Determination of ECoG information flow activity based on Granger causality and Hilbert transformation

Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm × 8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8 × 8 (0.016–300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub-cortex areas were capable of determining the information flow (causal influence) activity and communication frequencies between the populations of neurons successfully.     

An extended cell-average technique for a multi-dimensional population balance of granulation describing aggregation and breakage

This paper focuses on obtaining the numerical solution to a three-dimensional population balance model (PBM) of granulation using the cell-average technique first proposed by [22]. Conventionally, linear grids are used for the solution of PBMs, but the ability to incorporate non-linear grids would be more advantageous given that a larger size range can be covered using fewer number of grids, thus reducing computational overhead. Furthermore, the use of linear representation of grids in PBMs to represent industrial granulation processes that span a wide granule size range is computationally prohibitive and results show that a non-linear grid representation is computationally more efficient with comparable accuracy. Parallelization of the PBM via a multi-core strategy has also been incorporated in order to reduce the simulation time of the model. Incorporating the cell average technique along with parallelization of the overall model lends credence to the overall use of the model for effective granulation process design and analysis.     

Semantic views — Interactive hierarchical exploration for patent landscaping

In this paper, we investigate whether a semantic representation of patent documents provides added value for a multi-dimensional visual exploration of a patent landscape compared to traditional approaches that use tf–idf (term frequency–inverse document frequency). Word embeddings from a pre-trained word2vec model created from patent text are used to calculate pairwise similarities in order to represent each document in the semantic space. Then, a hierarchical clustering method is applied to create several semantic aggregation levels for a collection of patent documents. For visual exploration, we have seamlessly integrated multiple interaction metaphors that combine semantics and additional metadata for improving hierarchical exploration of large document collections.     

Modelling protein crystallisation using morphological population balance models

Protein crystallisation is known to be affected by many factors and inherently difficult to control. Being able to model the crystal growth behaviour, especially at process scale for the population of particles in a crystalliser will no doubt greatly help the formulation and controlled manufacture of protein crystals. In this paper, a morphological population balance model for crystallisation of tetragonal Hen-Egg-White (HEW) lysozyme is presented. Since the population balance model has incorporated crystal shape information, it is able to simulate the dynamic evolution of the shape distribution as well as size distribution. The morphological population balance model requires faceted growth kinetics data, which was obtained from published data in literature for the two identified independent crystallographic faces, {1 0 1} and {1 1 0}, of HEW lysozyme crystals.     

Simulating the early stage of high-shear granulation using a two-dimensional Monte-Carlo approach

A two-dimensional (2-D) model of a granulation process is presented in this paper. It aims to simulate an entire granulation batch without the use of an initial experimental or fictitious 2-D density function, by taking the experimental operating conditions into account. The mass of liquid and solid in the granules are the two predicted internal variables. The 2-D population balance equation is solved by a Constant Number Monte-Carlo method. This is a stochastic technique tracking the evolution of a population, whilst performing the calculations with a fixed number of particles. This is achieved by reducing or increasing the sample volume when an event results in a net production or a net decrease in the number of particles, respectively. An original multi-population approach is developed to describe the early stage of the process, where small numbers of granules are formed amongst a large number of primary particles. It consists of separating the primary particles from the granule population. A specific intensive variable is introduced to keep track of the repartition of masses. The overall density function is reconstructed a posteriori from the combination of the two populations. This approach allows the simulation to commence from the initial addition of liquid at the start of the process, rather than to start from an early granule size distribution. The early stage of the granulation process, frequently referred as nucleation, can therefore be studied numerically. Four different mechanisms are implemented. Nucleation and re-wetting describe the addition of liquid to the system. The interactions between liquid and solid phases are modelled by a layering process. An aggregation model is also included to simulate the growth of particles undergoing frequent collisions. Finally, the relevance of this new model is demonstrated by confronting the simulations to real experimental data.     

Experimental validation studies on a multi-dimensional and multi-scale population balance model of batch granulation

In this study, a dynamic model is presented for the granulation process, employing a three-dimensional population balance framework. As a first attempt to account for the multi-scale character of the process, the nucleation and aggregation kernels used in the population balance model are derived using mechanistic representations of the underlying particle physics such as wetting kinetics and energy dissipation effects. Thus, the fundamental properties of the powder and the liquid were used as parameters in the model to predict the granulator dynamics and granule properties. The population balance model is validated against experimental data from a calcite/PVOH-H₂O recipe obtained using a lab-scale drum granulator for granule size, fractional binder content and porosity. A reasonably good agreement between experimental and simulation results were obtained for the granule size distribution under different experimental conditions. In addition, accurate model predictions were made for the evolution of the average properties (i.e., size, fractional binder content and porosity) for various operating conditions.     

多维气相色谱法测定汽油的组成

在 AC 公司生产的多维色谱仪上建立了两种分析含不同烯烃浓度汽油组成的分析方法—PIONA 和PHONA,该方法适合国内汽油组成分析的要求.经研究得到结论:当烯烃含量高时,应该采用带有二段烯烃吸附温度的 PHONA 分析模式;烯烃含量在体积分数20%以下的汽油最好使用 PIONA 的分析模式.     

Development of a model for stochastic analysis of vehicle–track interaction considering spatial variability of track parameters

In this work, a model for achieving stochastic analysis of vehicle–track interaction systems is proposed based on random field realization of track parameters and the finite element coupling of rigid-elastic bodies. Aiming at the clarification of the spatial variability and correlation of track parameters on the influence of vehicle–track dynamic performance, a general scheme based on Karhunen–Loève Expansion and cyclic calculation method is developed to realize a multi-dimensional random field that holds an arbitrary marginal distribution function and correlation structure, and to consider the inhomogeneity of system parameters along the longitudinal direction efficiently. Furthermore, the validation of the random field expansion against the vehicle–track system is implemented, and the dynamic effects of random track parameters are investigated through two special issues. Numerical results show that the spatial correlation of fastener stiffness affects the extreme response, especially the low-frequency component of rail displacement. The spatial dependence of the filling layer has little influence on the dynamic behavior of the vehicle subsystem, but it obviously changes the extreme stress and strain of the filling layer. Ignoring the spatial correlation of random variables might underestimate the performance of the track system.     

一种基于多维支持向量机的P2P网络流量识别模型

提出一种多维支持向量机（MSVM）训练方法,并建立了一种基于多维支持向量机的P2P网络流量识别模型。理论分析与数值实验表明,该模型具有较好的实验结果和所期望的识别精确度。     

Self-Organising Maps as a Relevance Feedback Technique in Content-Based Image Retrieval

Self-Organising Maps (SOMs) can be used in implementing a powerful relevance feedback mechanism for Content-Based Image Retrieval (CBIR). This paper introduces the PicSOM CBIR system, and describes the use of SOMs as a relevance feedback technique in it. The technique is based on the SOM’s inherent property of topology-preserving mapping from a high-dimensional feature space to a two-dimensional grid of artificial neurons. On this grid similar images are mapped in nearby locations. As image similarity must, in unannotated databases, be based on low-level visual features, the similarity of images is dependent on the feature extraction scheme used. Therefore, in PicSOM there exists a separate tree-structured SOM for each different feature type. The incorporation of the relevance feedback and the combination of the outputs from the SOMs are performed as two successive processing steps. The proposed relevance feedback technique is described, analysed qualitatively, and visualised in the paper. Also, its performance is compared with a reference method.