Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Fractional Processing Based Adaptive Beamforming Algorithm

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing complexity.The goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity lower.The effectiveness of the algorithm will be studied and evaluated in this context.In this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of resources.This algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational complexity.The FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight adaptation.The derivation of the algorithm is provided and supported by mathematical convergence analysis.Performance is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various parameters.The results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter plots.FLMS outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

Adaptive Method’s Application for the Identification Systems in Dynamic Weighing

In this work,the possibility of adaptive algorithm in WIM(weight-in-motion)systems,in which fibre optic sensors are used,is shown.Appointment of dynamic weighing device consists in determining the weight and type of vehicle.In this work an algorithm for processing the input data and fiber optic sensor to create the database used in the algorithm is presented.The results of the algorithm for the identification of vehicles are given.The conclusions are made and options of increasing the accuracy of the identification algorithm are considered.

Range walk and array rotation in space-time adaptive processing:effects and compensations

This paper proposes a unified clutter model incorporating the effects of range walk and array rotation for space-time adaptive processing（STAP） in airborne multi-channel early-warning radar.Based on this clutter model,STAP performance is then analyzed from the perspective of covariance matrix tapering（CMT）.For STAP performance degradation due to array rotation,a determinate compensation method is proposed based on the CMT method.Numerical examples are provided to verify the analysis and the proposed compensation method.

SPACE-TIME ADAPTIVE PROCESSING FOR AIRBORNE RADAR:A CONVENIENT IMPLEMENTATION APPROACH

A convenient implementation approach to space-time adaptive processing for airborne radar has been proposed, which is added by some auxiliary array elements in the area of main-lobe clutter on the basis of 2-D Capon approach . It is of practical use for its small computational load. This approach possesses the ideal performance in the area of main-lobe clutter . In addition, the approach which is added by some auxiliary beams in the area of main-lobe clutter has also been discussed.

Multiconstraint adaptive three-dimensional guidance law using convex optimization

The traditional guidance law only guarantees the accuracy of attacking a target. However, the look angle and acceleration constraints are indispensable in applications. A new adaptive three-dimensional proportional navigation(PN) guidance law is proposed based on convex optimization. Decomposition of the three-dimensional space is carried out to establish threedimensional kinematic engagements. The constraints and the performance index are disposed by using the convex optimization method. PN guidance gains can be obtained by solving the optimization problem. This solution is more rapid and programmatic than the traditional method and provides a foundation for future online guidance methods, which is of great value for engineering applications.

Direct data domain approach to space-time adaptive processing

In non-homogeneous environment, traditional space-time adaptive processing doesn＇t effectively suppress interference and detect target, because the secondary data don＇ t exactly reflect the statistical characteristic of the range cell under test. A ravel methodology utilizing the direct data domain approach to space-time adaptive processing （ STAP ） in airbome radar non-homogeneous environments is presented. The deterministic least squares adaptive signal processing technique operates on a ＂snapshot-by-snapshot＂ basis to dethrone the adaptive adaptive weights for nulling interferences and estimating signal of interest （SOI）. Furthermore, this approach eliminates the requirement for estimating the covariance through the data of neighboring range cell, which eliminates calculating the inverse of covariance, and can be implemented to operate in real-time. Simulation results illustrate the efficiency of interference suppression in non-homogeneous environment.

STATISTICAL SPACE-TIME ADAPTIVE PROCESSING ALGORITHM

For the slowly changed environment-range-dependent non-homogeneity, a new statistical space-time adaptive processing algorithm is proposed, which uses the statistical methods, such as Bayes or likelihood criterion to estimate the approximative covariance matrix in the non-homogeneous condition. According to the statistical characteristics of the space-time snapshot data, via defining the aggregate snapshot data and corresponding events, the conditional probability of the space-time snapshot data which is the effective training data is given, then the weighting coefficients are obtained for the weighting method. The theory analysis indicates that the statistical methods of the Bayes and likelihood criterion for covariance matrix estimation are more reasonable than other methods that estimate the covariance matrix with the use of training data except the detected outliers. The last simulations attest that the proposed algorithms can estimate the covariance in the non-homogeneous condition exactly and have favorable characteristics.

Real-Time Discrete Adaptive Control of Robot Arm Based on Digital Signal Processing

A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.

PRECONDITIONED METHODS FOR SPACE-TIME ADAPTIVE PROCESSING

This paper introduces the preconditioned methods for Space-Time Adaptive Processing(STAP).Using the Block-Toeplitz-Toeplitz-Block(BTTB)structure of the clutter-plus-noise covari-ance matrix,a Block-Circulant-Circulant-Block(BCCB)preconditioner is constructed.Based on thepreconditioner,a Preconditioned Multistage Wiener Filter(PMWF)which can be implemented by thePreconditioned Conjugate Gradient(PCG)method is proposed.Simulation results show that thePMWF has faster convergence rate and lower processing rank compared with the MWF.

Classification and Analysis of Tube Hydroforming Processes with Respect to Adaptive FEM Simulations

Tube hydroforming process is a relative new process f or production of structural parts of low weight and high rigidity. The successfu lness of the process depends largely on the a proper selection of loading path w hich is axial feeding distance as related to the applied internal pressure. Due to the complicated nature of plastic deformation, a optimum loading path which w ill guarantee good hydroformed parts free of winkle and fracture has often to be determined by finite element analysis. In order to save trials and errors, adap tive FEM simulation method has been developed. To effectively apply the adaptive simulation method, we have to know the applicability of the method. In this pap er, a classification of tube hydroforming (THF) processes based on sensitivity to loading parameters has been suggested. Characteristics of the classification have been analyzed in terms of failure mode, dominant loading parameters and th eir working windows. It was discussed that the so called pressure dominant THF p rocess is the most difficult process for both simulation in FEM analysis and pra ctical operation in real manufacturing situation. To effectively find out the op timum loading path for pressure dominant THF process, adaptive FEM simulation st rategies are mostly needed.

Behavior of traditional concrete dams and three-dimensional printed concrete dams under the debris flow impact

This study investigated the resilience of traditional concrete dams compared to 3D printed concrete dams(3DPC)when subjected to debris flow.Three types of dams,namely check dams,arch dams,and curve dams,were numerically analyzed using a three-dimensional Coupled Eulerian-Lagrangian(CEL)methodology.The research focused on critical factors such as impact force and viscous energy dissipation to compare dam performance.Additionally,the study examined the printing and service phases of 3DPC models,determining potential failure modes and analyzing printing parameters.The results demonstrated that 3DPC dams outperformed traditional concrete dams,with filament deposition orientation,perpendicular to the debris flow direction,identified as a pivotal factor.Infill percentage and pattern were also found to influence the behavior of 3DPC models.Notably,curved dams exhibited superior performance based on dam geometry.These findings have significant potential for advancing the development of resilient dam structures capable of withstanding debris flow impacts.

A Method for Reducing Ocean Wave-Induced Magnetic Noises in Shallow-Water MT Data Using a Complex Adaptive Filter

In shallow-water areas,the marine magnetotelluric(MT)method faces a challenge in the investigation of seabed conductivity structures due to electrical and magnetic noises induced by ocean waves,which seriously contaminate MT data.Ocean waves can affect electric and magnetic fields to different extents.In general,their influence on magnetic fields is considerably greater than that on electric fields.In this paper,a complex adaptive filter is adopted to reduce wave-induced magnetic noises in the frequency domain.The processing results of synthetic and measured MT data indicate that the proposed method can effectively reduce wave-induced magnetic noises and provide reliable apparent resistivity and phase data.

Preparation of Dashanzha Wan by three-dimensional printing

Objective:To use three-dimensional(3D)printing technology to prepare Dashanzha Wan.Methods:The standard formula proportion of Dashanzha Wan was used to prepare printable materials(normally called the ink)for 3D printing,and different doses and shapes of Dashanzha Wan were prepared.Then,the rheological properties,texture characteristics,scanning electron microscopy,and content of ursolic acid were evaluated.Results:Dashanzha Wan ink showed good shear thinning properties,which is very suitable for 3D printing.The printed sample had a beautiful and regular shape with high resolution.Meanwhile,ursolic acid content in 3D-printed Dashanzha Wan aligned with the ursolic acid content shown in the Pharmacopoeia of the People's Republic of China 2020.Conclusion:The 3D-printed Dashanzha Wan has a better texture,and can be shaped into various shapes according to individual needs,which would increase patients’interest when taking medicine.Moreover,3D printing of Dashanzha Wan could be easily integrated into the digital life system,enabling online customization or use at home.This study reveals that 3D printing technology is a promising method for the production of traditional Chinese medicine with personalized appearance,dosage,and texture,which is suitable for a broader population.

Adaptive multiscale convolutional neural network model for chemical process fault diagnosis

Intelligent fault recognition techniques are essential to ensure the long-term reliability of manufacturing.Due to the variations in material,equipment and environment,the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating modes.Despite much progress in statistical learning and deep learning for fault recognition,most models are constrained by abundant diagnostic expertise,inefficient multiscale feature extraction and unruly multimode condition.To overcome the above issues,a novel fault diagnosis model called adaptive multiscale convolutional neural network(AMCNN)is developed in this paper.A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data,embedding the adaptive attention module to adjust the selection of relevant fault pattern information.The triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode condition.The benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed method.Compared with other common models,AMCNN shows its outstanding fault diagnosis performance and great generalization ability.

Three-Dimensional Rigid-Plastic FEM Simulation of Bulk Forming Processes with New Contact and Remeshing Techniques

Some techniques such as die surface description, contact judgement algorithm and remeshing are proposed to improve the robustness of the numerical solution. Based on these techniques, a three-dimensional rigid-plastic FEM code has been developed. Isothermal forging process of a cylindrical housing has been simulated. The simulation results show that the given techniques and the FEM code are reasonable and feasible for three-dimensional bulk forming processes.

Simulation-Based Construction of Three-Dimensional Process Model for Punching Cartridge Cases

A method of constructing three-dimensional process model for the punching cartridge cases is presented based on DEFORM simulation analysis. Using DEFORM software,the finite element simulation models for the punching and forming process of cartridge cases are established,and the corresponding simulation result model of each intermediate procedure is obtained by continuously performing the forming process simulation. The simulation model cannot annotate size and process information due to poor interface between DEFORM software and CAD software. Thus,a 3D annotation module is developed with secondary development technology of UG NX software. Consequently,the final process model with dimension and process information is obtained. Then,with the current 3D process management system,the 3D punching and forming process design of cartridge cases can be completed further. An example is also provided to illustrate that the relative error between the simulation process model and the physical model is less than 2%,which proves the validity and reliability of the proposed method in this study.

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

适应学习和一个高度非线性、变化时间的生物反应器基准过程的预言用神经原线，为在 G2 实时聪明的环境开发并且部署神经网络的一个图形的工具箱，和新修改 Broyden 被学习，弗莱彻， Goldfarb ，并且 Shanno ( BFGS )伪--牛顿算法。为神经网络被开发并且由介绍学习率到完整的存储器 BFGS 算法的一个新搜索方法嵌进神经原线的背繁殖(BP ) 的适应学习的修改 BFGS 算法。模拟结果证明适应学习和神经网络系统能快速追踪的预言生物反应器的变化时间、非线性的行为。

Investigation of control effects of end-wall selfadaptive jet on three-dimensional corner separation of a highly loaded compressor cascade

To overcome the limitations posed by three-dimensional corner separation,this paper proposes a novel flow control technology known as passive End-Wall(EW)self-adaptive jet.Two single EW slotted schemes(EWS1 and EWS2),alongside a combined(COM)scheme featuring double EW slots,were investigated.The results reveal that the EW slot,driven by pressure differentials between the pressure and suction sides,can generate an adaptive jet with escalating velocity as the operational load increases.This high-speed jet effectively re-excites the local low-energy fluid,thereby mitigating the corner separation.Notably,the EWS1 slot,positioned near the blade leading edge,exhibits relatively low jet velocities at negative incidence angles,causing jet separation and exacerbating the corner separation.Besides,the EWS2 slot is close to the blade trailing edge,resulting in massive low-energy fluid accumulating and separating before the slot outlet at positive incidence angles.In contrast,the COM scheme emerges as the most effective solution for comprehensive corner separation control.It can significantly reduce the total pressure loss and improve the static pressure coefficient for the ORI blade at 0°-4° incidence angles,while causing minimal negative impact on the aerodynamic performance at negative incidence angles.Therefore,the corner stall is delayed,and the available incidence angle range is broadened from -10°--2°to -10°-4°.This holds substantial promise for advancing the aerodynamic performance,operational stability,and load capacity of future highly loaded compressors.

Adaptive Consistent Parameter Estimation for Non－Gaussian MA Processes Using 4th Order Cumulant

This paper addresses the problem of adaptively estimating the consistent parameters for non Gaussian nonminimum MA processes with symmetric PDF using the fourth order cumulant of the underlying processes. The processes may be corrupted by additive noise.