Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

Prediction Model for Gas Outburst Intensity of Coal Mining Face Based on Improved PSO and LSSVM

For the problems of nonlinearity,uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face,the least squares support vector machine(LSSVM)is proposed to establish the prediction model.Firstly,considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization(PSO),an improved PSO(IPSO)algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate.Secondly,the penalty factor and kernel function parameter of LSSVM are searched automatically,and the regression accuracy and generalization performance is enhanced by applying IPSO.Finally,to verify the proposed prediction model,the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City,and the results are compared with that of PSO-SVM model,IGA-LSSVM model and BP model.The results show that the relative errors of the proposed model are not greater than 2.7%,and the prediction accuracy is higher than other three prediction models.The IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.

A Control Strategy for Smoothing Active Power Fluctuation of Wind Farm with Flywheel Energy Storage System Based on Improved Wind Power Prediction Algorithm

The fluctuation of active power output of wind farm has many negative impacts on large-scale wind power integration into power grid. In this paper, flywheel energy storage system (FESS) was connected to AC side of the doubly-fed induction generator (DFIG) wind farm to realize smooth control of wind power output. Based on improved wind power prediction algorithm and wind speed-power curve modeling, a new smooth control strategy with the FESS was proposed. The requirement of power system dispatch for wind power prediction and flywheel rotor speed limit were taken into consideration during the process. While smoothing the wind power fluctuation, FESS can track short-term planned output of wind farm. It was demonstrated by quantitative analysis of simulation results that the proposed control strategy can smooth the active power fluctuation of wind farm effectively and thereby improve power quality of the power grid.

IMPROVED METHOD FOR RNA SECONDARY STRUCTURE PREDICTION'

A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudoknots including the well known H-type pseudoknot were permitted to occur if reasonable.We have applied this approach to e number of RNA sequences.The prediction accuracies we obtained were higher than those in published papers.

A method for predicting the water-flowing fractured zone height based on an improved key stratum theory

In the process of using the original key stratum theory to predict the height of a water-flowing fractured zone(WFZ),the influence of rock strata outside the calculation range on the rock strata within the calculation range as well as the fact that the shape of the overburden deformation area will change with the excavation length are ignored.In this paper,an improved key stratum theory(IKS theory)was proposed by fixing these two shortcomings.Then,a WFZ height prediction method based on IKS theory was established and applied.First,the range of overburden involved in the analysis was determined according to the tensile stress distribution range above the goaf.Second,the key stratum in the overburden involved in the analysis was identified through IKS theory.Finally,the tendency of the WFZ to develop upward was determined by judging whether or not the identified key stratum will break.The proposed method was applied and verified in a mining case study,and the reasons for the differences in the development patterns between the WFZs in coalfields in Northwest and East China were also fully explained by this method.

Preface to the Special Issue:Towards Improving Understanding and Prediction of Arctic Change and its Linkage with Eurasian Mid-latitude Weather and Climate

The dramatic changes in the Arctic climate system during recent decades are one of the most prominent features of global climate change.Two most striking and fundamental characteristics are the amplified near-surface warming at a rate twice the global average since the mid 20th century(e.g.,Blunden and Arndt,2012;Huang et al.,2017),and the rapid

Study of improved gray predictive PID control algorithm

According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated by MATLAB.As a result that it can improve the response speed and stability of the system,and meet the demand of the system.

A Novel Tuning Method for Predictive Control of VAV Air Conditioning System Based on Machine Learning and Improved PSO

The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response,a novel tuning method based on machine learning and improved particle swarm optimization(PSO)is proposed.In this method,the relationship between MPC controller parameters and time domain performance indices is established via machine learning.Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices.In addition,the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method.Finally,the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.

Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology

In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.

Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm

Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.

GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems

Owing to the expansion of the grid interconnection scale,the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important.These characteristics can provide effective support in coordinated security control.However,traditional model-based frequencyprediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models.Therefore,this study presents a rolling frequency-prediction model based on a graph convolutional network(GCN)and a long short-term memory(LSTM)spatiotemporal network and named as STGCN-LSTM.In the proposed method,the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input.An improved GCN embedded with topology information is used to extract the spatial features,while the LSTM network is used to extract the temporal features.The spatiotemporal-network-regression model is further trained,and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information.The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response.The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.

Effect Evaluation and Intelligent Prediction of Power Substation Project Considering New Energy

The evaluation of the implementation effect of the power substation project can find out the problems of the project more comprehensively,which has important practical significance for the further development of the power substation project.To ensure accuracy and real-time evaluation,this paper proposes a novel hybrid intelligent evaluation and prediction model based on improved TOPSIS and Long Short-Term Memory(LSTM)optimized by a Sperm Whale Algorithm(SWA).Firstly,under the background of considering the development of new energy,the influencing factors of power substation project implementation effect are analyzed from three aspects of technology,economy and society.Moreover,an evaluation model based on improved TOPSIS is constructed.Then,an intelligent prediction model based on SWA optimized LSTM is designed.Finally,the scientificity and accuracy of the proposed model are verified by empirical analysis,and the important factors affecting the implementation effect of power substation projects are pointed out.

Progress of MJO Prediction at CMA from Phase I to Phase II of the Sub-Seasonal to Seasonal Prediction Project

As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates the models’capability to simulate and predict the Madden-Julian Oscillation(MJO).Three versions of the Beijing Climate Center Climate System Model(BCC-CSM)are used to conduct historical simulations and re-forecast experiments(referred to as EXP1,EXP1-M,and EXP2,respectively).In simulating MJO characteristics,the newly-developed high-resolution BCC-CSM outperforms its predecessors.In terms of MJO prediction,the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M,and further to 24 days in EXP2.Within the first forecast week,the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill.However,during forecast weeks 2–3,EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3.Particularly at initial phases 2–3,EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection,leading to the highest prediction skill of the MJO.Our results reveal that,during the participation of the CMA models in the S2S Project,both the improved model initialization and updated model physics played positive roles in improving MJO prediction.Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.

Improved Relative-transformation Principal Component Analysis Based on Mahalanobis Distance and Its Application for Fault Detection

主要部件分析(PCA ) 广泛地在过程工业被使用了，它能维持最大的差错察觉率。尽管许多问题在 PCA 被处理了，一些必要问题仍然保持未解决。这研究以下列方法为差错察觉性能改进 PCA。第一，一个相对转变计划基于 Mahalanobis 距离(MD ) 被介绍消除数据的尺寸的效果而不是无尺寸的标准化，并且改进精确性和差错察觉的即时性能。理论推导证明那相对转变能直接基于 MD 消除尺寸的效果并且在结果显示出的相对空间，分析和模拟给 PCA 的合理解释它的优势和有效性。第二，一个改进摆平的预言错误(SPE ) 统计数值被给改进标准化 PCA 的差错察觉表演，它能使标准化基于 PCA 的差错察觉方法成为对实际工业过程合适的更多。最后，二个改进方法被联合更有效地检测差错。建议方法被使用在热连续滚动过程检测 looper 系统的单个差错和多差错，模拟结果以易感知，精确性和差错察觉的即时性能为差错察觉性能表明这些改进的有效性。

Research on Optimize Prediction Model and Algorithm about Chaotic Time Series

We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means. Key words chaotic time series - parameter identification - optimal prediction model - improved change ruler method CLC number TP 273 Foundation item: Supported by the National Natural Science Foundation of China (60373062)Biography: JIANG Wei-jin (1964-), male, Professor, research direction: intelligent compute and the theory methods of distributed data processing in complex system, and the theory of software.

On the stability of two-step predictive controller based on state observer

For input saturated Hammerstein systems, the two-step predictive control strategy is adopted. The first step calculates the desired intermediate variable applying unconstrained linear modal and predictive control. The second step obtains the real-time control action by solving algebraic equation and desaturation. The case of immeasurable state is considered where the observer gain matrix is solved by Sylvester equation. The sufficient closed-loop stability condition is given and the designing and tuning algorithm for the domain of attraction is proposed. The theoretical results are validated by an example.

Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent years.The mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all things.The variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication environments.Ensuring data secure transmission is critical for mobile IIoT networks.This paper investigates the data secure transmission performance prediction of mobile IIoT networks.To cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first derived.Then,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction algorithm.For mobile signals,the important features may be removed by the pooling layers.This will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is designed.Out of the input and output layers,it removes the pooling layer and contains six convolution layers.Elman,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed algorithm.Through simulation analysis,good prediction accuracy is achieved by the CNN algorithm.The prediction accuracy obtains a 59%increase.

Smart Development Process Enactment Based on Context Sensitive Sequence Prediction

Actual software development processes define the different steps developers have to perform during a development project. Usually these development steps are not described independently from each other—a more or less formal flow of development step is an essential part of the development process definition. In practice, we observe that often the process definitions are hardly used and very seldom “lived”. One reason is that the predefined general process flow does not reflect the specific constraints of the individual project. For that reasons we claim to get rid of the process flow definition as part of the development process. Instead we describe in this paper an approach to smartly assist developers in software process execution. The approach observes the developer’s actions and predicts his next development step based on the project process history. Therefore we apply machine learning resp. sequence learning approaches based on a general rule based process model and its semantics. Finally we show two evaluations of the presented approach: The data of the first is derived from a synthetic scenario. The second evaluation is based on real project data of an industrial enterprise.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction:case study of the coastal waters of Beihai,China

Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.