A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency pulsations contained within active power flow. A primary concern is excitation of low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of interconnection between the Northern and Southern power system networks. In order to determine whether the prevalence of wind generation has a negative effect (excites modes) or positive impact (damping of modes) on the power system, oscillations must be measured and characterised. Using time – frequency methods, this paper presents work that has been conducted to extract features from low-frequency active power pulsations to determine the composition of oscillatory modes which may impact on dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

Impact of operator experience on information feedback and reusability

Keeping a record of operator experience remains a challenge to operation management and a major source of inefficiency in information management. The objective is to develop a framework that enables an explicit presentation of experience based on information use. A purposive sampling method is used to select four small and medium-sized enterprises as case studies. The unit of analysis is the production process in the machine shop. Data collection is by structured interview, observation and documentation. A comparative case analysis is applied. The findings suggest experience is an accumulation of tacit information feedback, which can be made explicit in information use interoperatability matrix. The matrix is conditioned upon information use typology, which is strategic in waste reduction. The limitations include difficulty of participant anonymity where the organisation nominates a participant. Areas for further research include application of the concepts to knowledge management and shop floor resource management.

Identification of dyes on ancient Chinese paper samples using the subtracted shifted Raman spectroscopy method

The Stein Collection in the British Library contains the Diamond Sutra, the world's oldest, dated, printed document. The paper of the Diamond Sutra and other documents from the Stein collection is believed to be dyed yellow by a natural extract, called huangbo, from the bark of Phellodendron amurense, which contains three major yellow chromophores: berberine, palmatine, and jatrorrhizine, Conservation of these documents requires definite information on the chemical composition of the dyes but no suitable, completely noninvasive analytical method is known. Here we report resonance Raman studies of a series of prate dyes, of plant materials and extracts, and of dyed ancient and modern paper samples. Resonance Raman spectroscopy is used to enhance the spectra of the dyes over the signals from the paper matrixes in which they are held. The samples an give resonance Raman spectra which are dominated by intense fluorescence, but by using SSRS (subtracted shifted Raman spectroscopy) we have obtained reliable spectra of the pure dyes, native bark from the Phellodendron amurense, modern paper dyed with huangbo extracted from this bark, and ancient paper samples. For both ancient paper samples whose pigment bands were detected, the relative intensities of the bands due to berberine and palmatine suggest that the ancient paper is richer in berberine than its modern counterpart, This is the first nondestructive in situ method for detection of these pigments in manuscripts, and as such has considerable potential benefit for the treatment of irreplaceable documents that are believed to be dyed with huangbo but documents on which conservation work cannot proceed without definite identification of the chemical compounds that they contain.

Moving Object Scanning Apparatus and Method

Apparatus for scanning a moving object includes a visible waveband sensor oriented to collect a series of images of the object as it passes through a field of view. An image processor uses the series of images to form a composite image. The image processor stores image pixel data for a current image and predecessor image in the series. It uses information in the current image and its predecessor to analyse images and derive likelihood measures indicating probabilities that current image pixels correspond to parts of the object. The image processor estimates motion between the current image and its predecessor from likelihood weighted pixels. It generates the composite image from frames positioned according to respective estimates of object image motion. Image motion may alternatively be detected be a speed sensor such as Doppler radar sensing object motion directly and providing image timing signals

Europe’s Information Society project and Digital Inclusion: universal service obligations or social solidarity?

The importance of digital inclusion to Europe is obvious: as we move towards an ever more internet-communicating society the lack of access to basic digital infrastructures for a significant segment of the population is both problematic for those individuals without access and also problematic for those providing services which should be efficient and fully utilised. The EU’s ‘Information Society’ project has been the central plank of the European attempt to build a European digital marketplace, a concept which necessitates digital inclusion of the population at large. It is a project which prefers universal service obligations to achieve inclusion. If that is to be the preferred solution I suggest that we must consider exclusion from the banking system, given that the Information Society is at root an economic community.

However, universal service obligations are not the only method whereby digital inclusion can be encouraged and I posit we may need to reconsider the role of the state as supplier of services through the concept of ‘social solidarity’.

Information disclosure and depositor discipline in the Chinese banking sector

We investigate the relationship between information disclosure and depositor behaviour in the Chinese banking sector. Specifically, we enquire whether enhanced information disclosure enables investors to more effectively infer a banking institution's risk profile, thereby influencing their deposit decisions. Utilising an unbalanced panel, incorporating financial data from 169 Chinese banks over the 1998–2009 period, we employ generalised-method-of-moments (GMM) estimation procedures to control for potential endogeneity, unobserved heterogeneity, and persistence in the dependent variable. We uncover evidence that: (i) the growth rate of deposits is sensitive to bank fundamentals after controlling for macroeconomic factors, diversity in ownership structure, and government intervention; (ii) a bank publicly disclosing more transparent information in its financial reports, is more likely to experience growth in its deposit base; and (iii) banks characterised by high information transparency, well-capitalised and adopted international accounting standards, are more able to attract funds by offering higher interest rates.

Assessment Method for a Power Analysis to Identify Differentially Expressed Pathways

Gene expression data can provide a very rich source of information for elucidating the biological function on the pathway level if the experimental design considers the needs of the statistical analysis methods. The purpose of this paper is to provide a comparative analysis of statistical methods for detecting the differentially expression of pathways (DEP). In contrast to many other studies conducted so far, we use three novel simulation types, producing a more realistic correlation structure than previous simulation methods. This includes also the generation of surrogate data from two large-scale microarray experiments from prostate cancer and ALL. As a result from our comprehensive analysis of 41,004 parameter configurations, we find that each method should only be applied if certain conditions of the data from a pathway are met. Further, we provide method-specific estimates for the optimal sample size for microarray experiments aiming to identify DEP in order to avoid an underpowered design. Our study highlights the sensitivity of the studied methods on the parameters of the system. © 2012 Tripahti and Emmert-Streib.

Road traffic congestion and public information - An experimental investigation

This paper reports laboratory experiments designed to study the impact of public information about past departure rates on congestion levels and travel costs. Our design is based on a discrete version of Arnott et al.'s (1990) bottleneck model. In all treatments, congestion occurs and the observed travel costs are quite similar to the predicted ones. Subjects' capacity to coordinate is not affected by the availability of public information on past departure rates, by the number of drivers or by the relative cost of delay. This seemingly absence of treatment effects is confirmed by our finding that a parameter-free reinforcement learning model best characterises individual behaviour.

A comparison of the application of STOPP/START to patients' drug lists with and without clinical information

Background A European screening tool (STOPP/START) has been formulated to identify the prescribing of potentially inappropriate medicines (PIMs) and potential prescribing omissions (PPOs). Pharmacists working in community pharmacies could use STOPP/START as a guide to conducting medication use reviews; however, community pharmacists do not routinely have access to patients' clinical records. Objective To compare the PIM and PPO detection rates from application of the STOPP/START criteria to patients' medication details alone with the detection rates from application of STOPP/START to information on patients' medications combined with clinical information. Setting Community Pharmacy. Method Three pharmacists applied STOPP/START to 250 patient medication lists, containing information regarding dose, frequency and duration of treatment. The PIMs and PPOs identified by each pharmacist were compared with those identified by consensus agreement of two other pharmacists, who applied STOPP/START criteria using patients' full clinical records. Main outcome measure The main outcome measures were: (1) PIM and PPO detection rates among pharmacists with access to patients' clinical information compared to PIM and PPO detection rates among pharmacists using patients' medication information only, and (2) the levels of agreement (calculated using Cohen's kappa statistic (k)) for the three most commonly identified PIMs and PPOs. Results Pharmacists with access to patients' clinical records identified significantly fewer PIMs than pharmacists without (p = 0.002). The three most commonly identified PIMs were benzodiazepines, proton pump inhibitors and duplicate drug classes, with kappa (k) statistic agreement ranges of 0.87-0.97, 0.60-0.68 and 0.39-0.85 respectively. PPOs were identified more often (p 

Living is Information Processing: From Molecules to Global Systems

We extend the concept that life is an informational phenomenon, at every level of organisation, from molecules to the global ecological system. According to this thesis: (a) living is information processing, in which memory is maintained by both molecular states and ecological states as well as the more obvious nucleic acid coding; (b) this information processing has one overall function-to perpetuate itself; and (c) the processing method is filtration (cognition) of, and synthesis of, information at lower levels to appear at higher levels in complex systems (emergence). We show how information patterns, are united by the creation of mutual context, generating persistent consequences, to result in 'functional information'. This constructive process forms arbitrarily large complexes of information, the combined effects of which include the functions of life. Molecules and simple organisms have already been measured in terms of functional information content; we show how quantification may be extended to each level of organisation up to the ecological. In terms of a computer analogy, life is both the data and the program and its biochemical structure is the way the information is embodied. This idea supports the seamless integration of life at all scales with the physical universe. The innovation reported here is essentially to integrate these ideas, basing information on the 'general definition' of information, rather than simply the statistics of information, thereby explaining how functional information operates throughout life. © 2013 Springer Science+Business Media Dordrecht.

Invariant Information Local Sub-map Filter (IILSF) for Efficient Simultaneous Localisation and Mapping of Large Environments

This paper presents an Invariant Information Local Sub-map Filter (IILSF) as a technique for consistent Simultaneous Localisation and Mapping (SLAM) in a large environment. It harnesses the benefits of sub-map technique to improve the consistency and efficiency of Extended Kalman Filter (EKF) based SLAM. The IILSF makes use of invariant information obtained from estimated locations of features in independent sub-maps, instead of incorporating every observation directly into the global map. Then the global map is updated at regular intervals. Applying this technique to the EKF based SLAM algorithm: (a) reduces the computational complexity of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. Simulation results show that the method was able to accurately fuse local map observations to generate an efficient and consistent global map, in addition to significantly reducing computational cost and data association ambiguities.

Estimation of the surface area and volume of the retinal capillary basement membrane using the stereologic method of vertical sections

The modern stereologic method of vertical sections was applied to the retina as a means of generating unbiased estimates of three-dimensional structure. The method is illustrated with real data on the volume and surface area of the capillary basement membrane from the central retina of the rat. Novel methods of estimating the volume of retina sampled and of creating accurate vertical sections are described. The advantages of using stereologic methods to generate quantitative information on the three-dimensional structure of the retina are discussed and compared to those of previous quantitative methods that provide data on two-dimensional structure only.

A Spherical Array Approach for Simulation of Binaural Impulse Responses using the Finite Difference Time Domain Method

The finite difference time domain (FDTD) method has direct applications in musical instrument modeling, simulation of environmental acoustics, room acoustics and sound reproduction paradigms, all of which benefit from auralization. However, rendering binaural impulse responses from simulated
data is not straightforward to accomplish as the calculated pressure at FDTD grid nodes does not contain any directional information. This paper addresses this issue by introducing a spherical array to capture sound pressure on a finite difference grid, and decomposing it into a plane-wave density
function. Binaural impulse responses are then constructed in the spherical harmonics domain by combining the decomposed grid data with free field head-related transfer functions. The effects of designing a spherical array in a Cartesian grid are studied, and emphasis is given to the relationships
between array sampling and the spatial and spectral design parameters of several finite-difference
schemes.

An Intelligent Threat Prevention Framework with Heterogeneous Information

Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects’ intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect’s further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address
this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.