We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of ...We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and,unlike the state of the art,requires neither weakcoupling Lamb-Dicke approximation nor perturbation treatment.With the application of gradient-based optimal control,it enables finding amplitude-and phase-modulated laser control protocols that work without the Lamb-Dicke approximation,promising gate speeds on the order of microseconds comparable to the characteristic trap frequencies.Also,robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections.Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation,and thus can find applications in near-future experiments.展开更多
In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint de...In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake judgments.Most of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial attacks.In addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual quality.In response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for DFFD.The ridge texture area within the fingerprint image has been identified and designated as the region for perturbation generation.Subsequently,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient variance.Additionally,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack performance.Experimental results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.展开更多
Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Indu...Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.展开更多
The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to an...The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.展开更多
The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the ...The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the joint sparsity and sparsifying transform learning(JTL)into the simultaneous auto-calibrating and k-space estimation(SAKE)structured low-rank model,named JTLSAKE.The alternate direction method of multipliers is exploited to solve the resulting optimization problem,and the optimized gradient method is used to improve the convergence speed.In addition,a graphics processing unit is used to accelerate the proposed algorithm.The experimental results on four in vivo human datasets demonstrate that the reconstruction quality of the proposed algorithm is comparable to that of JTL-based low-rank modeling of local k-space neighborhoods with parallel imaging(JTL-PLORAKS),and the proposed algorithm is 46 times faster than the JTL-PLORAKS,requiring only 4 s to reconstruct a 200×200 pixels MR image with 8 channels.展开更多
In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically ...In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically adjust the frequency regulation command based on the state of charge(SoC)of energy storage units,this paper proposes a secondary frequency regulation control strategy based on variable integral coefficients for multiple energy storage units.First,a power-uniform controller is designed to ensure that thermal power units gradually take on more regulation power during the frequency regulation process.Next,a control framework based on variable integral coefficients is proposed within the secondary frequency regulation model,along with an objective function that simultaneously considers both Automatic Generation Control(AGC)command tracking performance and SoC recovery requirements of energy storage units.Finally,a gradient descent optimization method is used to dynamically adjust the gain of the energy storage integral controller,allowingmultiple energy storage units to respond in real-time to AGC instructions and SoC variations.Simulation results confirmthe effectiveness of the proposedmethod.Compared to traditional strategies,the proposed approach takes into account the SoCdiscrepancies amongmultiple energy storage units and the duration of system net power imbalances.It successfully implements secondary frequency regulation while achieving dynamic power allocation among the units.展开更多
In atomic,molecular,and nuclear physics,the method of complex coordinate rotation is a widely used theoretical tool for studying resonant states.Here,we propose a novel implementation of this method based on the gradi...In atomic,molecular,and nuclear physics,the method of complex coordinate rotation is a widely used theoretical tool for studying resonant states.Here,we propose a novel implementation of this method based on the gradient optimization(CCR-GO).The main strength of the CCR-GO method is that it does not require manual adjustment of optimization parameters in the wave function;instead,a mathematically well-defined optimization path can be followed.Our method is proven to be very efficient in searching resonant positions and widths over a variety of few-body atomic systems,and can significantly improve the accuracy of the results.As a special case,the CCR-GO method is equally capable of dealing with bound-state problems with high accuracy,which is traditionally achieved through the usual extreme conditions of energy itself.展开更多
Attitude planning of rigid bodies has many applications in robotics and aerospace.However,because the attitude configuration space is non-Euclidean and the constraints are complex and non-linear,the design of the atti...Attitude planning of rigid bodies has many applications in robotics and aerospace.However,because the attitude configuration space is non-Euclidean and the constraints are complex and non-linear,the design of the attitude curve has always been a tricky problem.In this paper,a gradient-based attitude planning method is proposed to simultaneously handle attitude pointing,angular velocity,torque,and time constraints on Lie group SO(3).Firstly,the attitude interpolation algorithm on SO(3)gives an attitude curve connecting the initial and target attitudes.The shape of the curve is determined by the fitting coefficients and maneuvering time.Secondly,to match the curve with suitable angular velocity and control torque,a nonlinear planning model with fitting coefficients and maneuver time as decision variables is proposed.Solving the problem gives a smooth attitude curve that satisfies both kinematic and dynamic constraints and also avoids complicated time allocation.Then,to apply the gradientbased solver,analytical formulas for the derivatives of each order of the attitude curve with respect to the decision variables are given in this paper.Finally,the effectiveness of the proposed algorithm is verified by a series of numerical simulations.展开更多
The co-electrolysis of CO_(2)and H_(2)O through solid oxide electrolysis cells(SOECs),powered by renewable energy sources,offers a promising pathway to achieving carbon neutrality in the chemical industry.However,the ...The co-electrolysis of CO_(2)and H_(2)O through solid oxide electrolysis cells(SOECs),powered by renewable energy sources,offers a promising pathway to achieving carbon neutrality in the chemical industry.However,the inherent intermittency of renewable energy generation,such as wind power,leads to unstable power input for electrolysis.This variability induces significant thermal stress in SOECs,potentially causing cracks or even system failure.To address this challenge,a hybrid deep learning architecture(HDLA)was developed to control the temperature gradient of SOECs.The architecture combines a convolutional neural network(CNN)and a long short-term memory(LSTM)model for wind power prediction,a multi-physics model for temperature gradient simulation,and a linear neural network regression model to simulate the temperature distribution in SOECs.Training and verification are conducted using 16 datasets from an industrial wind farm.The results demonstrate that the application of HDLA successfully reduce the temperature gradient of SOECs from±20℃ to±5℃.Additionally,the potential wind power utilization achieved near-complete wind power utilization,increasing from 18%to 99%.This real-time control strategy,which optimizes flow regulation,effectively mitigates thermal stress,thereby extending the lifespan of SOECs and ensuring continuous carbon reduction,efficient conversion,and utilization.展开更多
Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations dur...Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.展开更多
We examine a simple averaging formula for the gradieni of linear finite elemelitsin Rd whose interpolation order in the Lq-norm is O(h2) for d < 2q and nonuniformtriangulations. For elliptic problems in R2 we deriv...We examine a simple averaging formula for the gradieni of linear finite elemelitsin Rd whose interpolation order in the Lq-norm is O(h2) for d < 2q and nonuniformtriangulations. For elliptic problems in R2 we derive an interior superconvergencefor the averaged gradient over quasiuniform triangulations. Local error estimatesup to a regular part of the boundary and the effect of numerical integration arealso investigated.展开更多
The basic principle of optimal method called “moving overlapping resolution mapping Method” to select the optimal binary mobile phase composition of multi-step linear gradient liquid chromatography is discussed with...The basic principle of optimal method called “moving overlapping resolution mapping Method” to select the optimal binary mobile phase composition of multi-step linear gradient liquid chromatography is discussed with simultaneously considering effects of position of solute inside the column and mobile phase composition on peak resolution and retention value, then a BASIC program based on this principle is developed in IBM-PC computer. The validities of both principle of optimization and BASIC program are confirmed by separation of samples Containing bile acids and PAHs in RP-HPLC.展开更多
China is one of the countries in the world carrying a heavy burden of tuberculosis.Due to the unbalanced economic development,the number of people working in other parts of country is huge,and the mobility of personne...China is one of the countries in the world carrying a heavy burden of tuberculosis.Due to the unbalanced economic development,the number of people working in other parts of country is huge,and the mobility of personnel has exacerbated the increase in tuberculosis cases.Most patients affected by this are in their middle and young ages.It is having a great impact among the family and society.Therefore,research on how to control this disease is absolutely necessary.The population is divided into two categories such as local population and the immigrant population.A pulmonary tuberculosis dynamic model with population heterogeneity is established.We calculate the basic reproductive number and the controlled reproductive number,and discuss the two types of population under the constraints given by the amount of vaccine and the optimal immunization ratio obtained is(0.118,0.107),which can reduce the effective reproduction number from 5.85 to 0.227.It is understood that immunizing the local population will control the spread of the epidemic to a large extent,and we simulate the final scale of infection after immunization under the optimal immunization ratio.It can take a minimum of at least 10 years to reduce the spread of this disease,but to eliminate it forever,it needs at least a minimum of 100 years.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12441502,12122506,12204230,and 12404554)the National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0300404)+6 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2021B1515020070)Shenzhen Science and Technology Program(Grant No.RCYX20200714114522109)China Postdoctoral Science Foundation(CPSF)(2024M762114)Postdoctoral Fellowship Program of CPSF(GZC20231727)supported by the National Natural Science Foundation of China(Grant Nos.92165206 and 11974330)Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301603)the Fundamental Research Funds for the Central Universities。
文摘We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and,unlike the state of the art,requires neither weakcoupling Lamb-Dicke approximation nor perturbation treatment.With the application of gradient-based optimal control,it enables finding amplitude-and phase-modulated laser control protocols that work without the Lamb-Dicke approximation,promising gate speeds on the order of microseconds comparable to the characteristic trap frequencies.Also,robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections.Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation,and thus can find applications in near-future experiments.
基金supported by the National Natural Science Foundation of China under Grant(62102189,62122032,61972205)the National Social Sciences Foundation of China under Grant 2022-SKJJ-C-082+2 种基金the Natural Science Foundation of Jiangsu Province under Grant BK20200807NUDT Scientific Research Program under Grant(JS21-4,ZK21-43)Guangdong Natural Science Funds for Distinguished Young Scholar under Grant 2023B1515020041.
文摘In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake judgments.Most of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial attacks.In addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual quality.In response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for DFFD.The ridge texture area within the fingerprint image has been identified and designated as the region for perturbation generation.Subsequently,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient variance.Additionally,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack performance.Experimental results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project under Grant No.(G:651-135-1443).
文摘Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.
基金Supported by the National Basic Research Program of China ("973" Program)the National Natural Science Foundation of China (60872112, 10805012)+1 种基金the Natural Science Foundation of Zhejiang Province(Z207588)the College Science Research Project of Anhui Province (KJ2008B268)~~
文摘The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.
基金the Yunnan Fundamental Research Projects(No.202301AT070452)the National Natural Science Foundation of China(No.61861023)。
文摘The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the joint sparsity and sparsifying transform learning(JTL)into the simultaneous auto-calibrating and k-space estimation(SAKE)structured low-rank model,named JTLSAKE.The alternate direction method of multipliers is exploited to solve the resulting optimization problem,and the optimized gradient method is used to improve the convergence speed.In addition,a graphics processing unit is used to accelerate the proposed algorithm.The experimental results on four in vivo human datasets demonstrate that the reconstruction quality of the proposed algorithm is comparable to that of JTL-based low-rank modeling of local k-space neighborhoods with parallel imaging(JTL-PLORAKS),and the proposed algorithm is 46 times faster than the JTL-PLORAKS,requiring only 4 s to reconstruct a 200×200 pixels MR image with 8 channels.
文摘In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically adjust the frequency regulation command based on the state of charge(SoC)of energy storage units,this paper proposes a secondary frequency regulation control strategy based on variable integral coefficients for multiple energy storage units.First,a power-uniform controller is designed to ensure that thermal power units gradually take on more regulation power during the frequency regulation process.Next,a control framework based on variable integral coefficients is proposed within the secondary frequency regulation model,along with an objective function that simultaneously considers both Automatic Generation Control(AGC)command tracking performance and SoC recovery requirements of energy storage units.Finally,a gradient descent optimization method is used to dynamically adjust the gain of the energy storage integral controller,allowingmultiple energy storage units to respond in real-time to AGC instructions and SoC variations.Simulation results confirmthe effectiveness of the proposedmethod.Compared to traditional strategies,the proposed approach takes into account the SoCdiscrepancies amongmultiple energy storage units and the duration of system net power imbalances.It successfully implements secondary frequency regulation while achieving dynamic power allocation among the units.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.91636216,11974382,and 11474316)the Chinese Academy of Sciences Strategic Priority Research Program(Grant No.XDB21020200)+1 种基金by the YIPA Programthe support of NSERC,SHARCnet,ACEnet of Canada。
文摘In atomic,molecular,and nuclear physics,the method of complex coordinate rotation is a widely used theoretical tool for studying resonant states.Here,we propose a novel implementation of this method based on the gradient optimization(CCR-GO).The main strength of the CCR-GO method is that it does not require manual adjustment of optimization parameters in the wave function;instead,a mathematically well-defined optimization path can be followed.Our method is proven to be very efficient in searching resonant positions and widths over a variety of few-body atomic systems,and can significantly improve the accuracy of the results.As a special case,the CCR-GO method is equally capable of dealing with bound-state problems with high accuracy,which is traditionally achieved through the usual extreme conditions of energy itself.
基金supported by Shenzhen Science and Technology Program(Grant No.JCYJ20220818102207015)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023B1515120018).
文摘Attitude planning of rigid bodies has many applications in robotics and aerospace.However,because the attitude configuration space is non-Euclidean and the constraints are complex and non-linear,the design of the attitude curve has always been a tricky problem.In this paper,a gradient-based attitude planning method is proposed to simultaneously handle attitude pointing,angular velocity,torque,and time constraints on Lie group SO(3).Firstly,the attitude interpolation algorithm on SO(3)gives an attitude curve connecting the initial and target attitudes.The shape of the curve is determined by the fitting coefficients and maneuvering time.Secondly,to match the curve with suitable angular velocity and control torque,a nonlinear planning model with fitting coefficients and maneuver time as decision variables is proposed.Solving the problem gives a smooth attitude curve that satisfies both kinematic and dynamic constraints and also avoids complicated time allocation.Then,to apply the gradientbased solver,analytical formulas for the derivatives of each order of the attitude curve with respect to the decision variables are given in this paper.Finally,the effectiveness of the proposed algorithm is verified by a series of numerical simulations.
基金The authors would like to acknowledge the financial support from the National Key Research and Development Program of China(No.2022YFB4500500)the National Natural Science Foundation of China(No.22250710676).
文摘The co-electrolysis of CO_(2)and H_(2)O through solid oxide electrolysis cells(SOECs),powered by renewable energy sources,offers a promising pathway to achieving carbon neutrality in the chemical industry.However,the inherent intermittency of renewable energy generation,such as wind power,leads to unstable power input for electrolysis.This variability induces significant thermal stress in SOECs,potentially causing cracks or even system failure.To address this challenge,a hybrid deep learning architecture(HDLA)was developed to control the temperature gradient of SOECs.The architecture combines a convolutional neural network(CNN)and a long short-term memory(LSTM)model for wind power prediction,a multi-physics model for temperature gradient simulation,and a linear neural network regression model to simulate the temperature distribution in SOECs.Training and verification are conducted using 16 datasets from an industrial wind farm.The results demonstrate that the application of HDLA successfully reduce the temperature gradient of SOECs from±20℃ to±5℃.Additionally,the potential wind power utilization achieved near-complete wind power utilization,increasing from 18%to 99%.This real-time control strategy,which optimizes flow regulation,effectively mitigates thermal stress,thereby extending the lifespan of SOECs and ensuring continuous carbon reduction,efficient conversion,and utilization.
文摘Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.
文摘We examine a simple averaging formula for the gradieni of linear finite elemelitsin Rd whose interpolation order in the Lq-norm is O(h2) for d < 2q and nonuniformtriangulations. For elliptic problems in R2 we derive an interior superconvergencefor the averaged gradient over quasiuniform triangulations. Local error estimatesup to a regular part of the boundary and the effect of numerical integration arealso investigated.
文摘The basic principle of optimal method called “moving overlapping resolution mapping Method” to select the optimal binary mobile phase composition of multi-step linear gradient liquid chromatography is discussed with simultaneously considering effects of position of solute inside the column and mobile phase composition on peak resolution and retention value, then a BASIC program based on this principle is developed in IBM-PC computer. The validities of both principle of optimization and BASIC program are confirmed by separation of samples Containing bile acids and PAHs in RP-HPLC.
基金This study was funded by the Natural Science Foundation of China(NSFC 11871093 and 11901027)Postgraduate Teaching Research and Quality Improvement Project of BUCEA(J2021010)BUCEA Post Graduate Innovation Project(PG2022139)。
文摘China is one of the countries in the world carrying a heavy burden of tuberculosis.Due to the unbalanced economic development,the number of people working in other parts of country is huge,and the mobility of personnel has exacerbated the increase in tuberculosis cases.Most patients affected by this are in their middle and young ages.It is having a great impact among the family and society.Therefore,research on how to control this disease is absolutely necessary.The population is divided into two categories such as local population and the immigrant population.A pulmonary tuberculosis dynamic model with population heterogeneity is established.We calculate the basic reproductive number and the controlled reproductive number,and discuss the two types of population under the constraints given by the amount of vaccine and the optimal immunization ratio obtained is(0.118,0.107),which can reduce the effective reproduction number from 5.85 to 0.227.It is understood that immunizing the local population will control the spread of the epidemic to a large extent,and we simulate the final scale of infection after immunization under the optimal immunization ratio.It can take a minimum of at least 10 years to reduce the spread of this disease,but to eliminate it forever,it needs at least a minimum of 100 years.
