Article
Engineering, Electrical & Electronic
Baris Cavus, Mustafa Aktas
Summary: This article recommends a new flux weakening control approach for induction motors operating at high speeds, particularly in working areas such as electric vehicles. The proposed approach is based on model predictive control (MPC) and aims to enhance criteria including the time to reach steady state, ripple, etc., in addition to enabling operation of the induction motor beyond its nominal speed. A direct torque controlled (DTC) induction motor is used in the flux weakening study due to its minimal dependence on parameters and its ability to quickly respond to torque/speed changes. By utilizing the proposed MPC-based flux weakening control, the DTC-controlled induction motor exhibits features such as fast adaptation to changes in torque/speed references, low dependency on system parameters, ability to reach high speeds, short time to reach steady-state, and low steady-state error.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Automation & Control Systems
Stepan Janous, Jakub Talla, Vaclav Smidl, Zdenek Peroutka
Summary: The study proposes a control method for dual induction motors based on optimization tasks, incorporating state-dependent Riccati equations and predictive control concepts, using a simple linear quadratic regulator and rule-based predictive controller. The control algorithm, validated on a laboratory prototype, successfully controls the dual induction motors with manual adjustment of two parameters.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Electrical & Electronic
Yanqing Zhang, Zhonggang Yin, Wei Li, Jing Liu, Yanping Zhang
Summary: This article proposes an adaptive sliding-mode-control-based MPTC method to improve the robustness of FCS-MPC. ASMC-MPTC exhibits the best disturbance rejection ability and robustness against motor parameters variation and load torque change.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Engineering, Multidisciplinary
Dionysios Spyropoulos, Panagiotis A. Panagiotou, Ioannis Arvanitakis, Epaminondas D. Mitronikas, Konstantinos N. Gyftakis
Summary: The article introduces an alternative methodology for diagnosing induction motors at steady state, relying on a two-stage signal processing technique to reliably detect faults and their severity, particularly suitable for large industrial motors.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2021)
Review
Engineering, Electrical & Electronic
Anton Dianov, Fabio Tinazzi, Sandro Calligaro, Silverio Bolognani
Summary: This article discusses the MTPA control of synchronous motors, explaining the nature of torque produced by these motors and presenting algorithms for operating at the maximum torque point. The authors classify the MTPA methods based on their features and discuss the modifications required for implementation. They review existing control algorithms, analyze their pros and cons, and provide insights into their potential applications.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
Article
Automation & Control Systems
Li Zhang, Xiaoyong Zhu, Lei Xu, Chao Zhang, Ying Fan
Summary: This article presents a simplified universal fault-tolerant direct torque control strategy for five-phase fault-tolerant permanent magnet motors under various open-circuit faults. The strategy overcomes the limitations of existing strategies and achieves minimal control drive system reconfiguration under different faults. It also ensures high-quality torque given under fault operation conditions. The strategy includes a steady-healthy controller to suppress torque ripples and a flux adaption-based dq coordinate system for reduced d-axis current in different operating conditions. The feasibility of the proposed strategy is verified through simulation and experimental results.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Marius Stender, Oliver Wallscheid, Joachim Boecker
Summary: The article proposes an adaptive Kalman filter for high-precision torque estimation and control in induction motor drives. By offline parameter and observer design optimization, uncertainty parameters are identified using a global optimization technique, leading to ideal performance in experimental validation.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Automation & Control Systems
Julian Kullick, Christoph M. Hackl
Summary: This paper presents a novel and simple approach for modeling, identification, and optimal torque control of induction machines. The approach utilizes a machine model that considers nonlinear flux linkages and iron losses, a machine identification procedure that produces temperature and frequency dependent machine maps, and an offline optimization method for generating optimal current references. Experimental results show that this approach can significantly improve the efficiency of induction machines.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Qixin Lei, Jinpeng Yu, Qing-guo Wang
Summary: This paper investigates a command filtered fault-tolerant control approach for induction motors discrete-time system, which can effectively handle actuator faults and unknown load disturbances, achieve error compensation and filtering, and demonstrate the validity of the method through simulation.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Engineering, Electrical & Electronic
Anton Dianov, Alecksey Anuchin
Summary: This article proposes an adaptive maximum torque per ampere control algorithm for IPMSM drives, which is insensitive to motor parameter variation and prioritizes reliability and efficiency. The algorithm uses a seeking technique to continuously vary the stator current phase angle and minimize the current magnitude, without using any motor parameters. It overcomes the main disadvantage of seeking algorithms and is suitable for operations with load torque varying over the mechanical revolution.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Guanghui Yang, Haseeb Hussain, Sheng Li, Jian Zhang, Jiaqiang Yang
Summary: This letter proposes a novel fault-tolerant strategy with harmonic injection for reducing torque ripple and enhancing torque operation range. By introducing higher order harmonics to reduce coupling to the fundamental plane, the effectiveness of the strategy is improved.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
Article
Energy & Fuels
Weitao Deng, Shanhu Li
Summary: A novel rotating vector-based direct torque control (DTC) method is proposed in this paper to improve the steady state torque and current performance of the traditional method. By dividing the vector plane into 12 sectors and utilizing virtual vectors synthesized by adjacent rotating vectors, the proposed method achieves significant improvements in steady state torque and current performances while maintaining zero common-mode voltage.
IEEE TRANSACTIONS ON ENERGY CONVERSION
(2022)
Article
Engineering, Electrical & Electronic
Panpan Ma, Jinpeng Yu, Qing-Guo Wang, Jiapeng Liu
Summary: In this article, a finite-time adaptive fuzzy control scheme based on filter and reduced-order observer is proposed for induction motors (IMs) with load variation. The rotor position and the angular velocity of IMs are estimated by a reduced-order observer. The unknown stochastic nonlinear functions are handled by the fuzzy logic systems. The proposed control strategy combines finite-time control with command filtering and introduces errors compensation signal to improve the convergence and robustness of the systems. The simulation and experimental results validate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Biomedical
Kang-Won Lee, Sang Hoon Kang, Soo-Chul Lim
Summary: Evaluation of position sense post-stroke is crucial for rehabilitation, and the development of a simple and reliable measurement device can greatly aid in this process.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Automation & Control Systems
Sagar Verma, Nicolas Henwood, Marc Castella, Al Kassem Jebai, Jean-Christophe Pesquet
Summary: This article introduces a neural network approach for estimating nonnoisy speed and torque in induction motors with variable speed drives. The proposed method includes a neural speed-torque estimator and a neural signal denoiser. The method improves upon existing noise removal techniques by learning to denoise as well as classify noisy signals.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Mathematics
Ahmad Kamal Hassan, Ubaid M. Al-Saggaf, Muhammad Moinuddin, Mohamed K. Alshoubaki
Summary: Multiple-input multiple-output (MIMO) radar offers improved performance compared to single antenna element radar system. Optimizing the transmitter and receiver waveform can lead to significant improvements in target detection.
Article
Energy & Fuels
M. Zulfiqar, M. Kamran, M. B. Rasheed, T. Alquthami, A. H. Milyani
Summary: This paper proposes a fast and accurate hybrid load forecasting model that enables distribution system operators to efficiently manage energy by engaging energy consumers in the intelligent demand-response program in the smart grid. The model integrates a locally weighted support vector regression (LWSVR) based forecaster with feature engineering (FE) and adaptive grasshopper optimization (AGO) based optimizers.
Article
Materials Science, Ceramics
M. Burhanuz Zaman, Vipin Shrotriya, Amzad Hossain, Ibrahim M. Mehedi, Md. Mottahir Alam
Summary: The synthesis and characterization of Cu2SnS3 (CTS) thin film photocatalysts via a simple and economical spin coating sol gel method was conducted. The films showed a single phase pristine tetragonal CTS structure with Cu, Sn, and S oxidation states of +1, +4, and -2 respectively. The films exhibited polycrystalline nature and had an optimum energy band gap suitable for visible light photocatalysis. They showed high efficiency in degrading methylene blue (MB) dye under visible light illumination and maintained their activity after multiple reuse.
CERAMICS INTERNATIONAL
(2023)
Article
Computer Science, Artificial Intelligence
Muhammad Shehzad Hanif, Muhammad Bilal, Abdullah Saeed Balamash, Ubaid M. Al-Saggaf
Summary: This article proposes a two-stage framework for crowd anomaly detection in single-scene or scene-dependent surveillance videos. The first stage generates hypotheses corresponding to potential anomalous regions in a video frame, and the second stage verifies them to reduce false alarms and identifies crowd anomalies. The effectiveness of the proposed framework is demonstrated on the UCSD anomaly detection benchmark dataset, showing comparable results against state-of-the-art methods.
TRAITEMENT DU SIGNAL
(2023)
Article
Chemistry, Physical
Nebras Sobahi, Mohd Imran, Mohammad Ehtisham Khan, Akbar Mohammad, Md. Mottahir Alam, Taeho Yoon, Ibrahim M. Mehedi, Mohammad A. Hussain, Mohammed J. Abdulaal, Ahmad A. Jiman
Summary: We synthesized Fe3O4/graphene nanocomposite for peroxide sensor with high selectivity and sensitivity. The nanocomposites were produced through a modified co-precipitation method and characterized using various techniques. The as-fabricated flexible electrode showed excellent electrocatalytic activity and exhibited a good linear range, high sensitivity, and low detection limit for H2O2 detection. The biosensor is cost-effective, stable, and highly reproducible.
Article
Medicine, General & Internal
Ibrahim M. Mehedi, K. Prahlad Rao, Fahad Mushhabbab Alotaibi, Hadi Mohsen Alkanfery
Summary: This study explores the use of wireless capsule endoscopy (WCE) in diagnosing, monitoring, and evaluating gastrointestinal disorders. It utilizes a miniature camera fitted in a wireless capsule that travels through the digestive tract to capture images. Research, design, simulation, testing, and analysis were conducted to enhance the WCE. The findings indicate that a spherical WCE shape and smaller size, high resolution, and frame rate can provide more accurate pictures, prolong battery life, and even reconstruct 3D images, making it more advantageous than commercial capsule-shaped endoscopic devices for wireless applications.
Article
Mathematics
Khizer Mehmood, Naveed Ishtiaq Chaudhary, Khalid Mehmood Cheema, Zeshan Aslam Khan, Muhammad Asif Zahoor Raja, Ahmad H. Milyani, Abdulellah Alsulami
Summary: This study investigates the parameter identification of an input nonlinear autoregressive exogenous (IN-ARX) model using swarm intelligence knacks of the nonlinear marine predators' algorithm (NMPA). A comparative analysis of the NMPA with other recent metaheuristics establishes its superiority in terms of accurate, robust, and convergent performances for different noise and generation variations. The statistics generated through multiple autonomous executions further confirm the reliability and stability of the NMPA for parameter estimation of IN-ARX systems.
Article
Engineering, Biomedical
Jing Yang, Muhammad Awais, Md. Amzad Hossain, Por Lip Yee, Ma. Haowei, Ibrahim M. Mehedi, A. I. M. Iskanderani
Summary: This paper presents Think2Type, an efficient Brain-Computer Interface (BCI) technology that allows users to translate their intentions into Morse code text using brain signals. It helps individuals with visual inefficiencies or different abilities overcome difficulties in using smartphones and computers and reduces their reliance on others.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Chemistry, Inorganic & Nuclear
Anjan Kumar, Mohammed Al-Bahrani, Md. Amzad Hossain, Ibrahim M. Mehedi, Ahmed I. M. Iskanderani, Juan Carlos Orosco Gavilan, Gurumurthy B. Ramaiah
Summary: C70 fullerenes are effective sensors and adsorbents for formaldehyde, and their sensitivity can be enhanced by nitrogen or boron doping. Nitrogen-doped C70 fullerenes are effective in removing formaldehyde from the air. In this study, the potential of transition metal-functionalized porphyrin C70 fullerenes (MF-PC70Fs) for sensing and removing formaldehyde was investigated. The HOMO-LUMO gaps and absorption energies of MF-PC70Fs were computed, and it was found that Ti-functionalized PC70Fs had the highest adsorption strength, while Ni-functionalized PC70Fs had the lowest. Cr-, Fe-, and Zn-functionalized PC70Fs showed potential for formaldehyde sensing, with Fe- and Zn-functionalized materials being more favorable.
INORGANIC CHEMISTRY COMMUNICATIONS
(2023)
Article
Mathematics
Ubaid M. Al-Saggaf, Jawwad Ahmad, Mohammed A. Alrefaei, Muhammad Moinuddin
Summary: Cooperative spectrum sensing (CSS) in cognitive radio (CR) utilizes multiple decisions from secondary user (SU) nodes to detect unused spectral holes. The energy detector (ED) is commonly used for spectrum sensing, but the challenge lies in having accurate knowledge of decision statistics distribution. Another challenge is choosing the optimal fusion strategy in CSS. To address these issues, we propose a beamforming-assisted ED with heuristic-optimized CSS using the characterization of the indefinite quadratic form (IQF). Genetic algorithm with multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO) algorithms are developed to design optimum beamforming and fusion weights for maximum detection probability while constraining false alarm probability.
Article
Mathematics, Applied
Abdul Latif, Ibrahim M. Mehedi, Mahendiran T. Vellingiri, Rahtul Jannat Meem, Thangam Palaniswamy
Summary: This article introduces an enhanced remora optimization algorithm with stacked bidirectional long short-term memory (EROA-SBiLSTM) approach for accurate temperature prediction of permanent magnet synchronous machine (PMSM) drives. The presented technique utilizes artificial intelligence and deep learning methods to achieve effective temperature prediction by analyzing correlations and optimizing parameters. Experimental results on electric motor temperature dataset verify the effectiveness of the proposed EROA-SBiLSTM technique.
Article
Computer Science, Information Systems
Mohammed J. Abdulaal, Mohamed M. E. A. Mahmoud, Saheed A. Bello, Junaid Khalid, Abdulah Jeza Aljohani, Ahmad H. Milyani, Abdullah M. Abusorrah, Mohamed I. Ibrahem
Summary: This paper investigates the problem of detecting power theft and protecting consumer privacy in advanced metering infrastructure (AMI). The paper proposes a detection method based on deep learning models and develops a privacy-preserving data encryption approach. Experimental results show that the method can accurately identify malicious consumers while preserving consumer privacy.
Article
Computer Science, Information Systems
Muhammad Bilal, Muhammad Shehzad Hanif, Khalid Munawar, Ubaid M. Al-Saggaf
Summary: Geometry-based Visual Odometry (VO) techniques estimate camera motion from visual data obtained from one or more cameras using multi-view geometry. Although effective, these methods do not perform well in challenging cases. In this work, we propose to integrate deep descriptors into the traditional VO pipeline to improve correspondence between image points. Experimental results using conventional and deep descriptors show that our VO method effectively minimizes drift and produces better camera trajectories, performing competitively with state-of-the-art works.
Article
Computer Science, Information Systems
Niaz Muhammad, Faisal Khan, Basharat Ullah, Saira Tariq, Ahmad H. Milyani
Summary: This paper proposes a design of an asymmetric spoke and delta-shape interior permanent magnet synchronous machine for electric vehicles. The design utilizes the magnetic-field-shifting technique to improve torque performance and reduce torque ripples. The asymmetry in the design is achieved through the rotor structure, permanent magnet placement, and flux barriers. The proposed design shows high efficiency and a good torque profile, making it suitable for electric vehicle applications.
Article
Computer Science, Information Systems
Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan, Faisal Althobiani, Salim Nasar Faraj Mursal, Saifur Rahman, Muawia Abdelkafi Magzoub, Muhammad Armghan Latif, Imran Khan Yousufzai
Summary: This study presents a novel approach to address imbalanced datasets in bearing fault diagnosis. By using Conditional Generative Adversarial Networks (CGANs) with spectral normalization and adaptive adversarial noise injection, high-quality bearing fault samples are generated to enhance generalization and robustness. A novel combination of involution and convolution feature extraction method is introduced to extract meaningful features from grayscale bearing fault images. The proposed oversampling methodology improves the performance of the classification scheme and benchmark transfer learning models.
