CURVE AND SURFACE INTERPOLATIONBY SUBDIVISION ALGORITHMS

Interpolatory subdivision algorithms for the generation of curves and surfaces play a veryimportant rule in shape design and modelling in CAD/CAM systems. In this paper, by using the dif-ference and divided difference analysis, a systematic method to construct Cn (n≥ 0) interpolatorycurves by subdivision from given data is described and the mask (filter) of the algorithm is presentedexplicitly. This algorithm generates a Cn smooth curve which interpolates the initial control points.Control parameters are also provided so that the shape of the final curve can be adjusted according torequirements. An immediate generalisation of the method is the construction of smooth interpolatorysubdivision algorithms over uniform triangular networks (tensor product type data) in Rm. The mainresults of this algorithm for smooth interpolatory surface subdivision algorrthm are also included.AMS(MOS) : 65D05 , 65D15 , 65D17.

SMOOTH SURFACE INTERPOLATION OVER ARBITRARY TRIANGULATIONS BY SUBDIVISION ALGORITHMS

A smooth interpolatory subdivision algorithm for the generation of surfaces over arbi-trary triangulations is introduced and its convergence properties over nonuniform triangulationsstudied. For uniform data, this method is a generalization of the analysis for univariatesubdivision algorithms and for nonuniform data, an extraordinary point analysis is introducedand the local subdivision matrix anaiysis presented. It is proved that the algorithm producessmooth surfaces over arbitrary triangular networks provided the shape parameters are kept with-in an appropriate range. Finally, two graphical examples of surface interpolation overnonuniform data are given to show the smoothing process of the algorithm.AMS (MOS): 65D05, 65D15,65D17.

THE PARALLEL RECURSIVE AP ADAPTIVE ALGORITHM BASED ON VOLTERRA SERIES

Aiming at the nonlinear system identification problem, a parallel recursive affine projection (AP) adaptive algorithm for the nonlinear system based on Volterra series is presented in this paper. The algorithm identifies in parallel the Volterra kernel of each order, recursively estimate the inverse of the autocorrelation matrix for the Volterra input of each order, and remarkably improve the convergence speed of the identification process compared with the NLMS and conventional AP adaptive algorithm based on Volterra series. Simulation results indicate that the proposed method in this paper is efficient.

Study of a Modified Fast Adaptive Algorithm for Digital Beamforming

This paper provides a modified fast adaptive algorithm for digital beamforming. It is analgorithm with strict constraint minimum power sampling matrix gradient (CSMG). It has merits ofboth traditional sampling mains gradient (SMG) and strictly constrained minimum power adaptivealgorithm. 16-element uniform circular array is selected. Some results of computer simulation aregiven. The results indicate that the beam direction will change with constraint angle and can beadaptable to adjust zero very well. The algorithm is fast convergent.

Stability and Robustness Analysis of an Adaptive Algorithm for Quasi-periodic Disturbances Rejection

Winding and web transport systems are subjected to quasi-periodic disturbances of the web tension due to the eccentricity and the non-circularity of the reel and rolls. The disturbances induced by the non-circularity and eccentricity of the rolls are quasi-periodic with a frequency that varies with their rotation speed. An adaptive method of rejection of these disturbances is proposed in this paper. It is based on a phase-locked loop structure that estimates simutaneously the phase and magnitude of the perturbation and then cancels it. This algorithm can be plugged in an existing industrial controller. The stability and robustness of the algorithm are also discussed. The ability of the algorithm to reject quasi-periodic disturbances with slowly varying frequencies is shown through simulation results.

Predictive FTF Adaptive Algorithm for Mobile Channels Estimation

The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation. A multi-ray Jakes mobile channel model with a Doppler frequency shift is used in the simulation. The channel estimator obtains the sampled channel impulse response (SIR) from the predetermined training sequence. The FTF is a computationally efficient implementation of the recursive least squares (RLS) algorithm of the conventional Kalman filter. A stabilization FTF is used to overcome the problem caused by the accumulation of roundoff errors, and, in addition, degree-one prediction is incorporated into the algorithm (Predictive FTF) to improve the estimation performance and to track changes of the mobile channel. The efficiency of the algorithm is confirmed by simulation results for slow and fast varying mobile channel. The results show about 5 to 15 dB improvement in the Mean Square Error (Deviation) between the estimated taps and the actual ones depending on the speed of channel time variations. Slow and fast vehicular channels with Doppler frequencies 100 Hz and 222 Hz respectively are used in these tests. The predictive FTF (PFTF) algorithm give a better channel SIR estimation performance than the conventional FTF algorithm, and it involves only a small increase in complexity.

An adaptive algorithm for pass adaptation in plate rolling

A new algorithm for pass adaptation in plate rolling is developedto improve thickness accuracy of plate products. The feature of thealgorithm is that it uses the measured data rather than the schedulecalculated data in adaptation, which leads to notable improvem- entin prediction accuracy of the rolling parameters and thicknessaccuracy of products can be improved according. Results show thatthis adaptive algorithm is effective in practice.

A Class of Generalized Approximate Inverse Solvers for Unsymmetric Linear Systems of Irregular Structure Based on Adaptive Algorithmic Modelling for Solving Complex Computational Problems in Three Space Dimensions

A class of general inverse matrix techniques based on adaptive algorithmic modelling methodologies is derived yielding iterative methods for solving unsymmetric linear systems of irregular structure arising in complex computational problems in three space dimensions. The proposed class of approximate inverse is chosen as the basis to yield systems on which classic and preconditioned iterative methods are explicitly applied. Optimized versions of the proposed approximate inverse are presented using special storage (k-sweep) techniques leading to economical forms of the approximate inverses. Application of the adaptive algorithmic methodologies on a characteristic nonlinear boundary value problem is discussed and numerical results are given.

ANN model of subdivision error based on genetic algorithm

According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating

Vibration Suppression for Active Magnetic Bearings Using Adaptive Filter with Iterative Search Algorithm

Active Magnetic Bearing(AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs,an adaptive filter based on Least Mean Square(LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm(ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table(LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

It is still a huge challenge for traditional Pareto-dominatedmany-objective optimization algorithms to solve manyobjective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front,resulting in poor performance of those algorithms.For this reason,we propose a reference vector-assisted algorithmwith an adaptive niche dominance relation,for short MaOEA-AR.The new dominance relation forms a niche based on the angle between candidate solutions.By comparing these solutions,the solutionwith the best convergence is found to be the non-dominated solution to improve the selection pressure.In reproduction,a mutation strategy of k-bit crossover and hybrid mutation is used to generate high-quality offspring.On 23 test problems with up to 15-objective,we compared the proposed algorithm with five state-of-the-art algorithms.The experimental results verified that the proposed algorithm is competitive.

Adaptive Fault Estimation for Dynamics of High Speed Train Based on Robust UKF Algorithm

This paper proposes an adaptive unscented Kalman filter algorithm(ARUKF)to implement fault estimation for the dynamics of high⁃speed train(HST)with measurement uncertainty and time⁃varying noise with unknown statistics.Firstly,regarding the actuator and sensor fault as the auxiliary variables of the dynamics of HST,an augmented system is established,and the fault estimation problem for dynamics of HST is formulated as the state estimation of the augmented system.Then,considering the measurement uncertainties,a robust lower bound is proposed to modify the update of the UKF to decrease the influence of measurement uncertainty on the filtering accuracy.Further,considering the unknown time⁃varying noise of the dynamics of HST,an adaptive UKF algorithm based on moving window is proposed to estimate the time⁃varying noise so that accurate concurrent actuator and sensor fault estimations of dynamics of HST is implemented.Finally,a five-car model of HST is given to show the effectiveness of this method.

An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

Adaptive genetic algorithm-based design of gamma-graphyne nanoribbon incorporating diamond-shaped segment with high thermoelectric conversion efficiency

The gamma-graphyne nanoribbons(γ-GYNRs) incorporating diamond-shaped segment(DSSs) with excellent thermoelectric properties are systematically investigated by combining nonequilibrium Green’s functions with adaptive genetic algorithm. Our calculations show that the adaptive genetic algorithm is efficient and accurate in the process of identifying structures with excellent thermoelectric performance. In multiple rounds, an average of 476 candidates(only 2.88% of all16512 candidate structures) are calculated to obtain the structures with extremely high thermoelectric conversion efficiency.The room temperature thermoelectric figure of merit(ZT) of the optimal γ-GYNR incorporating DSSs is 1.622, which is about 5.4 times higher than that of pristine γ-GYNR(length 23.693 nm and width 2.660 nm). The significant improvement of thermoelectric performance of the optimal γ-GYNR is mainly attributed to the maximum balance of inhibition of thermal conductance(proactive effect) and reduction of thermal power factor(side effect). Moreover, through exploration of the main variables affecting the genetic algorithm, it is revealed that the efficiency of the genetic algorithm can be improved by optimizing the initial population gene pool, selecting a higher individual retention rate and a lower mutation rate. The results presented in this paper validate the effectiveness of genetic algorithm in accelerating the exploration of γ-GYNRs with high thermoelectric conversion efficiency, and could provide a new development solution for carbon-based thermoelectric materials.

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.

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.

Blockchain technology‑based FinTech banking sector involvement using adaptive neuro‑fuzzy‑based K‑nearest neighbors algorithm

The study aims to investigate the financial technology(FinTech)factors influencing Chinese banking performance.Financial expectations and global realities may be changed by FinTech’s multidimensional scope,which is lacking in the traditional financial sector.The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization.The future of FinTech will be shaped by technologies like the Internet of Things,blockchain,and artificial intelligence.The involvement of these platforms in financial services is a major concern for global business growth.FinTech is becoming more popular with customers because of such benefits.FinTech has driven a fundamental change within the financial services industry,placing the client at the center of everything.Protection has become a primary focus since data are a component of FinTech transactions.The task of consolidating research reports for consensus is very manual,as there is no standardized format.Although existing research has proposed certain methods,they have certain drawbacks in FinTech payment systems(including cryptocurrencies),credit markets(including peer-to-peer lending),and insurance systems.This paper implements blockchainbased financial technology for the banking sector to overcome these transition issues.In this study,we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’algorithm.The chaotic improved foraging optimization algorithm is used to optimize the proposed method.The rolling window autoregressive lag modeling approach analyzes FinTech growth.The proposed algorithm is compared with existing approaches to demonstrate its efficiency.The findings showed that it achieved 91%accuracy,90%privacy,96%robustness,and 25%cyber-risk performance.Compared with traditional approaches,the recommended strategy will be more convenient,safe,and effective in the transition period.

Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.