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International Conference on Machine Learning and Applications (ICMLA)

作者： Olaoluwa Adigun Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute Los Angeles California

A bidirectional autoencoder learns or approximates an identity mapping as it trains a single network with a version of the new bidirectional backpropagation algorithm. Ordinary unidirectional autoencoders find many uses in image processing and in large language models. But they use separate networks for encoding and decoding. Bidirectional auto encoders use the same synaptic weights for encoding and decoding. The forward pass encodes while the backward pass decodes. Bidirectional auto encoders improved network performance and significantly reduced memory usage and used fewer parameters. Simulations compared unidirectional with bidirectional autoencoders for image compression and de noising. The models trained on the MNIST handwritten-digit and CIFAR-IO image datasets. The performance measures were the peak signal-to-noise ratio and the index of structural similarity. Bidirectional autoencoders outperformed unidirectional autoencoders and still reduced the number of trainable synaptic parameters by about 50%.

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Coherent Direction of Arrival Estimation using Multiple Toeplitz Space Time Spatial Smoothing

Coherent Direction of Arrival Estimation using Multiple Toep...

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Iranian Conference on electrical engineering (ICEE)

作者： Sepehr Kouzehgaran Masoumeh Azghani Electrical Engineering Department Wireless Communications and Signal Processing (WCSP) laboratory Sahand University of Technology Tabriz Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

The direction of arrival (DOA) estimation task becomes more challenging when the sources are coherent. In this paper, a method has been suggested for the coherent DOA estimation. At first, the received signal of the uniform linear antenna array is divided into subarrays. For each of the subarrays, the multiple Toeplitz technique has been applied to produce the correlation matrix. Then, the forward backward smoothing has been exploited and the resultant correlation matrices are averaged over various snapshots to provide the improved-rank covariance matrix. At the last step, one of the subspace based DOA estimation schemes are leveraged to estimate the DOAs of the coherent sources. The simulation results indicate that the proposed method is superior over its rivals in various scenarios.

关键词： electrical engineering Direction-of-arrival estimation Smoothing methods Correlation Simulation Estimation Receiving antennas

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

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Journal of engineering and Applied Science 2023年第1期70卷 56页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Hazem Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Chemical Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Electrical and Computer Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Monastir5019 Tunisia

This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps: first, a sine-cosine algorithm is used to extract and optimize features in order to only select the most descriptive ones. Second, to further improve the capability, thereby providing the highest accuracy performance, the newly gathered dataset is introduced as input to an ensemble learning paradigm, which merges the benefits of boosting and bagging techniques with an artificial neural network classifier. The essential goal of the developed proposal is to discriminate between the diverse operating conditions (one healthy and six faulty conditions). Three potential and frequent types of faults that can affect the system behaviors including short-circuit, open-circuit, and wear-out are considered and thereby injected at diverse locations and sides (grid and generator sides) in order to evaluate the availability and performance of the proposed technique when compared to the conventional FDD methods. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. The acquired outcomes demonstrate the efficiency of the suggested diagnostic paradigm compared to conventional FDD techniques (accuracy rate has been successfully achieved 98.35%). © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

关键词： Failure analysis

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Multi-modal signal integration for enhanced sleep stage classification: Leveraging EOG and 2-channel EEG data with advanced feature extraction

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Artificial Intelligence in Medicine 2025年 166卷 103152-103152页

作者： Samaee, Mahdi Yazdi, Mehran Massicotte, Daniel Signal and Image Processing Laboratory School of Electrical and Computer Engineering Shiraz University Shiraz Iran Laboratory of Signal and System Integration Department of Electrical and Computer Engineering Université du Québec à Trois-Rivières Trois-Rivières Canada

This paper introduces an innovative approach to sleep stage classification, leveraging a multi-modal signal integration framework encompassing Electrooculography (EOG) and two-channel electroencephalography (EEG) data. We explore the utility of various feature extraction techniques, including Short-Time Fourier Transform (STFT), Wavelet Transform, and raw signal processing, alongside the utilization of neural networks as feature extractors. This unique combination allows us to harness the benefits of traditional feature extraction methods while capitalizing on the power of neural networks to enhance classification performance. Our comprehensive classifier evaluation encompasses a range of models, including Long Short-Term Memory (LSTM) networks and XGBoost. Remarkably, our results reveal exceptional performance with the XGBoost classifier, achieving an overall accuracy of 84.57 % and a macro-F1 score of 78.21 % on the Sleep-EDF expanded dataset, and an overall accuracy of 86.02 % and a macro-F1 score of 81.96 % on the ISRUC-Sleep dataset. Class-specific accuracies highlight its proficiency, particularly in detecting wake and N2 stages, solidifying its superiority among the classifiers tested. This amalgamation of feature sets, complemented by Principal Component Analysis (PCA) for dimensionality reduction, underscores its significance in yielding top-tier classification outcomes. The integration of traditional feature extraction methods with neural networks as feature extractors creates a robust and comprehensive system for sleep stage classification, offering the advantages of both approaches to enhance the accuracy and reliability of the results. © 2025

关键词： Discrete wavelet transforms

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Dynamic Speech Emotion Recognition Using A Conditional Neural Process

Dynamic Speech Emotion Recognition Using A Conditional Neura...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Luz Martinez-Lucas Carlos Busso Department of Electrical and Computer Engineering Multimodal Signal Processing (MSP) Lab The University of Texas at Dallas Richardson TX USA

The problem of predicting emotional attributes from speech has often focused on predicting a single value from a sentence or short speaking turn. These methods often ignore that natural emotions are both dynamic and dependent on context. To model the dynamic nature of emotions, we can treat the prediction of emotion from speech as a time-series problem. We refer to the problem of predicting these emotional traces as dynamic speech emotion recognition. Previous studies in this area have used models that treat all emotional traces as coming from the same underlying distribution. Since emotions are dependent on contextual information, these methods might obscure the context of an emotional interaction. This paper uses a neural process model with a segment-level speech emotion recognition (SER) model for this problem. This type of model leverages information from the time-series and predictions from the SER model to learn a prior that defines a distribution over emotional traces. Our proposed model performs 21% better than a bidirectional long short-term memory (BiLSTM) baseline when predicting emotional traces for valence.

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Improved Dead-Beat Control for Single-Phase LC-Coupling Hybrid Active Power Filter (LC-HAPF) 32

Improved Dead-Beat Control for Single-Phase LC-Coupling Hybr...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Chan, Pak-Ian Sou, Wai-Kit Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Institute of Microelectronics University of Macau China University of Macau Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350399714

In this paper, an improved dead-beat controller (DBC) for a single-phase LC-coupling hybrid active power filter (LC-HAPF) is proposed to achieve both low steady-state error and fast transient response. The conventional DBC has an inherent discretized and predictive error, which can increase the steady-state error. Thus, a signal correction technique is used to reduce the steady-state error. Moreover, the improved DBC is implemented with a coupling capacitor voltage observer to replace the voltage sensor, thus reducing the system cost. After that, the stability analysis and parameter design of the proposed improved DBC for the LC-HAPF are given in detail. Subsequently, to verify the effectiveness of the improved DBC, the simulation results are provided in comparison to the conventional DBC. © 2023 IEEE.

关键词： Power quality

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Training Deep Neural Classifiers with Soft Diamond Regularizers

arXiv

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arXiv 2024年

作者： Adigun, Olaoluwa Kosko, Bart Signal and Image Processing Institute Department of Electrical and Computer Engineering Los AngelesCA90089-2564 United States

We introduce new soft diamond regularizers that both improve synaptic sparsity and maintain classification accuracy in deep neural networks. These parametrized regularizers outperform the state-of-the-art hard-diamond Laplacian regularizer of Lasso regression and classification. They use thick-tailed symmetric alpha-stable (SαS) bell-curve synaptic weight priors that are not Gaussian and so have thicker tails. The geometry of the diamond-shaped constraint set varies from a circle to a star depending on the tail thickness and dispersion of the prior probability density function. Training directly with these priors is computationally intensive because almost all SαS probability densities lack a closed form. A precomputed look-up table removed this computational bottleneck. We tested the new soft diamond regularizers with deep neural classifiers on the three datasets CIFAR-10, CIFAR-100, and Caltech-256. The regularizers improved the accuracy of the classifiers. The improvements included 4.57% on CIFAR-10, 4.27% on CIFAR-100, and 6.69% on Caltech-256. They also outperformed L2 regularizers on all the test cases. Soft diamond regularizers also outperformed L1 lasso or Laplace regularizers because they better increased sparsity while improving classification accuracy. Soft-diamond priors substantially improved accuracy on CIFAR-10 when combined with dropout, batch, or data-augmentation regularization. Copyright © 2024, The Authors. All rights reserved.

关键词： Probability density function

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Assimilation of satellite-based reflection measurements in an information system for a modern hop farm 44

Assimilation of satellite-based reflection measurements in a...

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Die 44. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft 2024 - 44th Annual Conference of the German Association for Informatics in Agriculture, Forestry, and the Food Sector 2024

作者： Linseisen, Hubert Manakos, Ioannis Katsikis, Eleftherios Delopoulos, Anastasios Landwirtschaftsberater Breitenwiese 6 Wolnzach85283 Germany 6 km Harilaou-Thermi Rd. Thessaloniki57001 Greece Information Processing Laboratory Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Information Processing Lab AUTH Campus ThessalonikiGR-54124 Greece

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Memristive band pass filter chaotic circuit: multistability and control

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International Journal of Electronics Letters 2023年第2期11卷 203-220页

作者： Kamdem Tchiedjo, Servet Kengne, Jacques Djuidje Kenmoe, Germaine Department of Electrical Engineering IUT-FV Bandjoun Laboratories d’Automatique et Informatique Appliquée (UR-AIA) Bandjoun Cameroon Laboratory of Mechanics Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Laboratory of Electronics and Signal Processing Department of Physics University of Dschang Dschang Cameroon

This paper studies the influence of an explicit symmetry break on the dynamics of a memristive band pass filter. It is shown that the circuit exhibits complex behaviours. The symmetry break is achieved by considering a memristor emulator with an asymmetric current-voltage characteristic. We describe how the bifurcation sequences, the number and types of coexisting solutions, and the topology of the basins of attraction metamorphose when monitoring both the system parameters and initial conditions. The theoretical results are validated by a series of simulations under PSPICE. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： antimonotonicity linear augmentation Memristor multistability pspice simulations

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