In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks.Traditional approaches often ...In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks.Traditional approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally intensive.This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy.Conventional classification methods are ill-suited for incomplete medical data.To enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data.Initially,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification algorithm.The effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and sensitivity.The encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.展开更多
The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in t...The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).展开更多
This paper mainly aims at proposing an effective method of speed control of the low power motors like Permanent Magnet Direct Current (PMDC) motor used in the orthopedic surgeries using a natural optimization techniqu...This paper mainly aims at proposing an effective method of speed control of the low power motors like Permanent Magnet Direct Current (PMDC) motor used in the orthopedic surgeries using a natural optimization technique called genetic algorithm. Using this method, better values of Performance parameters like rise time, settling time, fall time, peak overshoot and steady state are achieved compared to the conventional PI controller. The SIMUINK MODEL of both the controller operation is obtained using MATLAB version R2013a. The simulated results reveal that the proposed control drive exhibits reduced peak overshoot, rise time, settling time and steady state error. An experimental setup is devised to validate the simulation results. The comparative analysis made depicts the superiority of the proposed algorithm with reference to its conventional counterpart.展开更多
This paper presents an innovative way to enhance the performance of photovoltaic(PV)arrays under uneven shadowing conditions.The study focuses on a triple-series–parallel ladder configuration to exploit the benefits ...This paper presents an innovative way to enhance the performance of photovoltaic(PV)arrays under uneven shadowing conditions.The study focuses on a triple-series–parallel ladder configuration to exploit the benefits of increased power generation while ad-dressing the challenges associated with uneven shadowing.The proposed methodology focuses on the implementation of improved sliding-mode control technique for efficient global maximum power point tracking.Sliding-mode control is known for its robustness in the presence of uncertainties and disturbances,making it suitable for dynamic and complex systems such as PV arrays.This work employs a comprehensive simulation framework to comment on the performance of the suggested improved sliding-mode control strategy in uneven shadowing scenarios.Comparative analysis has been done to show the better effectiveness of the suggested method than the traditional control strategies.The results demonstrate a remarkable enhancement in the tracking accuracy of the global maximum power point,leading to enhanced energy-harvesting capabilities under challenging environmental conditions.Furthermore,the proposed approach exhibits robustness and adaptability in mitigating the effect of shading on the PV array,thereby increasing overall system efficiency.This research contributes valuable insights into the development of advanced control strategies for PV arrays,particularly in the context of triple-series–parallel ladder configurations operating under uneven shadowing conditions.Under short narrow shading conditions,the improved sliding-mode control method tracks the maximum power better compared with perturb&observe at 20.68%,incremental-conductance at 68.78%,fuzzy incremental-conductance at 19.8%,and constant-velocity sliding-mode control at 1.25%.The improved sliding-mode control method has 60%less chattering than constant-velocity sliding-mode control under shading conditions.展开更多
In this paper,the impact of communication time delays(CTDs)on combined load frequency control(LFC)and automatic voltage regulation(AVR)of a multi-area system with hybrid generation units is addressed.Investigation rev...In this paper,the impact of communication time delays(CTDs)on combined load frequency control(LFC)and automatic voltage regulation(AVR)of a multi-area system with hybrid generation units is addressed.Investigation reveals that CTDs have significant effect on system performance.A classical PID controller is employed as a secondary regulator and its parametric gains are optimized with a differential evolution-artificial electric field algorithm(DE-AEFA).The superior performance of the presented algorithm is established by comparing with various optimization algorithms reported in the literature.The investigation is further extended to integration of redox flow batteries(RFBs)and interline power flow controller(IPFC)with tie-lines.Analysis reveals that IPFC and RFBs coordinated control enhances system dynamic performance.Finally,the robustness of the proposed control methodology is validated by sensitivity analysis during wide variations of system parameters and load.展开更多
基金supported by the Researchers Supporting Project number(RSP2024R 34),King Saud University,Riyadh,Saudi Arabia。
文摘In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks.Traditional approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally intensive.This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy.Conventional classification methods are ill-suited for incomplete medical data.To enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data.Initially,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification algorithm.The effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and sensitivity.The encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.
文摘The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).
文摘This paper mainly aims at proposing an effective method of speed control of the low power motors like Permanent Magnet Direct Current (PMDC) motor used in the orthopedic surgeries using a natural optimization technique called genetic algorithm. Using this method, better values of Performance parameters like rise time, settling time, fall time, peak overshoot and steady state are achieved compared to the conventional PI controller. The SIMUINK MODEL of both the controller operation is obtained using MATLAB version R2013a. The simulated results reveal that the proposed control drive exhibits reduced peak overshoot, rise time, settling time and steady state error. An experimental setup is devised to validate the simulation results. The comparative analysis made depicts the superiority of the proposed algorithm with reference to its conventional counterpart.
文摘This paper presents an innovative way to enhance the performance of photovoltaic(PV)arrays under uneven shadowing conditions.The study focuses on a triple-series–parallel ladder configuration to exploit the benefits of increased power generation while ad-dressing the challenges associated with uneven shadowing.The proposed methodology focuses on the implementation of improved sliding-mode control technique for efficient global maximum power point tracking.Sliding-mode control is known for its robustness in the presence of uncertainties and disturbances,making it suitable for dynamic and complex systems such as PV arrays.This work employs a comprehensive simulation framework to comment on the performance of the suggested improved sliding-mode control strategy in uneven shadowing scenarios.Comparative analysis has been done to show the better effectiveness of the suggested method than the traditional control strategies.The results demonstrate a remarkable enhancement in the tracking accuracy of the global maximum power point,leading to enhanced energy-harvesting capabilities under challenging environmental conditions.Furthermore,the proposed approach exhibits robustness and adaptability in mitigating the effect of shading on the PV array,thereby increasing overall system efficiency.This research contributes valuable insights into the development of advanced control strategies for PV arrays,particularly in the context of triple-series–parallel ladder configurations operating under uneven shadowing conditions.Under short narrow shading conditions,the improved sliding-mode control method tracks the maximum power better compared with perturb&observe at 20.68%,incremental-conductance at 68.78%,fuzzy incremental-conductance at 19.8%,and constant-velocity sliding-mode control at 1.25%.The improved sliding-mode control method has 60%less chattering than constant-velocity sliding-mode control under shading conditions.
文摘In this paper,the impact of communication time delays(CTDs)on combined load frequency control(LFC)and automatic voltage regulation(AVR)of a multi-area system with hybrid generation units is addressed.Investigation reveals that CTDs have significant effect on system performance.A classical PID controller is employed as a secondary regulator and its parametric gains are optimized with a differential evolution-artificial electric field algorithm(DE-AEFA).The superior performance of the presented algorithm is established by comparing with various optimization algorithms reported in the literature.The investigation is further extended to integration of redox flow batteries(RFBs)and interline power flow controller(IPFC)with tie-lines.Analysis reveals that IPFC and RFBs coordinated control enhances system dynamic performance.Finally,the robustness of the proposed control methodology is validated by sensitivity analysis during wide variations of system parameters and load.
