Automatic hierarchy classification in venation networks using directional morphological filtering for hierarchical structure traits extraction

The extraction of vein traits from venation networks is of great significance to the development of a variety of research fields, such as evolutionary biology. However, traditional studies normally target to the extraction of reticulate structure traits (ReSTs), which is not sufficient enough to distinguish the difference between vein orders. For hierarchical structure traits (HiSTs), only a few tools have made attempts with human assistance, and obviously are not practical for large-scale traits extraction. Thus, there is a necessity to develop the method of automated vein hierarchy classification, raising a new challenge yet to be addressed. We propose a novel vein hierarchy classification method based on directional morphological filtering to automatically classify vein orders. Different from traditional methods, our method classify vein orders from highly dense venation networks for the extraction of traits with ecological significance. To the best of our knowledge, this is the first attempt to automatically classify vein hierarchy. To evaluate the performance of our method, we prepare a soybean transmission image dataset (STID) composed of 1200 soybean leaf images and the vein orders of these leaves are manually coarsely annotated by experts as ground truth. We apply our method to classify vein orders of each leaf in the dataset. Compared with ground truth, the proposed method achieves great performance, while the average deviation on major vein is less than 5 pixels and the average completeness on second-order veins reaches 54.28%.     

A field-reversal method for measuring the parameters of a ferroelectric liquid crystal

A method for measuring the spontaneous polarization P _s, the switching time, the rotational viscosity γ_φ, and the d.c. conductivity σ is presented. The possibilities of estimating the azimuthal angle φ₀, the dielectric anisotropy δε and the dielectric permittivity ε_⊥ in the same experiment are also discussed. It is explicitly shown that the switching delay, though primarily dependent on the material and the applied field, is also dependent on the geometry of the cell.     

BP neural network recognition algorithm for scour monitoring of subsea pipelines based on active thermometry

Scour monitoring is an important concern for subsea pipeline systems. The active-thermometry-based scour monitoring is based on the difference of heat transfer properties between sediment and sand, recognizes the surrounding media though temperature changing patterns during heating and cooling processes, and hence detects the free spans. Based on the scour monitoring system, a two-layer BP neural network was employed to process the monitoring data and achieved media recognition. The network's inputs were normalized temperature time histories. The network's outputs denoted different media: sediment and water. To validate the method, three experiments were conducted; one was used for training the network and the other two for testing. Also, the effect of noise on the network's performance was studied through simulation. The results demonstrated the feasibility and robustness of the neural network.     

Computational-Based Approach for Predicting Porosity of Electrospun Nanofiber Mats Using Response Surface Methodology and Artificial Neural Network Methods

Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of errors. The mean relative error for the RSM and ANN models were 1.97% and 2.62% and the root mean square errors (RMSE) were 1.50 and 1.95, respectively. The superiority of the RSM-based approach is due to its high prediction accuracy and the ability to compute the combined effects of the electrospinning factors on the porosity of the nanofiber mats.     

The (S−1,S) inventory model and its counterparts in queueing theory

We explore the relationship between the ($S?1,S$) inventory model and three well-known queueing models: the Erlang loss system, the machine-repair model and a two-node Jackson network. Exploiting this relationship allows us to obtain key performance measures of the ($S?1,S$) model, like the so-called virtual outdating time, the number of items on the shelf in steady state, the long-run rate of unsatisfied demands and the distribution of the empty shelf period.     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Continuum versus discrete networks,graph Laplacians,and reproducing kernel Hilbert spaces

Motivated by applications to machine learning, we construct a reversible and irreducible Markov chain whose state space is a certain collection of measurable sets of a chosen l.c.h. space $\mathfrak{X}$. We study the resulting network (connected undirected graph), including transience, Royden and Riesz decompositions, and kernel factorization. We describe a construction for Hilbert spaces of signed measures which comes equipped with a new notion of reproducing kernels and there is a unique solution to a regularized optimization problem involving the approximation of $L^2$ functions by functions of finite energy. The latter has applications to machine learning (for Markov random fields, for example).     

MAO‐free and extremely active catalytic system for ethylene tetramerization

The original Sasol catalytic system for ethylene tetramerization is composed of a Cr source, a PNP ligand, and MAO (methylaluminoxane). The use of expensive MAO in excess has been a critical concern in commercial operation. Many efforts have been made to replace MAO with non‐coordinating anions (e.g., [B(C₆F₅)₄]^?); however, most of such attempts were unsuccessful. Herein, an extremely active catalytic system that avoids the use of MAO is presented. The successive addition of two equivalent [H(OEt₂)₂]⁺[B(C₆F₅)₄]^? and one equivalent CrCl₃(THF)₃ to (acac)AlEt₂ and subsequent treatment with a PNP ligand [CH₃(CH₂)₁₆]₂C(H)N(PPh₂)₂ ( 1 ) yielded a complex presumably formulated as [ 1 ‐CrAl (acac)Cl₃(THF)]²⁺[B(C₆F₅)₄]^?₂, which exhibited high activity when combined with iBu₃Al (1120 kg/g‐Cr/h; ~4 times that of the original Sasol system composed of Cr (acac)₃, iPrN(PPh₂)₂, and MAO). Via the introduction of bulky trialkylsilyl substituents such as –SiMe₃, –Si(nBu)₃, or –SiMe₂(CH₂)₇CH₃ at the para‐position of phenyl groups in 1 (i.e., by using [CH₃(CH₂)₁₆]₂C(H)N[P(C₆H₄‐p‐SiR₃)₂]₂ instead of 1 ), the activities were dramatically improved, i.e., tripled (2960–3340 kg/g‐Cr/h; more than 10 times that of the original Sasol system). The generation of significantly less PE (<0.2 wt%) even at a high temperature is another advantage achieved by the introduction of bulky trialkylsilyl substituents. NMR studies and DFT calculations suggest that increase of the steric bulkiness on the alkyl‐N and P‐aryl moieties restrict the free rotation around (alkyl)N–P (aryl) bonds, which may cause the generation of more robust active species in higher proportion, leading to extremely high activity along with the generation of a smaller amount of PE.     

基于荧光光谱技术的菜籽油氧化状态智能评价

菜籽油在加工及贮藏过程中，易受氧气、温度、光照等因素的影响，产生氧化酸败现象。为准确判断油脂氧化程度，实现不同氧化模式下菜籽油品质的快速判别，采用三维同步荧光光谱技术结合平行因子分析法及BP神经网络法建立菜籽油氧化状态的智能评价模型。以冷榨菜籽油为原料，将样品分别置于常温、Schaal烘箱、高温模式中氧化处理，期间采集菜籽油的三维同步荧光光谱数据及理化指标，当理化指标超出国标限定范围时，停止采集数据。结果表明，菜籽油中荧光物质在不同氧化模式中的演变规律呈显著差异，氧化温度对菜籽油荧光光谱有明显影响。常温氧化350 d与第1 d相比，菜籽油的特征荧光峰位置无变化，仅在激发波长Ex为620和660 nm附近荧光峰强度发生微弱变化；Schaal烘箱氧化26 d后，在激发波长Ex为620和660 nm附近荧光峰强度显著减弱，且在激发波长Ex为350～450 nm之间有新的荧光峰生成；高温氧化48 h后，Ex为620和660 nm处荧光峰消失，在Ex为400～550 nm处产生显著荧光峰，相对Schaal烘箱氧化，荧光波长发生一定程度红移，这是由于高温氧化过程中油脂氧化生成的物质稳定性较差引起的。利用平行因子分析法对三维同步荧光光谱数据进行分解获取有效的二维荧光光谱数据，当组分数为6，Δλ=60 nm时激发波长的载荷值最大，不同样品间差异最显著。选定Δλ=60 nm波段的二维荧光光谱数据用于智能评价，作为BP神经网络模型的输入值，以极性组分作为模型输出值，分别对菜籽油三种氧化模式数据建模训练。实验结果表明，三种氧化模式对应的训练集、验证集、测试集模型相关系数r均能达到0.9以上，其中常温氧化模式中验证集及测试集模型的相关系数r为1，输出值与目标值较接近，模型的预测效果较好；综合三种氧化模式数据建模，对应训练集、验证集、测试集模型的相关系数分别为0.999，0.913和0.988，均方误差均较小，说明该模型能准确判断菜籽油的不同氧化状态。因此，三维同步荧光光谱技术结合平行因子分析法、BP神经网络法建立快速检测模型能实现菜籽油不同氧化状态的判别，为菜籽油的氧化程度的评价提供新方法，同时为其他食用油的品质评价提供参考。     

Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory

A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (T_me), mold temperature (T_mo), packing time (t_p), cooling time (t_c), and fill pressure (p_f), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was T_me. The rest of the process parameters, T_mo, t_p, t_c, and p_f, were found to be relatively less influential. Warpage increased with elevating T_mo. In contrast, an increase in Pp and T_me caused the warpage to decrease.