Information Source Estimation with Multi-Channel Graph Neural Network

2021 ◽  
pp. 1-27
Author(s):  
Xincheng Shu ◽  
Bin Yu ◽  
Zhongyuan Ruan ◽  
Qingpeng Zhang ◽  
Qi Xuan
Keyword(s):  
Neural Network ◽  
Information Source ◽  
Source Estimation

Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals

2020 ◽  
Vol 15 (1) ◽  
pp. 77-83
Author(s):  
R. Suresh Kumar ◽  
P. Manimegalai
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Information Source ◽  
Activation Function ◽  
Signal Extraction ◽  
Eeg Signal ◽  
Least Mean Square ◽  
Eeg Signals ◽  
Adaptive Line Enhancer ◽  
Field Programmable

Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime with the help of adaptive algorithms (LMS, RLS, Kalman Filter). Results: In this work, the least mean square algorithm was employed to obtain the coefficient update with respect to the corresponding input signal. Finally, Mat lab and verilog HDL language are used to simulate the signals and got the classification accuracy rate of 80%. Conclusion: Experiments show that this method can get high and accurate rate of classification. In this paper, it is proposed that a low-cost use of Field Programmable Gate Arrays (FPGAs) can be used to process EEG signals for extracting and denoising. As a preliminary study, this work shows the implementation of a Neural Network, integrated with ALE for EEG signal processing. The preliminary tests through the proposed architecture for the activation function shows to be reasonable both in terms of precision and in processing speed.

scholarly journals Family member information extraction via neural sequence labeling models with different tag schemes

2019 ◽  
Vol 19 (S10) ◽  
Author(s):  
Hong-Jie Dai
Keyword(s):  
Neural Network ◽  
Family Member ◽  
Network Architecture ◽  
Information Source ◽  
Family Members ◽  
Entity Recognition ◽  
Free Text ◽  
Sequence Labeling ◽  
Extraction Step ◽  
The Family

Abstract Background Family history information (FHI) described in unstructured electronic health records (EHRs) is a valuable information source for patient care and scientific researches. Since FHI is usually described in the format of free text, the entire process of FHI extraction consists of various steps including section segmentation, family member and clinical observation extraction, and relation discovery between the extracted members and their observations. The extraction step involves the recognition of FHI concepts along with their properties such as the family side attribute of the family member concept. Methods This study focuses on the extraction step and formulates it as a sequence labeling problem. We employed a neural sequence labeling model along with different tag schemes to distinguish family members and their observations. Corresponding to different tag schemes, the identified entities were aggregated and processed by different algorithms to determine the required properties. Results We studied the effectiveness of encoding required properties in the tag schemes by evaluating their performance on the dataset released by the BioCreative/OHNLP challenge 2018. It was observed that the proposed side scheme along with the developed features and neural network architecture can achieve an overall F1-score of 0.849 on the test set, which ranked second in the FHI entity recognition subtask. Conclusions By comparing with the performance of conditional random fields models, the developed neural network-based models performed significantly better. However, our error analysis revealed two challenging issues of the current approach. One is that some properties required cross-sentence inferences. The other is that the current model is not able to distinguish between the narratives describing the family members of the patient and those specifying the relatives of the patient’s family members.

A simple gust estimation algorithm and machine learning based nowcasting for wind turbines

2020 ◽  
Author(s):  
Irene Schicker ◽  
Petrina Papazek
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Construction Material ◽  
Information Source ◽  
Estimation Algorithm ◽  
Average Wind Speed ◽  
Average Wind

<p>Wind gusts and high wind speeds need to be considered in wind power industry and power grid management as they affect construction, material, siting and maintenance of turbines and power lines. Furthermore, gusts are an important information source on turbulence conditions in the atmosphere at the respective sites.<br>Often, the wind farm operators only provide basic data of the turbines such as average wind speed, direction, power and temperature. However, they require forecasts of gusts, too. Thus, a simple gust estimation algorithm based on the average wind speed was developed. The algorithm is tested at different mast measurement sites and WFIP2 data and applied to selected wind turbines. Results show that the algorithm is skillful enough to be used as a first guess gust estimation for single turbines and is, thus, used for nowcasting.<br>For nowcasting for the first two hours with a temporal fequency of ten minutes solely observations are used. A high-frequency wind speed and gust nowcasting ensemble based on different machine learning methodologies, including an ensemble for every method, was developd. Used are boosting, random forest, linear regression, a simple monte carlo method and a feed forward neural network. Results show that perturbing the observations provides a good forecasting spread for at least some of the methods. However, for other methods the spread is reduced significantly. Most of the used methods are able to provide good forecastst. However, hyperparameter tuning for the lightGBM boosting algorithm and the neural network is still needed.</p>

scholarly journals Estimating contaminant source in chemical industry park using UAV-based monitoring platform, artificial neural network and atmospheric dispersion simulation

RSC Advances ◽  
2017 ◽  
Vol 7 (63) ◽  
pp. 39726-39738 ◽  
Author(s):  
Sihang Qiu ◽  
Bin Chen ◽  
Rongxiao Wang ◽  
Zhengqiu Zhu ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chemical Industry ◽  
Atmospheric Dispersion ◽  
Emission Source ◽  
Contaminant Source ◽  
Source Estimation ◽  
Artificial Neural ◽  
Using Data ◽  
Monitoring Platform

The ANN-based source estimation approach can effectively locate and quantify the emission source using data from a UAV.

Research on the rapid design technology of automotive inspection structure based on MBD model

2021 ◽  
pp. 095440702110652
Author(s):  
Shuchun Wang ◽  
Qi Cheng ◽  
Lina Wang ◽  
Jingying Xu ◽  
Xifeng Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Information Source ◽  
Frame Structure ◽  
Kernel Principal Component Analysis ◽  
Design Efficiency ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Case Structure ◽  
Inspection Fixture

In view of the specificity and low efficiency of the design of automobile inspection fixture, a deformation design method of inspection fixture based on BP neural network algorithm is proposed. BP neural network algorithm is used to realize the learning and classification of case knowledge, and FCM (fuzzy c-means) algorithm and kernel principal component analysis are used to optimize the information source to improve the retrieval efficiency and accuracy. Based on the analysis of the existing fixture design case structure, the case structure is skeletonized to increase the applicability of the case structure. At the same time, the case frame structure is associated with the size chain, the priority deformation rule is proposed, and the relationship of each size chain is established to realize the mutual adjustment of each size chain. From the similarity of retrieval cases, the paper proposes the design scheme of inspection tools to improve the design efficiency. Finally, taking the front bumper model as the experimental object, the deformation rules are compared, and the priority deformation rule is more accurate than the ordinary basic rule. Compared with the manual design, the design efficiency of this method is improved by 55.71%, which proves the feasibility of this method.

scholarly journals A Model of Spatial Cell Development in Rat Hippocampus Based on Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Naigong Yu ◽  
Hejie Yu ◽  
Yishen Liao ◽  
Zongxia Wang ◽  
Ouattara Sie
Keyword(s):  
Neural Network ◽  
Development Process ◽  
Information Source ◽  
Cell Development ◽  
Place Cells ◽  
Rat Hippocampus ◽  
Cell Group ◽  
Grid Cells ◽  
Juvenile Period ◽  
Spatial Cell

Physiological studies have shown that the hippocampal structure of rats develops at different stages, in which the place cells continue to develop during the whole juvenile period of rats and mature after the juvenile period. As the main information source of place cells, grid cells should mature earlier than place cells. In order to make better use of the biological information exhibited by the rat brain hippocampus in the environment, we propose a position cognition model based on the spatial cell development mechanism of rat hippocampus. The model uses a recurrent neural network with parametric bias (RNNPB) to simulate changes in the discharge characteristics during the development of a single stripe cell. The oscillatory interference mechanism is able to fuse the developing stripe waves, thus indirectly simulating the developmental process of the grid cells. The output of the grid cells is then used as the information input of the place cells, whose development process is simulated by BP neural network. After the place cells matured, the position matrix generated by the place cell group was used to realize the position cognition of rats in a given spatial region. The experimental results show that this model can simulate the development process of grid cells and place cells, and it can realize high precision positioning in the given space area. Moreover, the experimental effect of cognitive map construction using this model is basically consistent with the effect of RatSLAM, which verifies the validity and accuracy of the model.

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