Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.

Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation(APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter(PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.

THE ADAPTIVE PARAMETER INCREMENTAL METHOD FOR THE ANALYSIS OF SNAPPING PROBLEMS

By the improvement of Riks’and Crisfield’s arc-length method,the adaptiveparameter incremental method is preasted for predicting the snapping response ofstructures. Its justification is fulfilled. Finally,the effectiveness of this method isdemonstrated by solving the snapping response of spherical caps subjected to centrallydistributed pressures.

A New Adaptive Regularization Parameter Selection Based on Expected Patch Log Likelihood

Digital images have been applied to various areas such as evidence in courts.However,it always suffers from noise by criminals.This type of computer network security has become a hot issue that can’t be ignored.In this paper,we focus on noise removal so as to provide guarantees for computer network security.Firstly,we introduce a well-known denoising method called Expected Patch Log Likelihood(EPLL)with Gaussian Mixture Model as its prior.This method achieves exciting results in noise removal.However,there remain problems to be solved such as preserving the edge and meaningful details in image denoising,cause it considers a constant as regularization parameter so that we denoise with the same strength on the whole image.This leads to a problem that edges and meaningful details may be oversmoothed.Under the consideration of preserving edges of the image,we introduce a new adaptive parameter selection based on EPLL by the use of the image gradient and variance,which varies with different regions of the image.Moreover,we add a gradient fidelity term to relieve staircase effect and preserve more details.The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.

Adaptive Parallel Particle Swarm Optimization Algorithm Based on Dynamic Exchange of Control Parameters

Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.

Virtual Synchronous Generator Adaptive Control of Energy Storage Power Station Based on Physical Constraints

The virtual synchronous generator(VSG)can simulate synchronous machine’s operation mechanism in the control link of an energy storage converter,so that an electrochemical energy storage power station has the ability to actively support the power grid,from passive regulation to active support.Since energy storage is an important physical basis for realizing the inertia and damping characteristics in VSG control,energy storage constraints of the physical characteristics on the system control parameters are analyzed to provide a basis for the system parameter tuning.In a classic VSG control,its virtual inertia and damping coefficient remain unchanged.When the grid load changes greatly,the constant control strategy most likely result in the grid frequency deviation beyond the stable operation standard limitations.To solve this problem,a comprehensive control strategy considering electrified wire netting demand and energy storage unit state of charge(SOC)is proposed,and an adaptive optimization method of VSG parameters under different SOC is given.The energy storage battery can maintain a safe working state at any time and be smoothly disconnected,which can effectively improve the output frequency performance of energy storage system.Simulation results further demonstrated the effectiveness of the VSG control theoretical analysis.

A Parameter Adaptive Method for Image Smoothing

Edge is the key information in the process of image smoothing. Some edges, especially the weak edges, are difficult to maintain, which result in the local area being over-smoothed. For the protection of weak edges, we propose an image smoothing algorithm based on global sparse structure and parameter adaptation. The algorithm decomposes the image into high frequency and low frequency part based on global sparse structure. The low frequency part contains less texture information which is relatively easy to smoothen. The high frequency part is more sensitive to edge information so it is more suitable for the selection of smoothing parameters. To reduce the computational complexity and improve the effect, we propose a bicubic polynomial fitting method to fit all the sample values into a surface. Finally, we use Alternating Direction Method of Multipliers (ADMM) to unify the whole algorithm and obtain the smoothed results by iterative optimization. Compared with traditional methods and deep learning methods, as well as the application tasks of edge extraction, image abstraction, pseudo-boundary removal, and image enhancement, it shows that our algorithm can preserve the local weak edge of the image more effectively, and the visual effect of smoothed results is better.

A New Neuro-Fuzzy Adaptive Genetic Algorithm

Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to control GA parameters.The self-learning ability of the cerebellar modelariculation controller (CMAC) neural network makes it possible for on-line learning the knowledge onGAs throughout the run.Automatically designing and tuning the fuzzy knowledge-base system,neuro-fuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learningmethod.The Results from initial experiments show a Dynamic Parametric AGA system designed by theproposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a widerange of combinatorial optimization.

Adaptive Sliding-Mode Control of an Automotive Electronic Throttle in the Presence of Input Saturation Constraint

In modern vehicles, electronic throttle(ET) has been widely utilized to control the airflow into gasoline engine. To solve the control difficulties with an ET, such as strong nonlinearity,unknown model parameters and input saturation constraints,an adaptive sliding-mode tracking control strategy for an ET is presented. Compared with the existing control strategies for an ET, input saturation constraints and parameter uncertainties are adequately considered in the proposed control strategy. At first, the nonlinear dynamic model for control of an ET is described. According to the dynamical model, the nonlinear adaptive sliding-mode tracking control method is presented,where parameter adaptive laws and auxiliary design system are employed. Parameter adaptive law is given to estimate the unknown parameter with an ET. An auxiliary system is designed,and its state is utilized in the tracking control method to handle the input saturation. Stability proof and analysis of the adaptive sliding-mode control method is performed by using Lyapunov stability theory. Finally, the reliability and feasibility of the proposed control strategy are evaluated by computer simulation.Simulation research shows that the proposed sliding-mode control strategy can provide good control performance for an ET.

Adaptive backtracking search optimization algorithm with pattern search for numerical optimization

The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

Convergence Track Based Adaptive Differential Evolution Algorithm(CTbADE)

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research paper.The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima problem.Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems.An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper.The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE algorithm.Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE algorithms.This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.

Strengthened Initialization of Adaptive Cross-Generation Differential Evolution

Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest algorithms.In order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant operator.In this paper,an enhanced version is proposed,namely SIACGDE.It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor.These improvements make the direction of crossgeneration mutation more clearly and the ability of searching more efficiently.The experiments show that the new algorithm has better diversity and improves convergence to a certain extent.At the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.

Adaptive On-line Operation Guide for Dry Gas-to-ethylbenzene Reactor

In this paper,a third-generation dry gas-to-ethylbenzene process in a factory of PetroChina is considered.For the gradual catalyst deactivation in the alkylation reactor,a model is established with the parameters estimated from the reaction rate equation of alkylation based on the on-site data and those from laboratory analysis. The real-time dynamic simulation of the alkylation process is carried out,in which the module accuracy is ensured by using OPC(Object linking and embedding for Process Control)technique and adaptive correction of model parameters.Both the current and future operation temperature can be predicted.

Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest-Guided Strategy

In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.

Adaptive linear active disturbance-rejection control strategy reduces the impulse current of compressed air energy storage connected to the grid

The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid regulation,using the traditional control mode with low accuracy can result in excess grid-connected impulse current and junction voltage.This occurs because the CAES output voltage does not match the frequency,amplitude,and phase of the power grid voltage.Therefore,an adaptive linear active disturbance-rejection control(A-LADRC)strategy was proposed.Based on the LADRC strategy,which is more accurate than the traditional proportional integral controller,the proposed controller is enhanced to allow adaptive adjustment of bandwidth parameters,resulting in improved accuracy and response speed.The problem of large impulse current when CAES is switched to the grid-connected mode is addressed,and the frequency fluctuation is reduced.Finally,the effectiveness of the proposed strategy in reducing the impact of CAES on the grid connection was verified using a hardware-in-the-loop simulation platform.The influence of the k value in the adaptive-adjustment formula on the A-LADRC was analyzed through simulation.The anti-interference performance of the control was verified by increasing and decreasing the load during the presynchronization process.

An improved flexible tolerance method for solving nonlinear constrained optimization problems:Application in mass integration

This paper proposes the use of the flexible tolerance method(FTM) modified with adaptive Nelder–Mead parameters and barrier to solve constrained optimization problems. The problems used to analyze the performance of the methods were taken from G-Suite functions, and the methods with the best performance were applied in mass integration problems. Four methods were proposed:(1) flexible tolerance method(FTM) using adaptive parameters(FTMA),(2) flexible tolerance method with scaling(FTMS) and with adaptive parameters(FTMAS),(3) FTMS including the barrier modification(MFTMS) and(4) MFTMS hybridized with PSO(MFTMS-PSO). The success rates of these methods were 100%(MFTMS), 85%(MFTMS-PSO), 40%(FTMAS) and 30%(FTMA).Numerical experiments indicated that the MFTMS could efficiently and reliably improve the accuracy of global optima. In mass integration, the method was able, from current process situation, to reach the optimum process configuration that includes integration issues, which was not possible using FTM in its standard formulation. The hybridization of FTMS with PSO(without barrier), FTMS-PSO, was also able to solve mass integration problems efficiently.

LAVRENTIEV'S REGULARIZATION METHOD FOR NONLINEAR ILL-POSED EQUATIONS IN BANACH SPACES

In this paper, we deal with nonlinear ill-posed problems involving m-accretive mappings in Banach spaces. We consider a derivative and inverse free method for the imple- mentation of Lavrentiev regularization method. Using general HSlder type source condition we obtain an optimal order error estimate. Also we consider the adaptive parameter choice strategy proposed by Pereverzev and Schock （2005） for choosing the regularization parameter.