Co-salient object detection with iterative purification and predictive optimization

Background Co-salient object detection(Co-SOD)aims to identify and segment commonly salient objects in a set of related images.However,most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the co-representation.These issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of detection.Methods To address this issue,this study introduces a novel Co-SOD method with iterative purification and predictive optimization(IPPO)comprising a common salient purification module(CSPM),predictive optimizing module(POM),and diminishing mixed enhancement block(DMEB).Results These components are designed to explore noise-free joint representations,assist the model in enhancing the quality of the final prediction results,and significantly improve the performance of the Co-SOD algorithm.Furthermore,through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM,POM,and DMEB,our experiments confirmed that these components are pivotal in enhancing the performance of the model,substantiating the significant advancements of our method over existing benchmarks.Experiments on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

2D multi-model general predictive iterative learning control for semi-batch reactor with multiple reactions

Batch to batch temperature control of a semi-batch chemical reactor with heating/cooling system was discussed in this study. Without extensive modeling investigations, a two-dimensional(2D) general predictive iterative learning control(2D-MGPILC) strategy based on the multi-model with time-varying weights was introduced for optimizing the tracking performance of desired temperature profile. This strategy was modeled based on an iterative learning control(ILC) algorithm for a 2D system and designed in the generalized predictive control(GPC) framework. Firstly, a multi-model structure with time-varying weights was developed to describe the complex operation of a general semi-batch reactor. Secondly, the 2 D-MGPILC algorithm was proposed to optimize simultaneously the dynamic performance along the time and batch axes. Finally, simulation for the controller design of a semi-batch reactor with multiple reactions was involved to demonstrate that the satisfactory performance could be achieved despite of the repetitive or non-repetitive disturbances.

Multi-loop Constrained Iterative Model Predictive Control Using ARX -PLS Decoupling Structure

A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares(ARX-PLS) framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restriction. ARX-PLS decoupling character enables to turn the multivariable model predictive control(MPC) controller design in original space into the multi-loop single input single output(SISO) MPC controllers design in latent space.An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control(IMC) method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.

Nonlinear system PID-type multi-step predictive control

A compound neural network was constructed during the process of identification and multi-step prediction. Under the PID-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller’s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Simulation study shows the effectiveness and good performance.

Chaotic time series multi-step direct prediction with partial least squares regression

Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.

Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction

Traffic flow prediction is an important part of the intelligent transportation system. Accurate multi-step traffic flow prediction plays an important role in improving the operational efficiency of the traffic network. Since traffic flow data has complex spatio-temporal correlation and non-linearity, existing prediction methods are mainly accomplished through a combination of a Graph Convolutional Network (GCN) and a recurrent neural network. The combination strategy has an excellent performance in traffic prediction tasks. However, multi-step prediction error accumulates with the predicted step size. Some scholars use multiple sampling sequences to achieve more accurate prediction results. But it requires high hardware conditions and multiplied training time. Considering the spatiotemporal correlation of traffic flow and influence of external factors, we propose an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction. This model models the traffic flow as diffusion on a digraph and extracts the spatial characteristics of traffic flow through GCN. We add meaningful time-slots attention to the encoder-decoder to form an Attention Encoder Network (AEN) to handle temporal correlation. The attention vector is used as a competitive choice to draw the correlation between predicted states and historical states. We considered the impact of three external factors (daytime, weekdays, and traffic accident markers) on the traffic flow prediction tasks. Experiments on two public data sets show that it makes sense to consider external factors. The prediction performance of our ABSTGCN-EF model achieves 7.2%–8.7% higher than the state-of-the-art baselines.

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems,it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances.In this paper,to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control(ILC),a novel real-time iterative compensation(RIC)control framework is proposed for precision motion systems without changing the inner closed-loop controller.Specifically,the RIC method can be divided into two parts,i.e.,accurate model prediction and real-time iterative compensation.An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times.In light of predicted errors,a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation.Both the prediction and compen-sation processes are finished in a real-time motion control sampling period.The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions.Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability,which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.

A Content-Aware Bitrate Selection Method Using Multi-Step Prediction for 360-Degree Video Streaming

A content-aware multi-step prediction control(CAMPC)algorithm is proposed to determine the bitrate of 360-degree videos,aim⁃ing to enhance the quality of experience(QoE)of users and reduce the cost of video content providers(VCP).The CAMPC algorithm first em⁃ploys a neural network to generate the content richness and combines it with the current field of view(FOV)to accurately predict the probability distribution of tiles being viewed.Then,for the tiles in the predicted viewport which directly affect QoE,the CAMPC algorithm utilizes a multi-step prediction for future system states,and accordingly selects the bitrates of multiple subsequent steps,instead of an instantaneous state.Meanwhile,it controls the buffer occupancy to eliminate the impact of prediction errors.We implement CAMPC on players by building a 360-degree video streaming platform and evaluating other advanced adaptive bitrate(ABR)rules through the real network.Experimental results show that CAMPC can save 83.5%of bandwidth resources compared with the scheme that completely transmits the tiles outside the viewport with the Dynamic Adaptive Streaming over HTTP(DASH)protocol.Besides,the proposed method can improve the system utility by 62.7%and 27.6%compared with the DASH official and viewport-based rules,respectively.

Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and hosts.In addition,there is a lack of long-term consideration for resource optimization in the VM consolidation,which results in unnecessary VM migration and increased energy consumption.To address these limitations,a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers(MPaAF-VMC)is proposed.The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs,and obtains the stage demand of resources in the future period through multi-step prediction,which is realized by iterative prediction.Then,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance.During the VM consolidation,the affinity value is used to select the migration VM and placement host.The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform.Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.

Distributed Model Predictive Control with Actuator Saturation for Markovian Jump Linear System

This paper is concerned with the distributed model predictive control (MPC) problem for a class of discrete-time Markovian jump linear systems (MJLSs) subject to actuator saturation and polytopic uncertainty in system matrices. The global system is decomposed into several subsystems which coordinate with each other. A set of distributed controllers is designed by solving a min-max optimization problem in terms of the solutions of linear matrix inequalities (LMIs). An iterative algorithm is developed to achieve the online computation. Finally, a simulation example is employed to show the effectiveness of the proposed algorithm. © 2014 Chinese Association of Automation.

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence.It is one of the crucial issues in computer vision and has many real-world applications,mainly focused on predicting future scenarios to avoid undesirable outcomes.However,modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene,such as occlusions,camera movements,delay and illumination.Direct frame synthesis or optical-flow estimation are common approaches used by researchers.However,researchers mainly focused on video prediction using one of the approaches.Both methods have limitations,such as direct frame synthesis,usually face blurry prediction due to complex pixel distributions in the scene,and optical-flow estimation,usually produce artifacts due to large object displacements or obstructions in the clip.In this paper,we constructed a deep neural network Frame Prediction Network(FPNet-OF)with multiplebranch inputs(optical flow and original frame)to predict the future video frame by adaptively fusing the future object-motion with the future frame generator.The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network.Using various real-world datasets,we experimentally verify that our proposed framework can produce high-level video frame compared to other state-ofthe-art framework.

基于GPC-ILC的秸秆捡拾致密成型机主轴转速控制方法研究

为解决秸秆捡拾致密成型机主轴转速自动控制问题,以利于致密成型机全程智能化作业,设计了电液控制系统的数学模型与转速预测模型,提出了一种基于GPC-ILC的致密成型机主轴转速控制方法,通过采集先前成型机运行过程中的输入、输出数据,使用带遗忘因子的最小二乘法辨识广义预测控制的参数模型并计算预测输出值,根据以往过程的累计平均模型误差修正预测输出值,并引出迭代学习控制律,在线实时计算新的控制量,实现主轴转速的控制。场地收获试验表明:增负荷时,转速最大动态偏差为3.21 r/min,与目标值的偏差为2.6%,最大余差为1.23 r/min;减负荷时,最大动态偏差为2.23 r/min,与目标值的偏差为2.47%,最大余差为0.89 r/min;增减负荷转速达到稳定时间小于5 s,超调量小于3%。田间试验表明:最大动态偏差为3.75 r/min,与目标值的偏差为3.47%,最大余差为1.79 r/min,满足成型机田间作业的需求。GPC-ILC算法可及时校正模型失配、干扰引起的转速控制的不确定性。

基于迭代协同克里金反演的非均质地基固结沉降预测研究

目前地基固结沉降反演预测多是以土体均质或分层假设为前提,然而天然地基水文、力学参数具有空间变异性是客观事实。鉴于此,提出了一种基于迭代协同克里金的非均质地基土体参数反演方法,据此开展了利用沉降和超孔隙水压力观测数据反演非均质地基土参数的数值试验研究,并结合敏感度分析解释了不同类型观测数据对非均质地基参数反演解析度的影响机制。结果表明:此方法反演的参数场是最优无偏估计;同时采用沉降和超孔隙水压力观测数据反演刻画非均质地基比单独采用沉降或超孔压数据反演刻画的非均质地基解析度更高,用于地基固结沉降预测效果也更好;不同类型观测信息对不同参数反演刻画解析度高低与观测信息对参数敏感度数量级呈正相关关系。

基于IDP的重型商用车自适应距离域预见性巡航控制策略

为降低重型商用车燃油消耗、减少运输成本,本文协调“人-车-路”交互体系,将车辆与智能网联环境下的多维度信息进行融合,提出了一种基于迭代动态规划(iterative dynamic programming,IDP)的自适应距离域预见性巡航控制策略(adaptive range predictive cruise control strategy,ARPCC)。首先结合车辆状态与前方环境多维度信息,基于车辆纵向动力学建立自适应距离域模型对路网重构,简化网格数量并利用IDP求取全局最优速度序列。其次,在全局最优速度序列的基础上,求取自适应距离域内的分段最优速度序列,实现车辆控制状态的快速求解。最后,利用Matlab/Simulink进行验证。结果表明,通过多次迭代缩小网格,该算法有效提高了计算效率和车辆燃油经济性。

基于倾斜摄影的混凝土3D打印成型精度分析与预测

混凝土3D打印技术是一项新型的增材制造技术,为了实现采用混凝土3D打印技术来快速打印高质量的成型构件,综合利用迭代最近点(iterative closest point,ICP)算法和倾斜摄影测量技术来分析3D打印构件的整体偏差、局部边界偏差和圆弧角半径,并根据整体偏差和边界偏差对成型精度及可建造性进行研究,最后利用边界偏差和圆弧角半径对打印模型进行预测。结果表明:在混凝土3D打印配合比为水泥∶砂∶粉煤灰∶减水剂∶混凝剂∶水=1∶1.12∶0.09∶0.004∶0.006∶0.32,打印速度V_(d)为45 mm/s,最佳挤出速度V_(j)为138 mm/s时构件成型精度最高;对倾斜摄影模型拟合对齐,并对其具体位置偏差标注色谱图,可知混凝土3D打印构件在第4层时压缩变形较大,可建造性较低;利用预测模型与混凝土3D打印实体构件进行拟合,可知预测模型与基准模型相比误差约1 mm,验证了所提方法及预测模型的合理性。

基于Transformer的盾构泥水舱液位智能预测与控制

泥水液位控制是泥水平衡盾构施工的关键环节,其通过调节泥水循环系统参数进行控制。由于长距离的管道泥水运输,系统存在显著的时滞效应,其控制特性也随地质和管道长度的变化而改变,依靠人工或传统控制方法难以实现准确控制。为解决这一难题并确保盾构推进过程中切口压力的稳定,通过对盾构泥水循环系统控制机制的深入分析,提出一种基于Transformer的盾构泥水舱液位智能预测与控制方法,该方法采用Transformer网络对泥水循环系统的动态特性进行建模,通过迭代多步预测方法对给定控制参数序列下的未来液位情况进行预测。此外,为了优化控制性能并满足系统约束,引入自适应梯度下降方法来解决优化问题及其约束条件,以获得系统的最优控制参数。该方法在苏州河段深层排水隧道的施工数据集上进行仿真验证,试验结果表明:1)在粉砂夹粉质黏土地层的泥水盾构隧道施工中,所提出的控制方法能够有效提高泥水循环系统的控制效果;2)通过与传统控制方法的比较,该智能控制方法显示出更高的控制精度和稳定性,证明其在盾构施工中的应用价值。

存在未知时滞非线性系统的迭代变区间预测迭代学习控制

本文针对机理模型未知的非线性非仿射多入多出(MIMO)离散时间系统,研究了系统同时存在未知时滞和迭代变化运行时间区间的预测迭代学习控制(PILC)问题.首先利用未知时滞的上下界信息建立了一种新型的动态线性化(DL)模型,理论分析表明该模型能够等价描述本文所考虑的存在未知时滞的未知非线性系统.同时,设计一种新的数据补偿机制用以处理由于系统运行时间区间迭代变化而引起的数据丢失问题.基于所建立的DL模型和数据补偿机制,设计了能够同时处理未知时滞和迭代变化运行时间区间的预测迭代学习控制方法.通过严格的理论分析同时给出了建模误差和跟踪控制误差的收敛性质.最后,通过仿真进一步验证了所提方法的有效性.

用于FPGA平台上图像快速旋转的改进CORDIC算法

坐标旋转数字算法(CORDIC)被广泛应用于消旋、相机边框等系统中。在对传统CORDIC算法分析的基础上提出了重编码预测和多倍迭代优化的方法,并用MATLAB进行了仿真,又在VIVADO上进行了FPGA验证与对比。实验结果表明,上述优化相对传统CORDIC算法以及VIVADO自带的CORDIC IP核显著减少了迭代次数,消耗了更少的资源,计算的精度也有了一定的提升。

CFD predictions of LBO limits for aero-engine combustors using fuel iterative approximation

Lean blow-out （LBO） is critical to operational performance of combustion systems in propulsion and power generation. Current predictive tools for LBO limits are based on decadesold empirical correlations that have limited applicability for modern combustor designs. According to the Lefebvre＇s model for LBO and classical perfect stirred reactor （PSR） concept, a load parameter （LP） is proposed for LBO analysis of aero-engine combustors in this paper. The parameters contained in load parameter are all estimated from the non-reacting flow field of a combustor that is obtained by numerical simulation. Additionally, based on the load parameter, a method of fuel iterative approximation （FIA） is proposed to predict the LBO limit of the combustor. Compared with experimental data for 19 combustors, it is found that load parameter can represent the actual combustion load of the combustor near LBO and have good relativity with LBO fuel/air ratio （FAR）. The LBO FAR obtained by FIA shows good agreement with experimental data, the maximum prediction uncertainty of FIA is about ±17.5%. Because only the non-reacting flow is simulated, the time cost of the LBO limit prediction using FIA is relatively low （about 6 h for one combustor with computer equipment of CPU 2.66 GHz · 4 and 4 GB memory）, showing that FIA is reliable and efficient to be used for practical applications.