Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

Wavelet-based hybrid natural image modeling using generalized Gaussian and α-stable distributions

Natural image is characterized by its highly kurtotic and heavy-tailed distribution in wavelet domain. These typical non-Gaussian statistics are commonly described by generalized Gaussian density (GGD) or α-stable distribution. However, each of the two models has its own deficiency to capture the variety and complexity of real world scenes. Considering the statistical properties of GGD and α-stable distributions respectively, in this paper we propose a hybrid statistical model of natural image’s wavelet coefficients which is better in describing the leptokurtosis and heavy tails simultaneously. Based on a clever fusion of GGD and α-stable functions, we establish the optimal parametric hybrid model, and a close-formed Kullback–Leibler divergence of the hybrid model is derived for evaluating model accuracy. Experiment results and comparative studies demonstrate that the proposed hybrid model is closer to the true distribution of natural image’s wavelet coefficients than the single modeling using GGD or α-stable, while is beneficial for applications such as image comparison.     

悬索桥跨径的空气动力极限

桥梁建设已经进入了一个建造跨海大桥的新时代。在跨海大桥工程的可行性研究中存在许多技术方面的挑战 ,对于桥梁设计者来说 ,超大跨径悬索桥的气动稳定问题始终是必须十分关注的问题。本文首先研究超大跨径悬索桥的技术可行性及潜在的要求 ,然后讨论各种气动措施的性能 ,其中包括缆索系统的调整、中央开槽方案、垂直及水平稳定板以及振动控制措施等。从技术及航运要求出发 ,设计了一个接近极限跨径的主跨为5 0 0 0m的悬索桥方案 ,以此为基础研究并阐述解决其空气静动力稳定性的方法     

改进LMD及高阶模糊度函数的管道泄漏定位

针对天然气管道泄漏定位的问题,提出一种基于改进局域均值分解(LMD)及高阶模糊度函数的时延估计方法。该方法首先采用改进的LMD对声发射信号进行分解,获得多个PF分量,进而提出根据K-L散度的PF分量自动选择算法,获取含有主要泄漏信息的PF分量,在此基础上,研究了基于高阶模糊度函数计算声发射信号的时频参数,并通过时频分析获取特征频率的到达时间差,最后结合泄漏产生的广义声发射信号的传播速度完成对天然气管道泄漏的定位。实验结果表明,提出的方法能够进行定位且精度较直接相关法明显提高。     

Sensors incipient fault detection and isolation of nuclear power plant using extended Kalman filter and Kullback–Leibler divergence

Sensor real-time monitoring is an indispensable to achieve reliable plant operation along with stricter safety and environmental measures. This paper presents a statistical algorithm for sensors time-varying incipient fault detection and isolation. The proposed approach formulates the fault detection index and fault signature using the extended Kalman filter. Algorithm relaxes assumption on a monitored system stability and a priori knowledge of the fault profile. Further, fault decision statistics has been devised using Kullback–Leibler Divergence (KLD) and mixed with an Exponential Weighted Moving Average (EWMA) control chart. Pressurized water reactor nuclear power plant temperature and neutron flux sensors incipient fault detection and isolation have been demonstrated to illustrate the effectiveness of proposed methodology.     

J‐divergence‐based Power Allocation in Wireless Sensor Networks with Distributed Detection under Correlated Noises

In this paper, the power allocation problem in a wireless sensor network (WSN) with binary distributed detection is considered. It is assumed that the sensors independently transmit their local decisions to a fusion center (FC) through a slow fading orthogonal multiple access channel (OMAC), where, in every channel, the interferences from other devices are considered as correlated noises. In this channel, the associated power allocation optimization problem with equal power constraint is established between statistical distributions under different hypotheses by using the Jeffrey divergence (J‐divergence) as a performance criterion. It is shown that this criterion for the power allocation problem is more efficient compared to other criteria such as mean square error (MSE). Moreover, several numerical simulations and examples are presented to illustrate the effectiveness of the proposed approach.     

3.705 μm紧凑型光参量振荡器

报道了一种基于工程应用的3.705 μm紧凑型高稳定性光参量振荡器。采用半导体制冷、强制气体对流冷却和热管导热技术相结合的方式,对主振荡功率放大结构的掺Yb光纤泵浦模块泵浦过程中产生的废热进行有效管理,再将经过耦合后的泵浦光注入到使用温控炉精确控温的PPMgLN晶体,在重复频率为55.56 kHz时,获得了功率不稳定度优于1.5%的1.78 W,脉冲宽度120 ns,峰值波长3.705 μm的线偏中红外激光输出,光光转换效率为12.6%。     

Higher-order Hamilton dynamics and Hamilton–Jacobi divergence PDE

This paper, using a non-standard Legendrian duality, investigates the Hamiltonian dynamics and formulates a Hamilton–Jacobi type divergence PDE governed by higher-order Lagrangians.     

Thermal analysis of EAST neutral beam injectors for long-pulse beam operation

Two sets of neutral beam injectors(NBI-1 and NBI-2) have been mounted on the EAST tokamak since 2014. NBI-1 and NBI-2 are co-direction and counter-direction, respectively. As with indepth physics and engineering study of EAST, the ability of long pulse beam injection should be required in the NBI system. For NBIs, the most important and difficult thing that should be overcome is heat removal capacity of heat loaded components for long-pulse beam extraction. In this article, the thermal state of the components of EAST NBI is investigated using water flow calorimetry and thermocouple temperatures. Results show that(1) operation parameters have an obvious influence on the heat deposited on the inner components of the beamline,(2) a suitable operation parameter can decrease the heat loading effectively and obtain longer beam pulse length, and(3) under the cooling water pressure of 0.25 MPa, the predicted maximum beam pulse length will be up to 260 s with 50 keV beam energy by a duty factor of 0.5. The results present that, in this regard, the EAST NBI-1 system has the ability of long-pulse beam injection.