scholarly journals Research on Fusion Predictive Control Method of Size and Roundness in Online Grinding

2021 ◽  
Author(s):  
Dongliang Liu ◽  
Peng Zheng ◽  
Manyi Cao ◽  
Zhiyong Zhang ◽  
Yingjie Xu
Keyword(s):  
Short Term Memory ◽  
Control Method ◽  
Calibration Method ◽  
Experimental Results ◽  
Online Measurement ◽  
Processing Efficiency ◽  
Error Accumulation ◽  
Measurement Experiment ◽  
Long Short Term Memory ◽  
The Relationship

Abstract In order to solve the problem that the error evaluation delay and the size and roundness of workpiece can not meet the processing requirements at the same time in online measurement. First this paper proposes an online fusion control method for the size and roundness error of workpiece, which can not only improve the processing efficiency, but also improve the consistency of workpiece quality. Then, the Long Short-Term Memory(LSTM) is used to predict the workpiece information of online measurement, and the error is calibrated according to the predicted value. The LSTM is used to predict the workpiece information in real time, and the process parameters are adjusted in time when the prediction value is out of the theoretical boundary to avoid error accumulation. Finallyr the online grinding measurement experiment based on the LSTM is designed and carried out, and the relationship between the dimension of input tensor and the prediction accuracy is analyzed through the experimental results. The results show that the LSTM can accurately predict the grinding size sequence and roundness sequence, and has good universality. The small batch machining is carried out according to the experimental results. Statistical analysis shows that the grinding accuracy is significantly improved by using the fusion prediction and calibration method.

scholarly journals Hi-C-LSTM: Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of 3D genome conformation

2021 ◽  
Author(s):  
Kevin Dsouza ◽  
Alexandra Maslova ◽  
Ediem Al-Jibury ◽  
Matthias Merkenschlager ◽  
Vijay Bhargava ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Regulatory Elements ◽  
Short Term ◽  
3D Genome ◽  
Nuclear Compartments ◽  
Long Short Term Memory ◽  
Latent Representations ◽  
Low Dimensional ◽  
The Relationship

Abstract Despite the availability of chromatin conformation capture experiments, discerning the relationship between the 1D genome and 3D conformation remains a challenge, which limits our understanding of their affect on gene expression and disease. We propose Hi-C-LSTM, a method that produces low-dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory (LSTM) neural network model. We find that these representations contain all the information needed to recreate the original Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.

Toward Downscaling Storage-Discharge Dynamics: Training Long Short-Term Memory (LSTM) model for Simulating Nonlinear Storage-Discharge Relations at The Rum River Watershed, MN

2021 ◽  
Author(s):  
Pai-Feng Teng ◽  
John Nieber
Keyword(s):  
Short Term Memory ◽  
Water Storage ◽  
Rainfall Runoff ◽  
Total Water ◽  
Short Term ◽  
Term Memory ◽  
Non Linear ◽  
Long Short Term Memory ◽  
The Relationship ◽  
Hydrological Variables

<p>Flooding is one of the most financially devastating natural hazards in the world. Studying storage-discharge relations can have the potential to improve existing flood forecasting systems, which are based on rainfall-runoff models. This presentation will assess the non-linear relation between daily water storage (ΔS) and discharge (Q) simulated by physical-based hydrological models at the Rum River Watershed, a HUC8 watershed in Minnesota, between 1995-2015, by training Long Short-Term Memory (LSTM) networks and other machine learning (ML) algorithms. Currently, linear regression models do not adequately represent the relationship between the simulated total ΔS and total Q at the HUC-8 watershed (R<sup>2</sup> = 0.3667). Since ML algorithms have been used for predicting the outputs that represent arbitrary non-linear functions between predictors and predictands, they will be used for improving the accuracy of the non-linear relation of the storage-discharge dynamics. This research will mainly use LSTM networks, the time-series deep learning neural network that has already been used for predicting rainfall-runoff relations. The LSTM network will be trained to evaluate the storage-discharge relationship by comparing two sets of non-linear hydrological variables simulated by the semi-distributed Hydrological Simulated Program-Fortran (HSPF): the relationship between the simulated discharges and input hydrological variables at selected HUC-8 watersheds, including air temperatures, cloud covers, dew points, potential evapotranspiration, precipitations, solar radiations, wind speeds, and total water storage, and the dynamics between simulated discharge and input variables that do not include the total water storage. The result of this research will lay the foundation for assessing the accuracy of downscaled storage-discharge dynamics by applying similar methods to evaluate the storage-discharge dynamics at small-scaled, HUC-12 watersheds. Furthermore, its results have the potentials for us to evaluate whether downscaling of storage-discharge dynamics at the HUC-12 watershed can improve the accuracy of predicting discharge by comparing the result from the HUC-8 and the HUC-12 watersheds.</p>

scholarly journals Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method

Universe ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 30
Author(s):  
Wanting Zhang ◽  
Xinhua Zhao ◽  
Xueshang Feng ◽  
Cheng’ao Liu ◽  
Nanbin Xiang ◽  
...  
Keyword(s):  
Solar Radio ◽  
Short Term Memory ◽  
Training Dataset ◽  
Radio Flux ◽  
Solar Cycles ◽  
Short Term ◽  
Term Memory ◽  
Solar Radio Flux ◽  
Long Short Term Memory ◽  
The Relationship

As an important index of solar activity, the 10.7-cm solar radio flux (F10.7) can indicate changes in the solar EUV radiation, which plays an important role in the relationship between the Sun and the Earth. Therefore, it is valuable to study and forecast F10.7. In this study, the long short-term memory (LSTM) method in machine learning is used to predict the daily value of F10.7. The F10.7 series from 1947 to 2019 are used. Among them, the data during 1947–1995 are adopted as the training dataset, and the data during 1996–2019 (solar cycles 23 and 24) are adopted as the test dataset. The fourfold cross validation method is used to group the training set for multiple validations. We find that the root mean square error (RMSE) of the prediction results is only 6.20~6.35 sfu, and the correlation coefficient (R) is as high as 0.9883~0.9889. The overall prediction accuracy of the LSTM method is equivalent to those of the widely used autoregressive (AR) and backpropagation neural network (BP) models. Especially for 2-day and 3-day forecasts, the LSTM model is slightly better. All this demonstrates the potentiality of the LSTM method in the real-time forecasting of F10.7 in future.

scholarly journals A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans

2020 ◽  
Author(s):  
Nhan T. Nguyen ◽  
Dat Q. Tran ◽  
Nghia T. Nguyen ◽  
Ha Q. Nguyen
Keyword(s):  
Intracranial Hemorrhage ◽  
Short Term Memory ◽  
Ct Scans ◽  
Single Model ◽  
Short Term ◽  
Novel Method ◽  
Long Short Term Memory ◽  
Almost All ◽  
The Relationship

AbstractWe propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans. The CNN plays the role of a slice-wise feature extractor while the LSTM is responsible for linking the features across slices. The whole architecture is trained end-to-end with input being an RGB-like image formed by stacking 3 different viewing windows of a single slice. We validate the method on the recent RSNA Intracranial Hemorrhage Detection challenge and on the CQ500 dataset. For the RSNA challenge, our best single model achieves a weighted log loss of 0.0522 on the leaderboard, which is comparable to the top 3% performances, almost all of which make use of ensemble learning. Importantly, our method generalizes very well: the model trained on the RSNA dataset significantly outperforms the 2D model, which does not take into account the relationship between slices, on CQ500. Our codes and models will be made public.

scholarly journals The Circular Economy and retail: using Deep Learning to predict business survival

2022 ◽  
Vol 34 (1) ◽  
Author(s):  
Juan Uribe-Toril ◽  
José Luis Ruiz-Real ◽  
Alejandro C. Galindo Durán ◽  
José Antonio Torres Arriaza ◽  
Jaime de Pablo Valenciano
Keyword(s):  
Deep Learning ◽  
Circular Economy ◽  
Short Term Memory ◽  
Retail Sector ◽  
Product Cycle ◽  
Business Survival ◽  
Long Short Term Memory ◽  
Circular Economy System ◽  
The Relationship ◽  
Economy System

Abstract Background The Circular Economy system can improve the product cycle and changes the system and mentality, both for production and the consumer and has become a significant alternative to the classic economic model. The retail sector has also started to advance along these lines. Following an analysis of the state of the art of the Circular Economy and retailing, using bibliometric techniques, our research focuses on understanding if the relationship between circularity and retailing can help us determine a business’ survivability and resilience. To this end, data pertaining to 658 commercial premises from four cities were studied over a period of 11 years. A Deep Learning technique is applied using Long Short-Term Memory to determine if there is a relationship between the resistance of the selected commercial premises, their status in previous periods of time, the type of business activity, and their classification in the Circular Economy plane. Results The system predicts, on the set of tests, with a 93.17% accuracy, the survival of a commercial premises based on the activity, and circularity information before 2012. The results of the training also show very significant precision values of the order of 94.15% with data from the post-depression period. Conclusions The results show that businesses with activities related to the Circular Economy are more likely to survive over extended periods of time.

scholarly journals Hi-C-LSTM: Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation

2021 ◽  
Author(s):  
Kevin Bradley Dsouza ◽  
Alexandra Maslova ◽  
Ediem Al-Jibury ◽  
Matthias Merkenschlager ◽  
Vijay K Bhargava ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Regulatory Elements ◽  
High Accuracy ◽  
Short Term ◽  
Nuclear Compartments ◽  
Long Short Term Memory ◽  
Latent Representations ◽  
Low Dimensional ◽  
The Relationship

Despite the availability of chromatin conformation capture experiments, understanding the relationship between regulatory elements and conformation remains a challenge. We propose Hi-C-LSTM, a method that produces low dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory (LSTM) neural network model. We find that these representations contain all the information needed to recreate the original Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.

Pattern-based dual learning for point-of-interest (POI) recommendation

2020 ◽  
Vol 120 (10) ◽  
pp. 1901-1921
Author(s):  
Tipajin Thaipisutikul ◽  
Yi-Cheng Chen
Keyword(s):  
Recommendation System ◽  
Short Term Memory ◽  
Real Life ◽  
Experimental Results ◽  
Short Term ◽  
Term Memory ◽  
Content Type ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Long Short Term Memory

PurposeTourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.Design/methodology/approachThe authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.FindingsThe authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.Originality/valueThis study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

scholarly journals Detection of Atrial Fibrillation Using a Machine Learning Approach

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 549
Author(s):  
Sidrah Liaqat ◽  
Kia Dashtipour ◽  
Adnan Zahid ◽  
Khaled Assaleh ◽  
Kamran Arshad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Short Term ◽  
Machine Learning Approach ◽  
Long Short Term Memory ◽  
Electrocardiogram Ecg

The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.

Coronavirus Genome Sequence Similarity and Protein Sequence Classification

2021 ◽  
Vol 3 (2) ◽  
pp. 3-18
Author(s):  
Partha Mukherjee ◽  
Youakim Badr ◽  
Srushti Karvekar ◽  
Shanmugapriya Viswanathan
Keyword(s):  
Short Term Memory ◽  
Genomic Structure ◽  
Genome Structure ◽  
Protein Family ◽  
Experimental Results ◽  
Protein Families ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

The world currently is going through a serious pandemic due to the coronavirus disease (COVID-19). In this study, we investigate the gene structure similarity of coronavirus genomes isolated from COVID-19 patients, Severe Acute Respiratory Syndrome (SARS) patients and bats genes. We also explore the extent of similarity between their genome structures to find if the new coronavirus is similar to either of the other genome structures. Our experimental results show that there is 82.42% similarity between the CoV-2 genome structure and the bat genome structure. Moreover, we have used a bidirectional Gated Recurrent Unit (GRU) model as the deep learning technique and an improved variant of Recurrent Neural networks (i.e., Bidirectional Long Short Term Memory model) to classify the protein families of these genomes to isolate the prominent protein family accession. The accuracy of Gated Recurrent Unit (GRU) is 98% for labeled protein sequences against the protein families. By comparing the performance of the Gated Recurrent Unit (GRU) model with the Bidirectional Long Short Term Memory (Bi-LSTM) model results, we found that the GRU model is 1.6% more accurate than the Bi-LSTM model for our multiclass protein classification problem. Our experimental results would be further support medical research purposes in targeting the protein family similarity to better understand the coronavirus genomic structure.

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