Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks

Fuel consumption is one of the main concerns for heavy-duty trucks.Predictive cruise control(PCC)provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information.In this study,a real-time implementable PCC,which simultaneously optimizes engine torque and gear shifting,is proposed for heavy-duty trucks.To minimize fuel consumption,the problem of the PCC is formulated as a nonlinear model predictive control(MPC),in which the upcoming road elevation information is used.Finding the solution of the nonlinear MPC is time consuming;thus,a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method.Dynamic programming(DP)algorithm,as a global optimization algorithm,is used as a performance benchmark for the proposed solver.Simulation,hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller.The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution,with less than 1%deviation for testing roads.Moreover,the proposed co-optimization controller is implementable in a real-truck,and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9%without compromising the truck’s travel time.

Machine learning as a tool predicting short-term postoperative complications in Crohn’s disease patients undergoing intestinal resection: What frontiers?

The recent study,“Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease:A machine learning-based study”invest-igated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn’s disease(CD)patients.Em-ploying a random forest analysis and Shapley Additive Explanations,the study prioritizes factors such as preoperative nutritional status,operative time,and CD activity index.Despite the retrospective design’s limitations,the model’s robu-stness,with area under the curve values surpassing 0.8,highlights its clinical potential.The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases,emphasizing the importance of compre-hensive assessment and optimization.While a significant advancement,further research is crucial for refining preoperative strategies in CD patients.

Comparison and development of machine learning for thalidomideinduced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

BACKGROUND Thalidomide is an effective treatment for refractory Crohn’s disease(CD).However,thalidomide-induced peripheral neuropathy(TiPN),which has a large individual variation,is a major cause of treatment failure.TiPN is rarely predictable and recognized,especially in CD.It is necessary to develop a risk model to predict TiPN occurrence.AIM To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic variables.METHODS A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the model.The National Cancer Institute Common Toxicity Criteria Sensory Scale(version 4.0)was used to assess TiPN.With 18 clinical features and 150 genetic variables,five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),specificity,sensitivity(recall rate),precision,accuracy,and F1 score.RESULTS The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248[P=0.0004,odds ratio(OR):8.983,95%confidence interval(CI):2.497-30.90],dose(mg/d,P=0.002),brainderived neurotrophic factor(BDNF)rs2030324(P=0.001,OR:3.164,95%CI:1.561-6.434),BDNF rs6265(P=0.001,OR:3.150,95%CI:1.546-6.073)and BDNF rs11030104(P=0.001,OR:3.091,95%CI:1.525-5.960).In the training set,gradient boosting decision tree(GBDT),extremely random trees(ET),random forest,logistic regression and extreme gradient boosting(XGBoost)obtained AUROC values>0.90 and AUPRC>0.87.Among these models,XGBoost and GBDT obtained the first two highest AUROC(0.90 and 1),AUPRC(0.98 and 1),accuracy(0.96 and 0.98),precision(0.90 and 0.95),F1 score(0.95 and 0.98),specificity(0.94 and 0.97),and sensitivity(1).In the validation set,XGBoost algorithm exhibited the best predictive performance with the highest specificity(0.857),accuracy(0.818),AUPRC(0.86)and AUROC(0.89).ET and GBDT obtained the highest sensitivity(1)and F1 score(0.8).Overall,compared with other state-of-the-art classifiers such as ET,GBDT and RF,XGBoost algorithm not only showed a more stable performance,but also yielded higher ROC-AUC and PRC-AUC scores,demonstrating its high accuracy in prediction of TiPN occurrence.CONCLUSION The powerful XGBoost algorithm accurately predicts TiPN using 18 clinical features and 14 genetic variables.With the ability to identify high-risk patients using single nucleotide polymorphisms,it offers a feasible option for improving thalidomide efficacy in CD patients.

Spatial distribution prediction and benefits assessment of green manure in the Pinggu District,Beijing,based on the CLUE-S model

Green manure use in China has declined rapidly since the 1980 s with the extensive use of chemical fertilizers.The deterioration of field environments and the demand for green agricultural products have resulted in more attention to green manure.Human intervention and policy-oriented behaviors likely have large impacts on promoting green manure planting.However,little information is available regarding on where,at what rates,and in which ways（i.e.,intercropping green manure in orchards or rotating green manure in cropland） to develop green manure and what benefits could be gained by incorporating green manure in fields at the county scale.This paper presents the conversion of land use and its effects at small region extent（CLUE-S） model,which is specifically developed for the simulation of land use changes originally,to predict spatial distribution of green manure in cropland and orchards in 2020 in Pinggu District located in Beijing,China.Four types of land use for planting or not planting green manure were classified and the future land use dynamics（mainly croplands and orchards） were considered in the prediction.Two scenarios were used to predict the spatial distribution of green manure based on data from 2011：The promotion of green manure planting in orchards（scenario 1） and the promotion of simultaneous green manure planting in orchards and croplands（scenario 2）.The predictions were generally accurate based on the receiver operating characteristic（ROC） and Kappa indices,which validated the effectiveness of the CLUE-S model in the prediction.In addition,the spatial distribution of the green manure was acquired,which indicated that green manure mainly located in the orchards of the middle and southern regions of Dahuashan,the western and southern regions of Wangxinzhuang,the middle region of Shandongzhuang,the eastern region of Pinggu and the middle region of Xiagezhuang under scenario 1.Green manure planting under scenario 2 occurred in orchards in the middle region of Wangxinzhuang,and croplands in most regions of Daxingzhuang,southern Pinggu,northern Xiagezhuang and most of Mafang.The spatially explicit results allowed for the assessment of the benefits of these changes based on different economic and ecological indicators.The economic and ecological gains of scenarios 1 and 2 were 175691 900 and143000 300 CNY,respectively,which indicated that the first scenario was more beneficial for promoting the same area of green manure.These results can facilitate policies of promoting green manure and guide the extensive use of green manure in local agricultural production in suitable ways.

Blended coal’s property prediction model based on PCA and SVM

In order to predict blended coal's property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and support vector machine (SVM) was established. PCA was used to transform the high-dimensional and correlative influencing factors data to low-dimensional principal component subspace. Well-trained SVM was used to extract influencing factors as input to predict blended coal's property. Then experiments were made by using the real data, and the results were compared with weighted averaging method (WAM) and BP neural network. The results show that PCA-SVM has higher prediction accuracy in the condition of few data, thus the hybrid model is of great use in the domain of power coal blending.

App Store Analysis: Using Regression Model for App Downloads Prediction

App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app description and app name currently. In this paper we propose an approach that App Store Analysis can be used to predict app downloads. We use data mining to extract app name and description and app rank information etc. from the Wandoujia App Store and AppCha App Store. We use questionnaire and sentimentanalysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman’s rank correlation analysis respectively. Finally, we establish a multiple nonlinear regression model which app downloads defined as dependent variable and three relevant attributes defined as independent variable. On average, 59.28 % of apps in Wandoujia App Store and 66.68 % of apps in AppCha App Store can be predicted accurately within threshold which error rate is 25 %. One can observe the more detailed classification of app store, the more accurate for regression modeling to predict app downloads. Our approach can help app developers to notice and optimize the vital factors which influence app downloads.

Rock critical porosity inversion and S-wave velocity prediction

A critical porosity model is often used to calculate the dry frame elastic modulus by the rock critical porosity value which is affected by many factors. In practice it is hard for us to obtain an accurate critical porosity value and we can generally take only an empirical critical porosity value which often causes errors. In this paper, we propose a method to obtain the rock critical porosity value by inverting P-wave velocity and applying it to predict S-wave velocity. The applications of experiment and log data both show that the critical porosity inversion method can reduce the uncertainty resulting from using an empirical value in the past and provide the accurate critical porosity value for predicting S-wave velocity which significantly improves the prediction accuracy.

D-S理论和Markov链组合的桥梁性能退化预测研究

为准确预测桥梁性能退化,考虑到数据随机性和微小扰动发生状态跳跃,提出了一种D-S(Dempster-Shafer)证据理论和Markov链组合的桥梁性能退化组合预测模型和性能退化率的概念.该模型基于指数平滑(exponential smoothing,ES)方法获得新的预测数据序列,并利用Markov链和D-S理论不断进行优化,从而实现桥梁性能退化的组合预测.实际工程的应用结果表明:性能退化率可以直观地表征在梁性能退化的速度.其次,该模型的平均相对误差为1.54%,较于回归、灰色和模糊加权Markov链模型,精度分别提高了1.11%,0.88%和2.8%,而后验差比值为0.242,小于0.35;模型的标准差为9.021,相比其他模型分别减小了3.978,3.405和7.500,而变异系数为0.109,均小于其他模型,验证了组合预测模型在精度和稳定性方面的优越性,可为在役桥梁结构性能退化预测与维护提供理论基础.

Flexible predictive power-split control for battery-supercapacitor systems of electric vehicles using IVHS

The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.

Implications from Subseasonal Prediction Skills of the Prolonged Heavy Snow Event over Southern China in Early 2008

An exceptionally prolonged heavy snow event(PHSE)occurred in southern China from 10 January to 3 February 2008,which caused considerable economic losses and many casualties.To what extent any dynamical model can predict such an extreme event is crucial for disaster prevention and mitigation.Here,we found the three S2S models(ECMWF,CMA1.0 and CMA2.0)can predict the distribution and intensity of precipitation and surface air temperature(SAT)associated with the PHSE at 10-day lead and 10−15-day lead,respectively.The success is attributed to the models’capability in forecasting the evolution of two important low-frequency systems in the tropics and mid-latitudes[the persistent Siberian High and the suppressed phase of the Madden−Julian Oscillation(MJO)],especially in the ECMWF model.However,beyond the 15-day lead,the three models show almost no skill in forecasting this PHSE.The bias in capturing the two critical circulation systems is responsible for the low skill in forecasting the 2008 PHSE beyond the 15-day lead.On one hand,the models cannot reproduce the persistence of the Siberian High,which results in the underestimation of negative SAT anomalies over southern China.On the other hand,the models cannot accurately capture the suppressed convection of the MJO,leading to weak anomalous southerly and moisture transport,and therefore the underestimation of precipitation over southern China.The Singular Value Decomposition(SVD)analyses between the critical circulation systems and SAT/precipitation over southern China shows a robust historical relation,indicating the fidelity of the predictability sources for both regular events and extreme events(e.g.,the 2008 PHSE).

Predictive Model for Corrosion Rate of Oil Tubes in CO_2/H_2S Coexistent Environment Part Ⅰ: Building of Model

Based on an analysis of the existing models of CO 2 corrosion in literatures and the autoclave simulative experiments, a predictive model of corrosion rate (r corr) in CO 2/H 2S corrosion for oil tubes has been established, in which r corr is expressed as a function of pH, temperature (T), pressure of CO 2 (P CO 2) and pressure of H 2S (P H 2S). The model has been verified by experimental data obtained on N80 steel. The improved features of the predictive model include the following aspects: (1) The influence of temperature on the protectiveness of corrosion film is taken into consideration for establishment of predictive model of the r corr in CO 2/H 2S corrosion. The Equations of scale temperature and scale factor are put forward, and they fit the experimental result very well. (2) The linear relationship still exists between ln r corr and ln P CO 2 in CO 2/H 2S corrosion (as same as that in CO 2 corrosion). Therefore, a correction factor as a function of P H 2S has been introduced into the predictive model in CO 2/H 2S corrosion. (3) The model is compatible with the main existing models.

Artificial network prediction on degradable properties of coal-filled films

Utilized degradable data of coal-filled films from the accelerated UV chamber ageing degradation experiments, and on the basis of control factors’ analysis, presented a predicting model on degradable properties of this film in photo-degradation according to back-propagation artificial neural network (BP ANN). 4 controlling factors in films degrada-tion, including temperature, the time of UV irradiation, the concentration and the type of coals were used as input parameters in the ANN model. While the degradable properties after film degradation, including the mechanical properties and carbonyl index, were used as output parameters. It was carried out by the neural network toolbox of Matlab 6.5 soft-ware and Visual Basic 6.0. Discussed partition of sample data and model’s parameters, and then selected the best configuration of ANN network. The accurate scope of predicting results was analyzed. This model has a high precision in predicting on properties of the coal-filled film degradation.

Analysis on Causes of Rural Land Expropriation Disputes Based on Smith's Model

Frequent occurrence of land expropriation disputes in rural areas of China has attracted attention of scholars to study causes. Most existing studies discuss causes from " structure- institution" level. In other words,land expropriation disputes are caused by existing imperfect institutions,regulations,and policies. Such analysis model points institutional causes,but pays little attention to policy implementation process,especially the subjective initiative of parties concerned. This paper described a decade-long land expropriation dispute case in detail.Through description of event and process and survey of reasons of actors,it revealed factors resulting in occurrence and upgrade of dispute,and analyzed factors and their interactions with the aid of Smith Process Model.

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

Constrained predictive control based on T-S fuzzy model for nonlinear systems

A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.

Temperature Control Based on Fuzzy Logic Two-degree-of-freedom Smith Internal Model

According to the characteristics of the large time delay,nonlinearity and the great inertia of temperature control system in biomass pyrolysis reactor,a two-degree-of-freedom Smith internal model controller based on fuzzy control is proposed.Firstly,the mathematical model of the temperature control system is established by using the step response method,and then the two-degree-of-freedom Smith internal model controller is designed,and the good tracking performance and disturbance suppression performance can be obtained by designing the set value tracking controller and interference rejection capability.Secondly,the fuzzy control algorithm is used to realize the on-line tuning of the control parameters of the two-degree-of-freedom Smith internal model algorithm.The simulation results show that,compared with the traditional internal model control,fuzzy internal model PID control and two-degree-of-freedom Smith internal model control,the algorithm proposed in this paper improves the influence of lag time on the control system,realizes the separation control of set point tracking and anti-jamming performance and the self-tuning of control parameters,and improves the control performance of the system.

Flatness predictive model based on T-S cloud reasoning network implemented by DSP

The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.

基于T-S模糊建模的气动肌肉拮抗关节预测角度控制

目的:为了提升气动肌肉拮抗关节的控制精度。方法:提出了一种用于气动肌肉拮抗关节角度跟踪的T-S模糊模型预测控制方法。建立了拮抗关节的T-S模糊模型,并对模型进行了离散化处理;基于离散后的T-S模糊模型,设计了拮抗关节的模型预测控制器,通过特征值法验证了系统的稳定性。结果:仿真实验表明,T-S模糊模型预测控制器跟踪±15°和±10°的角度时,相对于传统模型预测控制,平均绝对误差分别降低了29.05%和44.11%。结论:设计的T-S模糊模型预测控制器提高了气动肌肉拮抗关节的控制精度。

Model Predictive Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) with Online Parameter Estimation Based on Extended Kalman Filter

Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimation based on the extended Kalman filter for PMSM is designed. By predicting the errors of torque and flux based on the model and the current states of the system, the optimal voltage vector is selected to minimize the error of torque and flux. The stator resistance and inductance are estimated online via EKF to reduce the effect of model error and the current estimation can reduce the error caused by measurement noise. The stability of the EKF is proved in theory. The simulation experiment results show the method can estimate the motor parameters, reduce the torque, and flux ripples and improve the performance of direct torque control for permanent magnet synchronous motor (PMSM).

Building a Comprehensive Well-off Society in Yunnan＇s Minority Areas A Study of the Prediction on the Differences in Life Quality Indexes

Based on the gray theory and GM （1, 1） model, the life quality indexes in next few years are predicted using the statistical data of the life quality indexes of Yunnan＇ s 8 minority areas in recent years and also compared with the standard value （2020） of building the national well-off society, and the differences between them are measured and calculated. Thus, effective reference information is provided for the decision-making of government.