scholarly journals A High-Precision Magnetic-Assisted Heading Angle Calculation Method Based on a 1D Convolutional Neural Network (CNN) in a Complicated Magnetic Environment

Micromachines ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 642
Author(s):  
Guanghui Hu ◽  
Hong Wan ◽  
Xinxin Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Precision ◽  
Geomagnetic Field ◽  
Field Data ◽  
Time Window ◽  
Magnetic Data ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Heading Angle

Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading to reduced positioning accuracy of magnetic-assisted navigation systems. Therefore, there is an urgent need for methods which screen out undisturbed geomagnetic field data for realizing the high accuracy pedestrian inertial navigation indoors. In this paper, we propose an algorithm based on a one-dimensional convolutional neural network (1D CNN) to screen magnetic field data. By encoding the magnetic data within a certain time window to a time series, a 1D CNN with two convolutional layers is designed to extract data features. In order to avoid errors arising from artificial labels, the feature vectors will be clustered in the feature space to classify the magnetic data using unsupervised methods. Our experimental results show that this method can distinguish the geomagnetic field data from indoors disturbed magnetic data well and further significantly improve the calculation accuracy of the heading angle. Our work provides a possible technical path for the realization of high-precision indoor pedestrian navigation systems.

scholarly journals Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4446
Author(s):  
Do-In Kim
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Convolutional Neural Network ◽  
Time Window ◽  
Transient Signal ◽  
Measurement Unit ◽  
Transient Phenomena ◽  
Event Identification ◽  
Starting Point ◽  
Fast Decision

This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validation, as well as the fast decision in successive event conditions. The decision result includes information pertaining to various event types and locations based on various time delays for the protective operation. Finally, this work verifies the event identification method through a case study and analyzes the effects of successive events in addition to classification accuracy.

scholarly journals Ørsted satellite captures high-precision geomagnetic field data

Eos ◽  
2001 ◽  
Vol 82 (7) ◽  
pp. 81-88 ◽  
Author(s):  
Torsten Neubert ◽  
M. Mandea ◽  
G. Hulot ◽  
R. von Frese ◽  
F. Primdahl ◽  
...  
Keyword(s):  
High Precision ◽  
Geomagnetic Field ◽  
Field Data

scholarly journals Memristor Based Binary Convolutional Neural Network Architecture With Configurable Neurons

2021 ◽  
Vol 15 ◽  
Author(s):  
Lixing Huang ◽  
Jietao Diao ◽  
Hongshan Nie ◽  
Wei Wang ◽  
Zhiwei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Precision ◽  
Network Architecture ◽  
Large Scale ◽  
Network Performance ◽  
Recognition Accuracy ◽  
Activation Function ◽  
Data Set ◽  
Neuron Activation

The memristor-based convolutional neural network (CNN) gives full play to the advantages of memristive devices, such as low power consumption, high integration density, and strong network recognition capability. Consequently, it is very suitable for building a wearable embedded application system and has broad application prospects in image classification, speech recognition, and other fields. However, limited by the manufacturing process of memristive devices, high-precision weight devices are currently difficult to be applied in large-scale. In the same time, high-precision neuron activation function also further increases the complexity of network hardware implementation. In response to this, this paper proposes a configurable full-binary convolutional neural network (CFB-CNN) architecture, whose inputs, weights, and neurons are all binary values. The neurons are proportionally configured to two modes for different non-ideal situations. The architecture performance is verified based on the MNIST data set, and the influence of device yield and resistance fluctuations under different neuron configurations on network performance is also analyzed. The results show that the recognition accuracy of the 2-layer network is about 98.2%. When the yield rate is about 64% and the hidden neuron mode is configured as −1 and +1, namely ±1 MD, the CFB-CNN architecture achieves about 91.28% recognition accuracy. Whereas the resistance variation is about 26% and the hidden neuron mode configuration is 0 and 1, namely 01 MD, the CFB-CNN architecture gains about 93.43% recognition accuracy. Furthermore, memristors have been demonstrated as one of the most promising devices in neuromorphic computing for its synaptic plasticity. Therefore, the CFB-CNN architecture based on memristor is SNN-compatible, which is verified using the number of pulses to encode pixel values in this paper.

Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. B403-B417 ◽  
Author(s):  
Hao Wu ◽  
Bo Zhang ◽  
Tengfei Lin ◽  
Danping Cao ◽  
Yihuai Lou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Time Window ◽  
Training Data ◽  
Fault Surface ◽  
Unique Object ◽  
Segmentation Methods ◽  
Convolution Filters ◽  
Waveform Pattern

The seismic horizon is a critical input for the structure and stratigraphy modeling of reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for seismic data in which the lateral continuity of reflections is interrupted by faults and unconformities. The process of seismic horizon interpretation can be viewed as segmenting the seismic traces into different parts and each part is a unique object. Thus, we have considered the horizon interpretation as an object detection problem. We use the encoder-decoder convolutional neural network (CNN) to detect the “objects” contained in the seismic traces. The boundary of the objects is regarded as the horizons. The training data are the seismic traces located on a user-defined coarse grid. We give a unique training label to the time window of seismic traces bounded by two manually picked horizons. To efficiently learn the waveform pattern that is bounded by two adjacent horizons, we use variable sizes for the convolution filters, which is different than current CNN-based image segmentation methods. Two field data examples demonstrate that our method is capable of producing accurate horizons across the fault surface and near the unconformity which is beyond the current capability of horizon picking method.

scholarly journals Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Qiushuang Lin ◽  
Chunxiang Li ◽  
Chao Wu
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
High Precision ◽  
Health Monitoring ◽  
Wind Engineering ◽  
Stage Model ◽  
Health Monitoring System ◽  
Non Gaussian

Wind signal forecasting has become more and more crucial in the structural health monitoring system and wind engineering recently. It is a challenging subject owing to the complicated volatility of wind signals. The robustness and generalization of a predictor are significant as well as of high precision. In this paper, an adaptive residual convolutional neural network (CNN) is developed, aiming at achieving not only high precision but also high adaptivity for various wind signals with varying complexity. Afterwards, reinforced forecasting is adopted to enhance the robustness of the preliminary forecasting. The preliminary forecast results by adaptive residual CNN are integrated with historical observed signals as the new input to reconstruct a new forecasting mapping. Meanwhile, simplified-boost strategy is applied for more generalized results. The results of multistep forecasting for five kinds of nonstationary non-Gaussian wind signals prove the more excellent adaptivity and robustness of the developed two-stage model compared with single models.

scholarly journals Computed tomography angiography-based deep learning method for treatment selection and infarct volume prediction in anterior cerebral circulation large vessel occlusion

2021 ◽  
Vol 10 (11) ◽  
pp. 205846012110603
Author(s):  
Lasse Hokkinen ◽  
Teemu Mäkelä ◽  
Sauli Savolainen ◽  
Marko Kangasniemi
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Computed Tomography Angiography ◽  
Time Window ◽  
Infarct Volume ◽  
Correlation Coefficients ◽  
Late Time ◽  
Large Vessel Occlusion ◽  
Vessel Occlusion

Background Computed tomography perfusion (CTP) is the mainstay to determine possible eligibility for endovascular thrombectomy (EVT), but there is still a need for alternative methods in patient triage. Purpose To study the ability of a computed tomography angiography (CTA)-based convolutional neural network (CNN) method in predicting final infarct volume in patients with large vessel occlusion successfully treated with endovascular therapy. Materials and Methods The accuracy of the CTA source image-based CNN in final infarct volume prediction was evaluated against follow-up CT or MR imaging in 89 patients with anterior circulation ischemic stroke successfully treated with EVT as defined by Thrombolysis in Cerebral Infarction category 2b or 3 using Pearson correlation coefficients and intraclass correlation coefficients. Convolutional neural network performance was also compared to a commercially available CTP-based software (RAPID, iSchemaView). Results A correlation with final infarct volumes was found for both CNN and CTP-RAPID in patients presenting 6–24 h from symptom onset or last known well, with r = 0.67 ( p < 0.001) and r = 0.82 ( p < 0.001), respectively. Correlations with final infarct volumes in the early time window (0–6 h) were r = 0.43 ( p = 0.002) for the CNN and r = 0.58 ( p < 0.001) for CTP-RAPID. Compared to CTP-RAPID predictions, CNN estimated eligibility for thrombectomy according to ischemic core size in the late time window with a sensitivity of 0.38 and specificity of 0.89. Conclusion A CTA-based CNN method had moderate correlation with final infarct volumes in the late time window in patients successfully treated with EVT.

Seismic fault detection with convolutional neural network

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. O97-O103 ◽  
Author(s):  
Wei Xiong ◽  
Xu Ji ◽  
Yue Ma ◽  
Yuxiang Wang ◽  
Nasher M. AlBinHassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Field Data ◽  
Data Sets ◽  
3D Seismic ◽  
Learning Tools ◽  
Seismic Fault ◽  
Seismic Image ◽  
Coherence Estimation ◽  
Seismic Images

Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through several iterations to optimize how the different fault planes connect with each other. Many techniques have been developed to automate this process, such as seismic coherence estimation, edge detection, and ant-tracking, to name a few. However, these techniques do not take advantage of the valuable experience accumulated by the interpreters. We have developed a method that uses the convolutional neural network (CNN) to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters. This new technique is implemented in two steps: training and prediction. In the training step, a CNN model is trained with annotated seismic image cubes of field data, where every point in the seismic image is labeled as fault or nonfault. In the prediction step, the trained model is applied to compute fault probabilities at every location in other seismic image cubes. Unlike reported methods in the literature, our technique does not require precomputed attributes to predict the faults. We verified our approach on the synthetic and field data sets. We clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities. With the capability of emulating human experience and evolving through training using new field data sets, deep-learning tools manifest huge potential in automating and advancing seismic fault mapping.

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