EXPERT SYSTEM FOR SINTER CHEMICAL COMPOSITION CONTROL BASED ON ADAPTIVE PREDICTION

In order to stabilize the sinter chemical composition,the expert system for composition control based on adaptive prediction has been developed on the basis of modern control theory and artificial intelligence.It has been verified by using real data at No.3 Sintering Plant of Anshan Iron and Steel Co.,and satisfactory results have been obtained.

Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction and Labeling

Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction,and the pixel labeling scheme is inflexible.To solve these problems,this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling.First,we design an adaptive gradient prediction(AGP),which uses eight adjacent pixels and combines four scanning methods(i.e.,horizontal,vertical,diagonal,and diagonal)for prediction.AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel,which improves the prediction ability of the predictor for complex images.At the same time,we adopt an adaptive huffman coding labeling scheme,which can adaptively generate huffman codes for labeling according to different images,effectively improving the scheme’s embedding performance on the dataset.The experimental results show that the algorithm has a higher embedding rate.The embedding rate on the test image Jetplane is 4.2102 bpp,and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.

Adaptive Optics System and Its Application Predictions at 1.2m Telescope of Yunnan Observatory

A 61 element adaptive optical system has been preliminary tested in the Coudé path of the 1 2m telescope at the Yunnan observatory this year. The whole system will be fully operated next year. This paper describes the AO system performances and its first experiment results, and the possible astronomical research topics.

Research on Prediction Methods of Prevalence Perception under Information Exposure

With the rapid development of information technology,the explosive growth of data information has become a common challenge and opportunity.Social network services represented by WeChat,Weibo and Twitter,drive a large amount of information due to the continuous spread,evolution and emergence of users through these platforms.The dynamic modeling,analysis,and network information prediction,has very important research and application value,and plays a very important role in the discovery of popular events,personalized information recommendation,and early warning of bad information.For these reasons,this paper proposes an adaptive prediction algorithm for network information transmission.A popularity prediction algorithm is designed to control the transmission trend based on the gray Verhulst model to analyze the law of development and capture popular trends.Experimental simulations show that the proposed perceptual prediction model in this paper has a better fitting effect than the existing models.

Adaptive Predictive Inverse Control of Offshore Jacket Platform Based on Rough Neural Network

The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model. In this paper, an adaptive inverse control method is proposed on the basis of novel rough neural networks （RNN） to control the harmful vibration of the offshore jacket platform, and the offshore jacket platform model is established by dynamic stiffness matrix （DSM） method. Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory, RNN is utilized to identify the predictive inverse model of the offshore jacket platform system. Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads, and to deal with the delay problem caused by signal transmission in the control process. The numerical results show that the constructed novel RNN has advantages such as clear structure, fast training speed and strong error-tolerance ability, and the proposed method based on RNN can effectively control the harmful vibration of the offshore jacket platform.

Model Free Adaptive Predictive Control of Desulfurization Slurry pH Based on CPS Framework

In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process,a model free adaptive predictive control algorithm for the desulfurization slurry pH which is based on a cyber physical systems framework is proposed.First,aiming to address system characteristics of non-linearity and pure hysteresis in slurry pH change process,a model free adaptive predictive control algorithm based on compact form dynamic linearization is proposed by combining model free adaptive control algorithm with model predictive control algorithm.Then,by integrating information resources with the physical resources in the absorption tower slurry pH control process,an absorption tower slurry pH optimization control system based on cyber physical systems is constructed.It is turned out that the model free adaptive predictive control algorithm under the framework of the cyber physical systems can effectively realize the high-precision tracking control of the slurry pH of the absorption tower,and it has strong robustness.

Model-Based Adaptive Predictive Control with Visual Servo of a Rotary Crane System

This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.

High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction

To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)prediction.First,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images.In the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding space.To the best of our knowledge,quadtree partitioning is first applied to RDH-EI.Simulation results show that the proposed method is reversible and separable,and that its average embedding capacity is improved.For gray images with a size of 512×512,the average embedding capacity is increased by 25565 bits.For all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.

Control method on serial type pump-valve coordinated electro-hydraulic servo system

In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corresponding control strategy was proposed.The system was constituted of a pumpcontrolled part and a valve-controlled part,the pump controlled part is used to adjust the flow rate of oil source and the valve controlled part is used to complete the position tracking control of the hydraulic cylinder.Based on the system characteristics,a load flow grey prediction method was adopted in the pump controlled part to reduce the system overflow losses,and an adaptive robust control method was adopted in the valve controlled part to eliminate the effect of system nonlinearity and parametric uncertainties due to variable hydraulic parameters and system loads on the control precision.The experimental results validated that the adopted control strategy increased the system efficiency obviously with guaranteed high control accuracy.

Adaptive Fitting Capacity Prediction Method for Lithium‑Ion Batteries

Lithium-ion batteries have become the mainstream power source for electric vehicles because of their excellent performance.However,lithium-ion batteries still experience aging and capacity attenuation during usage.It is therefore critical to accu-rately predict battery remaining capacity for increasing battery safety and prolonging battery life.This paper first adopts the metabolism grey algorithm and a simplified electrochemical model to predict battery capacity under different operating conditions.To improve the prediction performance where the capacity changes nonlinearly,a decoupling analysis of battery capacity loss is then conducted based on the simplified electrochemical model.Finally,an adaptive fitting method is devel-oped for capacity prediction,aiming at improving the prediction accuracy at the inflection point of battery capacity diving.The prediction results indicate that the developed adaptive fitting method can achieve high prediction accuracy under battery capacity attenuation at different discharge stages with errors lower than 2.2%.And the battery capacity decay shows linear variation,and the proposed method effectively forecast the inflection point of battery capacity diving.

Adaptive Model Predictive Control for Yaw System of Variable-speed Wind Turbines

Due to varying characteristics of the wind condition, the performance of the wind turbines can be optimized by adapting the parameters of the control system. In this letter, an adaptive technique is proposed for the novel model predictive control(MPC) for the yaw system of the wind turbines. The control horizon is adapted to the one with the best predictive performance among multiple control horizons. The adaptive MPC is demonstrated by simulations using real wind data, and its performance is compared with the baseline MPC at fixed control horizon. Results show that the adaptive MPC provides better comprehensive performance than the baseline ones at different preview time of wind directions. Therefore, the proposed adaptive technique is potentially useful for the wind turbines in the future.

Composite Model-free Adaptive Predictive Control for Wind Power Generation Based on Full Wind Speed

Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance suppression.First,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch angle.Second,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions.Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally presented.The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions.

Estimation of IIR Systems with Binary-Valued Observations

Estimation and control problems with binary-valued observations exist widely in practical systems.However,most of the related works are devoted to finite impulse response(FIR for short)systems,and the theoretical problem of infinite impulse response(IIR for short)systems has been less explored.To study the estimation problems of IIR systems with binary-valued observations,the authors introduce a projected recursive estimation algorithm and analyse its global convergence properties,by using the stochastic Lyapunov function methods and the limit theory on double array martingales.It is shown that the estimation algorithm has similar convergence results as those for FIR systems under a weakest possible non-persistent excitation condition.Moreover,the upper bound for the accumulated regret of adaptive prediction is also established without resorting to any excitation condition.