scholarly journals Machine learning of electro-hydraulic motor dynamics

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Luca Baronti ◽  
Biao Zhang ◽  
Marco Castellani ◽  
Duc Truong Pham
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Analytical Model ◽  
Random Search ◽  
Bees Algorithm ◽  
Multi Layer Perceptron ◽  
Hydraulic Motor ◽  
Neural Network Models ◽  
Load Simulator ◽  
Intelligent Optimisation

AbstractIn this paper we propose an innovative machine learning approach to the hydraulic motor load balancing problem involving intelligent optimisation and neural networks. Two different nonlinear artificial neural network approaches are investigated, and their accuracy is compared to that of a linearised analytical model. The first neural network approach uses a multi-layer perceptron to reproduce the load simulator dynamics. The multi-layer perceptron is trained using the Rprop algorithm. The second approach uses a hybrid scheme featuring an analytical model to represent the main system behaviour, and a multi-layer perceptron to reproduce unmodelled nonlinear terms. Four techniques are tested for the optimisation of the parameters of the analytical model: random search, an evolutionary algorithm, particle swarm optimisation, and the Bees Algorithm. Experimental tests on 4500 real data samples from an electro-hydraulic load simulator rig reveal that the accuracy of the hybrid and the neural network models is comparable, and significantly superior to the accuracy of the analytical model. The results of the optimisation procedures suggest also that the inferior performance of the analytical model is likely due to the non-negligible magnitude of the unmodelled nonlinearities, rather than suboptimal setting of the parameters. Despite its limitations, the analytical linear model performs comparably to the state-of-the-art in the literature, whilst the neural and hybrid approaches compare favourably.

First-Break Picking Classification Models Using Recurrent Neural Network

2021 ◽  
Author(s):  
Mohammed Ayub ◽  
SanLinn Kaka
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Contextual Information ◽  
Classification Model ◽  
Superior Performance ◽  
Learning Models ◽  
Neural Network Models ◽  
Minimum Number ◽  
Machine Learning Models

Abstract Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.

scholarly journals Comparison of rule-based and neural network models for negation detection in radiology reports

2020 ◽  
pp. 1-22 ◽  
Author(s):  
D. Sykes ◽  
A. Grivas ◽  
C. Grover ◽  
R. Tobin ◽  
C. Sudlow ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Network Models ◽  
Neural Network Models ◽  
Test Set ◽  
Rule Based ◽  
Radiology Reports ◽  
The Neural Network ◽  
Negation Detection

Abstract Using natural language processing, it is possible to extract structured information from raw text in the electronic health record (EHR) at reasonably high accuracy. However, the accurate distinction between negated and non-negated mentions of clinical terms remains a challenge. EHR text includes cases where diseases are stated not to be present or only hypothesised, meaning a disease can be mentioned in a report when it is not being reported as present. This makes tasks such as document classification and summarisation more difficult. We have developed the rule-based EdIE-R-Neg, part of an existing text mining pipeline called EdIE-R (Edinburgh Information Extraction for Radiology reports), developed to process brain imaging reports, (https://www.ltg.ed.ac.uk/software/edie-r/) and two machine learning approaches; one using a bidirectional long short-term memory network and another using a feedforward neural network. These were developed on data from the Edinburgh Stroke Study (ESS) and tested on data from routine reports from NHS Tayside (Tayside). Both datasets consist of written reports from medical scans. These models are compared with two existing rule-based models: pyConText (Harkema et al. 2009. Journal of Biomedical Informatics42(5), 839–851), a python implementation of a generalisation of NegEx, and NegBio (Peng et al. 2017. NegBio: A high-performance tool for negation and uncertainty detection in radiology reports. arXiv e-prints, p. arXiv:1712.05898), which identifies negation scopes through patterns applied to a syntactic representation of the sentence. On both the test set of the dataset from which our models were developed, as well as the largely similar Tayside test set, the neural network models and our custom-built rule-based system outperformed the existing methods. EdIE-R-Neg scored highest on F1 score, particularly on the test set of the Tayside dataset, from which no development data were used in these experiments, showing the power of custom-built rule-based systems for negation detection on datasets of this size. The performance gap of the machine learning models to EdIE-R-Neg on the Tayside test set was reduced through adding development Tayside data into the ESS training set, demonstrating the adaptability of the neural network models.

scholarly journals A Novel Analytical-ANN Hybrid Model for Borehole Heat Exchanger

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6213
Author(s):  
Anjan Rao Puttige ◽  
Staffan Andersson ◽  
Ronny Östin ◽  
Thomas Olofsson
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Analytical Model ◽  
Hybrid Model ◽  
Time Resolution ◽  
Network Models ◽  
Borehole Heat Exchanger ◽  
Neural Network Models ◽  
Ground Source

Optimizing the operation of ground source heat pumps requires simulation of both short-term and long-term response of the borehole heat exchanger. However, the current physical and neural network based models are not suited to handle the large range of time scales, especially for large borehole fields. In this study, we present a hybrid model for long-term simulation of BHE with high resolution in time. The model uses an analytical model with low time resolution to guide an artificial neural network model with high time resolution. We trained, tuned, and tested the hybrid model using measured data from a ground source heat pump in real operation. The performance of the hybrid model is compared with an analytical model, a calibrated analytical model, and three different types of neural network models. The hybrid model has a relative RMSE of 6% for the testing period compared to 22%, 14%, and 12% respectively for the analytical model, the calibrated analytical model, and the best of the three investigated neural network models. The hybrid model also has a reasonable computational time and was also found to be robust with regard to the model parameters used by the analytical model.

scholarly journals NeuRiPP: Neural network identification of RiPP precursor peptides

2019 ◽  
Author(s):  
Emmanuel L.C. de los Santos
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Models ◽  
Gene Clusters ◽  
Learning Tools ◽  
Neural Network Models ◽  
Data Set ◽  
The Rich ◽  
Tailoring Enzymes ◽  
Rich Data

ABSTRACTSignificant progress has been made in the past few years on the computational identification biosynthetic gene clusters (BGCs) that encode ribosomally synthesized and post-translationally modified peptides (RiPPs). This is done by identifying both RiPP tailoring enzymes (RTEs) and RiPP precursor peptides (PPs). However, identification of PPs, particularly for novel RiPP classes remains challenging. To address this, machine learning has been used to accurately identify PP sequences. However, current machine learning tools have limitations, since they are specific to the RiPP-class they are trained for, and are context-dependent, requiring information about the surrounding genetic environment of the putative PP sequences. NeuRiPP overcomes these limitations. It does this by leveraging the rich data set of high-confidence putative PP sequences from existing programs, along with experimentally verified PPs from RiPP databases. NeuRiPP uses neural network models that are suitable for peptide classification with weights trained on PP datasets. It is able to identify known PP sequences, and sequences that are likely PPs. When tested on existing RiPP BGC datasets, NeuRiPP is able to identify PP sequences in significantly more putative RiPP clusters than current tools, while maintaining the same HMM hit accuracy. Finally, NeuRiPP was able to successfully identify PP sequences from novel RiPP classes that are recently characterized experimentally, highlighting its utility in complementing existing bioinformatics tools.

scholarly journals Thinking like a naturalist: enhancing computer vision of citizen science images by harnessing contextual data

2019 ◽  
Author(s):  
J. Christopher D. Terry ◽  
Helen E. Roy ◽  
Tom A. August
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Contextual Information ◽  
Network Models ◽  
List Type ◽  
British Isles ◽  
Automated Identification ◽  
Neural Network Models ◽  
Contextual Data

AbstractThe accurate identification of species in images submitted by citizen scientists is currently a bottleneck for many data uses. Machine learning tools offer the potential to provide rapid, objective and scalable species identification for the benefit of many aspects of ecological science. Currently, most approaches only make use of image pixel data for classification. However, an experienced naturalist would also use a wide variety of contextual information such as the location and date of recording.Here, we examine the automated identification of ladybird (Coccinellidae) records from the British Isles submitted to the UK Ladybird Survey, a volunteer-led mass participation recording scheme. Each image is associated with metadata; a date, location and recorder ID, which can be cross-referenced with other data sources to determine local weather at the time of recording, habitat types and the experience of the observer. We built multi-input neural network models that synthesise metadata and images to identify records to species level.We show that machine learning models can effectively harness contextual information to improve the interpretation of images. Against an image-only baseline of 48.2%, we observe a 9.1 percentage-point improvement in top-1 accuracy with a multi-input model compared to only a 3.6% increase when using an ensemble of image and metadata models. This suggests that contextual data is being used to interpret an image, beyond just providing a prior expectation. We show that our neural network models appear to be utilising similar pieces of evidence as human naturalists to make identifications.Metadata is a key tool for human naturalists. We show it can also be harnessed by computer vision systems. Contextualisation offers considerable extra information, particularly for challenging species, even within small and relatively homogeneous areas such as the British Isles. Although complex relationships between disparate sources of information can be profitably interpreted by simple neural network architectures, there is likely considerable room for further progress. Contextualising images has the potential to lead to a step change in the accuracy of automated identification tools, with considerable benefits for large scale verification of submitted records.

scholarly journals Energy and Policy Considerations for Modern Deep Learning Research

2020 ◽  
Vol 34 (09) ◽  
pp. 13693-13696
Author(s):  
Emma Strubell ◽  
Ananya Ganesh ◽  
Andrew McCallum
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Network Models ◽  
Intelligence Community ◽  
Environmental Costs ◽  
Neural Network Models ◽  
Learning Research ◽  
Specialized Hardware ◽  
Compute Time

The field of artificial intelligence has experienced a dramatic methodological shift towards large neural networks trained on plentiful data. This shift has been fueled by recent advances in hardware and techniques enabling remarkable levels of computation, resulting in impressive advances in AI across many applications. However, the massive computation required to obtain these exciting results is costly both financially, due to the price of specialized hardware and electricity or cloud compute time, and to the environment, as a result of non-renewable energy used to fuel modern tensor processing hardware. In a paper published this year at ACL, we brought this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training and tuning neural network models for NLP (Strubell, Ganesh, and McCallum 2019). In this extended abstract, we briefly summarize our findings in NLP, incorporating updated estimates and broader information from recent related publications, and provide actionable recommendations to reduce costs and improve equity in the machine learning and artificial intelligence community.

Klasifikasi motif batik menggunakan Convolutional Neural Network

JNANALOKA ◽  
2020 ◽  
pp. 45-50
Author(s):  
Rizki Mawan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Multi Layer Perceptron

Batik adalah bentuk seni visual pada bahan tekstil yang diproduksi menggunakan teknik menggambar tradisional yang berasal dari Indonesia. Oleh karena itu dibutuhkan penelitian untuk meneliti batik yang bertujuan untuk mengetahui motif dan melestarikannya. Convolutional Neural Network(CNN) adalah salah satu metode machine learning dari pengembangan Multi Layer Perceptron (MLP) yang didesain untuk mengolah data dua dimensi. CNN termasuk dalam jenis Deep Neural Network karena dalamnya tingkat jaringan dan banyak diimplementasikan dalam data citra. Eksperimen menggunakan Dataset 120 potongan foto Batik (3 kelas) menunjukkan bahwa model yang menggunakan CNN mencapai rata-rata akurasi 65% sedangkan model CNN dikombinasi dengan Grayscale mencapai rata-rata akurasi 70%. Meskipun demikian dengan penambahan Grayscale akurasi bertambah 5%.

Data processing using deep learning of the generative-adversarial neural network (GAN)

2021 ◽  
Author(s):  
V.Y. Ilichev ◽  
I.V. Chukhraev
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Effective Means ◽  
Network Models ◽  
Neural Network Models ◽  
High Quality ◽  
Python Language ◽  
And Training

The article is devoted to the consideration of one of the areas of application of modern and promising computer technology – machine learning. This direction is based on the creation of models consisting of neural networks and their deep learning. At present, there is a need to generate new, not yet existing, images of objects of different types. Most often, text files or images act as such objects. To achieve a high quality of results, a generation method based on the adversarial work of two neural networks (generator and discriminator) was once worked out. This class of neural network models is distinguished by the complexity of topography, since it is necessary to correctly organize the structure of neural layers in order to achieve maximum accuracy and minimal error. The described program is created using the Python language and special libraries that extend the set of commands for performing additional functions: working with neural networks Keras (main library), integrating with the operating system Os, outputting graphs Matplotlib, working with data arrays Numpy and others. A description is given of the type and features of each neural layer, as well as the use of library connection functions, input of initial data, compilation and training of the obtained model. Next, the implementation of the procedure for outputting the results of evaluating the errors of the generator and discriminator and the accuracy achieved by the model depending on the number of cycles (eras) of its training is considered. Based on the results of the work, conclusions were drawn and recommendations were made for the use and development of the considered methodology for creating and training generative and adversarial neural networks. Studies have demonstrated the procedure for operating with comparatively simple and accessible, but effective means of a universal Python language with the Keras library to create and teach a complex neural network model. In fact, it has been proved that the use of this method allows to achieve high-quality results of machine learning, previously achievable only when using special software systems for working with neural networks.

Klasifikasi motif batik menggunakan Convolutional Neural Network

JNANALOKA ◽  
2020 ◽  
pp. 45-50
Author(s):  
Rizki Mawan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Multi Layer Perceptron

Batik adalah bentuk seni visual pada bahan tekstil yang diproduksi menggunakan teknik menggambar tradisional yang berasal dari Indonesia. Oleh karena itu dibutuhkan penelitian untuk meneliti batik yang bertujuan untuk mengetahui motif dan melestarikannya. Convolutional Neural Network(CNN) adalah salah satu metode machine learning dari pengembangan Multi Layer Perceptron (MLP) yang didesain untuk mengolah data dua dimensi. CNN termasuk dalam jenis Deep Neural Network karena dalamnya tingkat jaringan dan banyak diimplementasikan dalam data citra. Eksperimen menggunakan Dataset 120 potongan foto Batik (3 kelas) menunjukkan bahwa model yang menggunakan CNN mencapai rata-rata akurasi 65% sedangkan model CNN dikombinasi dengan Grayscale mencapai rata-rata akurasi 70%. Meskipun demikian dengan penambahan Grayscale akurasi bertambah 5%.

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