基于概率模型的实时修正IMM目标跟踪算法

针对传统的IMM算法采用固定测量噪声协方差矩阵和Markov转移概率矩阵导致模型切换缓慢,跟踪精度下降的问题,提出了一种具有模型概率实时修正的IMM机动目标跟踪算法。该算法在监控区域上建立无线电指纹库,利用支持向量回归算法训练得到观测模型。引入模糊神经网络,在模型交互输出阶段自适应地调整测量误差协方差矩阵。根据IMM子模型中连续时间点之间的模型概率的比值,对Markov转移概率进行修正。仿真结果表明,提出的方法在实时性、跟踪精度方面具有良好的性能。     

MULTIPLICITY OF STATES IN SYSTEMS OF INTERACTING CATALYST PARTICLES

Presented here are several case studies, both theoretical and experimental, of the steady-state features of an assembly of interacting catalyst particles in a CSTR. Ostensibly, the results of these studies, though bused on simple string arrangements of two and three particles in a uniform environment, have meaning in terms of the behavioral characteristics of more complicated situations, including fixed-bed reactors.

Theoretical results exhibit complex bifurcation patterns and multiplicity features for steady-state curves when the particles are described as individual interacting entities. The complexity increases when the catalytic activities of the particles are not all alike.

Observations from experiments with the oxidation of hydrogen on spherical pellets of supported platinum resemble the theoretical cases. Individual pellet states in the laboratory work are depicted by their temperature maps obtained by means of a thermal infrared imaging system.     

Data-driven soft sensor of downhole pressure for a gas-lift oil well

Downhole pressure is a key variable in the operation of gas-lift oil wells. However, maintaining and replacing downhole sensors is a challenging task. In this context, we design and implement a data-driven soft sensor to estimate online the downhole pressure based on other (seabed and platform) available measurements. Such application is based on a two-step procedure. In the first step, discrete-time black-box and gray-box NARX models are identified offline and independently using historical data. Both polynomial and neural models are obtained. In the second step, recursive predictions of these multiple models are combined with current measured data (of variables other than the downhole pressure) by means of an interacting bank of unscented Kalman filters. In doing so, a closed-loop model prediction is performed. Three issues are investigated in this paper concerning: (i) the usage of a filter bank rather than a single filter approach, (ii) the availability of seabed variables as inputs of the models compared to the case where only platform variables are available, and (iii) the employment of gray-box models in the filters. Experimental results along 7 months of tests indicate that such closed-loop scheme improves estimation accuracy and robustness compared to the free-run model prediction or to the use of a single unscented Kalman filter. The method employed in this paper can also be applied to other soft sensing applications in industry.     

Optimisation of interacting particle systems for rare event estimation

The interacting particle system (IPS) is a recent probabilistic model proposed to estimate rare event probabilities for Markov chains. The principle of IPS is to apply alternatively selection and mutation stages to a set of initial particles in order to estimate probabilities or quantiles more accurately than with usual estimation techniques. The practical issue of IPS is the tuning of a parameter in the selection stage. Kriging-based optimisation strategy with a low simulation cost is thus proposed in order to minimise the probability estimate relative error. The efficiency of the proposed strategy is demonstrated on different test cases.     

An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

In this paper, the interaction and combination of Fuzzy Fading Memory (FFM) technique and Augmented Kalman Filtering (AUKF) method are presented for the state estimation of non-linear dynamic systems in presence of maneuver. It is shown that the AUKF method in conjunction with the FFM technique (FFM-AUKF) can estimate the target states appropriately since the FFM tunes the covariance matrix of the AUKF method in presence of unknown target accelerations by using a fuzzy system. In addition, the benefits of both FFM technique and AUKF method are employed in the scheme of well-known Interacting Multiple Model (IMM) algorithm. The proposed Fuzzy IMM (FIMM) algorithm does not need the predefinition and adjustment of sub-filters with respect to the target maneuver and reduces the number of required sub-filters to cover the wide range of unknown target accelerations. The Monte Carlo simulation analysis shows the effectiveness of the above-mentioned methods in maneuvering target tracking.     

Analysis of Dermal Papilla Cell Interactome Using STRING Database to Profile the ex Vivo Hair Growth Inhibition Effect of a Vinca Alkaloid Drug,Colchicine

Dermal papillae (DPs) control the formation of hair shafts. In clinical settings, colchicine (CLC) induces patients’ hair shedding. Compared to the control, the ex vivo hair fiber elongation of organ cultured vibrissa hair follicles (HFs) declined significantly after seven days of CLC treatment. The cultured DP cells (DPCs) were used as the experimental model to study the influence of CLC on the protein dynamics of DPs. CLC could alter the morphology and down-regulate the expression of alkaline phosphatase (ALP), the marker of DPC activity, and induce IκBα phosphorylation of DPCs. The proteomic results showed that CLC modulated the expression patterns (fold > 2) of 24 identified proteins, seven down-regulated and 17 up-regulated. Most of these proteins were presumably associated with protein turnover, metabolism, structure and signal transduction. Protein-protein interactions (PPI) among these proteins, established by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, revealed that they participate in protein metabolic process, translation, and energy production. Furthermore, ubiquitin C (UbC) was predicted to be the controlling hub, suggesting the involvement of ubiquitin-proteasome system in modulating the pathogenic effect of CLC on DPC.     

Progressive collapse analysis of thin-walled box columns

Box columns are often used as main strength members of various types of thin-walled structures such as ships, ship-shaped offshore structures, and aerospace structures. Until and after the ultimate limit state is reached, box columns exhibit highly nonlinear structural behavior in terms of geometrical and material aspects. In particular, the effects of local buckling, global buckling, and their interaction play a significant role in the resulting consequences of box columns under extreme actions. In order to calculate the maximum load-carrying capacity of box columns, it is thus highly required to perform the progressive collapse analysis to take into account progressive failures of individual components and their interacting effects. The aim of the present study is to demonstrate a method that is useful for the progressive collapse analysis of thin-walled box columns in terms of computational efficiency and accuracy. Theoretical outline of the method is addressed. Short, medium and long box columns in length are studied in terms of interacting effects between local component failure modes and global system failure modes. The effect of unloaded edge conditions of individual plate elements is also studied. A comparison of the method with more refined nonlinear finite element method computations is made.     

自适应交互式多模型目标跟踪算法

以防空火控系统的跟踪预测为背景,提出一种自适应的交互式多模型跟踪算法。该算法采用后验信息修正模型的噪声方差和马尔可夫转移矩阵,使IMM具有自适应能力。将该算法应用于由CA、CV两模型组成的交互式多模型算法中取得良好的效果。仿真结果表明,该算法跟踪精度比标准IMM有较大改善。     

日本耐硫酸露点腐蚀S-TEN1电焊钢管

耐硫酸露点腐蚀钢S-TENl已广泛应用于排烟处理设备(空气预热器、烟道及烟囱等)的低温硫氧化物腐蚀场合。在燃烧的废气处理设施中，这种钢对低温废气引起的盐酸露点腐蚀也有优良的防腐效果。S-TENl钢钢管以其高质量、经济性及耐腐蚀被用作电站锅炉管。文章对S-TENl钢管的质量特性作了概要介绍。     

N80油套管在油田采出液中的腐蚀与磨损协同作用研究

以长庆油田分公司樊家川油田采出液为介质,在自制的腐蚀磨损试验机中对比研究了N80油套管钢在其中的动静态腐蚀、腐蚀磨损以及缓蚀剂对腐蚀磨损(自行研发的NYS-9缓蚀剂)的协同作用。结果表明,在动态试验中,腐蚀产物膜在流体剪应力的作用下,使得N80油套管钢的局部腐蚀比静态试验严重得多;腐蚀磨损试验初期腐蚀磨损速率较大,随后逐渐减缓;NYS-9缓蚀剂对N80油套管钢具有很好的保护作用,加入缓蚀剂的质量分数为100×10-6时,缓蚀率可达到82%以上。