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.

Time Domain Full Waveform Inversion Based on Gradient Preconditioning with an Angle-Dependent Weighting Factor

There are lots of low wavenumber noises in the gradients of time domain full waveform inversion(FWI),which can seriously reduce the accuracy and convergence speed of FWI.Thus,we introduce an angle-dependent weighting factor to precondition the gradients so as to suppress the low wavenumber noises when the multi-scale FWI is implemented in the high frequency.Model experiments show that the FWI based on the gradient preconditioning with an angle-dependent weighting factor has faster convergence speed and higher inversion accuracy than the conventional FWI.The tests on real marine seismic data show that this method can adapt to the FWI of field data,and provide high-precision velocity models for the actual data processing.

Model Predictive Yaw Control Using Fuzzy-Deduced Weighting Factor for Large-Scale Wind Turbines

Yaw control system plays an important role in helping large-scale horizontal wind turbines capture the wind energy.To track the stochastic and fast-changing wind direction,the nacelle is rotated by the yaw control system.Therein,a difficulty consists in the variation speed of the wind direction much faster than the rotation speed of the nacelle.To deal with this difficulty,model predictive control has been recently proposed in the literature,in which the previewed wind direction is employed into the predictive model,and the estimated captured energy and yaw actuator usage are two contradictive objectives.Since the performance of the model predictive control strat-egy relies largely on the weighting factor that is designed to balance the two objectives,the weighting factor should be carefully selected.In this study,a fuzzy-deduced scheme is proposed to derive the weighting factor of the mod-el predictive yaw control.For the proposed fuzzy-deduced strategy,the variation degree and the increment of the wind direction during the predictive horizon are used as the inputs,and the weighting factor is the output,which is dynamically adjusted.The proposed model predictive yaw control is demonstrated by some simulations using real wind data and its performance is compared with the conventional model predictive control with thefixed weighting factor.Comparison results confirm the outweighing performance of the proposed control strategy over the conventional one.

WACPN:A Neural Network for Pneumonia Diagnosis

Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.

Cascaded Model Predictive Control of Six-phase Permanent Magnet Synchronous Motor with Fault Tolerant Ability

In the field of high-power electric drives, multiphase motors have the advantages of high power-density, excellent fault tolerance and control flexibility. But their decoupling control and modulation process are much more complicated compared with three-phase motors due to the increased degree of freedom. Finite control set model predictive control can reduce the difficulties of controlling six-phase motors because it does not require modulation process. In this paper, a cascaded model predictive control strategy is proposed for the optimal control of high-power six-phase permanent magnet synchronous motors. Firstly, the current prediction model of torque and harmonic subspaces are established by decoupling the six-phase spatial variables. Secondly, a cascaded cost function with fault-tolerant capability is proposed to eliminate the weighting factor in the cost function. And finally, the proposed strategy is demonstrated through theoretical analysis and experiments. It is validated that the proposed method is able to maintain excellent steady-state control accuracy and fast dynamic response while significantly reduce the control complexity of the system. Besides, it can easily achieve fault-tolerant operation under open-phase fault.

Low-complexity model predictive control of a four-levelactive neutral point clamped inverter without weighting factors

The four-level active neutral point clamped(ANPC)inverter has become increasingly widely used in the renewable energy indus-try since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter.The model predictive current control(MPCC)is a promising control method for multi-level inverters.However,the conven-tional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications.A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this paper.The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance.The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing.The efficacy of the proposed MPCC is verified by simulation and experimental results.

Coordinated Control Strategy for Harmonic Compensation of Multiple Active Power Filters

In order to minimize the harmonic distortion rate of the current at the common coupling point,this paper proposes a coordinated allocation strategy of harmonic compensation capacity considering the performance of active power filters(APF).On the premise of proportional distribution of harmonic compensation capacity,the harmonic compensation rate of each APF is considered,and the harmonic current value of each APF to be compensated is obtained.At the same time,the communication topology is introduced.Each APF takes into account the compensation ability of other APFs.Finally,three APFs with different capacity and performance are configured at the harmonic source to suppress the same harmonic source,and the harmonic distortion rate is reduced to 1.73%.The simulation results show that the strategy can effectively improve the compensation capability of the multiple APF cascaded system to the power grid without increasing the installed capacity.

多视点视频编码的码率控制优化算法(英文)

Rate control is one of the key factors influencing the multi-view video transmission.However,there is not a rate control algorithm in the existing Joint Multi-view Video Coding Model.In this paper,an efficient rate control algorithm and a bit allocation strategy for multi-view video coding are proposed.In order to obtain the consistent view quality,a bit allocation model based on the Lagrange optimum algorithm is firstly proposed.Secondly,considering the encoding statistical characteristics of different view types,a view weighting factor is introduced,and it will help improve the precision of bit allocation among views.Compared with the fixed QP control strategy,experiment results show that the proposed algorithm can efficiently control the bit rate and obtain more consistent views,with video visual quality improved.

Direct Power Control Based on Dead-beat Function and Extended Kalman Filter for PV Systems

In this paper,a new proposal for the implementation of the well-known direct power control(DPC)technique in grid-connected photovoltaic(PV)systems is suggested.Normally,the DPC is executed using a look-up table procedure based on the error between the actual and reference values of the active and reactive power.Thus,the structure of the DPC is simple and results in a fast transient behavior of the inner current loop(injected currents).Therefore,in the current study,the DPC is reformulated using a dead-beat function.In this formulation,the reference voltage vector(RVV)is obtained in theα-βreference frame.Consequently,the switching states for the inverter can be obtained based on the sign of the components of the RVV.The suggested DPC is compared with the conventional one and other switching tables,which are intended for performance enhancement.Furthermore,an extended Kalman filter(EKF)is utilized to eliminate all grid-voltage sensors.Moreover,the switching frequency of the proposed technique is minimized without any need for weighting factors or cost function evaluation.The overall control technique is validated using a hardware-in-the-loop(HIL)experimental set-up and compared with other schemes under different operating conditions.

Predictive Control Based on Ranking Multi-objective Optimization Approaches for a quasi-Z Source Inverter

In power converter control,predictive control has several merits,such as simple concept and fast response.However,the necessity to use the weighting factor inside the cost function makes the control design complex in the case of regulating multivariables where the value of the weighting factor is obtained by a nontrivial process.Also,it primarily depends on the system parameters and operating points of the control system.This paper aims to enhance the model predictive algorithm of the singlestage topology of a quasi-Z Source Inverter(qZSI).The concept of a multi-objective optimization approach is used in addition to the sub-cost function definition to remove the weighting factors.By using the sub-cost function definition,the inductor current is pushed away from the main loop of the predictive algorithm.Thus,no weighting factor is needed to manage the priority of the inductor current.The other two control targets,which are the capacitor voltage and load currents,will be controlled by the multi-objective optimization approach without using any weighting factors.A detailed theoretical analysis of the proposed technique will be given and validated based on simulation results.

A Multiple Emission Constrained Approach for Self-scheduling of GENCO Under Renewable Energy Penetration

The electric sector contributes substantially to both greenhouse gas(GHG)and non-greenhouse gas(NGHG)emissions,which means that both conventional and thermal generation companies(GENCOs)must follow certain environmental guidelines to address various emission requirements.This paper presents a methodology to investigate the feasibility of both GHG and NGHG emission reduction in a deregulated electricity market.The proposed model takes into consideration the effect of NGHG emission cost constraints in conjunction with classical GHG emission constraints for the scheduling aspects of GENCO.A profit based self-scheduling problem with conventional fossil fueled generators and renewable energy technologies(RETs)is formulated including emission penalties and avoidance costs of GHG and NGHG emissions,respectively.Thereafter,a set of pareto solutions is evaluated for different possible scheduling scenarios.A simple,effective optimality criteria is also postulated to identify the tradeoff solution.Finally,a sensitivity analysis of various technical,environmental,as well as economic aspects is presented to examine the effect of NGHG consideration and RET inclusion in scheduling.The simulation results are presented and discussed in detail to examine the effect of NGHG consideration in self-scheduling practices of GENCO in the electricity market,thus reflecting the benefits of the proposed approach over classical emission handling approaches.

Heuristic solution using decision tree model for enhanced XML schema matching of bridge structural calculation documents

Research on the quality of data in a structural calculation document(SCD)is lacking,although the SCD ofa bridge is used as an essential reference during the entire lifecycle of the facility.XML Schema matching enables qualitative improvement of the stored data.This study aimed to enhance the applicability of XML Schema matching,which improves the speed and quality of information stored in bridge SCDs.First,the authors proposed a method of reducing the computing time for the schema matching of bridge SCDs.The computing speed of schema matching was increased by 13 to 1800 times by reducing the checking process of the correlations.Second,the authors developed a heuristic solution for selecting the optimal weight factors used in the matching process to maintain a high accuracy by introducing a decision tree.The decision tree model was built using the content elements stored in the SCD,design companies,bridge types,and weight factors as input variables,and the matching accuracy as the target variable.The inverse-calculation method was applied to extract the weight factors from the decision tree model for high-accuracy schema matching results.