Joint Optimization Scheme for the Reconfigurable Intelligent Surface-Assisted Untrusted Relay Networks

To further improve the secrecy rate,a joint optimization scheme for the reconfigurable intelligent surface(RIS)phase shift and the power allocation is proposed in the untrusted relay(UR)networks assisted by the RIS.The eavesdropping on the UR is interfered by a source-based jamming strategy.Under the constraints of unit modulus and total power,the RIS phase shift,the power allocation between the confidential signal and the jamming signal,and the power allocation between the source node and the UR are jointly optimized to maximize the secrecy rate.The complex multivariable coupling problem is decomposed into three sub-problems,and the non-convexity of the objective function and the constraints is solved with semi-definite relaxation.Simulation results indicate that the secrecy rate is remarkably enhanced with the proposed scheme compared with the equal power allocation scheme,the random phase shift scheme,and the no-RIS scheme.

Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

A BIEM OPTIMIZATION METHOD FOR FRACTURE DYNAMICS INVERSE PROBLEM

In the present paper, based on the theory of dynamic boundary integral equation, an optimization method for crack identification is set up in the Laplace frequency space, where the direct problem is solved by the author's new type boundary integral equations and a method for choosing the high sensitive frequency region is proposed. The results show that the method proposed is successful in using the information of boundary elastic wave and overcoming the ill-posed difficulties on solution, and helpful to improve the identification precision.

Recent developments in the structural design and optimization of ITER neutral beam manifold

This paper describes a new design of the neutral beam manifold based on a more optimized support system.A proposed alternative scheme has presented to replace the former complex manifold supports and internal pipe supports in the final design phase.Both the structural reliability and feasibility were confirmed with detailed analyses.Comparative analyses between two typical types of manifold support scheme were performed.All relevant results of mechanical analyses for typical operation scenarios and fault conditions are presented.Future optimization activities are described,which will give useful information for a refined setting of components in the next phase.

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

RSs(Radar Systems)identify and trace targets and are commonly employed in applications like air traffic control and remote sensing.They are necessary for monitoring precise target trajectories.Estimations of RSs are non-linear as the parameters TDEs(time delay Estimations)and Doppler shifts are computed on receipt of echoes where EKFs(Extended Kalman Filters)and UKFs(Unscented Kalman Filters)have not been examined for computations.RSs,certain times result in poor accuracies and SNRs(low signal to noise ratios)especially,while encountering complicated environments.This work proposes IUKFs(Iterated UKFs)to track onlinefilter performances while using optimization techniques to enhance outcomes.The use of cost functions can assist state corrections while lowering costs.A new parameter is optimized using MCEHOs(Mutation Chaotic Elephant Herding Optimizations)by linearly approximating system non-linearity where OIUKFs(Optimized Iterative UKFs)predict a target's unknown parameters.To obtain optimal solutions theoretically,OIUKFs take less iteration,resulting in shorter execution times.The proposed OIUKFs provide numerical approximations which are derivative-free implementations.Simulation evaluation results with estimators show better performances in terms of reduced NMSEs(Normalized Mean Square Errors),RMSEs(Root Mean Squared Errors),SNRs,variances,and better accuracies than current approaches.

Optimizing Control of Bio-dissimilation Process of Glycerol to 1,3-Propanedlol

An iterative optimization strategy is proposed and applied to the steady state optimizing control of the bio-dissimilation process of glycerol to 1,3-propanediol in the presence of model-plant mismatch and input constraints. The scheme is based on the Augmented Integrated System Optimization and Parameter Estimation （AI- SOPE） technique, but a linearization of some performance function in the modified model-based optimization problem of AISOPE is introduced to overcome the difficulty of determining an appropriate penalty parameter. When carrying out the iterative optimization, the penalty coefficient is set to a larger value at the current iteration than at the previous iteration, which can promote the evolution rate of the iterative optimization. Simulation studies illustrate the potential ofthe approach presented for the optimizing control of the bioTdissimilation process of glycerol to 1,3-propanediol. The effects of measurement noise, measured and unmeasured disturbances on the proposed algorithm are also investigated.

Joint electricity and carbon market for Northeast Asia energy interconnection

Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades.The key challenges for achieving this goal are carbon emission trading and electricity sector regulation,which are also the major components of the carbon and electricity markets,respectively.In this paper,a joint electricity and carbon market model is proposed to investigate the relationships between electricity price,carbon price,and electricity generation capacity,thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework.The proposed model is a dynamically iterative optimization model consisting of upper-level and lower-level models.The upper-level model optimizes power generation and obtains the electricity price,which drives the lower-level model to update the carbon price and electricity generation capacity.The proposed model is verified using the Northeast Asia power grid.The results show that increasing carbon price will result in increased electricity price,along with further increases in renewable energy generation capacity in the following period.This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources,and hence the carbon price will decline.Moreover,the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation.Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050.It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.

Quality prediction and control of tube hollow

The quality prediction of tube hollow model based on the variance staged multiway partial least square (MPLS) method was proposed.The key aspects of staged decomposition of the productive data,calculation of the variance value,modeling,and on-lined prediction in the variance-staged MPLS method were introduced.Based on the model,iterative optimal control method was used for quality control of tube hollow.The experimental results show that the obvious benefits of this method are low maintenance cost,good real time function,high reliability precision,and practical application to on-line prediction and optimization on the quality of tube hollow.

Optimized finite difference iterative scheme based on POD technique for 2D viscoelastic wave equation

This study develops an optimized finite difference iterative （OFDI） scheme for the two-dimensional （2D） viscoelastic wave equation. The OFDI scheme is obtained using a proper orthogonal decomposition （POD） method. It has sufficiently high accuracy with very few unknowns for the 2D viscoelastic wave equation. Existence, stability, and convergence of the OFDI solutions are analyzed. Numerical simulations verify efficiency and feasibility of the proposed scheme.

Iterative optimal control based on support vector machine modeling within the Bayesian evidence framework

In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.

Sensitivity and inverse analysis methods for parameter intervals

This paper proposes a sensitivity analysis method for engineering parameters using interval analyses.This method substantially extends the application of interval analysis method.In this scheme,parameter intervals and decision-making target intervals are determined using the interval analysis method.As an example,an inverse analysis method for uncertainty is presented.The intervals of unknown parameters can be obtained by sampling measured data.Even for limited measured data,robust results can also be obtained with the inverse analysis method,which can be intuitively evaluated by the uncertainty expressed in terms of an interval.For complex nonlinear problems,an iteratively optimized inverse analysis model is proposed.In a given set of loose parameter intervals,all the unknown parameter intervals that satisfy the measured information can be obtained by an iteratively optimized inverse analysis model.The influences of measured precisions and the number of parameters on the results of the inverse analysis are evaluated.Finally,the uniqueness of the interval inverse analysis method is discussed.

Fast optical wavefront engineering for controlling light propagation in dynamic turbid media

While propagating inside the strongly scattering biological tssue,photons lose their incident directions beyond one transport mean free path(TMFP,~1 millimeter(mm)),which makes it challenging to achieve optical focusing or clear imaging deep inside tissue.By manipulating many degrees of the incident optical wavefront,the latest optical wavefront engineering(WFE)technology compensates the wavelfront distortions caused by the scattering media and thus is toward breaking this physical limit,bringing bright perspective to many applications deep inside tissue,eg,high resolution functional/molecular imaging,optical excitation(optogenetics)and optical tweezers.However,inside the dynamic turbid media such as the biological tissue,the wavefront distortion is a fast and continuously changing process whose decorrelation rate is on timescales from milliseconds(ms)to microseconds(μs),or even faster.This requires that the WFE technology should be capable of beating this rapid process.In this review,we discuss the major challenges faced by the WFE technology due to the fast decorrelation of dynamic turbid media such as living tissue when achieving light focusing/imaging and summarize the research progress achieved to date to overcome these challenges.

Run-to-run product quality control of batch processes

Batch processes have been increasingly used in the production of low volume and high value added products. Consequently, optimization control in batch processes is crucial in order to derive the maximum benefit. In this paper, a run-to-run product quality control based on iterative learning optimization control is developed. Moreover, a rigorous theorem is proposed and proven in this paper, which states that the tracking error under the optimal iterative learning control （ILC） law can converge to zero. In this paper, a typical nonlinear batch continuous stirred tank reactor （CSTR） is considered, and the results show that the performance of trajectory tracking is gradually improved by the ILC.

Fast Solvers of Fredholm Optimal Control Problems

The formulation of optimal control problems governed by Fredholm integral equations of second kind and an efficient computational framework for solving these control problems is presented. Existence and uniqueness of optimal solutions is proved.A collective Gauss-Seidel scheme and a multigrid scheme are discussed. Optimal computational performance of these iterative schemes is proved by local Fourier analysis and demonstrated by results of numerical experiments.

View Synthesis from multi-view RGB data using multi layered representation and volumetric estimation

Background Aiming at free-view exploration of complicated scenes,this paper presents a method for interpolating views among multi RGB cameras.Methods In this study,we combine the idea of cost volume,which represent 3 D information,and 2 D semantic segmentation of the scene,to accomplish view synthesis of complicated scenes.We use the idea of cost volume to estimate the depth and confidence map of the scene,and use a multi-layer representation and resolution of the data to optimize the view synthesis of the main object.Results/Conclusions By applying different treatment methods on different layers of the volume,we can handle complicated scenes containing multiple persons and plentiful occlusions.We also propose the view-interpolation→multi-view reconstruction→view interpolation pipeline to iteratively optimize the result.We test our method on varying data of multi-view scenes and generate decent results.

Iterative multi-photon adaptive compensation technique for deep tissue two-photon fluorescence lifetime imaging

Fluorescence lifetime imaging can reveal the high-resolution structure of various biophysical and chemical parameters in a microenvironment quantitatively.However,the depth of imaging is generally limited to hundreds of micrometers due to aberration and light scattering in biological tissues.This paper introduces an iterative multi-photon adaptive compensation technique(IMPACT)into a two-photon fluorescence lifetime microscopy system to successfully overcome aberrations and multiple scattering problems in deep tissues.It shows that 400 correction modes can be achieved within 5 min,which was mainly limited by the frame rate of a spatial light modulator.This system was used for high-resolution imaging of mice brain tissue and live zebrafish,further verifying its superior performance in imaging quality and photon accumulation speed.

Scenario-based Unit Commitment Optimization for Power System with Large-scale Wind Power Participating in Primary Frequency Regulation

Continuous increase of wind power penetration brings high randomness to power system,and also leads to serious shortage of primary frequency regulation(PFR)reserve for power system whose reserve capacity is typically provided by conventional units.Considering large-scale wind power participating in PFR,this paper proposes a unit commitment optimization model with respect to coordination of steady state and transient state.In addition to traditional operation costs,losses of wind farm de-loaded operation,environmental benefits and transient frequency safety costs in high-risk stochastic scenarios are also considered in the model.Besides,the model makes full use of interruptible loads on demand side as one of the PFR reserve sources.A selection method for high-risk scenarios is also proposed to improve the calculation efficiency.Finally,this paper proposes an inner-outer iterative optimization method for the model solution.The method is validated by the New England 10-machine system,and the results show that the optimization model can guarantee both the safety of transient frequency and the economy of system operation.

General Schemes of Iterative Optimization with Applications to Optimal Control Problems

A general(abstract)scheme of iterative improvement and optimization on the base of extension,localization principles which would help to generate new concrete methods and algorithms for new problems is proposed.Application to optimal control problems for continuous systems is considered.Visual example is given.

Iterative optimization of detection threshold and throughput for secondary users in multiband cognitive radio systems

In cognitive radio （CR） systems, efficient spectrum sensing ensures the secondary user （SU） to successfully access the spectrum hole. Typically, the detection problem has been considered separately from the optimization of transmission strategy. However, in practice, due to non-zero probabilities of miss detection and false alarm, the sensing phase has an impact on the throughput of SUs as well as on the transmission of primary user （PU）. In this paper, using energy detection, we maximize the total throughput of SUs by jointly optimizing the detection threshold and transmission strategy in multiband CR systems. A set of iteration based algorithms are proposed to solve this mix-integer programming problem, which show better performance compared with uniform detection threshold selection algorithm suggested by IEEE 802.22 standard.

An adaptive beamforming algorithm based on direction vector rotation and joint iterative optimization

An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.