Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.

Hyperspectral Image Super-Resolution Network Based on Reinforcing Inter-Spectral Incremental Information

Hyperspectral images typically have high spectral resolution but low spatial resolution,which impacts the reliability and accuracy of subsequent applications,for example,remote sensingclassification and mineral identification.But in traditional methods via deep convolution neural net-works,indiscriminately extracting and fusing spectral and spatial features makes it challenging toutilize the differentiated information across adjacent spectral channels.Thus,we proposed a multi-branch interleaved iterative upsampling hyperspectral image super-resolution reconstruction net-work(MIIUSR)to address the above problems.We reinforce spatial feature extraction by integrat-ing detailed features from different receptive fields across adjacent channels.Furthermore,we pro-pose an interleaved iterative upsampling process during the reconstruction stage,which progres-sively fuses incremental information among adjacent frequency bands.Additionally,we add twoparallel three dimensional(3D)feature extraction branches to the backbone network to extractspectral and spatial features of varying granularity.We further enhance the backbone network’sconstruction results by leveraging the difference between two dimensional(2D)channel-groupingspatial features and 3D multi-granularity features.The results obtained by applying the proposednetwork model to the CAVE test set show that,at a scaling factor of×4,the peak signal to noiseratio,spectral angle mapping,and structural similarity are 37.310 dB,3.525 and 0.9438,respec-tively.Besides,extensive experiments conducted on the Harvard and Foster datasets demonstratethe superior potential of the proposed model in hyperspectral super-resolution reconstruction.

An Incremental Model Transfer Method for Complex Process Fault Diagnosis

Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.

Studying Formability Limits By Combining Conventional and Incremental Sheet Forming Process

In this work it is assessed the potential of combining conventional and incremental sheet forming processes in a same sheet of metal.This so-called hybrid forming approach is performed through the manufacture of a pre-forming by conventional forming,followed by incremental sheet forming.The main objective is analyzing strain evolution.The pre-forming induced in the conventional forming stage will determine the strain paths,directly influencing the strains produced by the incremental process.To conduct the study,in the conventional processes,strains were imposed in three different ways with distinct true strains.At the incremental stage,the pyramid strategy was adopted with different wall slopes.From the experiments,the true strains and the final geometries were analyzed.Numerical simulation was also employed for the sake of comparison and correlation with the measured data.It could be observed that single-stretch pre-strain was directly proportional to the maximum incremental strains achieved,whereas samples subjected to biaxial pre-strain influenced the formability according to the degree of pre-strain applied.Pre-strain driven by the prior deep-drawing operation did not result,in this particular geometry,in increased formability.

Numerical prediction of the incremental melting and solidification process

A mathematical formulation is applied to represent the phenomena in theincremental melting and solidification process (IMSP), and the temperature and electromagneticfields and the depth of steel liquid phase are calculated by a finite difference technique using thecontrol volume method. The result shows that the predicted values are in good agreement with theobservations. In accordance with the calculated values for different kinds of materials anddifferent size of molds, the technological parameter of the IMS process such as the power supply andthe descending speed rate can be determined.

A NON－INCREMENTAL TIME－SPACE ALGORITHM FOR NUMERICAL SIMULATION OF FORMING PROCESS

A non-incremental time-space algorithm is proposed for numerical. analysis of forming process with the inclusion of geometrical, material, contact-frictional nonlinearities. Unlike the widely used Newton-Raphson counterpart, the present scheme features an iterative solution procedure on entire time and space domain. Validity and feasibility of foe present scheme are further justiced by the numerical investigation herewith presented.

Incremental multivariable predictive functional control and its application in a gas fractionation unit

The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process.In this work,an incremental multivariable predictive functional control(IMPFC) algorithm was proposed with less online computation,great precision and fast response.An incremental transfer function matrix model was set up through the step-response data,and predictive outputs were deduced with the theory of single-value optimization.The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm,and thereby making the control variable smoother.The predictive output error and future set-point were approximated by a polynomial,which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory.Then,the design of incremental multivariable predictive functional control was studied.Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.

Functional sample path properties of subsequence's C-R increments for a Wiener process in Hlder norm

In this paper, with the aid of large deviation formulas established in strong topology of functional space generated by HSlder norm, we discuss the functional sample path properties of subsequence＇s C-R increments for a Wiener process in HSlder norm. The obtained results, generalize the corresponding results of Chen and the classic Strassen＇s law of iterated logarithm for a Wiener process.

A GENERAL FORM OF THE INCREMENTS OF A TWO-PARAMETER WIENER PROCESS

In this paper, we consider a general form of the increments for a two-parameter Wiener process. Both the Csorgo-Revesz's increments and a class of the lag increments are the special cases of this general form of increments. Our results imply the theorem that have been given by Csorgo and Revesz (1978), and some of their conditions are removed.

A GENERAL FORM OF THE INCREMENTS OF TWO-PARAMETER FRACTIONAL WIENER PROCESS

A general form of the increments of two-parameter fractional Wiener process is given. The results of Csoergo-Révész increments are a special case,and it also implies the results of the increments of the two-parameter Wiener process.

Some limsup results for increments of stable processes in random scenery

In this paper,we prove some limsup results for increments and lag increments of G(t),which is a stable processe in random scenery.The proofs rely on the tail probability estimation of G(t).

单点渐进成形工艺参数对铝合金圆锥台表面粗糙度的影响

表面粗糙度是影响渐进成形零件质量的关键因素之一,为了探究工艺参数对零件表面粗糙度的影响,采用单点渐进成形技术对5052铝合金板料进行成形实验,研究工艺参数轴向进给量、主轴转速、进给速度对圆锥台表面质量的影响,并使用粗糙度仪测量外表面粗糙度R_(a)值。实验结果表明:降低轴向进给量可以改善零件表面质量;随着主轴转速增大,零件粗糙度R_(a)呈先减小后增大趋势;进给速度对粗糙度R_(a)影响较小,随着进给速度增大,粗糙度R_(a)呈先减小后增大趋势,受零件尺寸限制,进给速度超过2817 mm·min^(-1)时,内表面“鱼鳞纹”和粗糙度R_(a)几乎没有变化。

专业还是通俗创新型产品的广告语体风格对消费者产品采用的影响?

在新产品广告中,企业使用通俗语言进行描述的做法由来已久。随着新产品引入了许多新特性,体现产品科技效能的专业性语言逐渐被采用,以进行产品宣传。企业应该如何选择广告语体风格?基于语言线索和解释水平理论,本文验证了广告语体风格(专业术语vs.通俗用语)与产品创新类型(突破式vs.渐进式)对消费者购买意愿存在显著交互作用:专业术语的语体风格能有效提高消费者对突破式创新产品的购买意愿;通俗用语的语体风格能有效提高消费者对渐进式创新产品的购买意愿,加工流畅度中介了上述交互作用。此外,本研究还发现了不同产品属性条件下上述机制的适用性,并排除了感知风险与感知质量的替代性解释。本文结论将为企业提升新产品广告劝诱效果提供建议。

基于XGBoost增量实现业务流程执行结果的预测性监控方法

随着工业制造业务流程智能化提升,以预测执行结果为目标的监控技术成为必需。该技术基于历史执行构建预测模型,从而对正在执行的流程进行结果预测。但现有研究假定流程执行行为一直保持不变,实际上流程在运行中常发生变化(即流程执行发生漂移),因此预测模型也需要适应这种漂移。针对这种情况,受到在线学习思想的启发,提出了基于XGBoost增量实现以流程执行结果为目标的预测流程监控技术,并分别在真实数据集和合成数据集上进行了大量的实验。实验结果表明,基于XGBoost的增量学习技术能够很好地为工业制造真实场景中的预测性流程监控提供一种有效的解决方案。

用于紫外光谱仪的探测器温度控制系统

为了降低探测器的噪声与暗电流,使光谱仪的CMOS探测器能获得更准确的光谱曲线,设计了探测器温度控制系统。本系统核心采用基于现场可编程逻辑门阵列(FPGA)的增量式比例-积分-微分(PID)控制算法。在传统控制算法的基础上,增加了抗积分饱和控制,并且在PID算法的前端增加了对目标值的过渡过程。该系统在实现探测器温度变化速率可控的同时,也解决了超调过大的问题。多次整机环境实验结果表明:在轨环境温度条件下,40°C温差范围内该系统可以控制探测器以指定温变速率(4.5±0.05)°C/min达到任意温度;并且可在该温度下稳定工作;温度变化范围为±0.1°C。相比于传统模拟PID控制方法,其具有灵活度高,稳定性强等优点。当制冷到−10°C时,探测器的噪声得到了有效抑制。

空化水射流在渐进成形与冲蚀领域的对比分析

目的将高压水射流渐进成形技术与空化现象相结合,探索一种新的加工形式——空化水射流渐进成形技术,研究不同空化喷嘴内部结构、工作靶距对成形和冲蚀的影响。方法设计了5种不同内部结构的空化喷嘴,在相同工艺参数和不同靶距的条件下,对厚度为0.5 mm、尺寸为200 mm×200 mm的5052铝合金板以及厚度为10 mm、尺寸为50 mm×50 mm的1060铝合金块进行成形和冲蚀实验,测量样品成形高度和冲蚀损失质量,观察电子显微镜下样品的冲蚀外貌形态。结果喷嘴内部结构对空化效果有很大影响,有锥形扩散段的喷嘴空化效果更好,作用半径更大。喷嘴的作用区域呈3层圆环状,由许多涡旋组合而成。产生的空泡沿流动方向运动,在靶体附近溃灭,释放冲击力。在成形和冲蚀2种工况下,均存在最佳靶距,且随着靶距的增大,空化水射流的表面形态也发生变化,圆环的第1层直径保持不变,第2层、第3层直径显著增大。结论空化喷嘴的内部结构和靶距对空化水射流成形和冲蚀的影响较大,在实际生产应用中应根据使用场景、工作范围选择合适的工艺参数,以提高效率。

支持增量日志的业务流程剩余时间预测方法

流程预测性监控通过对业务流程及其属性的预测,预防运行中的实例未来可能会面临的风险,从而及时干预流程。流程剩余时间预测是避免业务超时风险的一项预测任务,然而业务执行是动态的过程,可能会随时间或业务规模的增长而发生变化。这就要求预测模型能够持续更新以捕捉这些变化,同时要有足够的输入信息来区分变化前后的差异,并且预测模型应具有充分的拟合与泛化能力。针对上述问题,本文提出支持增量日志的流程剩余时间预测框架。具体而言,提出特征自选取策略,构建多特征预测模型,丰富预测任务的已知信息,将所得特征组合作为模型输入,提高预测模型的拟合能力。然后,将定期和定量作为模型更新的判断依据,提出定期更新、定量更新和综合更新3种增量更新机制。最后,基于6个真实事件日志,实现了3种不同的预测模型,模拟了增量更新过程。实验结果验证了本文所提方法的有效性,提高了流程剩余时间预测的准确率。

5052铝合金板材渐进成形工艺参数双目标优化及试验验证

针对金属板材单点渐进成形工艺,选用具有较强成形性能的5052铝合金板材进行研究。基于Abaqus建立单点渐进成形有限元模型,以工具头直径、轴向进给量、板材初始厚度为研究变量,成形后圆锥台结构的最大Mises应力和最大减薄率为优化目标,设计了正交试验,并对每组参数进行模拟分析,采用田口分析和方差分析对工艺参数进行优化。优化结果表明,轴向进给量和板材初始厚度是对最大Mises应力影响的显著项,最终的优化工艺参数组合为工具头直径8 mm、轴向进给量0.5 mm、板材初始厚度0.5 mm,该工艺参数分析的圆锥台结构最大Mises为213.35 MPa,最大减薄率为38.32%。通过单点渐进成形试验进行验证,成形后的圆锥台结构表面质量较好,这也表明该工艺参数组合具备实际应用的可行性。

基于一阶ECM-IGPR的锂离子电池SOC及SOH联合估计框架

为解决锂电池荷电状态与健康状态互相耦合问题,提出一种基于等效电路模型-改进高斯过程回归的锂离子电池荷电状态(SOC)-健康状态(SOH)的联合估计框架。该框架通过提取容量增量曲线中的健康特征,进行主成分分析,然后建立电池老化的改进高斯过程回归模型进行SOH预测。在此基础上,建立锂电池一阶状态空间模型,并结合改进粒子滤波算法对后一周期的SOC更新,实现SOC及SOH的联合长期估计。牛津数据集中的8个电池被用来验证该框架的准确性和适应性,取得了较好的估计结果。

常用航天铝合金热渐进成形极限的研究进展

铝合金因其轻质、高强、良好的加工性能以及优异的耐腐蚀性,在航天领域得到了广泛的应用。随着科技的进步和航天技术的发展,对铝合金材料的性能要求越来越高,促使铝合金材料不断向高性能、轻量化的方向发展。热渐进成形作为一种先进的板料成形技术,为航天用铝合金零件的制造提供了新的解决方案,并获得了当今学者的广泛关注。根据渐进成形加热方式不同,总结了近年来典型的加热方法为电加热、激光加热、电磁感应加热。在此基础上,综述了铝合金热渐进成形极限方面的研究,并总结了多种工艺参数对材料成形极限的影响规律。