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Articoli di riviste sul tema "Ensemble neural noise"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 6 febbraio 2022

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1

Timme, Nicholas M., David Linsenbardt, and Christopher C. Lapish. "A Method to Present and Analyze Ensembles of Information Sources." Entropy 22, no. 5 (May 21, 2020): 580. http://dx.doi.org/10.3390/e22050580.

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Information theory is a powerful tool for analyzing complex systems. In many areas of neuroscience, it is now possible to gather data from large ensembles of neural variables (e.g., data from many neurons, genes, or voxels). The individual variables can be analyzed with information theory to provide estimates of information shared between variables (forming a network between variables), or between neural variables and other variables (e.g., behavior or sensory stimuli). However, it can be difficult to (1) evaluate if the ensemble is significantly different from what would be expected in a pure

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2

Nanni, Loris, Gianluca Maguolo, Sheryl Brahnam, and Michelangelo Paci. "An Ensemble of Convolutional Neural Networks for Audio Classification." Applied Sciences 11, no. 13 (June 22, 2021): 5796. http://dx.doi.org/10.3390/app11135796.

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Research in sound classification and recognition is rapidly advancing in the field of pattern recognition. One important area in this field is environmental sound recognition, whether it concerns the identification of endangered species in different habitats or the type of interfering noise in urban environments. Since environmental audio datasets are often limited in size, a robust model able to perform well across different datasets is of strong research interest. In this paper, ensembles of classifiers are combined that exploit six data augmentation techniques and four signal representation

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3

Sheng, Chunyang, Haixia Wang, Xiao Lu, Zhiguo Zhang, Wei Cui, and Yuxia Li. "Distributed Gaussian Granular Neural Networks Ensemble for Prediction Intervals Construction." Complexity 2019 (July 3, 2019): 1–17. http://dx.doi.org/10.1155/2019/2379584.

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To overcome the weakness of generic neural networks (NNs) ensemble for prediction intervals (PIs) construction, a novel Map-Reduce framework-based distributed NN ensemble consisting of several local Gaussian granular NN (GGNNs) is proposed in this study. Each local network is weighted according to its contribution to the ensemble model. The weighted coefficient is estimated by evaluating the performance of the constructed PIs from each local network. A new evaluation principle is reported with the consideration of the predicting indices. To estimate the modelling uncertainty and the data noise

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4

Chaouachi, Aymen, Rashad M. Kamel, and Ken Nagasaka. "Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (January 20, 2010): 69–75. http://dx.doi.org/10.20965/jaciii.2010.p0069.

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This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results

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5

Noh, Kyoungjin, and Joon-Hyuk Chang. "Deep neural network ensemble for reducing artificial noise in bandwidth extension." Digital Signal Processing 102 (July 2020): 102760. http://dx.doi.org/10.1016/j.dsp.2020.102760.

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6

He, Lei, Xiaohong Shen, Muhang Zhang, and Haiyan Wang. "Discriminative Ensemble Loss for Deep Neural Network on Classification of Ship-Radiated Noise." IEEE Signal Processing Letters 28 (2021): 449–53. http://dx.doi.org/10.1109/lsp.2021.3057539.

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7

Dai, Feng Yan, Zhao Yao Shi, and Jia Chun Lin. "Research of Defect Detection Method Noise for Bevel Gear." Advanced Materials Research 889-890 (February 2014): 722–25. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.722.

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Noise signal analysis method is widely available for gearbox bevel gear fault detection. However, the noise from the gearbox is usually concealed by background noise, which leads to poor efficiency analysis. This paper reports an ensemble empirical mode decomposition (EEMD) and neural network method for bevel gear fault detection. To extract useful signal, EEMD algorithm was firstly applied to get rid of the background noise. Characteristics from a group of discriminating defect status were then chosen to build the eigenvector. Finally, the eigenvector was imported into a back propagation (BP)

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8

Et. al., Rajesh Birok,. "ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2382–89. http://dx.doi.org/10.17762/turcomat.v12i2.2033.

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Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc. Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morpholo

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9

Jin, Dequan, Jigen Peng, and Bin Li. "A New Clustering Approach on the Basis of Dynamical Neural Field." Neural Computation 23, no. 8 (August 2011): 2032–57. http://dx.doi.org/10.1162/neco_a_00153.

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In this letter, we present a new hierarchical clustering approach based on the evolutionary process of Amari's dynamical neural field model. Dynamical neural field theory provides a theoretical framework macroscopically describing the activity of neuron ensemble. Based on it, our clustering approach is essentially close to the neurophysiological nature of perception. It is also computationally stable, insensitive to noise, flexible, and tractable for data with complex structure. Some examples are given to show the feasibility.

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10

Chen, Kai, Kai Xie, Chang Wen, and Xin-Gong Tang. "Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition." Sensors 20, no. 12 (June 15, 2020): 3373. http://dx.doi.org/10.3390/s20123373.

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In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is imp

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11

Zahid, Saadia, Fawad Hussain, Muhammad Rashid, Muhammad Haroon Yousaf, and Hafiz Adnan Habib. "Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/209814.

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Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an au

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12

HARTONO, PITOYO, and SHUJI HASHIMOTO. "EXTRACTING THE PRINCIPAL BEHAVIOR OF A PROBABILISTIC SUPERVISOR THROUGH NEURAL NETWORKS ENSEMBLE." International Journal of Neural Systems 12, no. 03n04 (June 2002): 291–301. http://dx.doi.org/10.1142/s0129065702001126.

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In this paper, we propose a model of a neural network ensemble that can be trained with a supervisor having two kinds of input-output functions where the occurrence probability of each function is not even. This condition can be likened to a learning condition, in which the learning data are hampered by noise. In this case, the neural network has the impression that the learning supervisor (object) has a probabilistic behavior in which the supervisor generates correct learning data most of the time but occasionally generates erroneous ones. The objective is to train the neural network to appro

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13

Dey, Subhrajit, Rajdeep Bhattacharya, Friedhelm Schwenker, and Ram Sarkar. "Median Filter Aided CNN Based Image Denoising: An Ensemble Approach." Algorithms 14, no. 4 (March 28, 2021): 109. http://dx.doi.org/10.3390/a14040109.

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Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model that uses the output of three image denoising models, namely ADNet, IRCNN, and DnCNN, in the ratio of 2:3:6, respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In the

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14

Hu, Sile, Qiaosheng Zhang, Jing Wang, and Zhe Chen. "Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity." Journal of Neurophysiology 119, no. 4 (April 1, 2018): 1394–410. http://dx.doi.org/10.1152/jn.00684.2017.

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Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have

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15

Ahn, J., L. J. Kreeger, S. T. Lubejko, D. A. Butts, and K. M. MacLeod. "Heterogeneity of intrinsic biophysical properties among cochlear nucleus neurons improves the population coding of temporal information." Journal of Neurophysiology 111, no. 11 (June 1, 2014): 2320–31. http://dx.doi.org/10.1152/jn.00836.2013.

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Reliable representation of the spectrotemporal features of an acoustic stimulus is critical for sound recognition. However, if all neurons respond with identical firing to the same stimulus, redundancy in the activity patterns would reduce the information capacity of the population. We thus investigated spike reliability and temporal fluctuation coding in an ensemble of neurons recorded in vitro from the avian auditory brain stem. Sequential patch-clamp recordings were made from neurons of the cochlear nucleus angularis while injecting identical filtered Gaussian white noise currents, simulati

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16

Ahn, Byeongyong, Gu Yong Park, Yoonsik Kim, and Nam Ik Cho. "A Self-ensemble Approach for Noise and Compression Artifacts Removal using Convolutional Neural Network." IEIE Transactions on Smart Processing & Computing 7, no. 4 (August 31, 2018): 296–304. http://dx.doi.org/10.5573/ieiespc.2018.7.4.296.

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17

Wu, Jianfeng, Yongzhu Hua, Shengying Yang, Hongshuai Qin, and Huibin Qin. "Speech Enhancement Using Generative Adversarial Network by Distilling Knowledge from Statistical Method." Applied Sciences 9, no. 16 (August 18, 2019): 3396. http://dx.doi.org/10.3390/app9163396.

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This paper presents a new deep neural network (DNN)-based speech enhancement algorithm by integrating the distilled knowledge from the traditional statistical-based method. Unlike the other DNN-based methods, which usually train many different models on the same data and then average their predictions, or use a large number of noise types to enlarge the simulated noisy speech, the proposed method does not train a whole ensemble of models and does not require a mass of simulated noisy speech. It first trains a discriminator network and a generator network simultaneously using the adversarial le

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18

Mężyk, Miłosz, Michał Chamarczuk, and Michał Malinowski. "Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network." Remote Sensing 13, no. 3 (January 23, 2021): 389. http://dx.doi.org/10.3390/rs13030389.

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Passive seismic experiments have been proposed as a cost-effective and non-invasive alternative to controlled-source seismology, allowing body–wave reflections based on seismic interferometry principles to be retrieved. However, from the huge volume of the recorded ambient noise, only selected time periods (noise panels) are contributing constructively to the retrieval of reflections. We address the issue of automatic scanning of ambient noise data recorded by a large-N array in search of body–wave energy (body–wave events) utilizing a convolutional neural network (CNN). It consists of computi

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19

Mashhadi, Peyman Sheikholharam, Sławomir Nowaczyk, and Sepideh Pashami. "Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life." Applied Sciences 10, no. 1 (December 20, 2019): 69. http://dx.doi.org/10.3390/app10010069.

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Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance

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20

Rajaraman, Sivaramakrishnan, Sudhir Sornapudi, Philip O. Alderson, Les R. Folio, and Sameer K. Antani. "Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs." PLOS ONE 15, no. 11 (November 12, 2020): e0242301. http://dx.doi.org/10.1371/journal.pone.0242301.

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Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affectin

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21

Kwon, Jihoon, and Nojun Kwak. "Radar Application: Stacking Multiple Classifiers for Human Walking Detection Using Micro-Doppler Signals." Applied Sciences 9, no. 17 (August 28, 2019): 3534. http://dx.doi.org/10.3390/app9173534.

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We propose a stacking method for ensemble learning to distinguish micro-Doppler signals generated by human walking from background noises using radar sensors. We collected micro-Doppler signals caused by four types of background noise (line of sight (LoS), fan, snow and rain) and additionally considered micro-Doppler signals caused by human walking combined with these four types of background noise. We firstly verified the effectiveness of a fully connected deep neural network (DNN) to classify 8 types of signals. The average accuracy was 88.79% for the test set. Then, we propose a stacking me

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22

Scheuerer, Michael, Matthew B. Switanek, Rochelle P. Worsnop, and Thomas M. Hamill. "Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California." Monthly Weather Review 148, no. 8 (July 31, 2020): 3489–506. http://dx.doi.org/10.1175/mwr-d-20-0096.1.

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Abstract Forecast skill of numerical weather prediction (NWP) models for precipitation accumulations over California is rather limited at subseasonal time scales, and the low signal-to-noise ratio makes it challenging to extract information that provides reliable probabilistic forecasts. A statistical postprocessing framework is proposed that uses an artificial neural network (ANN) to establish relationships between NWP ensemble forecast and gridded observed 7-day precipitation accumulations, and to model the increase or decrease of the probabilities for different precipitation categories rela

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23

Fallahian, Milad, Faramarz Khoshnoudian, and Viviana Meruane. "Ensemble classification method for structural damage assessment under varying temperature." Structural Health Monitoring 17, no. 4 (July 7, 2017): 747–62. http://dx.doi.org/10.1177/1475921717717311.

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Vibration-based damage assessment approaches use modal parameters, such as frequency response functions, mode shapes, and natural frequencies, as indicators of structural damage. Nevertheless, these parameters are sensitive not only to damage but also to temperature variations. Most civil engineering structures are exposed to varying environmental conditions, thus hindering vibration-based damage assessment. Therefore, in this article, a new damage assessment algorithm based on pattern recognition is proposed to scrutinize the healthy state of a structure in the presence of uncertainties such

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24

Hou, Sizu, and Wei Guo. "Faulty Line Selection Based on Modified CEEMDAN Optimal Denoising Smooth Model and Duffing Oscillator for Un-Effectively Grounded System." Mathematical Problems in Engineering 2020 (April 6, 2020): 1–21. http://dx.doi.org/10.1155/2020/5761642.

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As the un-effectively grounded system fails, the zero-sequence current contains strong noise and nonstationary features. This paper proposes a novel faulty line selection method based on modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and Duffing oscillator. Here, based on multiscale permutation entropy, fuzzy c-means clustering, and general regression neural network for abnormal signal detection, the MCEEMDAN is proposed. The endpoint mirror method is used to suppress the endpoint effect problem in the decomposition stage. The proposed algorithm is able

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25

Fishman, Yonatan I., and Mitchell Steinschneider. "Spectral Resolution of Monkey Primary Auditory Cortex (A1) Revealed With Two-Noise Masking." Journal of Neurophysiology 96, no. 3 (September 2006): 1105–15. http://dx.doi.org/10.1152/jn.00124.2006.

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An important function of the auditory nervous system is to analyze the frequency content of environmental sounds. The neural structures involved in determining psychophysical frequency resolution remain unclear. Using a two-noise masking paradigm, the present study investigates the spectral resolution of neural populations in primary auditory cortex (A1) of awake macaques and the degree to which it matches psychophysical frequency resolution. Neural ensemble responses (auditory evoked potentials, multiunit activity, and current source density) evoked by a pulsed 60-dB SPL pure-tone signal fixe

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Kim, Sungil, Baehyun Min, Seoyoon Kwon, and Min-gon Chu. "History Matching of a Channelized Reservoir Using a Serial Denoising Autoencoder Integrated with ES-MDA." Geofluids 2019 (April 16, 2019): 1–22. http://dx.doi.org/10.1155/2019/3280961.

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For an ensemble-based history matching of a channelized reservoir, loss of geological plausibility is challenging because of pixel-based manipulation of channel shape and connectivity despite sufficient conditioning to dynamic observations. Regarding the loss as artificial noise, this study designs a serial denoising autoencoder (SDAE) composed of two neural network filters, utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple data assimilation (ES-MDA), and improves the overall history matching performance. As a training datase

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Tian, Juan, and Yingxiang Li. "Convolutional Neural Networks for Steganalysis via Transfer Learning." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 02 (October 24, 2018): 1959006. http://dx.doi.org/10.1142/s0218001419590067.

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Recently, a large number of studies have shown that Convolutional Neural Networks are effective for learning features automatically for steganalysis. This paper uses the transfer learning method to help the training of CNNs for steganalysis. First, a Gaussian high-pass filter is designed for pretreatment of the images, that can enhance the weak stego noise in the stegos. Then, the classical Inception-V3 model is improved, and the improved network is used for steganalysis through the method of transfer learning. In order to test the effectiveness of the developed model, two spatial domain conte

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Jiang, Zhencun, Zhengxin Dong, Lingyang Wang, and Wenping Jiang. "Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model." Computational Intelligence and Neuroscience 2021 (August 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/7529893.

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Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B-lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B-lymphoid precursors (normal cells), it is difficult to distinguish between cancer cells and normal cells. Therefore, we propose the ViT-CNN ensemble model to classify cancer cells images and normal cells images to

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Knuth, Kevin H., Ankoor S. Shah, Wilson A. Truccolo, Mingzhou Ding, Steven L. Bressler, and Charles E. Schroeder. "Differentially Variable Component Analysis: Identifying Multiple Evoked Components Using Trial-to-Trial Variability." Journal of Neurophysiology 95, no. 5 (May 2006): 3257–76. http://dx.doi.org/10.1152/jn.00663.2005.

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Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response ampl

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Ma, Jianpeng, Zhenghui Li, Chengwei Li, Liwei Zhan, and Guang-Zhu Zhang. "Rolling Bearing Fault Diagnosis Based on Refined Composite Multi-Scale Approximate Entropy and Optimized Probabilistic Neural Network." Entropy 23, no. 2 (February 23, 2021): 259. http://dx.doi.org/10.3390/e23020259.

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A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-

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Xie, Yingchun, Yucheng Xiao, Xuyan Liu, Guijie Liu, Weixiong Jiang, and Jin Qin. "Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals." Sensors 20, no. 18 (September 4, 2020): 5040. http://dx.doi.org/10.3390/s20185040.

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Detection technology of underwater pipeline leakage plays an important role in the subsea production system. In this paper, a new method based on the acoustic leak signal collected by a hydrophone is proposed to detect pipeline leakage in the subsea production system. Through the pipeline leakage test, it is found that the radiation noise is a continuous spectrum of the medium and high-frequency noise. Both the increase in pipe pressure and the diameter of the leak hole will narrow the spectral structure and shift the spectrum center towards the low frequencies. Under the same condition, the p

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Antoniades, Andreas, Loukianos Spyrou, David Martin-Lopez, Antonio Valentin, Gonzalo Alarcon, Saeid Sanei, and Clive Cheong Took. "Deep Neural Architectures for Mapping Scalp to Intracranial EEG." International Journal of Neural Systems 28, no. 08 (August 26, 2018): 1850009. http://dx.doi.org/10.1142/s0129065718500090.

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Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly ma

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Wang, Jing, Guigen Nie, Shengjun Gao, Shuguang Wu, Haiyang Li, and Xiaobing Ren. "Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model." Remote Sensing 13, no. 6 (March 10, 2021): 1055. http://dx.doi.org/10.3390/rs13061055.

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The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide displacement prediction. The CEEMDAN method is implemented to ingest landslide Global Navigation Satellite System (GNSS) time series. The AMLSTM algorithm is then used to realize prediction work, jointly with multiple impact factors. The B

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Altuve, Miguel, Paula Lizarazo, and Javier Villamizar. "Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks." Biocybernetics and Biomedical Engineering 40, no. 3 (July 2020): 901–9. http://dx.doi.org/10.1016/j.bbe.2020.04.007.

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Cao, Yang, Xiaokang Zhou, and Ke Yan. "Deep Learning Neural Network Model for Tunnel Ground Surface Settlement Prediction Based on Sensor Data." Mathematical Problems in Engineering 2021 (August 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/9488892.

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Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. Data-driven techniques combining artificial intelligence (AI) and sensor networks are popular methods in the field, which have several advantages, including high prediction accuracy, efficiency, and low cost. Deep learning, as one of the advanced techniques in AI, is demanded for the tunnel settlement forecasting problem. However, deep neural networks often require a large amount of training data. Due to the tunnel construction,

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Akpudo, Ugochukwu Ejike, and Jang-Wook Hur. "A CEEMDAN-Assisted Deep Learning Model for the RUL Estimation of Solenoid Pumps." Electronics 10, no. 17 (August 25, 2021): 2054. http://dx.doi.org/10.3390/electronics10172054.

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Abstract (sommario):

This paper develops a data-driven remaining useful life prediction model for solenoid pumps. The model extracts high-level features using stacked autoencoders from decomposed pressure signals (using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm). These high-level features are then received by a recurrent neural network-gated recurrent units (GRUs) for the RUL estimation. The case study presented demonstrates the robustness of the proposed RUL estimation model with extensive empirical validations. Results support the validity of using the CEEMDAN fo

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Rudd, Michael E., and Lawrence G. Brown. "Noise Adaptation in Integrate-and-Fire Neurons." Neural Computation 9, no. 5 (July 1, 1997): 1047–69. http://dx.doi.org/10.1162/neco.1997.9.5.1047.

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Abstract (sommario):

The statistical spiking response of an ensemble of identically prepared stochastic integrate-and-fire neurons to a rectangular input current plus gaussian white noise is analyzed. It is shown that, on average, integrate-and-fire neurons adapt to the root-mean-square noise level of their input. This phenomenon is referred to as noise adaptation. Noise adaptation is characterized by a decrease in the average neural firing rate and an accompanying decrease in the average value of the generator potential, both of which can be attributed to noise-induced resets of the generator potential mediated b

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Wu, Jiang, Feng Miu, and Taiyong Li. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market." Energies 13, no. 7 (April 10, 2020): 1852. http://dx.doi.org/10.3390/en13071852.

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Abstract (sommario):

Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, nam

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Nti, Isaac Kofi, Adebayo Felix Adekoya, and Benjamin Asubam Weyori. "Efficient Stock-Market Prediction Using Ensemble Support Vector Machine." Open Computer Science 10, no. 1 (July 4, 2020): 153–63. http://dx.doi.org/10.1515/comp-2020-0199.

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Abstract (sommario):

AbstractPredicting stock-price remains an important subject of discussion among financial analysts and researchers. However, the advancement in technologies such as artificial intelligence and machine learning techniques has paved the way for better and accurate prediction of stock-price in recent years. Of late, Support Vector Machines (SVM) have earned popularity among Machine Learning (ML) algorithms used for predicting stock price. However, a high percentage of studies in algorithmic investments based on SVM overlooked the overfitting nature of SVM when the input dataset is of high-noise a

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Mousavi, Asma Alsadat, Chunwei Zhang, Sami F. Masri, and Gholamreza Gholipour. "Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study." Sensors 20, no. 5 (February 26, 2020): 1271. http://dx.doi.org/10.3390/s20051271.

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Abstract (sommario):

Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert–Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was

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Georges, Hassana Maigary, Dong Wang, and Zhu Xiao. "GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/682907.

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Abstract (sommario):

Among the inertial navigation system (INS) devices used in land vehicle navigation (LVN), low-cost microelectromechanical systems (MEMS) inertial sensors have received more interest for bridging global navigation satellites systems (GNSS) signal failures because of their price and portability. Kalman filter (KF) based GNSS/INS integration has been widely used to provide a robust solution to the navigation. However, its prediction model cannot give satisfactory results in the presence of colored and variational noise. In order to achieve reliable and accurate positional solution for LVN in urba

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Wu, Jiang, Tengfei Zhou, and Taiyong Li. "A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting." Complexity 2020 (October 22, 2020): 1–17. http://dx.doi.org/10.1155/2020/9318308.

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Abstract (sommario):

The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity. Therefore, accurately forecasting economic and financial time series is always a challenging research topic. In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise (ICEEMDANs), whale optimization algorithm (WOA), and random vector functional link (RVFL) neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to

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Krasnopolsky, Vladimir, Sudhir Nadiga, Avichal Mehra, Eric Bayler, and David Behringer. "Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6156513.

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Abstract (sommario):

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites andin situphysical observations. Satellite-derived “ocean color” (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-oce

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Opitz, D., and R. Maclin. "Popular Ensemble Methods: An Empirical Study." Journal of Artificial Intelligence Research 11 (August 1, 1999): 169–98. http://dx.doi.org/10.1613/jair.614.

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Abstract (sommario):

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly

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Han, Te, Dongxiang Jiang, Qi Zhao, Lei Wang, and Kai Yin. "Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery." Transactions of the Institute of Measurement and Control 40, no. 8 (June 1, 2017): 2681–93. http://dx.doi.org/10.1177/0142331217708242.

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Abstract (sommario):

Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery. Some popular classification algorithms such as artificial neural networks and support vector machine have been extensively studied and tested with many application cases, while the random forest, one of the present state-of-the-art classifiers based on ensemble learning strategy, is relatively unknown in this field. In this paper, the behavior of random forest for the intelligent diagnosis of rotating machinery is investig

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Saghi, Faramarz, and Mustafa Jahangoshai Rezaee. "An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays." PeerJ Computer Science 7 (April 7, 2021): e409. http://dx.doi.org/10.7717/peerj-cs.409.

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Abstract (sommario):

Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning. The purpose of this study is to provide an approach to forecast the natural gas price. First, optimal time delays are identified by a new appr

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Sarmiento, Carlos, and Jesus Savage. "Comparison of Two Objects Classification Techniques using Hidden Markov Models and Convolutional Neural Networks." Informatics and Automation 19, no. 6 (December 11, 2020): 1222–54. http://dx.doi.org/10.15622/ia.2020.19.6.4.

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Abstract (sommario):

This paper presents a comparison between discrete Hidden Markov Models and Convolutional Neural Networks for the image classification task. By fragmenting an image into sections, it is feasible to obtain vectors that represent visual features locally, but if a spatial sequence is established in a fixed way, it is possible to represent an image as a sequence of vectors. Using clustering techniques, we obtain an alphabet from said vectors and then symbol sequences are constructed to obtain a statistical model that represents a class of images. Hidden Markov Models, combined with quantization met

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48

Deweese, Michael R., and Anthony M. Zador. "Shared and Private Variability in the Auditory Cortex." Journal of Neurophysiology 92, no. 3 (September 2004): 1840–55. http://dx.doi.org/10.1152/jn.00197.2004.

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Abstract (sommario):

The high variability of cortical sensory responses is often assumed to impose a major constraint on efficient computation. In the auditory cortex, however, response variability can be very low. We have used in vivo whole cell patch-clamp methods to study the trial-to-trial variability of the subthreshold fluctuations in membrane potential underlying tone-evoked responses in the auditory cortex of anesthetized rats. Using methods adapted from classical quantal analysis, we partitioned this subthreshold variability into a private component (which includes synaptic, thermal, and other sources loc

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Жарикова, Е. П., Я. Ю. Григорьев, and А. Л. Григорьева. "Application of neural networks for water area analysis." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 2(52) (June 20, 2021): 129–33. http://dx.doi.org/10.37220/mit.2021.52.2.063.

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Abstract (sommario):

Современные задачи, связанные с эксплуатацией морских судов, транспортировкой нефтепродуктов различными способами в морских акваториях связаны с необходимостью контроля мониторинга возможности загрязнения нефтепродуктами вод мирового океана. В статье предлагается подход к решению задач оценки состояния акваторий на основе методов искусственного интеллекта. В исследовании рассматривается модель анализа состояния водной поверхности, основанная на расчете коэффициентов, определяемых отношением значений спектральных каналов. Применение метода обладает рядом недостатков, состоящих в необходимости п

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50

Redlich, A. Norman. "Redundancy Reduction as a Strategy for Unsupervised Learning." Neural Computation 5, no. 2 (March 1993): 289–304. http://dx.doi.org/10.1162/neco.1993.5.2.289.

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Abstract (sommario):

A redundancy reduction strategy, which can be applied in stages, is proposed as a way to learn as efficiently as possible the statistical properties of an ensemble of sensory messages. The method works best for inputs consisting of strongly correlated groups, that is features, with weaker statistical dependence between different features. This is the case for localized objects in an image or for words in a text. A local feature measure determining how much a single feature reduces the total redundancy is derived which turns out to depend only on the probability of the feature and of its compon

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