Evaluation of a pressure head and pressure zones in water distribution systems by artificial neural networks

Neural Computing and Applications - Water distribution system design is inherently associated with hydraulic calculations that require thorough evaluation of obtained results and accuracy of the...     

Efficient and Generic Algorithm for Rigorous Integration Forward in Time of dPDEs: Part I

We propose an efficient and generic algorithm for rigorous integration forward in time of partial differential equations written in the Fourier basis. By rigorous integration we mean a procedure which operates on sets and return sets which are guaranteed to contain the exact solution. The presented algorithm generates, in an efficient way, normalized derivatives which are used by the Lohner algorithm to produce a rigorous bound. The algorithm has been successfully tested on several partial differential equations (PDEs) including the Burgers equation, the Kuramoto-Sivashinsky equation, and the Swift-Hohenberg equation. The problem of rigorous integration in time of partial differential equations is a problem of large computational complexity and efficient algorithms are required to deal with PDEs on higher dimensional domains, like the Navier-Stokes equation. Technicalities regarding the various optimization techniques implemented in the software used in this paper will be reported elsewhere.     

Deterministic convergence of conjugate gradient method for feedforward neural networks

Conjugate gradient methods have many advantages in real numerical experiments, such as fast convergence and low memory requirements. This paper considers a class of conjugate gradient learning methods for backpropagation neural networks with three layers. We propose a new learning algorithm for almost cyclic learning of neural networks based on PRP conjugate gradient method. We then establish the deterministic convergence properties for three different learning modes, i.e., batch mode, cyclic and almost cyclic learning. The two deterministic convergence properties are weak and strong convergence that indicate that the gradient of the error function goes to zero and the weight sequence goes to a fixed point, respectively. It is shown that the deterministic convergence results are based on different learning modes and dependent on different selection strategies of learning rate. Illustrative numerical examples are given to support the theoretical analysis.     

Device-Independent Architecture for ubiquitous applications

The part of the Internet of Things composed of devices that directly interact with users has grown considerably in the past years. With new smartphones, tablets and other Internet-enabled devices that appear on the market, this trend is still increasing. However, existing application development processes and tools, designed for single device applications, do not allow developers to fully and efficiently address this opportunity. Applications are developed for a particular type of devices or a particular programming platform. This limits the number of potential users and makes it difficult to seamlessly use an application on multiple devices owned by users. To take full advantage of the Internet of Things, applications should be able to run on any device—they should be ubiquitous. In this paper, we present a concept of Device-Independent Architecture, which provides separation of applications from devices and facilitates development of device-independent applications. Additionally, the separation introduced by the Device-Independent Architecture enables implementation of multi-device scenarios where a single application employs multiple devices at the same time. The experiment described in the paper proves that such device-independent applications indeed may be used on any suitable device—they have a chance to become ubiquitous.     

Effects of the abdominal belt on muscle-generated spinal stability and L4/L5 joint compression force

The goals of this study were (1) to determine the effects of abdominal belts on muscle-generated active lumbar spine stability, (2) to determine their effect on the subsequent joint compression force at L4/L5 and (3) to determine whether the effective stability of the spine could be predicted by the active spine stability and belt condition. Electromyographic (EMG) and trunk stiffness data from a previously reported experiment in which 10 subjects performed quick-release tasks (perturbation) with and without an abdominal belt were used as inputs to biomechanical models to estimate the active spine stability and effective stability of the spine, respectively. The subjects exerted isometric trunk flexion, extension and lateral bending trials at 0 and 80% of maximum intra-abdominal pressure when the resisted force was suddenly released. Wearing an abdominal belt had no significant effect on either the muscle-generated lumbar spine stability or the L4/L5 joint compression force in any direction. The effective stability of the spine was adequately predicted by the active spine stability and the effect of the belt, which accounted for approximately 34% of the effective spine stability. The study demonstrated that the abdominal belt contributed to the passive stability of the lumbar spine and did not change the active stability for tests performed within the same experimental session.     

A formal semantics for the Taverna 2 workflow model

This paper presents a formal semantics for the Taverna 2 scientific workflow system. Taverna 2 is a successor to Taverna, an open-source workflow system broadly adopted within the e-science community worldwide. The new version improves upon the existing model in two main ways: (i) by adding support for data pipelining, which in turns enables input streams of indefinite length to be processed efficiently; and (ii) by providing new extensibility points that make it possible to add new operators to the workflow model. Consistent with previous work by some of the authors, we use trace semantics to describe the effect of workflow computations, and we show how they can be used to describe the new features in the Taverna 2 model.     

Fluid Simulation with Articulated Bodies

We present an algorithm for creating realistic animations of characters that are swimming through fluids. Our approach combines dynamic simulation with data-driven kinematic motions (motion capture data) to produce realistic animation in a fluid. The interaction of the articulated body with the fluid is performed by incorporating joint constraints with rigid animation and by extending a solid/fluid coupling method to handle articulated chains. Our solver takes as input the current state of the simulation and calculates the angular and linear accelerations of the connected bodies needed to match a particular motion sequence for the articulated body. These accelerations are used to estimate the forces and torques that are then applied to each joint. Based on this approach, we demonstrate simulated swimming results for a variety of different strokes, including crawl, backstroke, breaststroke, and butterfly. The ability to have articulated bodies interact with fluids also allows us to generate simulations of simple water creatures that are driven by simple controllers.     

INSIGMA: an intelligent transportation system for urban mobility enhancement

Intelligent Transportation Systems (ITS) aim to improve safety, mobility and environmental performance of road transport. The INSIGMA project provides a fresh look at the possible innovations in this field, by enhancing the functionality and accuracy of ITS in urban environments. This paper describes the architecture, sensors, processing algorithms, output modules and advantages of the developed system. A comparison of existing ITS systems has been provided as background. Special attention has been given to performance and privacy issues, as the system includes social aspects such as location monitoring.