Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unu...Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.展开更多
Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a...Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally.Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival rates.The significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening methods.Advances in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung cancer.CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung cancer.Consequently,there is growing interest in enhancing computer-aided detection(CAD)systems.These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis.This study aims to enhance the effectiveness of CAD systems through various methods.Initially,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual quality.Further refinement is achieved by integrating different optimization strategies with the CLAHE method.The CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and EfficientNet.The study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE algorithm.The empirical findings of the study demonstrate a significant reduction in the false positive rate(FPR)and an overall enhancement in diagnostic accuracy.This research not only contributes to the field of medical imaging but also holds significant implications for the early detection and treatment of lung cancer,ultimately aiming to reduce its mortality rates.展开更多
This paper proposes an analysis and a direct power control (DPC) design of a wind turbine driven doubly-fed induction generator (DFIG) under unbalanced network voltage conditions. A DFIG model described in the positiv...This paper proposes an analysis and a direct power control (DPC) design of a wind turbine driven doubly-fed induction generator (DFIG) under unbalanced network voltage conditions. A DFIG model described in the positive and negative synchronous reference frames is presented. Variations of the stator output active and reactive powers are fully deduced in the presence of negative sequence supply voltage and rotor flux. An enhanced DPC scheme is proposed to eliminate stator active power oscillation during network unbalance. The proposed control scheme removes rotor current regulators and the decomposition processing of positive and negative sequence rotor currents. Simulation results using PSCAD/EMTDC are presented on a 2-MW DFIG wind power generation system to validate the feasibility of the proposed control scheme under balanced and unbalanced network conditions.展开更多
In this paper,we report on the identification and modeling of unknown and higher order processes into first order plus dead time(FOPDT)plants based on the limit cycle information obtained from a single relay feedback ...In this paper,we report on the identification and modeling of unknown and higher order processes into first order plus dead time(FOPDT)plants based on the limit cycle information obtained from a single relay feedback test with an online fractional order proportional integral(FOPI)controller.The parameters of the test processes are accurately determined by the state space method while the FOPI controller settings are re-tuned to achieve enhanced performance based on the identified model parameters based on the balancedtuning method.A new performance index,integral time fractional order absolute error(ITFIAE)is introduced in this paper for balanced tuning of fractional order(FO)controllers.It requires minimum design specifications without a-priori knowledge of gain and phase crossover frequencies and is done non-iteratively without disrupting the closed loop.Four test processes and experimental analysis on a coupled tank system(CTS)validate the theory proposed.展开更多
The investigation explores the mechanical stress and electromagnetic performance for a wind-driven synchronous reluctance generator(SRG).The change in the mechanical stress due to the presence of centripetal force,win...The investigation explores the mechanical stress and electromagnetic performance for a wind-driven synchronous reluctance generator(SRG).The change in the mechanical stress due to the presence of centripetal force,wind speed,and rotor speed are evaluated for different thickness of tangential and radial ribs.Moreover,the variation in the electromagnetic feature such as the q−and d−axes flux,reactance ratio,inductance,torque and torque ripple are discussed for different thickness of tangential and radial ribs.Increasing both tangential and radial ribs thickness has an effect on the electromagnetic performance,but it is observed that effect is significantly more with the variation of tangential rib thickness.Similarly,the mechanical stress analysis for rotor design has been explored in this paper.It is observed that high concentration of peak stress on the rotor ribs,which limits the range of rotor speed.展开更多
Traditional security systems are exposed to many various attacks,which represents a major challenge for the spread of the Internet in the future.Innovative techniques have been suggested for detecting attacks using ma...Traditional security systems are exposed to many various attacks,which represents a major challenge for the spread of the Internet in the future.Innovative techniques have been suggested for detecting attacks using machine learning and deep learning.The significant advantage of deep learning is that it is highly efficient,but it needs a large training time with a lot of data.Therefore,in this paper,we present a new feature reduction strategy based on Distributed Cumulative Histograms(DCH)to distinguish between dataset features to locate the most effective features.Cumulative histograms assess the dataset instance patterns of the applied features to identify the most effective attributes that can significantly impact the classification results.Three different models for detecting attacks using Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)are also proposed.The accuracy test of attack detection using the hybrid model was 98.96%on the UNSW-NP15 dataset.The proposed model is compared with wrapper-based and filter-based Feature Selection(FS)models.The proposed model reduced classification time and increased detection accuracy.展开更多
Results of researches on plastic deformation of steels were obtained by acoustic emission and X-ray methods.The new numerical-analytical method of the thin structure parameter determination on one diffraction line was...Results of researches on plastic deformation of steels were obtained by acoustic emission and X-ray methods.The new numerical-analytical method of the thin structure parameter determination on one diffraction line was offered.It is supposed that with a growth of the deformation the cubic lattice will be transformed in the orthorhombic lattice.It is shown that changes of a condition of crystal structure of austenitic steel occur in four stages and of carbonaceous steel occur in six stages.Existence of two types of acoustic emission sources in carbonaceous steel and one type of acoustic emission source in austenitic steel was proposed.展开更多
This paper presents identification of second order plus dead time （SOPDT） integrating and critically damped systems based on relay feedback testing. Relay with hysteresis is applied to the unknown system to get the ...This paper presents identification of second order plus dead time （SOPDT） integrating and critically damped systems based on relay feedback testing. Relay with hysteresis is applied to the unknown system to get the sustained oscillations also called as limit cycle. The limit cycle parameters are utilized in mathematical expressions which are derived using state space technique so that exact process model parameters are estimated. As the relay with hysteresis helps in generating sustained oscillations and also reduces effect of measurement noise which is an important issue in system identification. Different types of processes in the form of transfer function models are considered to show the efficacy of the proposed method and results are compared with available methods in the literature with and without noise effect.展开更多
Graphene Oxide (GO) was chemically synthesized from Natural Flake Graphite (NFG). The GO was chemically reduced to Reduced Graphene Oxide (RGO) using hydrazine monohydrate. Thin films of GO and RGO were also deposited...Graphene Oxide (GO) was chemically synthesized from Natural Flake Graphite (NFG). The GO was chemically reduced to Reduced Graphene Oxide (RGO) using hydrazine monohydrate. Thin films of GO and RGO were also deposited on sodalime glass substrate using spray pyrolysis technique (SPT). The samples were characterized using Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM) with Energy Dispersive X-Ray (EDS) facility attached to it, UV-Visible Spectrometry and Four-Point probe. The FTIR spectra showed the addition of oxygen functionality groups in GO while such groups was drastically reduced in RGO. SEM micrograph of GO thin film showed a porous sponge-like structure while the micrograph of RGO thin film showed evenly distributed and well connected graphene structure. The EDX spectrum of RGO showed that there was decrease in oxygen content and increase in carbon content of RGO when compared to GO. The optical analysis of the GO and RGO thin films gave a direct energy bandgap of 2.7 eV and 2.2 eV respectively. The value of sheet resistance of GO and RGO films was determined to be 22.9 × 10<sup>6</sup>Ω/sq and 4.95 × 10<sup>6</sup>Ω/sq respectively.展开更多
Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,becaus...Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.展开更多
With the continuous improvement of automation,industrial robots have become an indispensable part of automated production lines.They widely used in a number of industrial production activities,such as spraying,welding...With the continuous improvement of automation,industrial robots have become an indispensable part of automated production lines.They widely used in a number of industrial production activities,such as spraying,welding,handling,etc.,and have a great role in these sectors.Recently,the robotic technology is developing towards high precision,high intelligence.Robot calibration technology has a great significance to improve the accuracy of robot.However,it has much work to be done in the identification of robot parameters.The parameter identification work of existing serial and parallel robots is introduced.On the one hand,it summarizes the methods for parameter calibration and discusses their advantages and disadvantages.On the other hand,the application of parameter identification is introduced.This overview has a great reference value for robot manufacturers to choose proper identification method,points further research areas for researchers.Finally,this paper analyzes the existing problems in robot calibration,which may be worth researching in the future.展开更多
This paper proposes a current control scheme for a grid-connected pulse width modulator(PWM) voltage source converter(GC-VSC) under imbalanced and distorted supply voltage conditions.The control scheme is implemented ...This paper proposes a current control scheme for a grid-connected pulse width modulator(PWM) voltage source converter(GC-VSC) under imbalanced and distorted supply voltage conditions.The control scheme is implemented in the positive synchronously rotating reference frame and composed of a single proportional integral(PI) regulator and multi-frequency resonant controllers tuned at the frequencies of 2ω and 6ω,respectively.The experimental results,with the target of eliminating the active power oscillations and current harmonics on a prototype GC-VSC system,validate the feasibility of the proposed current control scheme during supply voltage imbalance and distortion.展开更多
In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We ach...In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.展开更多
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ...Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.展开更多
In this study,a simple position synchronization control algorithm based on an integral sliding mode is developed for dualarm robotic manipulator systems.A first-order sliding surface is designed using cross-coupling e...In this study,a simple position synchronization control algorithm based on an integral sliding mode is developed for dualarm robotic manipulator systems.A first-order sliding surface is designed using cross-coupling error in order to ensure position synchronization of dual-arm manipulators.The design objective of the proposed controller is to ensure stability as well as to synchronize the movement of both arms while maintaining the trajectory as desired.The integral sliding mode eliminates the reaching phase and guarantees robustness throughout the whole operating period.Additionally,a low pass filter is used to smoothen the discontinuous element and minimize unwanted chattering.Lyapunov stability theory is utilized to prove the asymptotic stability of the controlled system.Simulation studies are performed to validate the proposed controller′s effectiveness.Also,to investigate the possibility of realizing the proposed dynamic control method in practical applications,experiments are conducted on a 14DoF coordinated links(COOL)dual-arm robotic manipulator system.Experimental evidence indicates adequate efficiency in trajectory tracking and guarantees robustness in the presence of parametric uncertainty and external disturbance.展开更多
This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and in...This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term.展开更多
This paper reviews various hybrid excited(HE)machines from the perspective of location of PM and DC excitation,series/parallel connection of PM and DC excited magnetic fields,and 2D/3D magnetic fields,respectively.The...This paper reviews various hybrid excited(HE)machines from the perspective of location of PM and DC excitation,series/parallel connection of PM and DC excited magnetic fields,and 2D/3D magnetic fields,respectively.The advantages as well as drawbacks of each category are analyzed.Since an additional control degree,i.e.DC excitation,is introduced in the HE machine,the flux weakening control strategies are more complex.The flux weakening performance as well as efficiency are compared with different control strategies.Then,the potential to mitigate the risk of uncontrolled overvoltage fault at high speed operation is highlighted by controlling the field excitation.Since additional DC coils are usually required for HE machines compared with pure PM excitation,the spatial confliction inevitably results in electromagnetic performance reduction.Finally,the technique to integrate the field and armature windings with open-winding drive circuit is introduced,and novel HE machines without a DC coil are summarized.展开更多
The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evoluti...The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.展开更多
Although single-pulse lasers are often used in traditional laser-induced breakdown spectroscopy （LIBS） measurements, their measurement outcomes are generally undesirable because of the low sensitivity of carbon in i...Although single-pulse lasers are often used in traditional laser-induced breakdown spectroscopy （LIBS） measurements, their measurement outcomes are generally undesirable because of the low sensitivity of carbon in iron-based alloys. In this article, a double-pulse laser was applied to improve the signal intensity of carbon. Both the inter-pulse delay and the combination of laser wavelengths in double-pulse laser-induced breakdown spectroscopy （DP-LIBS） were optimized in our experiment. At the optimized inter-pulse delay, the combination of a first laser of 532 nm and a second laser of 1,064 nm achieved the highest signal enhancement. The properties of the target also played a role in determining the mass ablation enhancement in DP-LIBS configuration.展开更多
文摘Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.
基金the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant number RGP-1444-0054.
文摘Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally.Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival rates.The significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening methods.Advances in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung cancer.CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung cancer.Consequently,there is growing interest in enhancing computer-aided detection(CAD)systems.These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis.This study aims to enhance the effectiveness of CAD systems through various methods.Initially,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual quality.Further refinement is achieved by integrating different optimization strategies with the CLAHE method.The CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and EfficientNet.The study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE algorithm.The empirical findings of the study demonstrate a significant reduction in the false positive rate(FPR)and an overall enhancement in diagnostic accuracy.This research not only contributes to the field of medical imaging but also holds significant implications for the early detection and treatment of lung cancer,ultimately aiming to reduce its mortality rates.
基金Project (No. 50577056) supported by the National Natural Science Foundation of China
文摘This paper proposes an analysis and a direct power control (DPC) design of a wind turbine driven doubly-fed induction generator (DFIG) under unbalanced network voltage conditions. A DFIG model described in the positive and negative synchronous reference frames is presented. Variations of the stator output active and reactive powers are fully deduced in the presence of negative sequence supply voltage and rotor flux. An enhanced DPC scheme is proposed to eliminate stator active power oscillation during network unbalance. The proposed control scheme removes rotor current regulators and the decomposition processing of positive and negative sequence rotor currents. Simulation results using PSCAD/EMTDC are presented on a 2-MW DFIG wind power generation system to validate the feasibility of the proposed control scheme under balanced and unbalanced network conditions.
文摘In this paper,we report on the identification and modeling of unknown and higher order processes into first order plus dead time(FOPDT)plants based on the limit cycle information obtained from a single relay feedback test with an online fractional order proportional integral(FOPI)controller.The parameters of the test processes are accurately determined by the state space method while the FOPI controller settings are re-tuned to achieve enhanced performance based on the identified model parameters based on the balancedtuning method.A new performance index,integral time fractional order absolute error(ITFIAE)is introduced in this paper for balanced tuning of fractional order(FO)controllers.It requires minimum design specifications without a-priori knowledge of gain and phase crossover frequencies and is done non-iteratively without disrupting the closed loop.Four test processes and experimental analysis on a coupled tank system(CTS)validate the theory proposed.
基金This work was sponsored by a Defense University from the National Defense of Ethiopia.
文摘The investigation explores the mechanical stress and electromagnetic performance for a wind-driven synchronous reluctance generator(SRG).The change in the mechanical stress due to the presence of centripetal force,wind speed,and rotor speed are evaluated for different thickness of tangential and radial ribs.Moreover,the variation in the electromagnetic feature such as the q−and d−axes flux,reactance ratio,inductance,torque and torque ripple are discussed for different thickness of tangential and radial ribs.Increasing both tangential and radial ribs thickness has an effect on the electromagnetic performance,but it is observed that effect is significantly more with the variation of tangential rib thickness.Similarly,the mechanical stress analysis for rotor design has been explored in this paper.It is observed that high concentration of peak stress on the rotor ribs,which limits the range of rotor speed.
文摘Traditional security systems are exposed to many various attacks,which represents a major challenge for the spread of the Internet in the future.Innovative techniques have been suggested for detecting attacks using machine learning and deep learning.The significant advantage of deep learning is that it is highly efficient,but it needs a large training time with a lot of data.Therefore,in this paper,we present a new feature reduction strategy based on Distributed Cumulative Histograms(DCH)to distinguish between dataset features to locate the most effective features.Cumulative histograms assess the dataset instance patterns of the applied features to identify the most effective attributes that can significantly impact the classification results.Three different models for detecting attacks using Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)are also proposed.The accuracy test of attack detection using the hybrid model was 98.96%on the UNSW-NP15 dataset.The proposed model is compared with wrapper-based and filter-based Feature Selection(FS)models.The proposed model reduced classification time and increased detection accuracy.
文摘Results of researches on plastic deformation of steels were obtained by acoustic emission and X-ray methods.The new numerical-analytical method of the thin structure parameter determination on one diffraction line was offered.It is supposed that with a growth of the deformation the cubic lattice will be transformed in the orthorhombic lattice.It is shown that changes of a condition of crystal structure of austenitic steel occur in four stages and of carbonaceous steel occur in six stages.Existence of two types of acoustic emission sources in carbonaceous steel and one type of acoustic emission source in austenitic steel was proposed.
基金I am highly grateful to PES Institute of Technology, Bangalore South Campus, Karnataka, India for deputing me to study at Indian Institute of Technology Guwahati (IITG), a prestigious institute in India and providing me with financial assistance
文摘This paper presents identification of second order plus dead time （SOPDT） integrating and critically damped systems based on relay feedback testing. Relay with hysteresis is applied to the unknown system to get the sustained oscillations also called as limit cycle. The limit cycle parameters are utilized in mathematical expressions which are derived using state space technique so that exact process model parameters are estimated. As the relay with hysteresis helps in generating sustained oscillations and also reduces effect of measurement noise which is an important issue in system identification. Different types of processes in the form of transfer function models are considered to show the efficacy of the proposed method and results are compared with available methods in the literature with and without noise effect.
文摘Graphene Oxide (GO) was chemically synthesized from Natural Flake Graphite (NFG). The GO was chemically reduced to Reduced Graphene Oxide (RGO) using hydrazine monohydrate. Thin films of GO and RGO were also deposited on sodalime glass substrate using spray pyrolysis technique (SPT). The samples were characterized using Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM) with Energy Dispersive X-Ray (EDS) facility attached to it, UV-Visible Spectrometry and Four-Point probe. The FTIR spectra showed the addition of oxygen functionality groups in GO while such groups was drastically reduced in RGO. SEM micrograph of GO thin film showed a porous sponge-like structure while the micrograph of RGO thin film showed evenly distributed and well connected graphene structure. The EDX spectrum of RGO showed that there was decrease in oxygen content and increase in carbon content of RGO when compared to GO. The optical analysis of the GO and RGO thin films gave a direct energy bandgap of 2.7 eV and 2.2 eV respectively. The value of sheet resistance of GO and RGO films was determined to be 22.9 × 10<sup>6</sup>Ω/sq and 4.95 × 10<sup>6</sup>Ω/sq respectively.
基金the Researchers Supporting Project Number(RSP2023R 102)King Saud University,Riyadh,Saudi Arabia.
文摘Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.
基金supported in part by the National Natural Science Foundation of China(61772493)in part by the Guangdong Province Universities and College Pearl River Scholar Funded Scheme(2019)in part by the Natural Science Foundation of Chongqing(cstc2019jcyjjq X0013)。
文摘With the continuous improvement of automation,industrial robots have become an indispensable part of automated production lines.They widely used in a number of industrial production activities,such as spraying,welding,handling,etc.,and have a great role in these sectors.Recently,the robotic technology is developing towards high precision,high intelligence.Robot calibration technology has a great significance to improve the accuracy of robot.However,it has much work to be done in the identification of robot parameters.The parameter identification work of existing serial and parallel robots is introduced.On the one hand,it summarizes the methods for parameter calibration and discusses their advantages and disadvantages.On the other hand,the application of parameter identification is introduced.This overview has a great reference value for robot manufacturers to choose proper identification method,points further research areas for researchers.Finally,this paper analyzes the existing problems in robot calibration,which may be worth researching in the future.
基金supported by the National Natural Science Foundation of China(No.50907057)the National High-Tech Research and Development Program (863) of China(No.2007AA05Z419)
文摘This paper proposes a current control scheme for a grid-connected pulse width modulator(PWM) voltage source converter(GC-VSC) under imbalanced and distorted supply voltage conditions.The control scheme is implemented in the positive synchronously rotating reference frame and composed of a single proportional integral(PI) regulator and multi-frequency resonant controllers tuned at the frequencies of 2ω and 6ω,respectively.The experimental results,with the target of eliminating the active power oscillations and current harmonics on a prototype GC-VSC system,validate the feasibility of the proposed current control scheme during supply voltage imbalance and distortion.
文摘In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.
文摘Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.
文摘In this study,a simple position synchronization control algorithm based on an integral sliding mode is developed for dualarm robotic manipulator systems.A first-order sliding surface is designed using cross-coupling error in order to ensure position synchronization of dual-arm manipulators.The design objective of the proposed controller is to ensure stability as well as to synchronize the movement of both arms while maintaining the trajectory as desired.The integral sliding mode eliminates the reaching phase and guarantees robustness throughout the whole operating period.Additionally,a low pass filter is used to smoothen the discontinuous element and minimize unwanted chattering.Lyapunov stability theory is utilized to prove the asymptotic stability of the controlled system.Simulation studies are performed to validate the proposed controller′s effectiveness.Also,to investigate the possibility of realizing the proposed dynamic control method in practical applications,experiments are conducted on a 14DoF coordinated links(COOL)dual-arm robotic manipulator system.Experimental evidence indicates adequate efficiency in trajectory tracking and guarantees robustness in the presence of parametric uncertainty and external disturbance.
文摘This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term.
文摘This paper reviews various hybrid excited(HE)machines from the perspective of location of PM and DC excitation,series/parallel connection of PM and DC excited magnetic fields,and 2D/3D magnetic fields,respectively.The advantages as well as drawbacks of each category are analyzed.Since an additional control degree,i.e.DC excitation,is introduced in the HE machine,the flux weakening control strategies are more complex.The flux weakening performance as well as efficiency are compared with different control strategies.Then,the potential to mitigate the risk of uncontrolled overvoltage fault at high speed operation is highlighted by controlling the field excitation.Since additional DC coils are usually required for HE machines compared with pure PM excitation,the spatial confliction inevitably results in electromagnetic performance reduction.Finally,the technique to integrate the field and armature windings with open-winding drive circuit is introduced,and novel HE machines without a DC coil are summarized.
文摘The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.
基金supported by National Natural Science Foundation of China(No.51374040)the National Key Scientific Instrument and Equipment Development Project of China(No.2014YQ120351)
文摘Although single-pulse lasers are often used in traditional laser-induced breakdown spectroscopy （LIBS） measurements, their measurement outcomes are generally undesirable because of the low sensitivity of carbon in iron-based alloys. In this article, a double-pulse laser was applied to improve the signal intensity of carbon. Both the inter-pulse delay and the combination of laser wavelengths in double-pulse laser-induced breakdown spectroscopy （DP-LIBS） were optimized in our experiment. At the optimized inter-pulse delay, the combination of a first laser of 532 nm and a second laser of 1,064 nm achieved the highest signal enhancement. The properties of the target also played a role in determining the mass ablation enhancement in DP-LIBS configuration.
