Relevant bibliographies by topics / EMD - Neural networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'EMD - Neural networks'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "EMD - Neural networks"

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Zheng, Jing Wen, Shi Xiao Li, and Yang Kun. "A New Hybrid Model for Forecasting Crude Oil Price and the Techniques in the Model." Advanced Materials Research 974 (June 2014): 310–17. http://dx.doi.org/10.4028/www.scientific.net/amr.974.310.

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Being able to predict crude oil prices with a reputation of intransigence to analysis or the directions of changing in crude oil price is of increasing value. We seek a method to forecast oil prices with precise predictions. In this paper, a hybrid model was proposed, which firstly decomposes the crude oil prices into several time series with different frequencies,then predict these time series which are not white noises, and at last integrate the predictions as the final results. We use Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD) separately as the technique to decompose crude oil prices. Then we use Dynamic Artificial Neural Network (DAN2) and Back Propagation (BP) Neural Network separately as the technique to predict the deposed time series, and finally integrate the predictions produced by DAN2 or BP by Adaptive Linear Neural Network (ALNN) as the final result of predictions. EEMD has been proved as a very useful method to decompose the nonlinear and non-stationary time series, and DAN2, different from traditional artificial neural networks, also has obvious advantages over traditional ones. In this paper, EEMD and DAN2 are used to predict crude oil prices at the first time。 All in all, we build four models-EEMD-DAN2-ALNN, EMD-BP-ALNN, EEMD-BP-ALNN and EMD-DAN2-ALNN to test which technique, EMD or EEMD, could do better job in decomposition of crude oil prices in this kind of hybrid model and whetherDAN2 could outshine BP when used in this hybrid model. Experimental results of four hybrid models indicate EEMD-DAN2-ALNN could gives the most precise predictions of crude oil prices, and DAN2 has a better performance than traditional neural networks-BP,when used in this hybrid model and EEMD could do a better job than EMD in decomposition of crude oil prices to yield precise predictions of crude oil prices in this model.
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Saâdaoui, Foued, and Othman Ben Messaoud. "Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting." International Journal of Neural Systems 30, no. 08 (June 26, 2020): 2050039. http://dx.doi.org/10.1142/s0129065720500392.

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Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data.
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Lei, Yu, Danning Zhao, and Hongbing Cai. "Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks." Artificial Satellites 51, no. 4 (December 1, 2016): 149–61. http://dx.doi.org/10.1515/arsa-2016-0013.

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Abstract It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques.
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Ge, Yujia, Yurong Nan, and Lijun Bai. "A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks." Energies 12, no. 24 (December 13, 2019): 4762. http://dx.doi.org/10.3390/en12244762.

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For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into a series of relatively stable component sequences. For improving the prediction accuracy further by utilizing the current day solar radiation profile in one-hour-ahead predictions, similar solar radiation profile data were selected for training LSTM neural networks. Simulation results show that the hybrid model achieves better prediction performance than traditional prediction methods, such as the exponentially-weighted moving average (EWMA), weather conditioned moving average (WCMA), and only LSTM models.
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Jiang, Qi, Yuxin Cheng, Haozhe Le, Chunquan Li, and Peter X. Liu. "A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting." Mathematics 10, no. 14 (July 13, 2022): 2446. http://dx.doi.org/10.3390/math10142446.

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It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting performance. Specifically, the stacking neural networks contain two types of networks: the base-layer and the meta-layer networks. During the pre-training process, the base-layer network integrates a radial basis function (RBF), random vector functional link (RVFL), and backpropagation neural network (BPNN) to provide a robust predictive model. The meta-layer network utilizes a deep belief network (DBN) and the improved broad learning system (BLS) to enhance predictive accuracy. Finally, the similar-days prediction method is developed to extract the relationship of electric load data in different time dimensions, further enhancing the robustness and accuracy of the model. To demonstrate the effectiveness of our model, it is evaluated using real data from five regions of the United States in three consecutive years. We compare our method with several state-of-the-art and conventional neural-network-based models. Our proposed algorithm improves the prediction accuracy by 16.08%, 16.83%, and 22.64% compared to DWT-EMD-RVFL, SWT-LSTM, and EMD-BLS, respectively. Empirical results demonstrate that our model achieves better accuracy and robustness compared with the baselines.
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Huang, Xiaoxin, and Xiuxiu Chen. "A Quantitative Model of International Trade Based on Deep Neural Network." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/9811358.

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This paper is an in-depth study of international trade quantification models based on deep neural networks. Based on an in-depth analysis of global trade characteristics, a summary of existing problems, and a comparative analysis of various prediction methods, this paper constructs the ARIMA model, BP neural network (BPNN) model, and deep neural network (DNN) model to make a comprehensive comparison of international trade quantification. The results show that the nonlinear model has a global trade quantification has some advantages over linear models, and the deep model shows better prediction performance than the shallow model. In addition, preprocessing of the time series is considered to improve the prediction accuracy or shorten the model training time. The empirical modal analysis method (EMD) is introduced to decompose the time series into eigenmodal functions (IMFs) of different scales. Then the decomposed IMF series are arranged into a matrix using principal component analysis (PCA) to reduce the dimensionality and extract the data containing the most stock index information features; these features are then input into BPNN and DNN for combined prediction, thus constructing the combined models EMD-PCA-BPNN and EMD-PCA-DNN. Based on Melitz’s heterogeneous firm trade theory and its development by Chaney, a quantitative trade model incorporating production heterogeneity is constructed through a multisector extension. This paper adopts a general equilibrium analysis, which makes the modeling process pulse clear. The completed model has high flexibility and scalability, which can be applied to quantitative analysis of various problems.
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Zhou, Shuyi, Brandon J. Bethel, Wenjin Sun, Yang Zhao, Wenhong Xie, and Changming Dong. "Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network." Journal of Marine Science and Engineering 9, no. 7 (July 5, 2021): 744. http://dx.doi.org/10.3390/jmse9070744.

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Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significant wave height forecasting, a method widely used in wind speed forecasting, but not yet for wave heights. Following a comparative analysis, the results demonstrate that EMD-LSTM significantly outperforms LSTM at every forecast horizon (3, 6, 12, 24, 48, and 72 h), considerably improving forecasting accuracy, especially for forecasts exceeding 24 h. Additionally, EMD-LSTM responds faster than LSTM to large waves. An error analysis comparing LSTM and EMD-LSTM demonstrates that LSTM errors are more systematic. This study also identifies that LSTM is not able to adequately predict high-frequency significant wave height intrinsic mode functions, which leaves room for further improvements.
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Zhang, Boning. "Foreign exchange rates forecasting with an EMD-LSTM neural networks model." Journal of Physics: Conference Series 1053 (July 2018): 012005. http://dx.doi.org/10.1088/1742-6596/1053/1/012005.

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Chengzhao, Zhang, Pan Heiping, and Zhou Ke. "Comparison of Back Propagation Neural Networks and EMD-Based Neural Networks in Forecasting the Three Major Asian Stock Markets." Journal of Applied Sciences 15, no. 1 (December 15, 2014): 90–99. http://dx.doi.org/10.3923/jas.2015.90.99.

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Shu, Wangwei, and Qiang Gao. "Forecasting Stock Price Based on Frequency Components by EMD and Neural Networks." IEEE Access 8 (2020): 206388–95. http://dx.doi.org/10.1109/access.2020.3037681.

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Dissertations / Theses on the topic "EMD - Neural networks"

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Post, David L. "Network Management: Assessing Internet Network-Element Fault Status Using Neural Networks." Ohio : Ohio University, 2008. http://www.ohiolink.edu/etd/view.cgi?ohiou1220632155.

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Lee, Yee Hui. "Evolutionary techniques for the optimisation of EMC antennas." Thesis, University of York, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270061.

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Donachy, Shaun. "Spiking Neural Networks: Neuron Models, Plasticity, and Graph Applications." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3984.

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Networks of spiking neurons can be used not only for brain modeling but also to solve graph problems. With the use of a computationally efficient Izhikevich neuron model combined with plasticity rules, the networks possess self-organizing characteristics. Two different time-based synaptic plasticity rules are used to adjust weights among nodes in a graph resulting in solutions to graph prob- lems such as finding the shortest path and clustering.
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Draper, Matthew C. "Neural algorithms for EMI based landmine detection." Honors in the Major Thesis, University of Central Florida, 2003. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/410.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.
Bachelors
Engineering and Computer Science
Computer Engineering
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Liu, Ming Ming. "Dynamic muscle force prediction from EMG signals using artificial neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq20875.pdf.

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Chan, Heather Y. "Gene Network Inference and Expression Prediction Using Recurrent Neural Networks and Evolutionary Algorithms." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2648.

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We demonstrate the success of recurrent neural networks in gene network inference and expression prediction using a hybrid of particle swarm optimization and differential evolution to overcome the classic obstacle of local minima in training recurrent neural networks. We also provide an improved validation framework for the evaluation of genetic network modeling systems that will result in better generalization and long-term prediction capability. Success in the modeling of gene regulation and prediction of gene expression will lead to more rapid discovery and development of therapeutic medicine, earlier diagnosis and treatment of adverse conditions, and vast advancements in life science research.
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Tarullo, Viviana. "Artificial Neural Networks for classification of EMG data in hand myoelectric control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19195/.

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This thesis studies the state-of-the-art in myoelectric control of active hand prostheses for people with trans-radial amputation using pattern recognition and machine learning techniques. Our work is supported by Centro Protesi INAIL in Vigorso di Budrio (BO). We studied the control system developed by INAIL consisting in acquiring EMG signals from amputee subjects and using pattern recognition methods for the classifcation of acquired signals, associating them with specifc gestures and consequently commanding the prosthesis. Our work consisted in improving classifcation methods used in the learning phase. In particular, we proposed a classifer based on a neural network as a valid alternative to the INAIL one-versus-all approach to multiclass classifcation.
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Hettinger, Christopher James. "Hyperparameters for Dense Neural Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7531.

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Neural networks can perform an incredible array of complex tasks, but successfully training a network is difficult because it requires us to minimize a function about which we know very little. In practice, developing a good model requires both intuition and a lot of guess-and-check. In this dissertation, we study a type of fully-connected neural network that improves on standard rectifier networks while retaining their useful properties. We then examine this type of network and its loss function from a probabilistic perspective. This analysis leads to a new rule for parameter initialization and a new method for predicting effective learning rates for gradient descent. Experiments confirm that the theory behind these developments translates well into practice.
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Atalla, Mauro J. "Model Updating Using Neural Networks." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-274210359611541/.

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Moncur, Tyler. "Optimal Learning Rates for Neural Networks." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8662.

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Neural networks have long been known as universal function approximators and have more recently been shown to be powerful and versatile in practice. But it can be extremely challenging to find the right set of parameters and hyperparameters. Model training is both expensive and difficult due to the large number of parameters and sensitivity to hyperparameters such as learning rate and architecture. Hyperparameter searches are notorious for requiring tremendous amounts of processing power and human resources. This thesis provides an analytic approach to estimating the optimal value of one of the key hyperparameters in neural networks, the learning rate. Where possible, the analysis is computed exactly, and where necessary, approximations and assumptions are used and justified. The result is a method that estimates the optimal learning rate for a certain type of network, a fully connected CReLU network.
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Books on the topic "EMD - Neural networks"

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Shishkin, Aleksey. Methods of digital processing and speech recognition. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1904325.

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The monograph discusses the theory, algorithms and practical methods of implementing digital processing and recognition of speech signals. The basics of mathematical analysis of digital signals necessary for speech processing are presented. The acoustic theory of speech formation with the construction of a general discrete model is briefly described. The main characteristic features of speech signals, as well as methods of their isolation are considered. Hidden Markov models and the architecture of traditional recognition systems based on them are described in detail. Weighted finite converters used to increase the efficiency and speed up the process of decoding acoustic signals are considered. The main architectures of artificial neural networks and examples of integrated (end-to-end) speech recognition systems based on them are presented. It is intended for students, postgraduates, researchers and specialists dealing with speech signal processing, pattern recognition and artificial intelligence.
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Kasabov, Nikola. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. The MIT Press, 1996. http://dx.doi.org/10.7551/mitpress/3071.001.0001.

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In a clear and accessible style, Kasabov describes rule-based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp. Bradford Books imprint
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Nolte, David D. Introduction to Modern Dynamics. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198844624.001.0001.

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Introduction to Modern Dynamics: Chaos, Networks, Space and Time (2nd Edition) combines the topics of modern dynamics—chaos theory, dynamics on complex networks and the geometry of dynamical spaces—into a coherent framework. This text is divided into four parts: Geometric Mechanics, Nonlinear Dynamics, Complex Systems, and Relativity. These topics share a common and simple mathematical language that helps students gain a unified physical intuition. Geometric mechanics lays the foundation and sets the tone for the rest of the book by emphasizing dynamical spaces, like state space and phase space, whose geometric properties define the set of all trajectories through those spaces. The section on nonlinear dynamics has chapters on chaos theory, synchronization, and networks. Chaos theory provides the language and tools to understand nonlinear systems, introducing fixed points that are classified through stability analysis and nullclines that shepherd system trajectories. Synchronization and networks are central paradigms in this book because they demonstrate how collective behavior emerges from the interactions of many individual nonlinear elements. The section on complex systems contains chapters on neural dynamics, evolutionary dynamics, and economic dynamics. The final section contains chapters on metric spaces and the special and general theories of relativity. In the second edition, sections on conventional topics, like applications of Lagrangians, have been strengthened, as well as being updated to provide a modern perspective. Several of the introductory chapters have been rearranged for improved logical flow and there are expanded homework problems at the end of each chapter.
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Lee, Joonkoo. Global Commodity Chains and Global Value Chains. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.201.

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A commodity chain refers to “a network of labor and production processes whose end result is a finished commodity.” The attention given to this concept has quickly translated into an expanding body of global chains literature. Research into global commodity chains (GCC), and later global value chains (GVC), is an endeavor to explain the social and organizational structure of the global economy and its dynamics by examining the commodity chains of a specific product of service. The GCC approach first emerged in the mid-1980s from world-system research and was reformulated in the early 1990s by development scholars. The development-oriented GCC approach turned the focus of GCC analysis to actor-centered processes in the global economy. One of the initial criticisms facing the GCC approach was its exclusive focus on internal conditions and organizational linkages, lacking systemic attention to the effect of domestic institutions and internal capacity on economic development. Other critics pointed to the narrow scope of GCC research. With the huge expansion in global chains literature in the past decade—not only in volume but also in depth and scope—efforts have been made to elaborate the global chains framework and to render it industry neutral, as partly reflected in the adoption of the term “global value chains.” Three key research themes surround these recent evolutions of global chains literature: GVC governance, “upgrading,” and the social construction of global value chains. Existing literature, however, still has theoretical and methodological gaps to redress.
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Book chapters on the topic "EMD - Neural networks"

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Meng, Qingfang, Shanshan Chen, Weidong Zhou, and Xinghai Yang. "Seizure Detection in Clinical EEG Based on Entropies and EMD." In Advances in Neural Networks – ISNN 2013, 323–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39068-5_40.

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Meng, Qingfang, Lei Du, Shanshan Chen, and Hanyong Zhang. "Epileptic Detection Based on EMD and Sparse Representation in Clinic EEG." In Advances in Neural Networks – ISNN 2018, 842–49. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92537-0_95.

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Meng, Qingfang, Shanshan Chen, Haihong Liu, Yunxia Liu, and Dong Wang. "Detection of Epileptic Seizure in EEG Using Sparse Representation and EMD." In Advances in Neural Networks - ISNN 2017, 516–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_60.

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Du, Lei, Yuwei Zhang, Qingfang Meng, Hanyong Zhang, and Yang Li. "Automatic Seizure Detection Based on a Novel Multi-feature Fusion Method and EMD." In Advances in Neural Networks – ISNN 2019, 512–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22808-8_50.

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Sun, Yuehui, and Di Zhang. "DT-CWT Feature Structure Representation for Face Recognition under Varying Illumination Using EMD." In Advances in Neural Networks – ISNN 2009, 429–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01513-7_47.

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Leo, Marco, David Looney, Tiziana D’Orazio, and Danilo P. Mandic. "Defective Areas Identification in Aircraft Components by Bivariate EMD Analysis of Ultrasound Signals." In Artificial Neural Networks in Pattern Recognition, 219–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12159-3_20.

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Wei, Yingda, Qingfang Meng, Haihong Liu, Jin Zhou, and Dong Wang. "An Improved Symbol Entropy Algorithm Based on EMD for Detecting VT and VF." In Advances in Neural Networks - ISNN 2017, 345–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_41.

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Labate, Domenico, Fabio La Foresta, Giuseppe Morabito, Isabella Palamara, and Francesco Carlo Morabito. "On the Use of Empirical Mode Decomposition (EMD) for Alzheimer’s Disease Diagnosis." In Advances in Neural Networks: Computational and Theoretical Issues, 121–28. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18164-6_12.

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Hagn, Korbinian, and Oliver Grau. "Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation." In Deep Neural Networks and Data for Automated Driving, 127–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_4.

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AbstractSynthetic, i.e., computer-generated imagery (CGI) data is a key component for training and validating deep-learning-based perceptive functions due to its ability to simulate rare cases, avoidance of privacy issues, and generation of pixel-accurate ground truth data. Today, physical-based rendering (PBR) engines simulate already a wealth of realistic optical effects but are mainly focused on the human perception system. Whereas the perceptive functions require realistic images modeled with sensor artifacts as close as possible toward the sensor, the training data has been recorded. This chapter proposes a way to improve the data synthesis process by application of realistic sensor artifacts. To do this, one has to overcome the domain distance between real-world imagery and the synthetic imagery. Therefore, we propose a measure which captures the generalization distance of two distinct datasets which have been trained on the same model. With this measure the data synthesis pipeline can be improved to produce realistic sensor-simulated images which are closer to the real-world domain. The proposed measure is based on the Wasserstein distance (earth mover’s distance, EMD) over the performance metric mean intersection-over-union (mIoU) on a per-image basis, comparing synthetic and real datasets using deep neural networks (DNNs) for semantic segmentation. This measure is subsequently used to match the characteristic of a real-world camera for the image synthesis pipeline which considers realistic sensor noise and lens artifacts. Comparing the measure with the well-established Fréchet inception distance (FID) on real and artificial datasets demonstrates the ability to interpret the generalization distance which is inherent asymmetric and more informative than just a simple distance measure. Furthermore, we use the metric as an optimization criterion to adapt a synthetic dataset to a real dataset, decreasing the EMD distance between a synthetic and the Cityscapes dataset from 32.67 to 27.48 and increasing the mIoU of our test algorithm () from 40.36 to $$47.63\%$$ 47.63 % .
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Malik, Hasmat, Yogesh Pandya, Aakash Parashar, and Rajneesh Sharma. "Feature Extraction Using EMD and Classifier Through Artificial Neural Networks for Gearbox Fault Diagnosis." In Advances in Intelligent Systems and Computing, 309–17. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1822-1_28.

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Conference papers on the topic "EMD - Neural networks"

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Miao, Yao, and Jianting Cao. "Comparison of EMD, MEMD and 2T-EMD by analyzing standard artificial signals and EEG." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966012.

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Ge Guangtao, Sang Enfang, Liu Zhuofu, and Zhu Beibei. "Bidimensional EMD infinite sifting." In 2008 International Conference on Neural Networks and Signal Processing (ICNNSP). IEEE, 2008. http://dx.doi.org/10.1109/icnnsp.2008.4590370.

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Djeddou, Messaoud, Xingang Zhao, Ibrahim A. Hameed, and Ahmed Rahmani. "Hybrid Improved Empirical Mode Decomposition and Artificial Neural Network Model for the Prediction of Critical Heat Flux (CHF)." In 2021 28th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icone28-64879.

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Abstract Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generalized regression neural networks (GRNN), and multi-layer perceptron (MLP) combined with empirical mode decomposition (EMD) are developed for CHF predictive modelling using CHF experimental databases. First, the original experimental inputs data series are decomposed into several intrinsic mode functions (IMFs) and one residual by EMD, whose components are divided into high, medium and low components. The performance parameters of the hybrid models indicates that the root mean square error (RMSE) are 0.8831, 0.6522, and 0.4149; the mean absolute error (MAE) are 0.6697, 0.4636, and 0.1935. The values of the R-square of the developed prediction approach utilizing EMD-RBF, EMD-GRNN, and EMD-MLP models are 0.8553, 0.9302, and 0.9818, and the index of agreement are 0.9464, 0.9700, and 0.9894., The value of the R-square and the index of agreement of the proposed models are much higher than those of the simple models. The Pearson’s test results show that the association strength between the measured and the predicted values of the proposed model EMD-MLP is the strongest. These results show the following: (a) compared with other related, recent studies, the prediction accuracy of the hybrid model EMD-MLP proposed in this research is the best hybrid model; (b) the proposed hybrid model (EMD-MLP) attains superior performance compared with simple models.
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Tianqiang Yuan, Nenghai Yu, and Xuelong Li. "Image retrieval with EMD for new perceptual color feature." In Proceedings of 2003 International Conference on Neural Networks and Signal Processing. IEEE, 2003. http://dx.doi.org/10.1109/icnnsp.2003.1280761.

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Jiao, Xiaoxuan, Bo Jing, Yifeng Huang, Wei Liang, and Guangyue Xu. "A fault diagnosis approach for airborne fuel pump based on EMD and probabilistic neural networks." In 2016 Prognostics and System Health Management Conference (PHM-Chengdu). IEEE, 2016. http://dx.doi.org/10.1109/phm.2016.7819831.

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YUAN, CAO, ZHOU YUHAN, and SUN YONGKUI. "FAULT DIAGNOSIS OF TURNOUT SWITCH MACHINE BASED ON BRAIN-INSPIRED INTELLIGENCE." In 3rd International Workshop on Structural Health Monitoring for Railway System (IWSHM-RS 2021). Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/iwshm-rs2021/36031.

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As the core equipment to control the running direction of railway vehicle, the turnout switch machine, its failure will lead to a series of problems. At present, more and more researchers conduct intelligent re-search on its fault diagnosis. In this paper, aiming at different types of faults during the operation of turnout switch machines, a spiking neural networks model based on apoptosis mechanism (BASNNs) is established as a classifier for fault diagnosis. The model uses neuron competition rules and intelligent unsupervised learning algorithms to update the network. The system uses the non-contact measurement of the sound signal as the input, and uses the combination of empirical mode decomposition (EMD) and wavelet packet decomposition energy entropy (WPDE) to extract features of the sound data. Experimental results show that the proposed method has better data recognition ability than traditional ANNs with the same number of layers.
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Lai, Chiayu, Zhe-Lun Jhang, and Deng-Neng Chen. "To Design and Implement a Recommender System based on Brainwave: Applying Empirical Model Decomposition (EMD) and Neural Networks." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2019. http://dx.doi.org/10.24251/hicss.2019.536.

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Auluck, Nandika, and Seetaram Maurya. "A Data-Driven Diagnosis and Prognosis method for Machinery Tools Based on EMD and Dual-Task Deep Neural Networks." In 2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON). IEEE, 2022. http://dx.doi.org/10.1109/catcon56237.2022.10077686.

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GAO, MINGLIANG, SHAN GAO, CHUANG YU, DEQUAN LI, SHIJI SONG, HAIMING SHI, HONGLIANG SUN, and HONGCHAO WANG. "RESEARCH AND APPLICATION OF RADIAL BASIS NETWORK BOGIE FAULT DIAGNOSIS MODEL BASED ON PARTICLE SWARM OPTIMIZATION." In 3rd International Workshop on Structural Health Monitoring for Railway System (IWSHM-RS 2021). Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/iwshm-rs2021/36030.

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Bogie system is the key system that affects the safety and quality of EMU operation. The construction of fault diagnosis model for bogie system can effectively improve the safety and comfort of EMU operation. The traditional modeling method uses BP neural network to model by fitting bogie system temperature and other parameters. However, BP neural network is prone to fall into local minimum, slow convergence and poor diagnostic accuracy. In this paper, particle radial basis function neural network (PSRB) is designed by using particle swarm optimization algorithm with high convergence. Particle Swarm optimization (PSO) is used to optimize the parameters of RBF Neural Networks. According to the complexity of the input parameters of the bogie system, the input and output parameters of the model are determined. Particle swarm optimization algorithm is used to search the optimal values of the center, width and output layer weight threshold of the RBF neural network. The hybrid algorithm is applied to the fault diagnosis of bogie system, and a bogie fault diagnosis model based on particle radial basis function neural network is designed. The experimental results show that the diagnosis model can effectively improve the identification accuracy of fault diagnosis, the minimum error accuracy is 0.0055, the operation time is saved, the operation time is reduced to 1.9s, and the influence of non-target parameters on the inversion results is eliminated. The model can also be used in other EMU systems, and has practical application value.
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Hu, Weifei, Yihan He, Zhenyu Liu, Jianrong Tan, Ming Yang, and Jiancheng Chen. "A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16500.

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Abstract Precise time series prediction serves as an important role in constructing a Digital Twin (DT). The various internal and external interferences result in highly non-linear and stochastic time series data sampled from real situations. Although artificial Neural Networks (ANNs) are often used to forecast time series for their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components, each of which is composed of single-frequency and stationary signal, and a residual signal. The decomposed signals are used to train the BO-LSTM neural networks, in which the hyper-parameters of the LSTM neural networks are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed hybrid method (EEMD-BO-LSTM), this paper conducts a case study of wind speed time series prediction and has a comprehensive comparison between the proposed method and other approaches including the persistence model, ARIMA, LSTM neural networks, B0-LSTM neural networks, and EEMD-LSTM neural networks. Results show an improved prediction accuracy using the EEMD-BO-LSTM method by multiple accuracy metrics.
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Reports on the topic "EMD - Neural networks"

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculation of recognition error and training accuracy are presented.
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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