Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.     

A Poisson–Boltzmann solver on irregular domains with Neumann or Robin boundary conditions on non-graded adaptive grid

We introduce a second-order solver for the Poisson–Boltzmann equation in arbitrary geometry in two and three spatial dimensions. The method differs from existing methods solving the Poisson–Boltzmann equation in the two following ways: first, non-graded Quadtree (in two spatial dimensions) and Octree (in three spatial dimensions) grid structures are used; Second, Neumann or Robin boundary conditions are enforced at the irregular domain’s boundary. The irregular domain is described implicitly and the grid needs not to conform to the domain’s boundary, which makes grid generation straightforward and robust. The linear system is symmetric, positive definite in the case where the grid is uniform, nonsymmetric otherwise. In this case, the resulting matrix is an M-matrix, thus the linear system is invertible. Convergence examples are given in both two and three spatial dimensions and demonstrate that the solution is second-order accurate and that Quadtree/Octree grid structures save a significant amount of computational power at no sacrifice in accuracy.     

Numerical modeling of transient two-dimensional viscoelastic waves

This paper deals with the numerical modeling of transient mechanical waves in linear viscoelastic solids. Dissipation mechanisms are described using the generalized Zener model. No time convolutions are required thanks to the introduction of memory variables that satisfy local-in-time differential equations. By appropriately choosing the relaxation parameters, it is possible to accurately describe a large range of materials, such as solids with constant quality factors. The evolution equations satisfied by the velocity, the stress, and the memory variables are written in the form of a first-order system of PDEs with a source term. This system is solved by splitting it into two parts: the propagative part is discretized explicitly, using a fourth-order ADER scheme on a Cartesian grid, and the diffusive part is then solved exactly. Jump conditions along the interfaces are discretized by applying an immersed interface method. Numerical experiments of wave propagation in viscoelastic and fluid media show the efficiency of this numerical modeling for dealing with challenging problems, such as multiple scattering configurations.     

Decentralized operation strategies for an integrated building energy system using a memetic algorithm

The emerging technology in net-zero building and smart grids drives research moving from centralized operation decisions on a single building to decentralized decisions on a group of buildings, termed a building cluster which shares energy resources locally and globally. However, current research has focused on developing an accurate simulation of single building energy usage which limits its application to building clusters as scenarios such as energy sharing and competition cannot be modeled and studied. We hypothesize that the study of energy usage for a group of buildings instead of one single building will result in a cost effective building system which in turn will be resilient to power disruption. To this end, this paper develops a decision model based on a building cluster simulator with each building modeled by energy consumption, storage and generation sub modules. Assuming each building is interested in minimizing its energy cost, a bi-level operation decision framework based on a memetic algorithm is proposed to study the tradeoff in energy usage among the group of buildings. Two additional metrics, measuring the comfort level and the degree of dependencies on the power grid are introduced for the analysis. The experimental result demonstrates that the proposed framework is capable of deriving the Pareto solutions for the building cluster in a decentralized manner. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity.     

Numerical simulation and convergence analysis for a system of nonlinear singularly perturbed differential equations arising in population dynamics

In this article, we consider a system of nonlinear singularly perturbed differential equations with two different parameters. To solve this system, we develop a weighted monotone hybrid scheme on a nonuniform mesh. The proposed scheme is a combination of the midpoint scheme and the upwind scheme involving the weight parameters. The weight parameters enable the method to switch automatically from the midpoint scheme to the upwind scheme as the nodal points start moving from the inner region to the outer region. The nonuniform mesh in particular the adaptive grid is constructed using the idea of equidistributing a positive monitor function involving the solution gradient. The method is shown to be second order convergent with respect to the small parameters. Numerical experiments are presented to show the robustness of the proposed scheme and indicate that the estimate is optimal.     

Generation of linear and nonlinear waves in numerical wave tank using clustering technique-volume of fluid method

A two-dimensional (2D) numerical model is developed for the wave simulation and propagation in a wave flume.The fluid flow is assumed to be viscous and incompressible,and the Navier-Stokes and continui...     

New Falling Film Reactor for Melt Polycondensation Process

Summary: A grid falling film tower (GFFT) has been invented as an ideal polycondensation reactor. In this reactor, polymer melt flows through multi-layers grids from top to bottom to form falling film owing to gravity without agitation and shear; large gas-liquid interfacial area is generated; the grids are perpendicular between adjacent layers to ensure film renewal and to achieve uniformly flowing. The fluid flow in this reactor has little back-mixing and dead zone, which is near to plug flow. All melts are under the state of thin film which avoids the negative effect of hydrostatic head on the mass transfer impetus. Furthermore, the GFFT has wide operation flexibility as well as adjustable configuration parameters to meet different demands. A pilot scale GFFT with the height of 4.0 meters has been applied to polyester polycondensation process. The intrinsic viscosity of polyethylene terephthalate increased from 0.45 dl/g to 0.8–0.9 dl/g successfully. GFFT is supposed to be an universal apparatus for many devolatilization processes.     

Two‐grid method for two‐dimensional nonlinear Schrödinger equation by finite element method

A conservative two‐grid finite element scheme is presented for the two‐dimensional nonlinear Schrödinger equation. One Newton iteration is applied on the fine grid to linearize the fully discrete problem using the coarse‐grid solution as the initial guess. Moreover, error estimates are conducted for the two‐grid method. It is shown that the coarse space can be extremely coarse, with no loss in the order of accuracy, and still achieve the asymptotically optimal approximation as long as the mesh sizes satisfy in the two‐grid method. The numerical results show that this method is very effective.     

多孔介质中两相不可压缩流体的时间局部网格加密有限差分格式

引入Charent压力变量,对于多孔介质中两相不可压缩流体的非混溶驱动问题,其模型表现为耦合的非线性偏微分方程组,一个是压力方程,另一个为饱和度方程．文中考虑一维问题且假定达西速度“已知,建立了在时间上进行局部加密的有限差分格式,给出了饱和度的最大模误差估计．最后给出了数值算例．     

一种基于并行混沌和SFLA的风电电网无功优化模型设计

针对已有方法在解决电网无功优化时,由于系统的无功不足和电网电压的不稳定,容易过早收敛到局部最优解的缺点,设计了一种基于并行混沌和混合蛙跳算法 (Shuffle Frog Leaping Algorithm, SFLA)的电网无功优化模型。首先,建立了最小化有功网损、最大化静态电压稳定裕度和最大化无功补偿单位投资收益的多目标数学优化模型,然后,对经典的SFLA进行改进,通过引入精英协同进化机制和划分种群的方式实现并行寻优,从而增加个体的多样性和加快最优解的求取速度,在不同种群中设计不同的适应度函数和个体更新进化方法。为了使得算法的初始解分布更为均匀,引入用混沌机制来对种群进行初始化,最后,对基于并行混沌和SFLA的总体算法进行了设计和分析。在Matlab环境下进行实验,实验结果表明文中方法得到的优化结果具有电网有功损耗小、单位投资收益高和静态电压稳定裕度大的优点,具有较强的可行性和适应性。