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Full-Text Articles in Engineering
Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar
Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar
Research outputs 2022 to 2026
Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …
Prediction Of Drilling Fluid Lost-Circulation Zone Based On Deep Learning, Yili Kang, Chenglin Ma, Chengyuan Xu, Lijun You, Zhenjiang You
Prediction Of Drilling Fluid Lost-Circulation Zone Based On Deep Learning, Yili Kang, Chenglin Ma, Chengyuan Xu, Lijun You, Zhenjiang You
Research outputs 2022 to 2026
Lost circulation has become a crucial technical problem that restricts the quality and efficiency improvement of the drilling operation in deep oil and gas wells. The lost-circulation zone prediction has always been a hot and difficult research topic on the prevention and control of lost circulation. This study applied machine learning and statistical methods to deeply mine 105 groups and 29 features of loss data from typical loss block M. After removing 10 sets of noise data, the methods of mean removal, range scaling and normalization were used to pre-treat the 95 sets of the loss data. The multi-factor analysis …
A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung
A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung
Research outputs 2022 to 2026
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we …
Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain
Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain
Research outputs 2022 to 2026
Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation …
Deep Learning For Robust Adaptive Inverse Control Of Nonlinear Dynamic Systems: Improved Settling Time With An Autoencoder, Nuha A. S. Alwan, Zahir M. Hussain
Deep Learning For Robust Adaptive Inverse Control Of Nonlinear Dynamic Systems: Improved Settling Time With An Autoencoder, Nuha A. S. Alwan, Zahir M. Hussain
Research outputs 2022 to 2026
An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be …
Deep Learning Inspired Feature Engineering For Classifying Tremor Severity, Ahmed Al Taee, Seyedehmarzieh Hosseini, Rami N. Khushaba, Tanveer Zia, Chin-Teng Lin, Adel Al-Jumaily
Deep Learning Inspired Feature Engineering For Classifying Tremor Severity, Ahmed Al Taee, Seyedehmarzieh Hosseini, Rami N. Khushaba, Tanveer Zia, Chin-Teng Lin, Adel Al-Jumaily
Research outputs 2022 to 2026
Bio-signals pattern recognition systems can be impacted by several factors with a potential to limit their associated performance and clinical translation. Among these factors, selecting the optimum feature extraction method, that can effectively exploit the interaction between the temporal and spatial information, is the most prominent. Despite the potential of deep learning (DL) models for extracting temporal, spatial, or temporal-spatial information, they are typically restricted by their need for a large amount of training data. The deep wavelet scattering transform (WST) is a relatively recent advancement within the DL literature to replace expensive convolution neural networks models with computationally less …
Rgb-D Data-Based Action Recognition: A Review, Muhammad Bilal Shaikh, Douglas Chai
Rgb-D Data-Based Action Recognition: A Review, Muhammad Bilal Shaikh, Douglas Chai
Research outputs 2014 to 2021
Classification of human actions is an ongoing research problem in computer vision. This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The increase in the number of action recognition datasets intersects with advances in deep learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality—such as RGB, depth, skeleton, and infrared (IR)—has distinct characteristics; therefore, it is important to exploit the value of …
Deep Learning Control For Digital Feedback Systems: Improved Performance With Robustness Against Parameter Change, Nuha A. S. Alwan, Zahir M. Hussain
Deep Learning Control For Digital Feedback Systems: Improved Performance With Robustness Against Parameter Change, Nuha A. S. Alwan, Zahir M. Hussain
Research outputs 2014 to 2021
Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a …
A Statistical Approach To Provide Explainable Convolutional Neural Network Parameter Optimization, Saman Akbarzadeh, Selam Ahderom, Kamal Alameh
A Statistical Approach To Provide Explainable Convolutional Neural Network Parameter Optimization, Saman Akbarzadeh, Selam Ahderom, Kamal Alameh
Research outputs 2014 to 2021
Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications. Finding the best network and optimizing its hyperparameters for a specific application are central challenges for CNNs. Most state-of-the-art CNNs are manually designed, while techniques for automatically finding the best architecture and hyperparameters are computationally intensive, and hence, there is a need to severely limit their search space. This paper proposes a fast statistical method for CNN parameter optimization, which can be applied in many CNN applications …
Review Of Deep Learning Methods In Robotic Grasp Detection, Shehan Caldera, Alexander Rassau, Douglas Chai
Review Of Deep Learning Methods In Robotic Grasp Detection, Shehan Caldera, Alexander Rassau, Douglas Chai
Research outputs 2014 to 2021
For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods …