scholarly journals Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuliang Guo
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Data Storage ◽  
Prediction Accuracy ◽  
Video Monitor ◽  
Advanced Technologies ◽  
The Neural Network ◽  
5G Network ◽  
Processing Module ◽  
Track Analysis

Roller skating is an important and international physical exercise, which has beautiful body movements to be watched. However, the falling of roller athletes also happens frequently. Upon the roller athletes’ fall, it means that the whole competition is over and even the roller athletes are perhaps injured. In order to stave off the tragedy, the roller track can be analyzed and be notified the roller athlete to terminate the competition. With such consideration, this paper analyzes the roller track by using two advanced technologies, i.e., pattern recognition and neural network, in which each roller athlete is equipped with an automatic movement identifier (AMI). Meanwhile, AMI is connected with the remote video monitor referee via the transmission of 5G network. In terms of AMI, its function is realized by pattern recognition, including data collection module, data processing module, and data storage module. Among them, the data storage module considers the data classification based on roller track. In addition, the neural network is used to train the roller tracks stored at AMI and give the further analysis results for the remote video monitor referee. Based on NS3, the devised AMI is simulated and the experimental results reveal that the prediction accuracy can reach 100% and the analyzed results can be used for the falling prevention timely.

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 The Use of Neural Networks in High-Energy Physics

1993 ◽  
Vol 5 (4) ◽  
pp. 505-549 ◽  
Author(s):  
Bruce Denby
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
High Energy Physics ◽  
High Energy ◽  
High Quality ◽  
Network Algorithms ◽  
The Past ◽  
The Neural Network ◽  
Energy Physics

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.

scholarly journals Pattern Recognition using a Neural Network on a Microcontroller with I2C Ultrasonic Sensors

2019 ◽  
Vol 3 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Fazal Noor
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Integrated Circuit ◽  
High Accuracy ◽  
Ultrasonic Sensors ◽  
Motion Patterns ◽  
The Neural Network ◽  
Proposed Model ◽  
Neural Network Algorithm

Ultrasonic sensors have been used in a variety of applications to measure ranges to objects. Hand gestures via ultrasonic sensors form unique motion patterns for controls. In this research, patterns formed by placing a set of objects in a grid of cells are used for control purposes. A neural network algorithm is implemented on a microcontroller which takes in range signals as inputs read from ultrasonic sensors and classifies them in one of four classes. The neural network is then trained to classify patterns based on objects’ locations in real-time. The testing of the neural network for pattern recognition is performed on a testbed consisting of Inter-Integrated Circuit (I2C) ultrasonic sensors and a microcontroller. The performance of the proposed model is presented and it is observed the model is highly scalable, accurate, robust and reliable for applications requiring high accuracy such as in robotics and artificial intelligence.

scholarly journals Hard-threshold neural network-based prediction of organic synthetic outcomes

2020 ◽  
Author(s):  
Haoyang Hu ◽  
Zhihong Yuan
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Organic Molecules ◽  
Backpropagation Algorithm ◽  
Forward Reaction ◽  
Drug Discovery And Development ◽  
Reaction Prediction ◽  
The Neural Network ◽  
Reaction Data ◽  
Retrosynthetic Analysis

Abstract Retrosynthetic analysis is a canonical technique for planning the synthesis route of organic molecules in drug discovery and development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is far from completing this step independently. Previous studies attempted to apply a neural network to forward reaction prediction, but the accuracy was not satisfying. Through using the Edit Vector-based description and extended-connectivity fingerprints to transform the reaction into a vector, this study focuses on the update of the neural network to improve the template-based forward reaction prediction. Hard-threshold activation and the target propagation algorithm are implemented by introducing mixed convex-combinatorial optimization. Comparative tests were conducted to explore the optimal hyperparameter set. Using 15,000 experimental reaction data extracted from granted United States patents, the proposed hard-threshold neural network was systematically trained and tested. The results demonstrated that a higher prediction accuracy was obtained than that for the traditional neural network with backpropagation algorithm. Some successfully predicted reaction examples are also briefly illustrated.

scholarly journals The approach to the formation of the neural network architecture for pattern recognition

2016 ◽  
Vol 12 (2) ◽  
pp. 61-64 ◽  
Author(s):  
Vitaly M Tatyankin
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Architecture ◽  
Test Sample ◽  
Recognition Algorithm ◽  
Neural Network Architecture ◽  
Pattern Recognition Algorithm ◽  
The Neural Network

An approach to the formation of an efficient pattern recognition algorithm. Under efficiency, understood as a zero error, resulting in the identification of the images on the test sample. As a test sample is considered an open database of images of handwritten digits MNIST.

Application of adaptive neural network to localization of objects using pressure array transducer

Robotica ◽  
1996 ◽  
Vol 14 (4) ◽  
pp. 407-414 ◽  
Author(s):  
Anderson Leung ◽  
Shahram Payandeh
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Sensor Array ◽  
Pressure Sensors ◽  
Object Localization ◽  
Tactile Sensors ◽  
Neural Network Approach ◽  
Array Transducer ◽  
The Neural Network ◽  
Fixed Reference

SUMMARYPattern recognition and object localization, using various sensors such as vision and tactile sensors, are two important areas of research in the application of robotic systems. This paper demonstrates the feasibility of using some relatively inexpensive array of pressure sensors and a neural network approach to achieve object localization and pattern recognition. The sensors that are used are force sensing resistors (FSRs), more specifically, a 16 x 16 array of FSRs. Because of the nonlinearity associated with a FSR, three possible approaches for gathering output from the sensor array are used. The neural network that is used consists of two 2-layer counterpropagation networks (CPNs). One of the CPNs is trained to recognize contact signatures of different objects placed on a fixed reference position on the sensor array.

scholarly journals On the Use of the FuzzyARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design

2010 ◽  
Vol 5 (2) ◽  
pp. 205 ◽  
Author(s):  
J. A. Vazquez-Lopez ◽  
I. Lopez-Juarez ◽  
M. Peña-Cabrera
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Behaviour Pattern ◽  
Control Parameters ◽  
Network Efficiency ◽  
Statistical Process ◽  
The Neural Network ◽  
Statistical Pattern ◽  
Experimental Design Methodology

Time-series statistical pattern recognition is of prime importance in statistics, especially in quality control techniques for manufacturing processes. A frequent problem in this application is the complexity when trying to determine the behaviour (pattern) from sample data. There have been identified standard patterns which are commonly present when using the X chart; its detection depends on human judgement supported by norms and graphical criteria. In the last few years, it has been demonstrated that Artificial Neural Networks (ANN’s) are useful to predict the type of time-series pattern instead of the use of rules. However, the ANN control parameters have to be fixed to values that maximize its performance. This research proposes an experimental design methodology to determine the most appropriate values for the control parameters of the FuzzyARTMAP ANN such as: learning rate (β ) and network vigilance (ρa, ρb, ρab) in order to increment the neural network efficiency during unnatural pattern recognition.

scholarly journals An Overview of Data Mining with Neural Network

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Learning Ability ◽  
Management Systems ◽  
Important Data ◽  
The Neural Network ◽  
Base Management

With increasing data base management systems applications, large amounts of important data are available much of its knowledge is preserved and concealed. The methods used to extract data from is Data Mining. Various tools are available to forecast the trends that will support decision of people. Neural Networks or Artificial Neural Networks (ANN) have been a promising system in many applications due to their learning ability from data and generalization ability. Neural Networks are used for prediction, classification, forecasting, and pattern recognition. This paper provides a brief overview of Data Mining with the Neural Network, its tools and process

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