The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding

2011 ◽  
Vol 11 (8) ◽  
pp. 4839-4846 ◽  
Author(s):  
Enkhsaikhan Boldsaikhan ◽  
Edward M. Corwin ◽  
Antonette M. Logar ◽  
William J. Arbegast
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Friction Stir Welding ◽  
Real Time ◽  
Discrete Fourier Transform ◽  
Friction Stir ◽  
Time Evaluation

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

Effect of process parameters on temperature and force distribution during friction stir welding of armor-marine grade aluminum alloy

2020 ◽  
Vol 235 (1-2) ◽  
pp. 144-154 ◽  
Author(s):  
Shubham Verma ◽  
Joy Prakash Misra
Keyword(s):  
Friction Stir Welding ◽  
Real Time ◽  
Process Parameters ◽  
Tilt Angle ◽  
Welding Speed ◽  
Low Cost ◽  
Maximum Force ◽  
Maximum Temperature ◽  
Force Distribution ◽  
Friction Stir

This research investigates the effect of process parameters on real-time temperature and forces distribution during friction stir welding of AA7039. Experiments are conducted at different rotational speed, welding speed, and tilt angle conditions. For the experimentation, a low-cost real-time force-measuring fixture is indigenously developed in-house. However, eight K-type L-shaped thermocouples are used to examine the real-time temperature distribution. The forces in the z-direction are of a higher magnitude than the x-direction. The maximum force in the z-direction of 3.25 kN is witnessed for 2° tilt angle and a minimum of 2.1 kN for 26 mm/min of welding speed. The maximum force in the x-direction of 0.97 kN is obtained at 2° tilt angle and a minimum of 0.27 kN is obtained at 1.3° tilt angle. The maximum temperature of 390 °C is observed at 1812 r/min, whereas a minimum of 283 °C is observed at 43 mm/min of welding speed. The variations in temperature and force distribution during friction stir welding are also evaluated by utilizing two phenomenological models.

Non-Invasive Optical Blood Glucose Measurement based on Discrete Fourier Transform and Fast Artificial Neural Network: Fasting Normal Glucose Participants Case Study

2019 ◽  
Author(s):  
Renan Prasta Jenie ◽  
Evy Damayanthi ◽  
Irzaman Irzaman ◽  
Rimbawan Rimbawan ◽  
Dadang Sukandar ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Trial ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Blood Glucose ◽  
Discrete Fourier Transform ◽  
Glucose Measurement ◽  
Non Invasive ◽  
Artificial Neural

A prototype non-invasive blood glucose level measurement optical device (NI-BGL-MOD) has been developed. The NI-BGL-MOD uses a discrete Fourier transform (DFT) method and a fast artificial neural network algorithm to optimize device performance. The appropriate light-emitting diode for the sensory module was selected based on near-infrared spectrophotometry of a blood glucose model and human blood. DFT is implemented in an analog-to-digital converter module. An in vitro trial using the blood glucose model along with a clinical trial involving 110 participants were conducted to evaluate the performance of the prototype. The root-mean-square error of the prototype was 10.8 mg/dl in the in vitro trial and 3.64 mg/dl in the clinical trial, which is lower than the ISO-15197:2016 mandated value of 10 mg/dl. In each trial, consensus error grid analysis indicated that the measurement error was within the safe range. The sensitivity and specificity of the prototype were 0.83 (0.36, 1.00) and 0.90 (0.55, 1.00) in the in vitro trial and 0.81 (0.75, 0.85) and 0.83 (0.78, 0.87) in the clinical trial, respectively. In general, the proposed NI-BGL-MOD demonstrated good performance than gold-standard measurement. Key words: Non-invasive blood glucose measurement, optical device, discrete Fourier transform, multi-formulatric regression, fast artificial neural network

Comb-Assisted Real-Time Discrete Fourier Transform Processor

2017 ◽  
Author(s):  
H. Hu ◽  
D. J. Esman ◽  
V. Ataie ◽  
E. Temprana ◽  
B. P.-P. Kuo ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Real Time ◽  
Discrete Fourier Transform

scholarly journals Improving ENIGMA-style Clause Selection while Learning From History

2021 ◽  
pp. 543-561
Author(s):  
Martin Suda
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Theorem Prover ◽  
Central Idea ◽  
Theorem Provers ◽  
Recursive Neural Network ◽  
Time Evaluation ◽  
Automatic Theorem ◽  
Learning From History

AbstractWe re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a recursive neural network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41 % improvement on a relevant subset of SMT-LIB in a real time evaluation.

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