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.

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.

Application of single neuron adaptive PID controller during the process of timber drying

The paper presents a method of using single neuron adaptive PID control for adjusting system or servo system to implement timber drying process control, which combines the thought of parameter adaptive PID control and the character of neural network on exactly describing nonlinear and uncertainty dynamic process organically. The method implements functions of adaptive and self-learning by adjusting weighting parameters. Adaptive neural network can make some output trail given hoping value to decouple in static state. The simulation result indicates the validity, veracity and robustness of the method used in the timber drying process

An Improved Adaptive Multi-way Principal Component Analysis for Monitoring Streptomycin Fermentation Process

Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.

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.

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

The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment,and a new modified Broyden, Fletcher, Goldfarb, and Shanno （BFGS） quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation （BP） neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quicklv track the time-varving and nonlinear behavior of the bioreactor.

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.

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.

Adaptive predictive functional control based on Takagi-Sugeno model and its application to pH process

In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.

Adaptive control of machining process based on extended entropy square error and wavelet neural network

Combining information entropy and wavelet analysis with neural network,an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error(EESE)and wavelet neural network(WNN).Extended entropy square error function is defined and its availability is proved theoretically.Replacing the mean square error criterion of BP algorithm with the EESE criterion,the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter,translating parameter of the wavelet and neural network weights.Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network.The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions,thus improving the machining efficiency and protecting the tool.

Flexible,thermal processable,self-healing,and fully bio-based starch plastics by constructing dynamic imine network

The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.

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.

Climate change vulnerability assessment in the new urban planning process in Tanzania

Climate change vulnerability assessment is an essential tool for identifying regions that are most susceptible to the impacts of climate change and designing effective adaptation actions that can reduce vulnerability and enhance long-term resilience of these regions.This study explored a framework for climate change vulnerability assessment in the new urban planning process in Jangwani Ward,Tanzania.Specifically,taking flood as an example,this study highlighted the steps and methods for climate change vulnerability assessment in the new urban planning process.In the study area,95 households were selected and interviewed through purposeful sampling.Additionally,10 respondents(4 females and 6 males)were interviewed for Focus Group Discussion(FGD),and 3 respondents(1 female and 2 males)were selected for Key Informant Interviews(KII)at the Ministry of Lands,Housing and Human Settlements Development.This study indicated that climate change vulnerability assessment framework involves the assessment of climatic hazards,risk elements,and adaptive capacity,and the determination of vulnerability levels.The average hazard risk rating of flood was 2.3.Socioeconomic and livelihood activities and physical infrastructures both had the average risk element rating of 3.0,and ecosystems had the average risk element rating of 2.9.Adaptive capacity ratings of knowledge,technology,economy or finance,and institution were 1.6,1.9,1.4,and 2.2,respectively.The vulnerability levels of socioeconomic and livelihood activities and physical infrastructure were very high(4.0).Ecosystems had a high vulnerability level(3.8)to flood.The very high vulnerability level of socioeconomic and livelihood activities was driven by high exposure and sensitivity to risk elements and low adaptive capacity.The study recommends adoption of the new urban planning process including preparation,planning,implementation,and monitoring-evaluation-review phases that integrates climate change vulnerability assessment in all phases.