and yellow. Using colour histogram feature extraction, which is one of the image processing techniques, the features that distinguish these colours are determined. These features increase the effectiveness of the KNN classifier. The training data consist of saved frames and the test data are obtained from the video camera in real-time. The video consists of consecutive frames. The frames are 100 × 70 in size. Each frame is tested with K = 3,5,7,9 and the obtained results are recorded. In general, the best results are obtained when used K = 5.   Keywords: KNN algorithm, classifier, application, neural network, image processing, developed, colour, dataset, colour recognition.

Cotton Leaf Disease Detection by Feature Extraction

2016 ◽  
pp. 89-104
Author(s):  
Savita N. Ghaiwat ◽  
Parul Arora
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Early Stage ◽  
Cotton Leaf ◽  
Control Methods ◽  
The Neural Network ◽  
The World ◽  
Leaf Image

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

scholarly journals Rethinking the Random Cropping Data Augmentation Method Used in the Training of CNN-Based SAR Image Ship Detector

2020 ◽  
Vol 13 (1) ◽  
pp. 34
Author(s):  
Rong Yang ◽  
Robert Wang ◽  
Yunkai Deng ◽  
Xiaoxue Jia ◽  
Heng Zhang
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Back Propagation ◽  
Detection Performance ◽  
Training Data ◽  
Sar Image ◽  
Optical Images ◽  
The Neural Network ◽  
Effective Training ◽  
Standard Configuration

The random cropping data augmentation method is widely used to train convolutional neural network (CNN)-based target detectors to detect targets in optical images (e.g., COCO datasets). It can expand the scale of the dataset dozens of times while consuming only a small amount of calculations when training the neural network detector. In addition, random cropping can also greatly enhance the spatial robustness of the model, because it can make the same target appear in different positions of the sample image. Nowadays, random cropping and random flipping have become the standard configuration for those tasks with limited training data, which makes it natural to introduce them into the training of CNN-based synthetic aperture radar (SAR) image ship detectors. However, in this paper, we show that the introduction of traditional random cropping methods directly in the training of the CNN-based SAR image ship detector may generate a lot of noise in the gradient during back propagation, which hurts the detection performance. In order to eliminate the noise in the training gradient, a simple and effective training method based on feature map mask is proposed. Experiments prove that the proposed method can effectively eliminate the gradient noise introduced by random cropping and significantly improve the detection performance under a variety of evaluation indicators without increasing inference cost.

Application of ANN Back-Propagation for Fracture Design Parameters in Extra-Low Cycle Rotating Bending Fatigue

2009 ◽  
Vol 610-613 ◽  
pp. 450-453
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Jin Zhang ◽  
Gui Ping He
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Bending Fatigue ◽  
The Neural Network ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

scholarly journals Computer image processing and neural network technology for boiler thermal energy diagnosis

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3059-3068
Author(s):  
Qinghong Wu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Thermal Energy ◽  
Processing System ◽  
Heat Energy ◽  
Image Feature ◽  
The Neural Network ◽  
Computer Image Processing ◽  
On Line ◽  
Parameter Values

The paper uses the flame image processing technology to diagnose the furnace flame combustion achieve the measurement of boiler heat energy. The paper obtains the combustion image of the flame image processing system, and extracts the flame image characteristics of the boiler thermal energy diagnosis, constructs the neural network model of the boiler thermal energy diagnosis, and trains and tests the extracted flame image feature parameter values as the input of the neural network. A rough diagnosis of the boiler?s thermal energy is obtained while predicting the state of combustion. According to the research results, a boiler thermal energy diagnosis system was designed and tested on the boiler of 200 MW unit. The experimental results confirmed the applicability of the system, which can realize on-line monitoring of boiler heat energy and evaluate the combustion situation.

scholarly journals Bootstrapping a Neural Morphological Generator from Morphological Analyzer Output for Inuktitut

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jeffrey Micher
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Data Set ◽  
Set Size ◽  
The Neural Network ◽  
Surface Character ◽  
Finite State ◽  
Character Sequences ◽  
Finite State Transducer

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.

Analysis of Fin-Tube Evaporator Performance With Limited Experimental Data Using Artificial Neural Networks

2000 ◽  
Author(s):  
Arturo Pacheco-Vega ◽  
Mihir Sen ◽  
Rodney L. McClain
Keyword(s):  
Neural Network ◽  
Heat Rate ◽  
Network Models ◽  
Activation Function ◽  
Operating Conditions ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Artificial Neural ◽  
Fin Tube

Abstract In the current study we consider the problem of accuracy in heat rate estimations from artificial neural network models of heat exchangers used for refrigeration applications. The network configuration is of the feedforward type with a sigmoid activation function and a backpropagation algorithm. Limited experimental measurements from a manufacturer are used to show the capability of the neural network technique in modeling the heat transfer in these systems. Results from this exercise show that a well-trained network correlates the data with errors of the same order as the uncertainty of the measurements. It is also shown that the number and distribution of the training data are linked to the performance of the network when estimating the heat rates under different operating conditions, and that networks trained from few tests may give large errors. A methodology based on the cross-validation technique is presented to find regions where not enough data are available to construct a reliable neural network. The results from three tests show that the proposed methodology gives an upper bound of the estimated error in the heat rates.

Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

2019 ◽  
Vol 14 (2) ◽  
pp. 158-164 ◽  
Author(s):  
G. Emayavaramban ◽  
A. Amudha ◽  
T. Rajendran ◽  
M. Sivaramkumar ◽  
K. Balachandar ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Network Models ◽  
Prosthetic Hand ◽  
Neural Network Models ◽  
The Neural Network ◽  
Semg Signals

Background: Identifying user suitability plays a vital role in various modalities like neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper analysis the user suitability based on neural networks (NN), subjects, age groups and gender for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand. Six parametric feature extraction algorithms are used to extract the features from sEMG signals such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural Network. Methods: sEMG signals generated from forearm muscles of the participants are collected through an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the user is identified in the sEMG recognition module using signal processing, feature extraction and machine learning techniques. The information about the identified movement is passed to microcontroller wherein a control is developed to command the prosthetic hand to emulate the identified movement. Results: From the six feature extraction algorithms and two neural network models used in the study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best suited for this study as the neural network model is specially designed for pattern matching problem. Moreover, it has simple architecture and low computational complexity. AR Burg is found to be the best feature extraction technique in this study due to its high resolution for short data records and its ability to always produce a stable model. In all the neural network models, the maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are best suited for the study due to their better muscle contractions. Better muscle fatigue resistance has contributed for better performance of female subjects as compared to male subjects. From the single trial analysis, it can be observed that the hand close movement has achieved best recognition rate for all neural network models. Conclusion: In this paper a study was conducted to identify user suitability for designing hand prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements. The suitability of the user was identified using two neural networks with six parametric features. From the result, it was concluded thatfit women doing regular physical exercises aged between 26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition Neural Network with AR Burg extraction features using extension movements will be a better way to design the HMI. However, Signal acquisition based on wireless method is worth considering for the future.

scholarly journals Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3691
Author(s):  
Jian Liang ◽  
Junchao Zhang ◽  
Jianbo Shao ◽  
Bofan Song ◽  
Baoli Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
3D Imaging ◽  
Phase Unwrapping ◽  
Training Data ◽  
Fringe Projection ◽  
Second Step ◽  
Network Configuration ◽  
Simulation Data ◽  
The Neural Network ◽  
Key Features

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.

Prediction of stress in fillet portion of spur gears using artificial neural networks

2007 ◽  
Vol 22 (1) ◽  
pp. 41-51
Author(s):  
M.S. Shunmugam ◽  
N. Siva Prasad
Keyword(s):  
Neural Network ◽  
Gear Tooth ◽  
Spur Gear ◽  
Activation Function ◽  
Training Data ◽  
Spur Gears ◽  
Pressure Angle ◽  
The Neural Network ◽  
Artificial Neural ◽  
Taguchi’S Design Of Experiments

AbstractA fillet curve is provided at the root of the spur gear tooth, as stresses are high in this portion. The fillet curve may be a trochoid or an arc of suitable size as specified by designer. The fillet stress is influenced by the fillet geometry as well as the number of teeth, modules, and the pressure angle of the gear. Because the relationship is nonlinear and complex, an artificial neural network and a backpropagation algorithm are used in the present work to predict the fillet stresses. Training data are obtained from finite element simulations that are greatly reduced using Taguchi's design of experiments. Each simulation takes around 30 min. The 4-5-1 network and a sigmoid activation function are chosen. TRAINLM function is used for training the network with a learning rate parameter of 0.01 and a momentum constant of 0.8. The neural network is able to predict the fillet stresses in 0.03 s with reasonable accuracy for spur gears having 25–125 teeth, a 1–5 mm module, a 0.05–0.45 mm fillet radius, and a 15°–25° pressure angle.

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