Continuous Drilling Sensor Data Reconstruction and Prediction via Recurrent Neural Networks

2020 ◽  
Author(s):  
Andrzej T. Tunkiel ◽  
Tomasz Wiktorski ◽  
Dan Sui
Keyword(s):  
Neural Networks ◽  
Absolute Error ◽  
Sensor Data ◽  
Neural Network Approach ◽  
Hard Drives ◽  
Exhaust Temperature ◽  
Novel Approach ◽  
Weight On Bit ◽  
Curved Section ◽  
Oilfield Operations

Abstract There is an ever-increasing amount of data being recorded in oilfield operations. During drilling a well a large number of parameters is being monitored and saved, often reaching several hundreds. We are seemingly monitoring everything, from basic parameters such as Weight on Bit, Torque, and Rate of Penetration (ROP), to the exhaust temperature of engine no. 3. Unfortunately, the quality of collected data does not match the quantity. Critical sensors, such as gamma and inclination, are often lagging many meters behind the bit. Despite best efforts, sensors stop working, hard drives corrupt files, and data mud pulse telemetry uplinks fail. Methods of infilling data spanning many meters or minutes are necessary. We present a novel approach that enables reliable prediction of data lagging behind the bit through deep neural networks by merging trend-based prediction with traditional neural network approach. We were able to predict continuous inclination data in a curved section of a well with an average absolute error of only 0.4 degrees up to 20 meters from last known value.

scholarly journals Prediction by using Artificial Neural Networks and Box-Jenkins methodologies: Comparison Study

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Habib Al- Sharoot ◽  
Emaan Yousif Abdoon
Keyword(s):  
Neural Networks ◽  
Exchange Rate ◽  
Absolute Error ◽  
Percentage Error ◽  
Neural Network Approach ◽  
Decision Making Processes ◽  
The Mean ◽  
Us Dollar ◽  
Stable Economy ◽  
Better Than

The variations in exchange rate, especially the sudden unexpected increases and decreases, have significant impact on the national economy of any country. Iraq is no exception; therefore, the accurate forecasting of exchange rate of Iraqi dinar to US dollar plays an important role in the planning and decision-making processes as well as the maintenance of a stable economy in Iraq. This research aims to compare Box-Jenkins methodology to neural networks in terms of forecasting the exchange rate of Iraqi dinar to US dollar based on data provided by the Iraqi Central Bank for the period  30/01/2004 and 30/12/2014. Based on the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE) as criteria to compare the two methodologies, it was concluded that Box-Jenkins is better than neural network approach in forecasting.

scholarly journals Neural network cloud top pressure and height for MODIS

2018 ◽  
Author(s):  
Nina Håkansson ◽  
Claudia Adok ◽  
Anke Thoss ◽  
Ronald Scheirer ◽  
Sara Hörnquist
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absolute Error ◽  
Climate Data ◽  
Neural Network Approach ◽  
Brightness Temperatures ◽  
The Neural Network ◽  
The Neural Networks ◽  
Platform System ◽  
Cloud Top Pressure

Abstract. Cloud top height retrieval from imager instruments is important for Nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP dataset. Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 level 2 height product and the cloud top temperature and height algorithm (CTTH) in the 2014 version of the NWCSAF Polar Platform System (PPS-v2014). All three techniques are evaluated using both CALIOP and CPR (CloudSat) height. Instruments like AVHRR and VIIRS contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighbouring pixels are very important. Overall results for the neural network height retrievals are very promising. The neural networks using the brightness temperatures at 11 μm and 12 μm show at least 33 % (or 627 m) lower mean absolute error (MAE) compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 433 m) reduction of MAE. For the network trained with a channel combination available for AVHRR1, the MAE is at least 542 m better when validated with CALIOP and 414 m when validated with CPR (CloudSat) compared to the two operational reference algorithms. The NWCSAF PPS-2018 release will contain a neural network based cloud height algorithm.

scholarly journals Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Narendhar Gugulothu ◽  
Vishnu TV ◽  
Pankaj Malhotra ◽  
Lovekesh Vig ◽  
Puneet Agarwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Missing Values ◽  
Multivariate Time Series ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Novel Approach ◽  
Useful Life ◽  
Robust To Noise

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art (Malhotra, TV, et al., 2016) on several metrics.

THE COMBINATION OF MULTIPLE CLASSIFIERS BY A NEURAL NETWORK APPROACH

1995 ◽  
Vol 09 (03) ◽  
pp. 579-597 ◽  
Author(s):  
YEA S. HUANG ◽  
KE LIU ◽  
CHING Y. SUEN
Keyword(s):  
Neural Networks ◽  
Recognition Rate ◽  
Data Classification ◽  
Data Transformation ◽  
Neural Net ◽  
Multi Layer Perceptron ◽  
Neural Network Approach ◽  
Multiple Classifiers ◽  
Novel Approach ◽  
Transformation Functions

Due to different writing styles and various kinds of noise, the recognition of handwritten numerals is an extremely complicated problem. Recently, a new trend has emerged to tackle this problem by the use of multiple classifiers. This method combines individual classification decisions to derive the final decisions. This is called "Combination of Multiple Classifiers" (CME). In this paper, a novel approach to CME is developed and discussed in detail. It contains two steps: data transformation and data classification. In data transformation, the output values of each classifier are first transformed into a form of likeness measurement. The larger a likeness measurement is, the more probable the corresponding class has the input. In data classification, neural networks have been found very suitable to aggregate the transformed output to produce the final classification decisions. Some strategies for further improving the performance of neural networks have also been proposed in this paper. Experiments with several data transformation functions and data classification approaches have been performed on a large number of handwritten samples. The best result among them is achieved by using both the proposed data transformation function and the multi-layer perceptron neural net, which increased the recognition rate of three individual classifications considerably.

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

scholarly journals Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

2021 ◽  
Author(s):  
Haitham Baomar ◽  
Peter J. Bentley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Training Data ◽  
Aviation Industry ◽  
Control Models ◽  
Novel Approach ◽  
Network Modules ◽  
Artificial Neural

AbstractWe describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

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