A general result on precise asymptotics for linear processes of positively associated sequences

Let {εt; t ∈ Z^＋} be a strictly stationary sequence of associated random variables with mean zeros, let 0〈Eε1^2〈∞ and σ^2=Eε1^2＋1∑j=2^∞ Eε1εj with 0〈σ^2〈∞.{aj;j∈Z^＋} is a sequence of real numbers satisfying ∑j=0^∞｜aj｜〈∞.Define a linear process Xt=∑j=0^∞ ajεt-j,t≥1,and Sn=∑t=1^n Xt,n≥1.Assume that E｜ε1｜^2＋δ′〈 for some δ′〉0 and μ（n）=O（n^-ρ） for some ρ〉0.This paper achieves a general law of precise asymptotics for {Sn}.

Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

A batch-to-batch optimal iterative learning control （ILC） strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation （LTVP） model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained, A rigorous theorem is proposed, to prove the convergence of tracking error under ILC, The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation processes.BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors.When the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring.Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant analysis.In addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.

Evaluating Traffic Congestion Using the Traffic Occupancy and Speed Distribution Relationship: An Application of Bayesian Dirichlet Process Mixtures of Generalized Linear Model

Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream parameters, which has been used to quantify the traffic conditions. Previous studies have shown that multi-modal probability distribution of speeds gives excellent results when simultaneously evaluating congested and free-flow traffic conditions. However, most of these previous analytical studies do not incorporate the influencing factors in characterizing these conditions. This study evaluates the impact of traffic occupancy on the multi-state speed distribution using the Bayesian Dirichlet Process Mixtures of Generalized Linear Models (DPM-GLM). Further, the study estimates the speed cut-point values of traffic states, which separate them into homogeneous groups using Bayesian change-point detection (BCD) technique. The study used 2015 archived one-year traffic data collected on Florida’s Interstate 295 freeway corridor. Information criteria results revealed three traffic states, which were identified as free-flow, transitional flow condition (congestion onset/offset), and the congested condition. The findings of the DPM-GLM indicated that in all estimated states, the traffic speed decreases when traffic occupancy increases. Comparison of the influence of traffic occupancy between traffic states showed that traffic occupancy has more impact on the free-flow and the congested state than on the transitional flow condition. With respect to estimating the threshold speed value, the results of the BCD model revealed promising findings in characterizing levels of traffic congestion.

The SCUSUM Estimation of Variance Change Point in Linear Process

This paper considers the estimation problem of a variance change-point in linear process.Consistency of a SCUSUM type change-point estimator is proved and its rate of convergence is established.The mean-unknown case is also considered.

Image Processing System for Air Classification Using Linear Discriminant Analysis

An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wastes. In these factories, operators manually adjust the air flow rate while checking the wastes discharged from the separator outlet. However, the adjustments are basically done by trial and error, and it is difficult to do them appropriately. In this study, we tried to develop the image processing system that calculates the ratio of copper (Cu) product and polyvinyl chloride (PVC) in the wastes as a substitute for the operator’s eyes. Six colors of PVC (white, gray, green, blue, black, and red) were used in the present work. An image consists of foreground and background. An image’s regions of interest are objects (Cu particles) in its foreground. However, the particles having a color similar to the background color are buried in the background. Using the difference of two color backgrounds, we separated particles and background without dependent of background. The Otsu’ thresholding was employed to choose the threshold to maximize the degree of separation of the particles and background. The ratio of Cu to PVC pixels from mixed image was calculated by linear discriminant analysis. The error of PVC pixels resulted in zero, whereas the error of Cu pixels arose to 4.19%. Comparing the numbers of Cu and PVC pixels within the contour, the minority of the object were corrected to the majority of the object. The error of Cu pixels discriminated as PVC incorrectly became zero percent through this correction.

DATA PROCESSING ON LINEARIZATION OF HOT-WIRE ANEMOMETER

A method of data processing to determine the coefficients of linearization equations for 1050 anemometer (produced by Thermo-Systems Inc. -TSI, USA) with the sensors made of domestic hot wire using the program preferred in this Paper is described. By calculation and test, it is indicated that the error resulting from this method is about 0. 5% of the full scale and less than TSl's. By using this method we can set up the calibration curve according to the measurement range and the diameter of the hot wire at a certain accuracy.

Application of Dynamic Linear Detecting Method in Data Processing of the Portable Blood Sugar Analyzer

In this paper,a dynamic linear detecting method,that the non-linear coefficient NL% was led and the non-linearity of data were estimated continuously and dynamically and determined when NL% exceeded reference value (5%),was used for data processing and could solve the problem caused by the phenomenon of substrate depleting occurred following the redox reaction in portable blood sugar analyzer.By contrast to the conventional end-point method,the dynamic linear detecting method is based on multipoint data collecting.Experiments of measuring the calibration glucose solution with 8 various concentrations from 50 mg/dl to 400 mg/dl were carried out with the analyzer developed by our group.The linear regression curve,whose correlation for the data was 0.9995 and the residual was 2.8080,were obtained.The obtained correlation,residual, and the computation workload are all fit for the portable blood sugar analyzer.

Some Applications of Higher Moments of the Linear Gaussian White Noise Process

The Linear Gaussian white noise process is an independent and identically distributed (iid) sequence with zero mean and finite variance with distribution N (0, σ2 ) . Hence, if X1, x2, …, Xn is a realization of such an iid sequence, this paper studies in detail the covariance structure of X1d, X2d, …, Xnd, d=1, 2, …. By this study, it is shown that: 1) all powers of a Linear Gaussian White Noise Process are iid but, not normally distributed and 2) the higher moments (variance and kurtosis) of Xtd, d=2, 3, … can be used to distinguish between the Linear Gaussian white noise process and other processes with similar covariance structure.

Study on linear bio-structure print process based on alginate bio-ink in 3D bio-fabrication

Ink-jet printing is a non-impact printing technology in which drops jetted from an orifice onto a designated position.This technology can digitally transport fluids containing cells precisely onto desired substrates to construct three-dimensional organs.In order to obtain the stable uniform droplets,a stream is the key point of this technology.However,there are so many factors that affect the uniform droplet stream construction process:print parameters,material parameters,control method,etc.A good understanding of the various coupled transport processes that occur during bio-ink impact and spreading on bio-structure can improve the success of print-ability.This paper aims to obtain a good linear bio-structure with ink-jet printing technology.First,a typical droplet deposition process model is constructed;including droplet dynamics impact models and droplet diffusion cap models.Second,a model of successive droplet overlap,to form linear bio-structures,is constructed.Third,the finite element method is used to simulate the droplet impact,collision,and fusion process.Finally,the main influencing factors of the continuous injection printing process,namely the time interval between consecutive droplets and the droplet contact angles,are discussed.Sodium alginate is selected as bio-ink to verify the theory,and it is found that a good linear bio-structure could be obtained if the printing parameters are controlled optimally,i.e.,if the initial contact angle is set as 60 degrees and the trigger frequency is set as 150 kHz.With a proper printing speed and gel coating,a good survival rate of printed cells could be obtained.

Central limit theolrem for linear processes generated byⅡD random variables under the sub-linear expectation

In this paper,we investigate the central limit theorem and the invariance principle for linear processes generated by a new notion of independently and identically distributed(IID)random variables for sub-linear expectations initiated by Peng[19].It turns out that these theorems are natural and fairly neat extensions of the classical Kolmogorov's central limit theorem and invariance principle to the case where probability measures are no longer additive.

THE MONITORING FOR PHYSICAL DEVELOPING PROCESS WITH A LINEAR CCD

In this work, we used a linear CCD to detect the whole physical developing process of silver diffusion transfer reversal process in photographic chemistry. The influence of the ingredient of the working solution was studied.

Decomposition of Supercritical Linear-Fractional Branching Processes

It is well known that a supercritical single-type Bienayme-Galton-Watson process can be viewed as a decomposable branching process formed by two subtypes of particles: those having infinite line of descent and those who have finite number of descendants. In this paper we analyze such a decomposition for the linear-fractional Bienayme-Galton-Watson processes with countably many types. We find explicit expressions for the main characteristics of the reproduction laws for so-called skeleton and doomed particles.

基于对数线性过程的数控机床不完全预防性维修策略研究

针对数控机床维修时产生较高维修成本和未能充分利用系统剩余寿命的问题,提出了一种基于对数线性过程的数控机床非等周期不完全预防性维修方法。建立对数线性过程下机床不完全预防性维修的混合故障强度模型,根据经验取值法给出役龄递减因子和故障强度递增因子的表达式,以可靠度为约束,推导出不同预防性维修阈值和更换阈值下的动态维修间隔期。在此基础上,以平均维修费用最小为目标给出设备维修周期内的最佳预防维修次数。以数控机床主轴系统为例对所提方法进行验证,通过分别与未考虑更换阈值的维修模型以及混合故障强度因子服从均匀分布的维修模型进行对比分析,结果表明该方法可有效降低维修成本且能充分利用设备的剩余寿命。研究结果为制定合理的数控机床预防性维修策略提供了一个新方法。

A min-max optimization approach for weight determination in analytic hierarchy process

A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.

SOLVERS FOR SYSTEMS OF LARGE SPARSE LINEAR AND NONLINEAR EQUATIONS BASED ON MULTI-GPUS

Numerical treatment of engineering application problems often eventually results in a solution of systems of linear or nonlinear equations.The solution process using digital computational devices usually takes tremendous time due to the extremely large size encountered in most real-world engineering applications.So,practical solvers for systems of linear and nonlinear equations based on multi graphic process units（GPUs）are proposed in order to accelerate the solving process.In the linear and nonlinear solvers,the preconditioned bi-conjugate gradient stable（PBi-CGstab）method and the Inexact Newton method are used to achieve the fast and stable convergence behavior.Multi-GPUs are utilized to obtain more data storage that large size problems need.

Processing Modflow模型在预测地面沉降中的应用

研究目的:针对开采地下水引起的区域地面沉降对线性工程的影响问题,以京津城际铁路为例,采用地下水三维渗流与地面沉降耦合模型计算软件—Processing Modflow,选择沿线地面沉降较严重区段,建立区域地下水流场与地面沉降的耦合模型,运用参数修正后的模型,进行地面沉降预测。研究结论:利用实测地面沉降值对模型进行验证,沉降中心位置及沉降量的模拟结果与实测结果基本吻合;设计不同的地下水开采方案,利用模型预测地面沉降,结果表明:合理压采地下水可以降低地面沉降量及沉降坡度,减缓地面沉降的不均匀性。

基于连续时间表示的炼钢-连铸过程调度建模与求解

面向某钢铁企业在炼钢-连铸阶段的复杂生产调度问题,提出了一种基于连续时间表示的调度建模方法。依据炼钢-连铸过程的每个浇次中各炉次的保序性,针对每一台生产设备,将每个炉次的作业开始与结束时间作为决策变量,并根据避免作业冲突的原则构建约束;同时,定义离散决策变量,保证炉次在同一类生产设备中进行作业的唯一性。最终,调度模型被构建为一个混合整数规划问题,从而便于使用商业求解器高效求解。在同一生产计划中对基于不同时间表示方法的调度模型进行仿真测试,仿真结果表明,所提方法能够在要求的运行时间内得到满足工艺要求的最优决策,在工程应用中具有可行性。

基于YOLOv5的焊缝图像特征信息检测方法研究

为了满足自动化工艺的需要和获取更为清晰的焊缝原始图像,提出了一种基于YOLOv5和结构光的焊缝特征点提取算法。首先利用激光视觉系统获取焊缝图像,使用改进后的YOLOv5算法进行预训练;然后通过Steger算法提取结构光中心线,使用Harris算法对中心线图像进行处理,并确定焊缝坐标点的坐标;最后将坐标信息传输给PLC,控制十字滑台带动焊枪进行焊缝的跟踪操作。实验结果表明,改进后的YOLOv5算法mAP值更大、稳定性更高、鲁棒性更强。