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Ensemble of deep learning techniques to human activity recognition using smart phone signals

Multimedia Tools and Applications 2024年第42期83卷 89635-89664页

作者： Imanzadeh, Soodabeh Tanha, Jafar Jalili, Mahdi Electrical and Computer Engineering Department University of Tabriz Tabriz Iran School of Engineering RMIT University Melbourne Australia

Human Activity Recognition (HAR) has become a significant area of study in the fields of health, human behavior analysis, the Internet of Things, and human–machine interaction in recent years. Smartphones are a popular choice for HAR as they are common devices used in daily life. However, most available HAR datasets are gathered in laboratory settings, which do not reflect real-world scenarios. To address this issue, a real-world dataset using smartphone inertial sensors, involving 62 individuals, is collected. The collected dataset is noisy, small, and has variable frequency. On the other hand, in the context of HAR, algorithms face additional challenges due to intra-class diversity (which refers to differences in the characteristics of performing an activity by different people or by the same individual under different conditions) and inter-class similarity (which refers to different activities that are highly similar). Consequently, it is essential to extract features accurately from the dataset. Ensemble learning, which combines multiple models, is an effective approach to improve generalization performance. In this paper, a weighted ensemble of hybrid deep models for HAR using smartphone sensors is proposed. The proposed ensemble approach demonstrates superior performance compared to current methods, achieving impressive results across multiple evaluation metrics. Specifically, the experimental analysis demonstrates an accuracy of 97.15%, precision of 96.41%, recall of 95.62%, and an F1-score of 96.01%. These results demonstrate the effectiveness of our ensemble approach in addressing the challenges of HAR in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smartphones

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Design and Modeling of a Photonic Crystal Multiplexer Using Artificial Intelligence

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 59-64页

作者： Karami, Pouya Yahya, Salah I. Palash, Behnam Chaudhary, Muhammad Akmal Assaad, Maher Parandin, Fariborz Roshani, Saeed Hazzazi, Fawwaz Roshani, Sobhan Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University-Erbil Erbil44001 Iraq Department of Software Engineering Faculty of Engineering Koya University KOY45 Koya Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj11492 Saudi Arabia

In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference between the output powers in logical states, which is critical for enhancing the system ability to distinguish between these states. In this study, an ANN model is employed to accurately predict the normalized output power of the designed photonic crystal multiplexer, providing a time-efficient alternative to conventional simulation methods for analyzing multiplexer behavior across various logical states. The results demonstrate significant improvements in signal separation and overall performance compared to previous works. Additionally, a detailed comparison of the normalized output power for different logic states is provided, highlighting the advantages of the proposed design. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Silicon photonics

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Meta-ETI: Meta-Reinforcement Learning with Explicit Task Inference for UAV-IoT Coverage

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IEEE Internet of Things Journal 2025年第13期12卷 23852-23865页

作者： Huang, Songjun Sun, Chuanneng Pompili, Dario Rutgers University-New Brunswick Department of Electrical and Computer Engineering NJ United States

To better enhance the network service for different user devices in various scenarios, unmanned aerial vehicles (UAVs) are increasingly used as aerial base stations (ABSs). However, optimizing coverage for user devices via UAV team control is an NP-hard problem and escalates exponentially in complexity with the growing number of user devices. To address this challenge, researchers have turned to reinforcement learning (RL) for a more practical solution. With the growing prevalence of the Internet of Things (IoT), the diversity of user devices increases, posing challenges for traditional RL, as i) the spatial distribution of devices becomes more complex;ii) variations in device types and device mobility increase the training latency;iii) the high-speed movement of IoT devices can lead to performance deterioration in widely used RL algorithms with discrete action space;and iv) traditional RL struggles to adapt to new environments. To solve these problems, we propose a new meta-RL framework, Meta-RL with Explicit Task Inference (Meta-ETI). Then, we apply this framework to efficiently train an energy-efficient UAV control policy for fair and effective coverage in 3D dynamic environments. Meta-ETI is evaluated in both theoretical and application-related aspects and demonstrates superior performance compared to the baseline frameworks. The result shows that Meta-ETI demonstrates 2 to 3 times faster adaptation speed and a decent performance in sample efficiency. Furthermore, in the UAV-IoT coverage application, Meta-ETI shows 30% to 50% better in energy efficiency and 40% to 60% more served devices because of the fair coverage. © 2014 IEEE.

关键词： Reinforcement learning

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Switching Dynamic State Estimation and Event Detection for Inverter-Based Resources with Multiple Control Modes

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IEEE Transactions on Power Systems 2024年第4期40卷 3439-3451页

作者： Huang, Heqing Lin, Yuzhang Department of Electrical and Computer Engineering New York University Brooklyn NY USA

The Dynamic State Estimation (DSE) for Inverter-Based Resources (IBRs) is an emerging topic as IBRs gradually replace synchronous generators (SGs) in power systems. Unlike SGs, the dynamic models of IBRs heavily depend on their control algorithms, and conventional DSE methods for SGs, which assume a unchanged state space and dynamic model, cannot handle IBRs with control mode changes in real time, particularly when the power grid operators are unaware of the current control mode of the IBRs. In response to these challenges, an Expectation-Maximization Sliding-Window Iterated Extended Kalman Filter (EM-SW-IEKF) method is proposed in this paper. It theoretically achieves maximum likelihood estimation under different modes through the EM algorithm, providing the most probable control mode of the system as well as the corresponding state estimate. This method is validated in various IBR systems (battery energy storage systems and solar photovoltaic systems) and under different control mode transitions (switching between grid-following and grid-forming controls and between low voltage ride through and maximum power point tracking controls). © 1969-2012 IEEE.

关键词： Expectation maximization algorithm

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Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1217-1232页

作者： Alali, Mohammad Imani, Mahdi Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States

Hidden Markov models (HMMs) are a powerful class of dynamical models for representing complex systems that are partially observed through sensory data. Existing data collection methods for HMMs, typically based on active learning or heuristic approaches, face challenges in terms of efficiency in stochastic domains with costly data. This article introduces a Bayesian lookahead data collection method for inferring HMMs with finite state and parameter spaces. The method optimizes data collection under uncertainty using a belief state that captures the joint distribution of system states and models. Unlike traditional approaches that prioritize short-term gains, this policy accounts for the long-term impact of data collection decisions to improve inference performance over time. We develop a deep reinforcement learning policy that approximates the optimal Bayesian solution by simulating system trajectories offline. This pretrained policy can be executed in real time, dynamically adapting to new conditions as data is collected. The proposed framework supports a wide range of inference objectives, including point based, distribution based, and causal inference. Experimental results across three distinct systems demonstrate significant improvements in inference accuracy and robustness, showcasing the effectiveness of the approach in uncertain and data-limited environments. © 2020 IEEE.

关键词： Deep reinforcement learning

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Multi-Model emotion recognition from speech using StarGAN, DCNN, and SVM

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Multimedia Tools and Applications 2025年 1-18页

作者： Banihosseini, Najmeh Sadat Ghods, Vahid Department of Electrical and Computer Engineering Semnan Branch Islamic Azad University Semnan Iran

Emotion recognition from speech is a significant research area in human–computer interaction and psychological assessments. This study proposes a novel three-stage process for emotion recognition from speech signals. In the first stage, pre-processing operations, including noise reduction and normalization, are applied to improve data quality. Then, StarGAN is employed for data augmentation, addressing the challenge of limited emotional speech data. The second stage involves extracting meaningful features from high-dimensional data using deep convolutional neural networks (DCNN). Finally, support vector machines (SVM) are used to classify the extracted features into distinct emotional categories. The main advantage of the proposed method over traditional deep learning techniques is the use of StarGAN for data augmentation, which improves performance on limited datasets. Additionally, the combination of DCNN and SVM enhances accuracy and efficiency in feature extraction and classification. This method has been evaluated using four different datasets, RAVDESS, Emo-DB, SAVEE, and CASIA, achieving accuracy rates of 98.25%, 95.5%, 96.8%, and 96.45%, respectively. The results indicate that the proposed method significantly outperforms existing methods and offers improved capabilities for practical application. Despite achieving high accuracy, challenges such as increased computational complexity and variability in performance across different datasets are discussed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A Scalable Unsupervised and Back Propagation Free Learning With SACSOM: A Novel Approach to SOM-Based Architectures

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 955-967页

作者： Hirani, Gaurav R. Wang, Kevin I-Kai Abdulla, Waleed H. The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand

The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alternative avenues such as self-organizing maps (SOM)-based architectures, which offer significant advantages such as tractability, the absence of back-propagation, and feed-forward unsupervised learning. However, these SOM-based approaches frequently suffer from lower accuracy and limited generalization capabilities. To address these shortcomings, we propose a novel model called split and concur SOM (SACSOM). SACSOM overcomes the limitations of closely related SOM-based algorithms by utilizing multiple parallel branches, each equipped with its own SOM modules that process data independently with varying patch sizes. Furthermore, by creating groups of classes and using respective training samples to train independent subbranches in each branch, our approach accommodates datasets with a large number of classes. SACSOM employs a simple yet effective labeling technique requiring minimal labeled samples. The outputs from each branch, filtered by a threshold, contribute to the final prediction. Experimental validation on MNIST-digit, Fashion-MNIST, CIFAR-10, and CIFAR-100 demonstrates that SACSOM achieves competitive accuracy with significantly reduced computation time. Furthermore, it exhibits superior performance and generalization capabilities, even in high-noise scenarios. The weights of the single-layered SACSOM provide meaningful insights into the patch-based learning pattern, enhancing its tractability and making it ideal from the perspective of explainable AI. This study addresses the limitations of current clustering techniques, such as K-means and traditional SOMs, by proposing a lightweight, manageable, and fast architecture that does not require a GPU, making it suitable for low-powered devices. © 2024 IEEE.

关键词： Unsupervised learning

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Development of a machine-learning-based method for early fault detection in photovoltaic systems

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Journal of engineering and Applied Science 2023年第1期70卷 27页

作者： Voutsinas, Stylianos Karolidis, Dimitrios Voyiatzis, Ioannis Samarakou, Maria Department of Informatics and Computer Engineering University of West Attica Athens Greece

In the process of the decarbonization of energy production, the use of photovoltaic systems (PVS) is an increasing trend. In order to optimize the power generation, the fault detection and identification in PVS is significant. The purpose of this work is the study and implementation of such an algorithm, for the detection as many as faults arising on the DC side of a photovoltaic system. A machine learning technique was chosen. The dataset used to train the algorithm was based on a year’s worth of irradiance and temperature data, as well as data from the PV cell used. The method uses logistic regression with cross validation as a new approach to detect and identify faults in PVS. It is applied to smart PV arrays, that can transmit voltage and current measurements from each PV cell of the array individually. The results are satisfactory since the algorithm can detect the majority of faults that occur on the DC side of a photovoltaic (open-circuit fault, short-circuit fault, mismatch faults). The accuracy of the algorithm (97.11%) is comparable to other methods presented by the literature. Moreover, the computational cost of the proposed method is significantly lower than the methods presented in the literature. In summary, the performance of the implemented algorithm is considered particularly satisfactory and can be easily applied to PVS. © 2023, The Author(s).

关键词： Machine learning

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Suppression of over voltage in SiC-based inverter fed induction motor

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Australian Journal of electrical and Electronics engineering 2024年第1期21卷 11-28页

作者： Salau, Ayodeji Olalekan Kanchana, Kadirvel Anoop, Kuttichakku Johny Markus, Elisha Didam Braide, Sepiribo Lucky Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electrical Electronics Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering VISAT Engineering College Kerala Ernakulam India Department of Electrical/Electronic and Computer Engineering Central University of Technology Free State Bloemfontein South Africa Department of Electrical/Electronic Engineering University of Port Harcourt Rivers State Choba Nigeria

Silicon carbide (SiC) based inverters are widely used due to their fast switching and ability to withstand high temperatures and Voltage. This helps to increase efficiency and reduces switching losses compared to the silicon-based inverter. However, this fast-switching device causes an increase in the Voltage at the junction of the cable and motor due to the reflected wave. The main causes of overvoltage are cable/motor impedance mismatch, fast-switching inverter devices (IGBT), and inverter-motor separation. To extend the induction motor lifespan, overvoltage reduction is necessary. Passive and active filters solve the problem, but passive filters are bulky and have high power loss. This paper presents a shunt active filter approach for an inverter-fed induction motor to reduce overvoltage. To show the effectiveness of the proposed approach, the findings were compared to the topology of the clamp-type active filter already in use. The shunt active filter used Proportional Integral (PI) and fuzzy controllers to generate reference current according to the Synchronous Reference Frame (SRF) theory. Harmonics in the power system were removed using the SRF theory. The unique aspect of this work is how the reference current is produced in the shunt active filter using the SRF theory. The proposed shunt active filter with a fuzzy controller was added to the hardware implementation to achieve better results than previous active filters. ©, Engineers Australia.

关键词： Silicon carbide

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Matrix Completion from One-Bit Dither Samples

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IEEE Transactions on Signal Processing 2024年 1-14页

作者： Eamaz, Arian Yeganegi, Farhang Soltanalian, Mojtaba Department of Electrical and Computer Engineering University of Illinois Chicago Chicago IL USA

We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with random dither levels. In particular, instead of observing a subset of high-resolution entries of a low-rank matrix, we have access to a small number of one-bit samples, generated as a result of these comparisons. In order to recover the low-rank matrix using its coarsely quantized known entries, we begin by transforming the problem of one-bit matrix completion (one-bit MC) with random dithering into a nuclear norm minimization problem. The one-bit sampled information is represented as linear inequality feasibility constraints. We then develop the popular singular value thresholding (SVT) algorithm to accommodate these inequality constraints, resulting in the creation of the One-Bit SVT (OBSVT). Our findings demonstrate that incorporating multiple random dither sequences in one-bit MC can significantly improve the performance of the matrix completion algorithm. In pursuit of achieving this objective, we utilize diverse dithering schemes, namely uniform, Gaussian, and discrete dithers. To accelerate the convergence of our proposed algorithm, we introduce three variants of the OB-SVT algorithm. Among these variants is the randomized sketched OB-SVT, which departs from using the entire information at each iteration, opting instead to utilize sketched data. This approach effectively reduces the dimension of the operational space and accelerates the convergence. We perform numerical evaluations comparing our proposed algorithm with the maximum likelihood estimation method previously employed for one-bit MC, and demonstrate that our approach can achieve a better recovery performance. Authors

关键词： Linear matrix inequalities

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