scholarly journals Prediction of Flexion and Extension Movements of 4 Fingers of the Hand Using a New Labeled Method

2021 ◽  
Vol 2008 (1) ◽  
pp. 012015
Author(s):  
J A García Torres ◽  
A Ibarra Fuentes ◽  
E Morales Sánchez ◽  
A Hernández Zavala
Keyword(s):  
Neural Network ◽  
Ring Finger ◽  
Middle Finger ◽  
Mean Value ◽  
Semg Signal ◽  
The Neural Network ◽  
The Time Domain ◽  
Labeling Method ◽  
Time Segmentation ◽  
Flexion And Extension

Abstract This work presents a neural network classifier for identifying the flexion and extension movements for four fingers from the hand, out of the surface electromyography signals in the forearm muscles. A new labeled data method was proposed based on time segmentation to relate the sEMG signal with the corresponding finger movement. This is a different way of labeling the data for training the neural network, a llowing to reduce the amount of training gesture hand. The experiment consists of 10 sessions in which the fingers are flexed randomly, one at a time for 2 minutes with a 16ms sample time. The absolute mean value (MAV) is used as a feature extractor in the time domain to a verage 5 samples a nd the normalized data is used for the neural network. Results show that this system with the labeled data method, provides a 98.83% precision value for the index finger, 93.46% for the ring finger, 80.34% for the middle finger, and 68.46% for the little finger. The results are simila r to those found in the literature where they classify different gestures using the conventional labeling method.

scholarly journals Identification of 7 Movements of the Human Hand Using sEMG - 360° on the Forearm

2021 ◽  
Author(s):  
◽  
A. Ibarra-Fuentes
Keyword(s):  
Time Domain ◽  
Index Finger ◽  
Ring Finger ◽  
Middle Finger ◽  
Svm Classifier ◽  
Human Hand ◽  
Absolute Value ◽  
The Time Domain ◽  
Flexion And Extension ◽  
Semg Signals

This document shows the identification of 7 gestures (movements) of the human hand from sEMG – 360° signals in the forearm. sEMG – 360° is the sEMG measurement through 8 channels every 45° making a total of 360°. When making a hand gesture, there will be 8 independents sEMG signals that will be used to identify the movement. The 7 gestures to identify are: relaxed hand (closed), open hand (fingers extended), flexion and extension of the little finger, the ring finger, the middle finger, the index finger and the thumb separately. 100 samples of each of the gesture were captured and 3 feature extractors were applied in the time domain (mean absolute value (MAV), root mean square value (RMS) and area vale under the curve (CUA)), then a vector support machine (SVM) classifier was applied to each extractor. The movements were identified and the percentage of accuracy in the identification was calculated for each extractor + SVM classifier. The calculation of the percentage of accuracy took into account the 8 channels for each gesture. 97.61 % accuracy was achieved in the identification of human hand gestures by applying sEMG – 360°.

Research on the Machining Status Monitoring of CNC Machine Tools Based on Artificial Neural Network

2011 ◽  
Vol 338 ◽  
pp. 685-688
Author(s):  
Jin Hua Li ◽  
Yong Xian Liu ◽  
Deng Li Yi ◽  
De Qiang Zhang
Keyword(s):  
Neural Network ◽  
Layer Structure ◽  
Mean Value ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Characteristic Quantity ◽  
Network Training ◽  
The Neural Network ◽  
Ae Signal ◽  
The Time Domain

Vibration acceleration and AE signal were analyzed in the time domain, and they sampled the mean value and mean value of energy. The two values were used as the basis of the state recognition and tool wear prediction. In Labview, the neural network model was established, and it had three-layer structure. the nonlinear mapping was realized between machining state and characteristic quantity. The outcome could show that the machining state was monitored by the neural network training.

scholarly journals A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Qi ◽  
Yanan Zhao ◽  
Yufang Huang ◽  
Yang Wang ◽  
Wei Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Phones ◽  
Mean Value ◽  
Wheat Cultivars ◽  
N Content ◽  
Color Parameters ◽  
The Neural Network ◽  
The Mean ◽  
Leaf N Content ◽  
Leaf N

AbstractThe accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.

Discovering Gathering Pattern Using a Taxicab Service Rate Analysis Method based on Neural Network

2020 ◽  
pp. 408-428
Author(s):  
Junming Zhang ◽  
Jinglin Li
Keyword(s):  
Neural Network ◽  
Moving Objects ◽  
High Volume ◽  
Mean Value ◽  
Service Rate ◽  
Trajectory Data ◽  
Rate Analysis ◽  
The Neural Network ◽  
Time Period ◽  
Effectiveness And Efficiency

Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, how to improve the effectiveness and efficiency of the gathering pattern discovering method still remains as a challenging issue since the large number of moving objects will generate high volume of trajectory data. In order to address this issue, the authors propose a method to discovering the gathering pattern by analyzing the taxicab demand. This paper first introduces the concept of Taxicab Service Rate (TSR). In this method, they use the KS measures to test the distribution of TSR and calculate the mean value of the TSR of a certain time period. Then, the authors use a neural network based method Neural Network Gathering Discovering (NNGD) to detect the gathering pattern. The neural network is based on the knowledge of historical gathering pattern data. The authors have implemented their method with experiments based on real trajectory data. The results show the both effectiveness and efficiency of their method.

scholarly journals Novel Alphabet Deduction Using MATLAB by Neural Networks and Comparison with the Fuzzy Classifier

2021 ◽  
Vol 36 (1) ◽  
pp. 623-628
Author(s):  
Bapatu Siva Kumar Reddy ◽  
P. Vishnu Vardhan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Recognition Rate ◽  
Software Tool ◽  
Mean Value ◽  
Fuzzy Classifier ◽  
The Neural Network ◽  
Accuracy Level ◽  
The Neural Networks

Aim: The study aims to identify or recognize the alphabets using neural networks and fuzzy classifier/logic. Methods and materials: Neural network and fuzzy classifier are used for comparing the recognition of characters. For each classifier sample size is 20. Character recognition was developed using MATLAB R2018a, a software tool. The algorithm is again compared with the Fuzzy classifier to know the accuracy level. Results: Performance of both fuzzy classifier and neural networks are calculated by the accuracy value. The mean value of the fuzzy classifier is 82 and the neural network is 77. The recognition rate (accuracy) with the data features is found to be 98.06%. Fuzzy classifier shows higher significant value of P=0.002 < P=0.005 than the neural networks in recognition of characters. Conclusion: The independent tests for this study shows a higher accuracy level of alphabetical character recognition for Fuzzy classifier when compared with neural networks. Henceforth, the fuzzy classifier shows higher significant than the neural networks in recognition of characters.

GEAR FAULT DIAGNOSIS IN TIME DOMAINS VIA BAYESIAN NETWORKS

2013 ◽  
Vol 37 (3) ◽  
pp. 665-672 ◽  
Author(s):  
Chun-Chieh Wang ◽  
Yuan Kang ◽  
Chin-Chi Liao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bayesian Networks ◽  
Rolling Bearing ◽  
Vibration Signals ◽  
The Neural Network ◽  
Training Samples ◽  
Time Domains ◽  
Gear Fault ◽  
The Time Domain

In gear or rolling bearing systems, it is difficult to extract symptoms from vibration signals where shock vibration signals are present. However, the neural network method cannot provide satisfactory diagnosis results without adequate training samples. Bayesian networks provide an effective approach for fault diagnosis in cases given uncertain and incomplete information. In this study, the statistical factors of vibration signals in the time-domain were used and the diagnosis results by using Bayesian networks were superior to other neural network methods.

Wavelet-Based Neural Network Adaptive Filter for sEMG Denoising

2011 ◽  
Vol 121-126 ◽  
pp. 4259-4264 ◽  
Author(s):  
Jian Hui Wang ◽  
Na Chen ◽  
Qian Xiao ◽  
Jian You Xu ◽  
Shu Sheng Gu
Keyword(s):  
Neural Network ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Signal Denoising ◽  
Semg Signal ◽  
Filter Model ◽  
The Neural Network ◽  
Filtering Effect

For the large computation workload of the adaptive filter algorithm and the low filtering speed of the adaptive filter model based on wavelet transform, a wavelet-based neural network adaptive filter model is constructed in this paper. As the neural network has the capacity of distributed storage and fast self-evolution, Hopfield neural network is used to implement adaptive filtering algorithm LMS, so as to increase the computing speed. The model applied to sEMG signal denoising can achieve a better filtering effect.

scholarly journals Collective Dynamics of Neural Networks With Sleep-Related Biological Drives in Drosophila

2021 ◽  
Vol 15 ◽  
Author(s):  
Shuihan Qiu ◽  
Kaijia Sun ◽  
Zengru Di
Keyword(s):  
Neural Network ◽  
Field Potential ◽  
Mean Value ◽  
Network Structures ◽  
Regular Spike ◽  
Wake Time ◽  
The Neural Network ◽  
The Mean ◽  
The Brain ◽  
Local Field Potential Lfp

The collective electrophysiological dynamics of the brain as a result of sleep-related biological drives in Drosophila are investigated in this paper. Based on the Huber-Braun thermoreceptor model, the conductance-based neurons model is extended to a coupled neural network to analyze the local field potential (LFP). The LFP is calculated by using two different metrics: the mean value and the distance-dependent LFP. The distribution of neurons around the electrodes is assumed to have a circular or grid distribution on a two-dimensional plane. Regardless of which method is used, qualitatively similar results are obtained that are roughly consistent with the experimental data. During wake, the LFP has an irregular or a regular spike. However, the LFP becomes regular bursting during sleep. To further analyze the results, wavelet analysis and raster plots are used to examine how the LFP frequencies changed. The synchronization of neurons under different network structures is also studied. The results demonstrate that there are obvious oscillations at approximately 8 Hz during sleep that are absent during wake. Different time series of the LFP can be obtained under different network structures and the density of the network will also affect the magnitude of the potential. As the number of coupled neurons increases, the neural network becomes easier to synchronize, but the sleep and wake time described by the LFP spectrogram do not change. Moreover, the parameters that affect the durations of sleep and wake are analyzed.

Discovering Gathering Pattern Using a Taxicab Service Rate Analysis Method based on Neural Network

2016 ◽  
Vol 8 (2) ◽  
pp. 23-42 ◽  
Author(s):  
Junming Zhang ◽  
Jinglin Li
Keyword(s):  
Neural Network ◽  
Moving Objects ◽  
High Volume ◽  
Mean Value ◽  
Service Rate ◽  
Trajectory Data ◽  
Rate Analysis ◽  
The Neural Network ◽  
Time Period ◽  
Effectiveness And Efficiency

Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, how to improve the effectiveness and efficiency of the gathering pattern discovering method still remains as a challenging issue since the large number of moving objects will generate high volume of trajectory data. In order to address this issue, the authors propose a method to discovering the gathering pattern by analyzing the taxicab demand. This paper first introduces the concept of Taxicab Service Rate (TSR). In this method, they use the KS measures to test the distribution of TSR and calculate the mean value of the TSR of a certain time period. Then, the authors use a neural network based method Neural Network Gathering Discovering (NNGD) to detect the gathering pattern. The neural network is based on the knowledge of historical gathering pattern data. The authors have implemented their method with experiments based on real trajectory data. The results show the both effectiveness and efficiency of their method.

scholarly journals Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7187
Author(s):  
Chia-Ming Tsai ◽  
Chiao-Sheng Wang ◽  
Yu-Jen Chung ◽  
Yung-Da Sun ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rapid Development ◽  
Sensor Fault ◽  
The Neural Network ◽  
Signal Input ◽  
Diagnosis Method ◽  
The One ◽  
The Time Domain ◽  
Single Neural Network

With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller’s health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.

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