This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Select...This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Selection(AHB-AHS)method.A finite element dynamic equation for the AEDR system is introduced,considering complex nonlinearities of the intershaft bearing,unbalanced excitations,and high-frequency excitation.A solving strategy combining the AHB-AHS method and improved arclength continuation method is proposed to solve highdimensional dynamic equations containing complex nonlinearities and to track periodic solutions with parameter variations.The Floquet theory is used to analyze the types of bifurcation points in the system and the stability of periodic motions.The results indicate that high-frequency excitation can couple high-order and low-order modes,especially when the system undergoes superharmonic resonance.High-frequency excitation leads to more combination frequency harmonics,among which N_(f)ω_(1)-2ω_(2)dominates.Furthermore,changing the parameters(amplitude and frequency)of high-frequency excitation widens or shifts the unstable regions of the system.These findings contribute to understanding the mechanism of high-frequency excitation on aero-engines and demonstrate that the proposed AHB-AHS method is a powerful tool for analyzing highdimensional complex nonlinear dynamic systems under multi-frequency excitation.展开更多
This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with ...This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with the Lagrangian mechanics,representing a two-degree-of-freedom nonlinear electromechanical system subject to harmonic base excitation under a 1:1 internal resonance condition.The model is normalized,and the conditions dictating monostable and bistable oscillation modes are identified.The bifurcation characteristics of the coupled system are analyzed in both oscillation modes by means of harmonic balance and continuation methods.The vibration isolation performance,with and without the coupled harvester,is evaluated in terms of displacement transmissibility to assess its dual functionalities for vibration isolation and energy harvesting.Analytical results demonstrate that integrating a piezoelectric harvester into a monostable QZS isolator under 1:1 internal resonance does not compromise its vibration isolation capability while enabling efficient energy harvesting at extremely low-frequency base excitation.Furthermore,the system's response under strong base excitation is investigated exclusively for energy harvesting in both monostable and bistable modes,leading to optimal structural parameter design.The conditions for intra-well and inter-well periodic oscillation modes,as well as chaotic responses,are analyzed analytically and validated numerically through stability charts,basins of attraction,bifurcation diagrams,time histories,and Poincarémaps.This work provides a comprehensive understanding of the oscillation dynamics of QZS isolators and offers valuable insights for optimizing their geometric parameters to function as high-performance vibration isolators and/or energy harvesters.展开更多
An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control...An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.展开更多
In this article,the reduced differential transform method is introduced to solve the nonlinear fractional model of Tumor-Immune.The fractional derivatives are described in the Caputo sense.The solutions derived using ...In this article,the reduced differential transform method is introduced to solve the nonlinear fractional model of Tumor-Immune.The fractional derivatives are described in the Caputo sense.The solutions derived using this method are easy and very accurate.The model is given by its signal flow diagram.Moreover,a simulation of the system by the Simulink of MATLAB is given.The disease-free equilibrium and stability of the equilibrium point are calculated.Formulation of a fractional optimal control for the cancer model is calculated.In addition,to control the system,we propose a novel modification of its model.This modification is based on converting the model to a memristive one,which is a first time in the literature that such idea is used to control this type of diseases.Also,we study the system’s stability via the Lyapunov exponents and Poincare maps before and after control.Fractional order differential equations(FDEs)are commonly utilized to model systems that have memory,and exist in several physical phenomena,models in thermoelasticity field,and biological paradigms.FDEs have been utilized to model the realistic biphasic decline manner of elastic systems and infection of diseases with a slower rate of change.FDEs are more useful than integer-order in modeling sophisticated models that contain physical phenomena.展开更多
This paper presents a method using range deception jamming to evaluate the safety performance of the autonomous vehicle with millimetre wave(MMW)radar.The working principle of this method is described.Combined with a ...This paper presents a method using range deception jamming to evaluate the safety performance of the autonomous vehicle with millimetre wave(MMW)radar.The working principle of this method is described.Combined with a waveform edition software,an experimental platform is developed to generate a deceptive signal that contains false distance information.According to related theories and its principle,the configuration parameters of the experimental setup are calculated and configured.The MMW radar of evaluated vehicle should identify an objective when it receives the deceptive signal from the experimental setup.Even if no obstacle,the evaluated vehicle can immediately brake in order that its braking distance is measured.The experimental results show that the proposed method can meet the requirements of the safety performance evaluation for the autonomous vehicle with MMW radar,and it also overcomes some deficiencies of previous methods.展开更多
Vibration of structures is often an undesirable phenomena and should be avoided or controlled. There are two techniques to control the vibration of a system, that is, active and passive control techniques. In this pap...Vibration of structures is often an undesirable phenomena and should be avoided or controlled. There are two techniques to control the vibration of a system, that is, active and passive control techniques. In this paper, a negative feedback velocity is applied to a dynamical system, which is represented by two coupled second order nonlinear differential equations having both quadratic and cubic nonlinearties. The system describes the vibration of an aircraft tail. The system is subjected to multi-external excitation forces. The method of multiple time scale perturbation is applied to solve the nonlinear differential equations and obtain approximate solutions up to third order of accuracy. The stability of the system is investigated applying frequency response equations. The effects of the different parameters are studied numerically. Various resonance cases are investigated. A comparison is made with the available published work.展开更多
In this paper,the nonlinear dynamic behavior of a string-beam coupled system subjected to external,parametric and tuned excitations is presented.The governing equations of motion are obtained for the nonlinear transve...In this paper,the nonlinear dynamic behavior of a string-beam coupled system subjected to external,parametric and tuned excitations is presented.The governing equations of motion are obtained for the nonlinear transverse vibrations of the string-beam coupled system which are described by a set of ordinary differential equations with two degrees of freedom.The case of 1：1 internal resonance between the modes of the beam and string,and the primary and combined resonance for the beam is considered.The method of multiple scales is utilized to analyze the nonlinear responses of the string-beam coupled system and obtain approximate solutions up to and including the second-order approximations.All resonance cases are extracted and investigated.Stability of the system is studied using frequency response equations and the phase-plane method.Numerical solutions are carried out and the results are presented graphically and discussed.The effects of the different parameters on both response and stability of the system are investigated.The reported results are compared to the available published work.展开更多
The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related ...The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.展开更多
Coronavirus(COVID-19)is a contagious disease that causes exceptional effect on healthcare organizationsworldwide with dangerous impact on medical services within the hospitals.Because of the fast spread of COVID-19,th...Coronavirus(COVID-19)is a contagious disease that causes exceptional effect on healthcare organizationsworldwide with dangerous impact on medical services within the hospitals.Because of the fast spread of COVID-19,the healthcare facilities could be a big source of disease infection.So,healthcare video consultations should be used to decrease face-to-face communication between clinician and patients.Healthcare video consultations may be beneficial for some COVID-19 conditions and reduce the need for faceto-face contact with a potentially positive patient without symptoms.These conditions are like top clinicians who provide remote consultations to develop treatment methodology and follow-up remotely,patients who consult about COVID-19,and those who have mild symptoms suggestive of the COVID-19 virus.Video consultations are a supplement to,and not a substitute for,telephone consultations.It may also form part of a broader COVID-19 distance care strategy that contains computerized screening,separation of possibly infectious patients within medical services,and computerized video-intensive observing of their intensive care that helps reduce mixing.Nowadays,the spread of the COVID-19 virus helps to expand the use of video healthcare consultations because it helps to exchange experiences and remote medical consultations,save costs and health procedures used to cope with the pandemic of the COVID-19 virus,and monitor the progress of treatment plans,moment by moment from a distance with precision,clarity and ease.From this perspective,this paper introduces a high-efficiency video coding(HEVC)ChaCha20-based selective encryption(SE)scheme for secure healthcare video Consultations.The proposed HEVC ChaCha20-based SE scheme uses the ChaCha20 for encrypting the sign bits of the Discrete Cosine Transform(DCT)and Motion Vector Difference(MVD)in the HEVC entropy phase.The main achievement of HEVC ChaCha20-based SE scheme is encrypting the most sensitive video bits with keeping low delay time,fixed bit rate of the HEVC,and format compliance.Experimental tests guarantee that the proposed HEVC ChaCha20-based SE scheme can ensure the confidentiality of the healthcare video consultations which has become easy to transmit through the internet.展开更多
Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging su...Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.展开更多
Background:Contrast enhancement plays an important role in the image processing field.Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the i...Background:Contrast enhancement plays an important role in the image processing field.Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image.Objective:This paper proposed a novel method based on statistical data from the local mean and local standard deviation.Method:The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories:background,foreground,and problematic(contrast&luminosity)region.Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast variation problem effectively compared to Histogram Equalization(HE),Difference of Gaussian(DoG),and Butterworth Homomorphic Filtering(BHF).Seven(7)types of binarization methods were tested on the corrected image and produced a positive and impressive result.Result:Finally,a comparison in terms of Signal Noise Ratio(SNR),Misclassification Error(ME),F-measure,Peak Signal Noise Ratio(PSNR),Misclassification Penalty Metric(MPM),and Accuracy was calculated.Each binarization method shows an incremented result after applying it onto the corrected image compared to the original image.The SNR result of our proposed image is 9.350 higher than the three(3)other methods.The average increment after five(5)types of evaluation are:(Otsu=41.64%,Local Adaptive=7.05%,Niblack=30.28%,Bernsen=25%,Bradley=3.54%,Nick=1.59%,Gradient-Based=14.6%).Conclusion:The results presented in this paper effectively solve the contrast problem and finally produce better quality images.展开更多
In this paper, the problem of communication security in an underlay wiretap cognitive radio network is addressed and statistically investigated. We rely on a simple dual-hop communication model of decode and forward r...In this paper, the problem of communication security in an underlay wiretap cognitive radio network is addressed and statistically investigated. We rely on a simple dual-hop communication model of decode and forward relay assisted network. Regarding the interference from primary users, interference power and maximum transmit power constraints; this network is subjected to multiple eavesdropping attacks which employ a specific interception strategy. To confound this eavesdropping, proposed selection schemes are exploited that aim at maximizing the minimum of the dual secrecy rates in order to strengthen the physical layer security. Moreover, exact and asymptotic closed form expressions are derived for specific performance metrics over independent and identically distributed Rayleigh fading channels. At high signal to interference noise ratio(SINR), tangential system bounds are also derived and discussed. Monte Carlo simulation results emphasize our assumption. It is found out that at the full diversity of the system, any additional node that enters the cooperative eavesdropping system becomes significantly of no effect.展开更多
In this study we investigate the dynamic recovery effects in IRF9520 commercial p-channel power vertical double diffused metal-oxide semiconductor field-effect transistors（VDMOSFETs） subjected to negative bias tempe...In this study we investigate the dynamic recovery effects in IRF9520 commercial p-channel power vertical double diffused metal-oxide semiconductor field-effect transistors（VDMOSFETs） subjected to negative bias temperature（NBT）stressing under the particular pulsed bias. Particular values of the pulsed stress voltage frequency and duty cycle are chosen in order to analyze the recoverable and permanent components of stress-induced threshold voltage shift in detail. The results are discussed in terms of the mechanisms responsible for buildup of oxide charge and interface traps. The partial recovery during the low level of pulsed gate voltage is ascribed to the removal of recoverable component of degradation, i.e., to passivation/neutralization of shallow oxide traps that are not transformed into the deeper traps（permanent component）.Considering the value of characteristic time constant associated with complete removal of the recoverable component of degradation, it is shown that by selecting an appropriate combination of the frequency and duty cycle, the threshold voltage shifts induced under the pulsed negative bias temperature stress conditions can be significantly reduced, which may be utilized for improving the device lifetime in real application circuits.展开更多
Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,w...Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.展开更多
This article presents the generation of Orbital AngularMomentum(OAM)vortex waves with mode 1 using Uniform Circular Array(UCA)antenna.Two different designs,namely,UCA-1(4-element array antenna)and UCA-2(8-element arra...This article presents the generation of Orbital AngularMomentum(OAM)vortex waves with mode 1 using Uniform Circular Array(UCA)antenna.Two different designs,namely,UCA-1(4-element array antenna)and UCA-2(8-element array antenna),were designed and fabricated using FR-4 substrate to generate OAM mode 1 at 3.5 GHz(5G mid-band).The proposed antenna arrays comprised rectangular microstrip patch elements with inset fed technique.The elements were excited by a carefully designed feeding phase shift network to provide similar output energy at output ports with desired phase shift value.The generated OAM waves were confirmed by measuring the null in the bore sight of their 2D radiation patterns,simulated phase distribution and intensity distribution.The measurement results agree well with the simulation results.Moreover,a detailed mode purity analysis of the generated OAM waves was carried out considering different factors.The investigation found that the greater the number of elements,the higher the purity of the generated OAM wave.Compared with other previous works,the proposed antenna design of this paper is very simple to design and fabricate.In addition,the proposed antennas are compact in design even at lower frequency band with very wide bandwidth to meet the requirements of 5G mid-band applications.展开更多
This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest cl...This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.展开更多
This study presents the synchronisation between two non-linear Lorenz chaotic systems using an adaptive intuitionistic fuzzy controller(AIFC).This problem is solved in the presence of external disturbances and uncerta...This study presents the synchronisation between two non-linear Lorenz chaotic systems using an adaptive intuitionistic fuzzy controller(AIFC).This problem is solved in the presence of external disturbances and uncertainties.Two classes of Lorenz systems have been formulated one as the master and the other as the slave.The master and slave systems have different initial conditions.Under different initial conditions,both systems diverge.This is called the butterfly effect that rules the behaviour of chaotic systems.AIFC is chosen as a different method that has not been used before to synchronise the chaotic systems.The scaling factors of the inputs and the output of the intuitionistic fuzzy controller(IFC)are tuned using the artificial bee colony(ABC)algorithm depending on a specified objective function.Then IFC stability is studied when it is applied to synchronise the Lorenz system.The simulation results achieved to assess the adequacy of the proposed control and to exhibit its dependability to synchronise the Lorenz system.展开更多
In this paper,we studied the vibration performance,energy transfer and stability of the offshore wind turbine tower system under mixed excitations.The method of multiple scales is utilized to calculate the approximate...In this paper,we studied the vibration performance,energy transfer and stability of the offshore wind turbine tower system under mixed excitations.The method of multiple scales is utilized to calculate the approximate solutions of wind turbine system.The proportional-derivative controller was applied for reducing the oscillations of the controlled system.Adding the controller to single degree of freedom system equation is responsible for energy transfers in offshore wind turbine tower system.The steady state solution of stability at worst resonance cases is studied and examined.The offshore wind turbine system behavior was studied numerically at its different parameters values.Furthermore,the response and numerical results were obtained and discussed.The stability is also analyzed using technique of phase plane and equations of frequency response.In addition,the numerical results and comparison between analytical and numerical solutions were obtained with MAPLE and MATLAB algorithms.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
In this paper, an enhanced greedy bit and power allocation algorithms for orthogonal frequency division multiplexing (OFDM) communication systems are introduced. These algorithms combine low complexity greedy power al...In this paper, an enhanced greedy bit and power allocation algorithms for orthogonal frequency division multiplexing (OFDM) communication systems are introduced. These algorithms combine low complexity greedy power allocation algorithms with a simplified maximum ratio combining (MRC) precoding technique at the transmitter for maximizing the average data throughput of OFDM communication systems. Results of computer simulations show that precoding is an effective technique for improving the throughput performance of the proposed bit and power allocation algorithms.展开更多
基金the financial support from the National Key R&D Program of China(No.2023YFE0125900)National Natural Science Foundation of China(Nos.12372008 and 12102234)+1 种基金Natural Science Foundation of Heilongjiang Province,China(No.YQ2022A008)Taif University,Saudi Arabia,for supporting this work through Project number(TU-DSPP-2024-73).
文摘This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Selection(AHB-AHS)method.A finite element dynamic equation for the AEDR system is introduced,considering complex nonlinearities of the intershaft bearing,unbalanced excitations,and high-frequency excitation.A solving strategy combining the AHB-AHS method and improved arclength continuation method is proposed to solve highdimensional dynamic equations containing complex nonlinearities and to track periodic solutions with parameter variations.The Floquet theory is used to analyze the types of bifurcation points in the system and the stability of periodic motions.The results indicate that high-frequency excitation can couple high-order and low-order modes,especially when the system undergoes superharmonic resonance.High-frequency excitation leads to more combination frequency harmonics,among which N_(f)ω_(1)-2ω_(2)dominates.Furthermore,changing the parameters(amplitude and frequency)of high-frequency excitation widens or shifts the unstable regions of the system.These findings contribute to understanding the mechanism of high-frequency excitation on aero-engines and demonstrate that the proposed AHB-AHS method is a powerful tool for analyzing highdimensional complex nonlinear dynamic systems under multi-frequency excitation.
基金Project supported by the National Key R&D Program of China(No.2023YFE0125900)。
文摘This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with the Lagrangian mechanics,representing a two-degree-of-freedom nonlinear electromechanical system subject to harmonic base excitation under a 1:1 internal resonance condition.The model is normalized,and the conditions dictating monostable and bistable oscillation modes are identified.The bifurcation characteristics of the coupled system are analyzed in both oscillation modes by means of harmonic balance and continuation methods.The vibration isolation performance,with and without the coupled harvester,is evaluated in terms of displacement transmissibility to assess its dual functionalities for vibration isolation and energy harvesting.Analytical results demonstrate that integrating a piezoelectric harvester into a monostable QZS isolator under 1:1 internal resonance does not compromise its vibration isolation capability while enabling efficient energy harvesting at extremely low-frequency base excitation.Furthermore,the system's response under strong base excitation is investigated exclusively for energy harvesting in both monostable and bistable modes,leading to optimal structural parameter design.The conditions for intra-well and inter-well periodic oscillation modes,as well as chaotic responses,are analyzed analytically and validated numerically through stability charts,basins of attraction,bifurcation diagrams,time histories,and Poincarémaps.This work provides a comprehensive understanding of the oscillation dynamics of QZS isolators and offers valuable insights for optimizing their geometric parameters to function as high-performance vibration isolators and/or energy harvesters.
文摘An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
基金funded by“Taif University Researchers Supporting Project number(TURSP-2020/160),Taif University,Taif,Saudi Arabia”.
文摘In this article,the reduced differential transform method is introduced to solve the nonlinear fractional model of Tumor-Immune.The fractional derivatives are described in the Caputo sense.The solutions derived using this method are easy and very accurate.The model is given by its signal flow diagram.Moreover,a simulation of the system by the Simulink of MATLAB is given.The disease-free equilibrium and stability of the equilibrium point are calculated.Formulation of a fractional optimal control for the cancer model is calculated.In addition,to control the system,we propose a novel modification of its model.This modification is based on converting the model to a memristive one,which is a first time in the literature that such idea is used to control this type of diseases.Also,we study the system’s stability via the Lyapunov exponents and Poincare maps before and after control.Fractional order differential equations(FDEs)are commonly utilized to model systems that have memory,and exist in several physical phenomena,models in thermoelasticity field,and biological paradigms.FDEs have been utilized to model the realistic biphasic decline manner of elastic systems and infection of diseases with a slower rate of change.FDEs are more useful than integer-order in modeling sophisticated models that contain physical phenomena.
基金National Natural Science Foundation of China(No.61471289)Natural Science Foundation of Shaanxi Province of China(No.2015JM5189)。
文摘This paper presents a method using range deception jamming to evaluate the safety performance of the autonomous vehicle with millimetre wave(MMW)radar.The working principle of this method is described.Combined with a waveform edition software,an experimental platform is developed to generate a deceptive signal that contains false distance information.According to related theories and its principle,the configuration parameters of the experimental setup are calculated and configured.The MMW radar of evaluated vehicle should identify an objective when it receives the deceptive signal from the experimental setup.Even if no obstacle,the evaluated vehicle can immediately brake in order that its braking distance is measured.The experimental results show that the proposed method can meet the requirements of the safety performance evaluation for the autonomous vehicle with MMW radar,and it also overcomes some deficiencies of previous methods.
文摘Vibration of structures is often an undesirable phenomena and should be avoided or controlled. There are two techniques to control the vibration of a system, that is, active and passive control techniques. In this paper, a negative feedback velocity is applied to a dynamical system, which is represented by two coupled second order nonlinear differential equations having both quadratic and cubic nonlinearties. The system describes the vibration of an aircraft tail. The system is subjected to multi-external excitation forces. The method of multiple time scale perturbation is applied to solve the nonlinear differential equations and obtain approximate solutions up to third order of accuracy. The stability of the system is investigated applying frequency response equations. The effects of the different parameters are studied numerically. Various resonance cases are investigated. A comparison is made with the available published work.
文摘In this paper,the nonlinear dynamic behavior of a string-beam coupled system subjected to external,parametric and tuned excitations is presented.The governing equations of motion are obtained for the nonlinear transverse vibrations of the string-beam coupled system which are described by a set of ordinary differential equations with two degrees of freedom.The case of 1：1 internal resonance between the modes of the beam and string,and the primary and combined resonance for the beam is considered.The method of multiple scales is utilized to analyze the nonlinear responses of the string-beam coupled system and obtain approximate solutions up to and including the second-order approximations.All resonance cases are extracted and investigated.Stability of the system is studied using frequency response equations and the phase-plane method.Numerical solutions are carried out and the results are presented graphically and discussed.The effects of the different parameters on both response and stability of the system are investigated.The reported results are compared to the available published work.
文摘The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.
基金This study was funded by the Deanship of Scientific Research,Taif University Researchers Supporting Project number(TURSP-2020/08),Taif University,Taif,Saudi Arabia.
文摘Coronavirus(COVID-19)is a contagious disease that causes exceptional effect on healthcare organizationsworldwide with dangerous impact on medical services within the hospitals.Because of the fast spread of COVID-19,the healthcare facilities could be a big source of disease infection.So,healthcare video consultations should be used to decrease face-to-face communication between clinician and patients.Healthcare video consultations may be beneficial for some COVID-19 conditions and reduce the need for faceto-face contact with a potentially positive patient without symptoms.These conditions are like top clinicians who provide remote consultations to develop treatment methodology and follow-up remotely,patients who consult about COVID-19,and those who have mild symptoms suggestive of the COVID-19 virus.Video consultations are a supplement to,and not a substitute for,telephone consultations.It may also form part of a broader COVID-19 distance care strategy that contains computerized screening,separation of possibly infectious patients within medical services,and computerized video-intensive observing of their intensive care that helps reduce mixing.Nowadays,the spread of the COVID-19 virus helps to expand the use of video healthcare consultations because it helps to exchange experiences and remote medical consultations,save costs and health procedures used to cope with the pandemic of the COVID-19 virus,and monitor the progress of treatment plans,moment by moment from a distance with precision,clarity and ease.From this perspective,this paper introduces a high-efficiency video coding(HEVC)ChaCha20-based selective encryption(SE)scheme for secure healthcare video Consultations.The proposed HEVC ChaCha20-based SE scheme uses the ChaCha20 for encrypting the sign bits of the Discrete Cosine Transform(DCT)and Motion Vector Difference(MVD)in the HEVC entropy phase.The main achievement of HEVC ChaCha20-based SE scheme is encrypting the most sensitive video bits with keeping low delay time,fixed bit rate of the HEVC,and format compliance.Experimental tests guarantee that the proposed HEVC ChaCha20-based SE scheme can ensure the confidentiality of the healthcare video consultations which has become easy to transmit through the internet.
基金This research received the support from Taif University Researchers Supporting Project Number(TURSP-2020/147),Taif university,Taif,Saudi Arabia.
文摘Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.
文摘Background:Contrast enhancement plays an important role in the image processing field.Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image.Objective:This paper proposed a novel method based on statistical data from the local mean and local standard deviation.Method:The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories:background,foreground,and problematic(contrast&luminosity)region.Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast variation problem effectively compared to Histogram Equalization(HE),Difference of Gaussian(DoG),and Butterworth Homomorphic Filtering(BHF).Seven(7)types of binarization methods were tested on the corrected image and produced a positive and impressive result.Result:Finally,a comparison in terms of Signal Noise Ratio(SNR),Misclassification Error(ME),F-measure,Peak Signal Noise Ratio(PSNR),Misclassification Penalty Metric(MPM),and Accuracy was calculated.Each binarization method shows an incremented result after applying it onto the corrected image compared to the original image.The SNR result of our proposed image is 9.350 higher than the three(3)other methods.The average increment after five(5)types of evaluation are:(Otsu=41.64%,Local Adaptive=7.05%,Niblack=30.28%,Bernsen=25%,Bradley=3.54%,Nick=1.59%,Gradient-Based=14.6%).Conclusion:The results presented in this paper effectively solve the contrast problem and finally produce better quality images.
文摘In this paper, the problem of communication security in an underlay wiretap cognitive radio network is addressed and statistically investigated. We rely on a simple dual-hop communication model of decode and forward relay assisted network. Regarding the interference from primary users, interference power and maximum transmit power constraints; this network is subjected to multiple eavesdropping attacks which employ a specific interception strategy. To confound this eavesdropping, proposed selection schemes are exploited that aim at maximizing the minimum of the dual secrecy rates in order to strengthen the physical layer security. Moreover, exact and asymptotic closed form expressions are derived for specific performance metrics over independent and identically distributed Rayleigh fading channels. At high signal to interference noise ratio(SINR), tangential system bounds are also derived and discussed. Monte Carlo simulation results emphasize our assumption. It is found out that at the full diversity of the system, any additional node that enters the cooperative eavesdropping system becomes significantly of no effect.
基金Project supported by the Fund from the Ministry of Education,Science and Technological Development of the Republic of Serbia(Grant Nos.OI-171026 and TR-32026)the Ei PCB Factory,Ni
文摘In this study we investigate the dynamic recovery effects in IRF9520 commercial p-channel power vertical double diffused metal-oxide semiconductor field-effect transistors（VDMOSFETs） subjected to negative bias temperature（NBT）stressing under the particular pulsed bias. Particular values of the pulsed stress voltage frequency and duty cycle are chosen in order to analyze the recoverable and permanent components of stress-induced threshold voltage shift in detail. The results are discussed in terms of the mechanisms responsible for buildup of oxide charge and interface traps. The partial recovery during the low level of pulsed gate voltage is ascribed to the removal of recoverable component of degradation, i.e., to passivation/neutralization of shallow oxide traps that are not transformed into the deeper traps（permanent component）.Considering the value of characteristic time constant associated with complete removal of the recoverable component of degradation, it is shown that by selecting an appropriate combination of the frequency and duty cycle, the threshold voltage shifts induced under the pulsed negative bias temperature stress conditions can be significantly reduced, which may be utilized for improving the device lifetime in real application circuits.
文摘Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.
基金supported by Ministry of Higher Education through the FundamentalResearch Grant Scheme(FRGS)under a grant number of FRGS/1/2020/ICT09/UNIMAP/02/2.
文摘This article presents the generation of Orbital AngularMomentum(OAM)vortex waves with mode 1 using Uniform Circular Array(UCA)antenna.Two different designs,namely,UCA-1(4-element array antenna)and UCA-2(8-element array antenna),were designed and fabricated using FR-4 substrate to generate OAM mode 1 at 3.5 GHz(5G mid-band).The proposed antenna arrays comprised rectangular microstrip patch elements with inset fed technique.The elements were excited by a carefully designed feeding phase shift network to provide similar output energy at output ports with desired phase shift value.The generated OAM waves were confirmed by measuring the null in the bore sight of their 2D radiation patterns,simulated phase distribution and intensity distribution.The measurement results agree well with the simulation results.Moreover,a detailed mode purity analysis of the generated OAM waves was carried out considering different factors.The investigation found that the greater the number of elements,the higher the purity of the generated OAM wave.Compared with other previous works,the proposed antenna design of this paper is very simple to design and fabricate.In addition,the proposed antennas are compact in design even at lower frequency band with very wide bandwidth to meet the requirements of 5G mid-band applications.
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number(IFP2021-043).
文摘This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.
文摘This study presents the synchronisation between two non-linear Lorenz chaotic systems using an adaptive intuitionistic fuzzy controller(AIFC).This problem is solved in the presence of external disturbances and uncertainties.Two classes of Lorenz systems have been formulated one as the master and the other as the slave.The master and slave systems have different initial conditions.Under different initial conditions,both systems diverge.This is called the butterfly effect that rules the behaviour of chaotic systems.AIFC is chosen as a different method that has not been used before to synchronise the chaotic systems.The scaling factors of the inputs and the output of the intuitionistic fuzzy controller(IFC)are tuned using the artificial bee colony(ABC)algorithm depending on a specified objective function.Then IFC stability is studied when it is applied to synchronise the Lorenz system.The simulation results achieved to assess the adequacy of the proposed control and to exhibit its dependability to synchronise the Lorenz system.
基金This work was supported by Taif University under research grant 1-439-6067.The authors would like to acknowledge the scientific support provided by the university.
文摘In this paper,we studied the vibration performance,energy transfer and stability of the offshore wind turbine tower system under mixed excitations.The method of multiple scales is utilized to calculate the approximate solutions of wind turbine system.The proportional-derivative controller was applied for reducing the oscillations of the controlled system.Adding the controller to single degree of freedom system equation is responsible for energy transfers in offshore wind turbine tower system.The steady state solution of stability at worst resonance cases is studied and examined.The offshore wind turbine system behavior was studied numerically at its different parameters values.Furthermore,the response and numerical results were obtained and discussed.The stability is also analyzed using technique of phase plane and equations of frequency response.In addition,the numerical results and comparison between analytical and numerical solutions were obtained with MAPLE and MATLAB algorithms.
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In this paper, an enhanced greedy bit and power allocation algorithms for orthogonal frequency division multiplexing (OFDM) communication systems are introduced. These algorithms combine low complexity greedy power allocation algorithms with a simplified maximum ratio combining (MRC) precoding technique at the transmitter for maximizing the average data throughput of OFDM communication systems. Results of computer simulations show that precoding is an effective technique for improving the throughput performance of the proposed bit and power allocation algorithms.
