Modified Kanerva model. Results for real time word recognition

This paper describes results obtained using the modified Kanerva model to perform word recognition in continuous speech after being trained on the multi-speaker Alvey 'Hotel' speech corpus. Theoretical discoveries have recently enabled us to increase the speed of execution of part of the model by two orders of magnitude over that previously reported by Prager & Fallside. The memory required for the operation of the model has been similarly reduced. The recognition accuracy reaches 95% without syntactic constraints when tested on different data from seven trained speakers. Real time simulation of a model with 9,734 active units is now possible in both training and recognition modes using the Alvey PARSIFAL transputer array. The modified Kanerva model is a static network consisting of a fixed nonlinear mapping (location matching) followed by a single layer of conventional adaptive links. A section of preprocessed speech is transformed by the non-linear mapping to a high dimensional representation. From this intermediate representation a simple linear mapping is able to perform complex pattern discrimination to form the output, indicating the nature of the speech features present in the input window.

An experimental model of relay deployment planning tool for a wireless sensor network system to monitor civil engineering structures

It is essential to monitor deteriorated civil engineering structures cautiously to detect symptoms of their serious disruptions. A wireless sensor network can be an effective system for monitoring civil engineering structures. It is fast to deploy sensors especially in difficult-to-access areas, and it is extendable without any cable extensions. Since our target is to monitor deteriorations of civil engineering structures such as cracks at tunnel linings, most of the locations of sensors are known, and sensors are not required to move dynamically. Therefore, we focus on developing a deployment plan of a static network in order to reduce the value of a cost function such as initial installation cost and summation of communication distances of the network. The key issue of the deployment is the location of relays that forward sensing data from sensors to a data collection device called a gateway. In this paper, we propose a relay deployment-planning tool that can be used to design a wireless sensor network for monitoring civil engineering structures. For the planning tool, we formalize the model and implement a local search based algorithm to find a quasi-optimal solution. Our solution guarantees two routings from a sensor to a gateway, which can provide higher reliability of the network. We also show the application of our experimental tool to the actual environment in the London Underground.

Optimal design of Functionally Graded Materials using a procedural model and Particle Swarm Optimization

A new method for the optimal design of Functionally Graded Materials (FGM) is proposed in this paper. Instead of using the widely used explicit functional models, a feature tree based procedural model is proposed to represent generic material heterogeneities. A procedural model of this sort allows more than one explicit function to be incorporated to describe versatile material gradations and the material composition at a given location is no longer computed by simple evaluation of an analytic function, but obtained by execution of customizable procedures. This enables generic and diverse types of material variations to be represented, and most importantly, by a reasonably small number of design variables. The descriptive flexibility in the material heterogeneity formulation as well as the low dimensionality of the design vectors help facilitate the optimal design of functionally graded materials. Using the nature-inspired Particle Swarm Optimization (PSO) method, functionally graded materials with generic distributions can be efficiently optimized. We demonstrate, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials. The underlying reason for this performance boost is also elucidated with the help of benchmarked examples. © 2011 Elsevier Ltd. All rights reserved.

Multi-Modal Image Retrieval from Construction Databases and Model-Based Systems

In the modern and dynamic construction environment it is important to access information in a fast and efficient manner in order to improve the decision making processes for construction managers. This capability is, in most cases, straightforward with today’s technologies for data types with an inherent structure that resides primarily on established database structures like estimating and scheduling software. However, previous research has demonstrated that a significant percentage of construction data is stored in semi-structured or unstructured data formats (text, images, etc.) and that manually locating and identifying such data is a very hard and time-consuming task. This paper focuses on construction site image data and presents a novel image retrieval model that interfaces with established construction data management structures. This model is designed to retrieve images from related objects in project models or construction databases using location, date, and material information (extracted from the image content with pattern recognition techniques).

Seafloor-riser interaction model

Fatigue stresses associated with extreme storms, vessel movements, and vortex-induced vibrations are critical to the performance of steel catenary risers. The critical location for fatigue damage often occurs within the touchdown zone, where cyclic interaction of the riser with the seabed occurs. Developing a model for seabed stiffness requires characterization of a number of complex nonlinear processes including trench formation, nonlinear soil stiffness, soil suction, and breakaway of the riser from the seafloor. The analytical framework utilized in this research considers the riser-seafloor interaction problem in terms of a pipe resting on a bed of springs, the stiffness characteristics of which are described by nonlinear load-deflection (P-y) curves. The P-y model allows for first penetration and uplift, as well as repenetration and small range motions within the bounding loop defined by extreme loading. The backbone curve is constructed from knowledge of the soil strength, the rate of strength increase with depth, trench width, and two additional parameters, while three parameters are necessary for the cyclic response. © ASCE 2009.

Delayed control of a Moore-Greitzer axial compressor model

Several feedback control laws have appeared in the literature concerning the stabilization of the nonlinear Moore-Greitzer axial compression model. Motivated by magnitude and rate limitations imposed by the physical implementation of the control law, Larsen et al. studied a dynamic implementation of the S-controller suggested by Sepulchre and Kokotović. They showed the potential benefit of implementing the S-controller through a first-order lag: while the location of the closed-loop equilibrium achieved with the static control law was sensitive to poorly known parameters, the dynamic implementation resulted in a small limit cycle at a very desirable location, insensitive to parameter variations. In this paper, we investigate the more general case when the control is applied with a time delay. This can be seen as an extension of the model with a first-order lag. The delay can either be a result of system constraints or be deliberately implemented to achieve better system behavior. The resulting closed-loop system is a set of parameter-dependent delay differential equations. Numerical bifurcation analysis is used to study this model and investigate whether the positive results obtained for the first-order model persist, even for larger values of the delay.

Experimental study of the effects of temperature variation on droplet size and wetness fraction in a low-pressure model steam turbine

The three-stage low-pressure model steam turbine at the Institute of Thermal Turbomachinery and Machinery Laboratory (ITSM) was used to study the impact of three different steam inlet temperatures on the homogeneous condensation process and the resulting wetness topology. The droplet spectrum as well as the particle number concentration were measured in front of the last stage using an optical-pneumatic probe. At design load, condensation starts inside the stator of the second stage. A change in the steam inlet temperature is able to shift the location of condensation onset within the blade row up- or downstream and even into adjoining blade passages, which leads to significantly different local droplet sizes and wetness fractions due to different local expansion rates. The measured results are compared to steady three-dimensional computational fluid dynamics calculations. The predicted nucleation zones could be largely confirmed by the measurements. Although the trend of measured and calculated droplet size across the span is satisfactory, there are considerable differences between the measured and computed droplet spectrum and wetness fractions. © IMechE 2013 Reprints and permissions: sagepub.co.uk/ journalsPermissions.nav.