scholarly journals Chaotic Resonance in Izhikevich Neural Network Motifs Under Electromagnetic Induction

2021 ◽  
Author(s):  
Guowei Wang ◽  
Lijian Yang ◽  
Xuan Zhan ◽  
Anbang Li ◽  
Ya Jia
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Electromagnetic Induction ◽  
Current Intensity ◽  
Signal Frequency ◽  
Network Motifs ◽  
Neuronal System ◽  
Weak Signal ◽  
Weak Signals ◽  
The Impact

Abstract Chaotic resonance (CR) is the response of a nonlinear system to weak signals enhanced by internal or external chaotic activity (such as the signal derived from Lorenz system). In this paper, the triple-neuron feed-forward loop (FFL) Izhikevich neural network motifs with eight types are constructed as the nonlinear systems, and the effects of EMI on CR phenomenon in FFL neuronal network motifs are studied. It is found that both the single Izhikevich neural model under electromagnetic induction (EMI) and its network motifs exhibit CR phenomenon depending on the chaotic current intensity. There exists an optimal chaotic current intensity ensuring the best detection of weak signal in single Izhikevich neuron or its network motifs via CR. The EMI can enhance the ability of neuron to detect weak signals. For T1-FFL and T2-FFL motifs, the adjustment of EMI parameters makes T2-FFL show a more obvious CR phenomenon than that for T1-FFL motifs, which is different from the impact of system parameters (e.g., the weak signal frequency, the coupling strength, and the time delay) on CR. Another interesting phenomenon is that the variation of CR with time delay exhibits quasi periodic characteristics. Our results showed that CR effect is a robust phenomenon which is observed in both single Izhikevich neuron and network motifs, which might help one understand how to improve the ability of weak signal detection and propagation in neuronal system.

scholarly journals Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3373
Author(s):  
Kai Chen ◽  
Kai Xie ◽  
Chang Wen ◽  
Xin-Gong Tang
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Short Term Memory ◽  
Ensemble Empirical Mode Decomposition ◽  
Generative Adversarial Networks ◽  
Natural Mode ◽  
Weak Signal ◽  
Weak Signals ◽  
Mode Decomposition ◽  
Enhancement Method

In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.

scholarly journals Weak Signal Enhancement For Small Drill Condition Monitoring In PCB Drilling Process By Using Adaptive Multistable Stochastic Resonance

2021 ◽  
Author(s):  
Qifeng Tan ◽  
Guodong Liu ◽  
Yong Li ◽  
Hao Tong
Keyword(s):  
Tool Wear ◽  
Stochastic Resonance ◽  
Condition Monitoring ◽  
Printed Circuit Boards ◽  
Hole Drilling ◽  
Drilling Process ◽  
Weak Signal ◽  
Weak Signals ◽  
The Impact ◽  
Sr Method

Abstract The on-line tool condition monitoring is demanded to detect the tool wear and to ensure the hole drilling process of printed circuit boards (PCB) goes on smoothly. However, due to the impact of ambient noise caused by the limited size of small drill and the laminated material of PCB, the tool wear signal features are too weak to extract. The stochastic resonance (SR) method has been proven to be effective in enhancing weak signals among various weak signal extraction. In this paper, an adaptive multistable stochastic resonance is presented to improve performance of the SR method and process the tool wear signals for PCB drilling. The differential evolution (DE) algorithm is applied to adaptively optimize potential parameters and compensation factor, which makes the SR method suitable for high frequency signal. Moreover, tool wear experiments with different drill wear are carried out to verify the effectiveness of the proposed method. The results indicate that the proposed method improves the signal-to-noise ratio and has great potential in enhancing weak signals for small drill condition monitoring in PCB drilling process.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

European Economies Stability Faced With Potential Outburst of Sovereign Debt Crisis. An Empirical Study Using Neural Network

2013 ◽  
Vol 12 (2) ◽  
pp. 3255-3260
Author(s):  
Stelian Stancu ◽  
Alexandra Maria Constantin
Keyword(s):  
Neural Network ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Banking System ◽  
Sovereign Debt Crisis ◽  
European Level ◽  
Continuous Growth ◽  
Economic Context ◽  
European Economies ◽  
The Impact

Instilment, on a European level, of a state incompatible with the state of stability on a macroeconomic level and in the financial-banking system lead to continuous growth of vulnerability of European economies, situated at the verge of an outburst of sovereign debt crises. In this context, the current papers main objective is to produce a study regarding the vulnerability of European economies faced with potential outburst of sovereign debt crisis, which implies quantitative analysis of the impact of sovereign debt on the sensitivity of the European Unions economies. The paper also entails the following specific objectives: completing an introduction in the current European economic context, conceptualization of the notion of “sovereign debt crisis, presenting the methodology and obtained empirical results, as well as exposition of the conclusions.

A Time Delay Neural Network with Shared Weight Self-Attention for Small-Footprint Keyword Spotting

2019 ◽  
Author(s):  
Ye Bai ◽  
Jiangyan Yi ◽  
Jianhua Tao ◽  
Zhengqi Wen ◽  
Zhengkun Tian ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Keyword Spotting ◽  
Small Footprint

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

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