Now showing items 1-20 of 148

    • Enhancing Leukemia Detection: An Automated Approach Using Deep Learning and Ensemble Techniques

      Saad Ahmed Syed; Humaira Nisar; Rabeea Jaffari; Yan Chai Hum; Lee Yu Jen; Mian Qaisar, Saeed; External Collaboration; NA; NA; NA; et al. (Elsevier, 2024-01-11)
      As leukemia ranks high among the global causes of death, it's crucial to identify it early to enhance the prognosis for patients. The majority of diagnostic processes used today rely on medical experts inspecting samples manually. This is a laborious process that lacks an automated detection mechanism and takes a lot of time. With a focus on acute lymphoblastic leukemia (ALL), this work suggests an automated diagnostic method that uses Deep Learning (DL)-based ensembles to improve leukemia detection and prediction. We propose to utilize a combination of ten DL techniques (ResNet, ResNeXt, SE-ResNet, Inception V3, VGG, and its variants) and three ensemble techniques (Max voting, Averaging, and Stacking) to constitute the leukemia detection models and observe their performances. The ALL IDB benchmark leukemia dataset was evaluated using these techniques, with performances measured across several metrics namely: classification accuracy, F1 score, precision, recall (sensitivity), Kappa index, and ROC-AUC score. The findings from the experiments demonstrate a notable enhancement in leukemia detection performance when utilizing the proposed techniques. In particular, the proposed Ensemble Max Voting technique surpasses all other stateof-the-art detection models in the literature with an accuracy of 100.0% and an F1 score of 0.997. The main achievement of this study is the identification of the most effective method among various models and techniques for detecting leukemia.
    • Artificial Intelligence Assistive Fire Detection and Seeing the Invisible Through Smoke Using Hyperspectral and Multi-Spectral Images

      Ahed Alboody; Mian Qaisar, Saeed; Gilles Roussel; External Collaboration; NA; NA; NA; Electrical and Computer Engineering; NA; Ahed Alboody (IEEE, 2023-11-10)
      The global warming has serious impact on our climate. Due to this, the frequency and the intensity of forest fires is increasing. It has shown serious challenges such as the protection of resources, human and wild life, health, and property. This study focuses on developing an artificial intelligence assistive innovative solution for active fire detection in the context of smart cities and vicinities. This paper addresses spectral analysis, detection and classification of active fires and seeing the invisible through smoke and thin clouds. The appealing applications are in urban surveillance, smart cities, future industries, forests and earth observation. The idea is realizable by using an intelligent hybridization of machine/deep learning models and using multi-sensor images (aerial, satellite). For this purpose, we use hyperspectral images (Visible, Near Infra-red (NIR) and Short-Wave Infrared (SWIR)) from AVIRIS aerial and Multi-Spectral Sentinel-2 satellite images. AVIRIS images are 224 spectral bands of wavelengths with a spatial resolution of 15 meters, which varies from 366nm (nanometers) up to 2500nm. However, AVIRIS image studied for their spectral richness of wavelengths not yet completely exploited by machine and deep learning and in SWIR to detect active fires. While, Sentinel-2 image has 13 spectral bands (Visible, NIR and SWIR) with three spatial resolutions (10, 20 and 60 meters). First, we explain and describe the preparation phase of hyperspectral and multispectral image databases of forest fires. These databases contain hyperspectral and multispectral endmembers data of different sites for forest fires. Then, we conduct a spectral analysis from these endmembers to characterize the hyperspectral/multispectral reflectance of active fires to identify the distinct wavelengths for fire detection. We identify the wavelengths that can be used for an effective identification of fire and to see through fires smoke and thin clouds. Onward, the selected feature set is processed by robust machine/deep learning algorithms and their performance is compared for automated identification of fire and invisible vision amelioration. The proposed machine/deep learning method secured an overall test accuracy of 99.1%.
    • Satellite Imagery-Based Cloud Classification Using Deep Learning

      Rukhsar Yousaf; Hafiz Zia Rehman; Khurram Jadoon; Zeashan H. Khan; Adnan Fazil; Zahid Mahmood; Mian Qaisar, Saeed; Abdul Jabbar Siddiqui; External Collaboration; NA; et al. (MDPI, 2023-12-01)
      A significant amount of satellite imaging data is now easily available due to the continued development of remote sensing (RS) technology. Enabling the successful application of RS in real-world settings requires efficient and scalable solutions to extend their use in multidisciplinary areas. The goal of quick analysis and precise classification in Remote Sensing Imaging (RSI) is often accomplished by utilizing approaches based on deep Convolution Neural Networks (CNNs). This research offers a unique snapshot-based residual network (SnapResNet) that consists of fully connected layers (FC-1024), batch normalization (BN), L2 regularization, dropout layers, dense layer, and data augmentation. Architectural changes overcome the inter-class similarity problem while data augmentation resolves the problem of imbalanced classes. Moreover, the snapshot ensemble technique is utilized to prevent over-fitting, thereby further improving the network’s performance. The proposed SnapResNet152 model employs the most challenging Large-Scale Cloud Images Dataset for Meteorology Research (LSCIDMR), having 10 classes with thousands of high-resolution images and classifying them into respective classes. The developed model outperforms the existing deep learning-based algorithms (e.g., AlexNet, VGG-19, ResNet101, and EfficientNet) and achieves an overall accuracy of 97.25%.
    • Boosting Regression Assistive Predictive Maintenance of the Aircraft Engine with Random-Sampling Based Class Balancing

      Ibrahima Barry; Meriem Hafsi; Mian Qaisar, Saeed; External Collaboration; NA; NA; NA; Electrical and Computer Engineering; NA; Ibrahima Barry (Springer, 2024-01-17)
      This study presents the development of a data-driven predictive maintenance model in the context of industry 4.0. The solution is based on a novel hybridization of Remaining Useful Life (RUL) generation, Min-Max normalization, random-sampling based class balancing, and XGBoost regressor. The applicability is tested using the NASA's C-MAPSS dataset, which contains aircraft engine simulation data. The objective is to develop an effective and Artificial Intelligence (AI) assistive automated aircraft engine's RUL predictor. It can maximize the benefits of predictive maintenance. The rules based RUL generation provides a ground truth for evaluating the performance of intended regressors. The Min-Max normalization linearly transforms the intended dataset and scales the multi subject's data in a common range. The imbalance presentation among intended classes can lead towards a biasness in findings. This issue is intelligently resolved using the uniformly distributed random sub-sampling. Onward, the performance of robust machine learning and ensemble learning algorithms is compared for predicting the RUL of the considered aircraft engine by processing the balanced dataset. The results have shown that the XGBoost regressor, uses an ensemble of decision trees, outperforms other considered models. The root mean square error (RMSE) and mean absolute error (MAE) indicators will be used to evaluate the prediction performances. The devised method secures the RMSE value of 12.88%. It confirms a similar or better performance compared to the state-of-the-art counterparts.
    • 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, 18 Decembe)
      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.
    • Development of a Battery Management System for Enhancing the Performance and Safety of Lithium-Ion Battery Packs

      Abdulmajid, Mohammed; Abdulmajid; Binsalim, Haya; Badaam, Salma; Electrical and Computer Engineering
      Electrical grids generate energy using diverse power sources, including fossil fuels (gas and coal) and renewable sources (e.g., solar panels). However, the variability in power generation from these sources can lead to inefficiencies within the grid, resulting in energy wastage and potential damage to energy storage systems. In this context, developing robust energy storage solutions is crucial to maintain grid stability and optimize energy utilization. Battery packs integrated into the grid offer a promising solution for energy storage, but their efficient operation requires precise monitoring and control, which is achieved through Battery Management Systems (BMS). It manages individual cells' charging and discharging processes to maximize efficiency and extend their lifespan. Additionally, the BMS continuously monitors voltage and current levels to ensure they remain within safe limits, mitigating the risk of heat damage. This research investigates the challenges associated with energy generation and storage in electrical grids, emphasizing the need for efficient energy storage systems to prevent energy wastage and battery damage. The proposed solution focuses on BMS to monitor and control the energy storage process. This study offers a novel BMS design, incorporating Extended Kalman Filtering and a CCCV-based passive balancing algorithm to manage battery states, state of charge (SOC), state of health (SOH), and thermal characteristics. The research also includes a comprehensive simulation study conducted in SIMULINK with the Simscape toolbox to assess the effectiveness of the proposed BMS in a simulated grid environment. The simulation consists of a plant model representing the grid-connected battery pack, the BMS Electronic Control Unit (ECU) system, and various operational scenarios. The simulation results demonstrate that the proposed BMS design effectively monitors the battery pack's state, maintains cell balancing, estimates SOC, and regulates temperature and current levels within safe limits.
    • Performance Evaluation of Classic and Intelligent Controllers for Actuators

      Salem, Nema; Ali, Mirna; Kamal, Jana; Electrical and Computer Engineering
      This capstone project focuses on optimizing actuator control, specifically for DC motors. The project aims to design and evaluate various controllers, ranging from classic to intelligent. Finding the optimal controller for a DC motor offers many advantages, including stability, robustness, and precise control, which are useful in fields like robotics. This paper proposes several controllers: pole placement, different configurations of PID, LQR, and LQI with different optimization techniques, fuzzy logic, and adaptive neuro-fuzzy controller schemes. The MATLAB environment was used to develop and test these controllers. Their performance was measured in terms of rise time, settling time, and overshoot. Based on the performance, the I-PD controller is overall the optimal controller for the DC motor. It achieves the fastest rise time of 507.7 msec, a settling time of 2.3 sec, and an overshoot of 0.51%
    • 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.