Now showing items 1-20 of 135

    • A computational study of a laminar methane–air flame assisted by nanosecond repetitively pulsed discharges

      Xiao Shao; Kabbaj, Narjisse; Deanna A Lacoste; Hong G Im; External Collaboration; NA; NA; NA; Electrical and Computer Engineering; NA; et al. (IOP Science, 2024-02-19)
      Nanosecond repetitively pulsed (NRP) discharges have been considered a promising technique for enhancing combustion efficiency and control. For successful implementation, it is necessary to understand the complex plasma–combustion interactions involving chemical, thermal, and hydrodynamic pathways. This paper aims to investigate the mechanisms enhancing a laminar methane–air flame assisted by NRP discharges by high fidelity simulations of the jet-wall burner employed in a previous experimental study. A phenomenological plasma model is used to represent the plasma energy deposition in two channels: (1) the ultrafast heating and dissociation of $\mathrm{O_2}$ resulting from the relaxation of electronically excited $\mathrm{N_2}$, and (2) slow gas heating stemming from the relaxation of $\mathrm{N_2}$ vibrational states. The flame displacement, key radical distribution and flame response under plasma actuation are compared with experimental results in good agreement. The computational model allows a systematic investigation of the dominant physical mechanism by isolating different pathways. It is found that the kinetic effect from atomic O production dominates the flame dynamics, while the thermal effect plays a minor role. Hydrodynamic perturbations arising from weak shock wave propagation appear to be sensitive to burner geometry and is found to be less significant in the case under study.
    • Exploring the Maze: A Comparative Study of Path Finding Algorithms for PAC-Man Game

      Salem, Nema; Haneya, Hala; Balbaid, Hanin; Asrar, Manal; No Collaboration; NA; 3; 0; Electrical and Computer Engineering; 0; et al. (IEEE, 2024)
      Artificial Intelligence (AI) has become an integral part of our lives, finding applications across various industries. Search algorithms play a crucial role in AI. This paper focuses on the comparison of different search algorithms within the context of path-planning in the UC Berkeley’s PAC-Man’s game. The algorithms under consideration include Depth-First Search (DFS), Breadth-First Search (BFS), Uniform Cost Search (UCS), Iterative Deepening Depth First Search (IDDFS), and A∗ Search. The objective is to identify the most effective algorithm in terms of path-finding performance. The study’s findings reveal that the A∗ search algorithm outperforms the others in terms of score, cost, and node expansion, making it the most suitable choice for finding the shortest path in the PAC-Man’s game.
    • Photovoltaics Maximum Power Tracking by the Hybrid Perturb-Observe and Sliding Mode Control Strategies

      Salem, Nema; Alammari, Eithar; Alamro, Joud; Alashwali, Sara; No Collaboration; NA; 3; 0; Electrical and Computer Engineering; 0; et al. (IEEE, 2024)
      To increase photovoltaic PV power, the point of maximum power, MPPT, must be tracked effectively. The oscillation around the operating point is the main drawback of MPPT, obtaining techniques. This study suggests combining the Perturb and Observe, PO, and Sliding Mode Control, SMC, strategies to reduce this problem and deal with the nonlinearities of the solar panels under different climate situations. The SMC creates a sliding surface that establishes the operational point and increases the stability of the PO. The gate of a DC-DC converter quickly reaches this defined surface, and the duty cycle adjustment ensures maximum power in all conditions. This study utilized MATLAB/Simulink to design and analyze this combined control system. The outcomes supported the PO and SMC strategy’s reliability and successful operation under various environmental circumstances.
    • Boosting Wind Harvest: FOPID Pitch Angle Controller for Turbines

      Jamjoom, Jude; Qashqari, Maha; Alzahrani, Mariah; No Collaboration; NA; 3; 0; Electrical and Computer Engineering; 0; Salem, Nema (IEEE, 2024)
      Wind turbine blades are subjected to a variety of loads, including aerodynamic and gravitational loads. These loads produce aerodynamic strain and vibration in the blades, resulting in rotor blade damage and a reduction in the wind turbine’s system efficiency. This could be prevented by implementing a proper pitch angle controller that plays a crucial role in boosting the energy capture and overall performance of wind power systems. The conventional Proportional-Integral-Derivative, PID, controller has been widely utilized for pitch control, but it often faces challenges in meeting the requirements of complex and dynamic wind conditions. To address these limitations, this study explores the implementation of the Fractional Order Proportional-Integral-Derivative, FOPID, controller for wind turbine pitch control. This paper presents a comparative analysis between the PID and FOPID controllers for wind turbine pitch control. The performance of both controllers is evaluated through Simulink. The results demonstrate that the FOPID controller exhibits superior performance in terms of faster response time, and improved steady-state error compared to the PID controller.
    • Analysis and Design of Various Types of DC-DC Converters: A Comprehensive Study

      Salem, Nema; Almatrafi, Lina; Basmah Shigdar; Alaidaroos, Batool; No Collaboration; NA; 3; 0; Electrical and Computer Engineering; 0; et al. (IEEE, 2024)
      DC-DC converters are essential components in power electronics systems, enabling efficient voltage conversion and regulation. This paper presents a comprehensive study on the analysis and design of different types of DC-DC converter topology including Boost, Cuk, SEPIC, and Zeta converters. The design aspect of DC-DC converters covers the component selection to assist in achieving desired converter performance and meeting specific application requirements. The analysis focuses on examining key performance parameters such as efficiency, voltage ripple, transient response, and output regulation. To validate the theoretical analysis and design principles, simulation tools such as Simulink and MATLAB are employed.
    • Modeling and Analysis of a Thermoelectric Power Generator

      Salem, Nema; No Collaboration; NA; 0; 0; Electrical and Computer Engineering; 0; Salem, Nema (IEEE, 2023)
      Thermoelectric Generator (TEG) was developed using the Seebeck phenomenon. It consists of many thermocouples connected thermally in parallel and electrically in series to increase energy efficiency. TEGs instantly convert thermal energy to electrical energy with no rotating parts and are less likely to fail due to no moving parts. With the rising cost of fossil fuels and their negative impact on the atmosphere, it is time to consider TEGs as renewable energy sources with applications ranging from mW to W power. This study derives a mathematical model of a TEG module and validates it with MATLAB/SIMULINK.
    • Pole-Placement and Different PID Controller Structures Comparative Analysis for a DC Motor Optimal Performance

      Salem, Nema; Ali, Mirna; No Collaboration; NA; Mirna Ali; 0; Electrical and Computer Engineering; 0; Salem, Nema (IEEE, 2024)
      The pole-placement method is a popular technique used in control system design to assign desired closed loop system poles. By strategically placing these poles, the system’s dynamic response can be tailored to meet specific performance requirements. This study focuses on the design and simulation of the pole-placement method and various structures of Proportional-Integral-Derivative P ID controllers to determine the best-performing controller for a modeled DC motor. The study explores different PID controller structures, including parallel, series, (P I − D), and (I − P D). In addition, it employs a comprehensive analysis by utilizing a range of performance metrics such as settling time, overshoot, and rise time. The pole placement utilizes the state space technique to assign the desired closed loop poles while the controllers are tuned using Ziegler-Nichols, to achieve optimal performance. The results shows that the (I − P D) controller is the optimal controller for this application, with a rise time of 0.507 seconds, a settling time of 2.3 seconds, and an 0.51% overshoot.
    • Performance of LQR and PID Controllers for RS-550VC Motor Speed Enhancement

      Salem, Nema; Mateen, Kulsoom; Alharbi, Wafa; Kamal, Jana; No Collaboration; NA; 3; 0; Electrical and Computer Engineering; 0; et al. (IEEE, 2023-09-25)
      Motor speed control is a critical aspect of many industrial and commercial applications. This paper investigates the performance of two widely used control techniques, Linear Quadratic Regulator LQR and Proportional-Integral-Derivative PID, for motor speed control. We perform a simulation study using MATLAB to compare the performance of these two controllers in terms of their ability to track the reference speed profile. The simulation results show that LQR and PID controllers can achieve accurate speed tracking, but their performance characteristics differ significantly. LQR is better in the steady state and overshoot but slower than PID. Our study provides insights into the trade-offs between these two controllers and can guide the selection of an appropriate control technique for a given motor speed control application.
    • Enhancing Cruise Performance through PID Controller Tuned with Particle Swarm Optimization Technique

      Salem, Nema; Hassan, Rana; Muthanna, Lina; No Collaboration; NA; 2; 0; Electrical and Computer Engineering; 0; Salem, Nema (IEEE, 2023)
      The Proportional-Integral-Derivative (PID) controller is a widely used feedback control mechanism in various applications, including automobile cruise control systems. The performance of a PID controller is highly dependent on the values of its tuning parameters, which can be challenging to determine in practice. Particle Swarm Optimization (PSO) has emerged as a powerful optimization algorithm that can tune the PID controller parameters for optimal performance. The PSO is a metaheuristic optimization algorithm inspired by the social behavior of birds and fish. The PSO-PID is a variant of the PID controller that employs PSO to optimize its tuning parameters. PSO-PID offers several advantages over traditional PID tuning methods, including improved accuracy, stability, and robustness. This paper briefly overviews the PSO-PID algorithm and its application to automobile cruise control systems. The paper discusses the key steps involved in PSO-PID tuning, including initialization, evaluation, update, and termination. It provides an example of how PSO-PID can achieve optimal vehicle speed control. The paper highlights the advantages of PSO-PID over traditional PID tuning methods. PSO-PID is a promising technology for cruise control systems and has the potential to significantly improve the safety, comfort, and efficiency of modern automobiles.
    • An Efficient Approach for the Detection and Prevention of Gray-Hole Attacks in VANETs

      Malik, Abdul; Khan, Muhammad Zahid; Mian Qaisar, Saeed; Faisal, Mohammad; Mehmood, Gulzar; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Electrical and Computer Engineering; et al. (IEEE, 2023-09-15)
      Vehicular Ad-Hoc Networks (VANETs) deliver a wide range of commercial as well as safety applications and further motivate the advancements of Internet of Vehicles (IoV), Intelligent Transportation Systems (ITS), and Vehicles to Everything (V2X) communication. Despite their potential benefits, VANETs are susceptible to a variety of security attacks due to their open, distributed, and dynamic nature, which includes intrinsic protocol design issues. One such an infamous security attack is the Gray-Hole Attack (GHA), typically has two variants: Smart GHA and Sequence Number-based GHA. In Smart GHA, the malicious node behaves normally during the route discovery process, while in Sequence Number-based GHA, the malicious node starts misbehaving during the route discovery process. In either case, once the route is successfully established, it starts dropping the packets. In this paper, a novel security approach called ‘‘Detection and Prevention of GHA’’ (DPGHA) is proposed to detect and prevent both variants of GHA in Ad Hoc On-Demand Distance Vector (AODV) based VANETs. The approach is based on the generation of dynamic threshold values of abnormal differences of received, forwarded, and generated control or data packets among nodes and their sequence numbers. The proposed DPGHA is implemented and tested in NS-2 and SUMO simulators and its various performances are compared with the most relevant benchmark approaches. The results showed that the proposed DPGHA performed better than the benchmark approaches in terms of reduced routing overhead by 10.85% and end-to-end delay by 3.85%, increased Packet Delivery Ratio (PDR) by 4.67% and throughput by 6.58%, and achieved a maximum detection rate of 2.3%.
    • A Comprehensive Review of the Li-Ion Batteries Fast-Charging Protocols

      Mouais, Talal; Mian Qaisar, Saeed; Department Collaboration; Energy Lab; 0; 0; Electrical and Computer Engineering; 1; Mouais, Talal (Wiley, 2023-09-15)
      One of the significant drawbacks of renewable energy sources, such as solar and wind, is their intermittent pattern of functioning. One promising method to overcome this limitation is to use a battery pack to enable renewable energy generation to be stored until required. Batteries are known for their high commercial potential, fast response time, modularity, and flexible installation. Therefore, they are a very attractive option for renewable energy storage, peak shaving during intensive grid loads, and a backup system for controlling the voltage drops in the energy grid. The lithium-ion (Li-Ion) is considered one of the most promising battery technologies. It has a high energy density, fair performance-to-cost ratio, and long life compared to its counterparts. With an evolved deployment of Li-Ion batteries, the latest trend is to investigate the opportunities of fast Li-Ion battery charging protocols. The aim is to attain around the 70-80% State of Charge (SoC) within a few minutes. However, fast charging is a challenging approach. The cathode particle monitoring and electrolyte transportation limitations are the major bottlenecks in this regard. Additionally, sophisticated process control mechanisms are necessary to avoid overcharging, which can cause a rapid diminishing in the battery capacity and life. This chapter mainly focuses on an important aspect of realizing the effective and fast-charging protocols of Li-Ion batteries. It presents a comprehensive survey on the advancement of fast-charging battery materials and protocols. Additionally, the state-of-the-art approaches of optimizing the configurations of concurrent fast-charging protocols to maximize the Li-Ion batteries life cycle are also presented.
    • Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER Pro

      Basheer, Yasir; Mian Qaisar, Saeed; Waqar, Asad; Lateef, Fahad; Alzahrani, Ahmad; External Collaboration; Energy Lab; 0; 0; Electrical and Computer Engineering; et al. (IEEE, 2023-10-01)
      The cement industry is a major energy consumer, with most of its costs associated with fuel and energy requirements. While traditional thermal power plants generate electricity, they are both harmful and inefficient. In this study, battery depth of discharge (DOD) is evaluated for four different battery technologies in the context of the cement industry. The battery technologies evaluated are lead-acid (LA), lithium-ion (Li-ion), vanadium redox (VR), and nickel-iron (Ni-Fe). Five cement plants in Pakistan are considered, including Askari Cement Plant, Wah (ACPW), Bestway Cement Plant, Kalar Kahar (BCPKK), Bestway Cement Plant, Farooqia (BCPF), Bestway Cement Plant, Hattar (BCPH), and DG Cement Plant, Chakwal (DGCPC). Four hybrid energy generation models (HEGMs) were proposed using the HOMER pro software. HEGM-1 combines a diesel generator, photovoltaic system, converter, and battery system, while HEGM-2 consists of a photovoltaic system, converter, and battery system. HEGM-3 is a grid-connected version of HEGM-1 and HEGM-4 is the grid-connected version of HEGM-2. A reference base model using only grid connection is also considered. A multi-criteria decision analysis (MCDA) was performed using a cumulative objective function (COF) that includes net present cost (NPC), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions. The main objective was to maximize COF while minimizing NPC, LCOE, and GHG emissions using optimal battery technology and DOD. The results indicate that VR is the most optimal battery technology, with a DOD of 10% achieved in DGCPC using HEGM-3. This results in a 61.49% reduction in NPC, 78.62% reduction in LCOE, and 84.00% reduction in GHG emissions compared to the base model.
    • ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networks

      Khandelwal, Khandelwal; Salankar, Nilima; Mian Qaisar, Saeed; Upadhyay, Jyoti; Pławiak, Paweł; External Collaboration; Biometrics and Sensory Systems Lab; 0; 0; Electrical and Computer Engineering; et al. (Plos, 2023-11-02)
      Sleep Apnea (SA) can cause health complications including heart stroke and neurological disorders. The Polysomnography (PSG) test can detect the severity of sleep disturbance. However, it is expensive and requires a dedicated sleep laboratory and expertise to examine the patients. Therefore, it is not available to a large population in developing countries. This leads to the development of cost-effective and automated patient examination methods for the detection of sleep apnea. This study suggests an approach of using the ECG signals to categorize sleep apnea. In this work, we have devised an original technique of feature space designing by intelligently hybridizing the multirate processing, a mix of wavelet-empirical mode decomposition (W-EMD), modes-based Hjorth features extraction, and Adam-based optimized Multilayer perceptron neural network (MLPNN) for automated categorization of apnea. A publicly available ECG dataset is used for evaluating the performance of the suggested approach. Experiments are performed for four different sub-bands of the considered ECG signals. For each selected sub-band, five "Intrinsic Mode Functions" (IMFs) are extracted. Onward, three Hjorth features: complexity, activity, and mobility are mined from each IMF. In this way, four feature sets are formed based on wavelet-driven selected sub-bands. The performance of optimized MLPNN, for the apnea categorization, is compared for each feature set. Five different evaluation parameters are used to assess the performance. For the same dataset, a systematic comparison with current state-of-the-artwork has been done. Results have shown a classification accuracy of 98.12%.
    • A Novel Integration Technique for Optimal Location & Sizing of DG Units With Reconfiguration in Radial Distribution Networks Considering Reliability

      Raza, Ali; Zahid, Muhammad; Chen, Jinfu; Mian Qaisar, Saeed; Ilahi, Tehseen; Waqar, Asad; Alzahrani, Ahmad; External Collaboration; Energy Lab; 0; et al. (IEEE, 2023-11-02)
      This paper introduces an advanced approach for optimizing the distribution network reconfiguration (DNR) with the placement and sizing of multiple types of distributed generators (DGs). The method employs the ant colony optimization algorithm (ACOA), which is an innovative adaptive optimization algorithm, while also considering the system’s reliability. The primary objectives of the optimization problem are to minimize active power loss (APL), reduce voltage drop ( $V_{D}$ ) on buses, enhance system stability (SS), and improve overall reliability by reducing energy not supplied (ENS) to end-users. The optimization process involves determining the optimal location and size of DGs in the radial distribution network (RDN) using the ACOA meta-heuristic. The method maintains the radial structure of the system by selectively opening lines during the DNR process. The proposed technique is evaluated through simulations carried out on the IEEE-33 & -69 bus RDNs under various scenarios. The optimal solution is achieved by combining DG Type-1 with integration of DNR to reduce the APL and amplify the $V_{p}$ of buses in both RDNs. In this scenario APL is reduced to 87.97% (IEEE-33) and 92.83% (IEEE-69), respectively. Similarly, the $V_{p}$ of the buses significantly improved to 0.9776 p.u. (IEEE-33) and 0.9888 p.u. (IEEE-69), respectively. The results demonstrate the superiority of the presented ACOA-based approach over other techniques, such as fireworks algorithm (FWA) and adaptive shuffled frogs leaping algorithm (ASFLA). Combining the DNR and DGs placement in a simultaneous manner yields the best performance for the distribution network, resulting in lower APL, reduced $V_{D}$ , improved SS, and enhanced reliability. Furthermore, considering reliability in the optimization process significantly reduces ENS for customers and enables meeting their maximum load demand. Overall, the concurrent consideration of DNR and DGs placement using ACOA proves to be more effective than alternative algorithms.
    • EEG-based schizophrenia classification using penalized sequential dictionary learning in the context of mobile healthcare

      Haider, Usman; Hanif, Muhammad; Rashid, Ahmer; Mian Qaisar, Saeed; Subasi, Abdulhamit; External Collaboration; Biometrics and Sensory Systems Lab; 0; 0; Electrical and Computer Engineering; et al. (Elsevier, 2024-04-12)
      Mobile healthcare is an appealing approach based on the Internet of Medical Things (IoMT) and cloud computing. It can lead to unobstructed, economical, and patient-centric healthcare solutions. The key performance indicators of such systems are dimensionality reduction, computational effectiveness, low latency, and accuracy. In this context, a novel approach is devised for EEG-based schizophrenia, a severe mental disorder that adversely affects a person’s behavior and classification. A multichannel EEG recording with suitable granularity is required for precise analysis. It can increase exponentially the data dimensionality plus complexity and computational load. The proposed solution attains an interesting trade-off between dimensionality reduction plus computational effectiveness versus accuracy. It uses the penalized sequential dictionary learning (PSDL) that incorporates channel selection. First, PSDL learns a dictionary from the input data and evaluates its performance on all EEG channels. Based on this evaluation, a subset of six channels is selected for further training in the dictionary. The proposed PSDL algorithm then incorporates a penalty term that enhances the power of the learned dictionary on the selected channels. We evaluate the proposed approach on the multi-channel EEG dataset from the Institute of Psychiatry and Neurology in Warsaw, Poland. A performance comparison is also made with counterparts. The models’ performance depends on the EEG signals’ complexity. Therefore, we tried to make our models robust and straightforward, achieving appropriate performance with minimal computational cost. The proposed method reduces the dimension in two steps. First, the count of channels is reduced to 68.42%. In the second step, the kept information, 31.58% of channels, is further reduced to 83.75% using dictionary learning. The proposed framework secures a remarkable data dimension reduction and a lower computational cost and latency than the counterparts while attaining the sparse representation classification accuracy of 89.12%. These findings are promising and confirm the potential of investing in incorporating the proposed method in contemporary mobile healthcare solutions.
    • EEG-based emotion recognition using modified covariance and ensemble classifiers

      Subasi, Abdulhamit; Mian Qaisar, Saeed; College collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Electrical and Computer Engineering; 0; Subasi, Abdulhamit (Springer, 2023-11-01)
      The Electroencephalography (EEG)-based precise emotion identification is one of the most challenging tasks in pattern recognition. In this paper, an innovative EEG signal processing method is devised for an automated emotion identification. The Symlets-4 filters based “Multi Scale Principal Component Analysis” (MSPCA) is used to denoise and reduce the raw signal’s dimension. Onward, the “Modified Covariance” (MCOV) is used as a feature extractor. In the classification step, the ensemble classifiers are used. The proposed method achieved 99.6% classification accuracy by using the ensemble of Bagging and Random Forest (RF). It confirms effectiveness of the devised method in EEG-based emotion recognition.
    • A Secured Blockchain Framework for Healthcare Data Management System

      Ahmed, Toqeer; Mian Qaisar, Saeed; Waqar, Asad; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Electrical and Computer Engineering; 0; Ahmed, Toqeer (IEEE, 2023-12-11)
      In the healthcare system, electronic medical records are very critical, and they must be authenticated and verified. During the medical check-up, a large amount of patient medical data is generated which includes reports related to blood, life-threatening diseases, and personal information such as credit card numbers and addresses. Any privacy breach in patient medical records will bring various risks. A simple blockchain (Ethereum) can be effective to validate and authenticate stored data by deploying an immutable ledger. However, the main challenge in the simple blockchain is that its data can be easily accessible. In this paper, the authors create a business network for healthcare using Hyperledger fabric, which ensures that data is only available to the concerned person and its access rights are granted and revoked by the concerned participant. Additionally, the authors tested different scenarios to access blockchain security and its benefits.
    • Investigation of vibration’s effect on driver in optimal motion cueing algorithm

      Hazoor, Ahmad; Tariq, Muhammad; Yasin, Awais; Razzaq, Sohail; Ahmad Chaudhry, Muhammad; Shaikh, Inam Ul Hasan; Ali, Ahsan; Mian Qaisar, Saeed; Iqbal, Jamshed; External Collaboration; et al. (Plos, 2023-11-30)
      The increased sensation error between the surroundings and the driver is a major problem in driving simulators, resulting in unrealistic motion cues. Intelligent control schemes have to be developed to provide realistic motion cues to the driver. The driver’s body model incorporates the effects of vibrations on the driver’s health, comfort, perception, and motion sickness, and most of the current research on motion cueing has not considered these factors. This article proposes a novel optimal motion cueing algorithm that utilizes the driver’s body model in conjunction with the driver’s perception model to minimize the sensation error. Moreover, this article employs H∞ control in place of the linear quadratic regulator to optimize the quadratic cost function of sensation error. As compared to state of the art, we achieve decreased sensation error in terms of small root-mean-square difference (70%, 61%, and 84% decrease in case of longitudinal acceleration, lateral acceleration, and yaw velocity, respectively) and improved coefficient of cross-correlation (3% and 1% increase in case of longitudinal and lateral acceleration, respectively).
    • Hybradization of Emperical Mode Decomposition and Machine Learning for Categorization of Cardiac Diseases

      Milyani, Danah; Mian Qaisar, Saeed; Mohammad, Nouf; Alhamdan, Alhanoof; Slama, Rim; Hamour, Nora; Department Collaboration; External Collaboration; Biometrics and Sensory Systems Lab; 3; et al. (IEEE, 2023-11-10)
      The arrhythmia is one of the cardiovascular diseases which has several types. In literature, researchers have presented a broad study on the strategies utilized for Electrocardiogram (ECG) signal investigation. Automated arrhythmia detection by analyzing the ECG data is reported using a number of intriguing techniques and discoveries. In order to effectively categorize arrhythmia, a novel approach based on the hybridization of the denoising filter, QRS complex segmentation, “Empirical Mode decomposition” (EMD), “Intrinsic Mode Functions” (IMFs) based features extraction, and machine learning techniques is developed in this study. To evaluate the categorization accuracy, the 10-fold cross validation (10-CV) strategy is used. Using an arrhythmia dataset that is publically available for research, the performance of our method is evaluated. A 97% average accuracy score is secured by our method for the problem of 5-class arrhythmias. These findings are comparable or better than counterparts.
    • Machine Learning Assistive State of Charge Estimation of Li-Ion Battery

      Mian Qaisar, Saeed; Alboody, Ahed; Aldossary, Shahad; Alhamdan, Alhanoof; Moahammad, Nouf; Turki Almaktoom, Abdulaziz; Department Collaboration; University Collaboration; External Collaboration; Energy Lab; et al. (IEEE, 2023-11-10)
      For an effective and economical deployment of battery-powered electric vehicles, mobile phones, laptops, and medical gadgets, the State of Charge (SoC) of the batteries must be properly assessed. It permits a safe operation, have a longer usable battery life, and prevent malfunctions. In this context, the battery management systems provide diverse SoC estimation solutions. However, the Machine Learning (ML) based SoC estimation mechanisms are becoming popular because of their robustness and higher precision. In this study, the features set is prepared using the intended battery cell charge/discharge curves for voltage, current, and temperature. Utilizing statistical analysis and the shape context, the attributes are extracted. Following that, three credible machine learning (ML) algorithms-decision trees, random forests, and linear regression-process the set of mined attributes. The applicability is tested using the Panasonic Lithium-Ion (Li-Ion) battery cells, publicly provided by the McMaster University. The feature extraction and the ML based SoC prediction modules are implemented in MATLAB. The “correlation coefficient”, “mean absolute error”, and “root mean square error” are used to assess the prediction performance. The results show an outperformance of the random forest regressor among the intended ones by attaining the correlation coefficient value of 0.9988.