Identification of reduced models of power system areas using phasor measurement units

In this paper an approach is presented for identification of a reduced model for coherent areas in power systems using phasor measurement units to represent the inter-area oscillations of the system. The generators which are coherent in a wide range of operating conditions form the areas in power systems and the reduced model is obtained by representing each area by an equivalent machine. The reduced nonlinear model is then identified based on the data obtained from measurement units. The simulation is performed on three test systems and the obtained results show high accuracy of identification process.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.

Automatic de-identification of electronic health records : an Australian perspective

We present an approach to automatically de-identify health records. In our approach, personal health information is identified using a Conditional Random Fields machine learning classifier, a large set of linguistic and lexical features, and pattern matching techniques. Identified personal information is then removed from the reports. The de-identification of personal health information is fundamental for the sharing and secondary use of electronic health records, for example for data mining and disease monitoring. The effectiveness of our approach is first evaluated on the 2007 i2b2 Shared Task dataset, a widely adopted dataset for evaluating de-identification techniques. Subsequently, we investigate the robustness of the approach to limited training data; we study its effectiveness on different type and quality of data by evaluating the approach on scanned pathology reports from an Australian institution. This data contains optical character recognition errors, as well as linguistic conventions that differ from those contained in the i2b2 dataset, for example different date formats. The findings suggest that our approach compares to the best approach from the 2007 i2b2 Shared Task; in addition, the approach is found to be robust to variations of training size, data type and quality in presence of sufficient training data.

De-Identification of health records using Anonym: Effectiveness and robustness across datasets

Objective Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. Methods and Materials The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Results Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Conclusion Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data.

An overview of the marine systems simulator (MSS) : a Simulink toolbox for marine control systems

The Marine Systems Simulator (MSS) is an environment which provides the necessary resources for rapid implementation of mathematical models of marine systems with focus on control system design. The simulator targets models¡Xand provides examples ready to simulate¡Xof different floating structures and its systems performing various operations. The platform adopted for the development of MSS is Matlab/Simulink. This allows a modular simulator structure, and the possibility of distributed development. Openness and modularity of software components have been the prioritized design principles, which enables a systematic reuse of knowledge and results in efficient tools for research and education. This paper provides an overview of the structure of the MSS, its features, current accessability, and plans for future development.

Rapid identification of cytochrome P450cam variants by in vivo screening of active site libraries

The selection of cytochrome P450 enzymes from large variant libraries, and the subsequent use of these enzymes in preparative scale biotransformations, remains a formidable challenge due to the complexities of the associated electron transport systems. Here, a powerful approach for the generation and screening of P450cam libraries for new function is presented that is both flexible and robust. A targeted library was generated wherein only the P450cam active-site amino acids Y96 and F98 were fully randomized and biotransformations, using a novel P450cam whole-cell system, were screened by GC–MS for the hydroxylation of diphenylmethane. One in 50 of the reactions screened, including 16 different variants, produced 4-hydroxydiphenylmethane with up to 92% conversion observed in the case of the Y96A variant. These results demonstrate a primary example of the screening of P450cam libraries in a format that is compatible with extension to preparative scale reactions.

Developing transactive memory systems : theoretical contributions from a social identity perspective

Transactive memory system (TMS) theory explains how expertise is recognized and coordinated in teams. Extending current TMS research from a group information-processing perspective, our article presents a theoretical model that considers TMS development from a social identity perspective. We discuss how two features of communication (quantity and quality) important to TMS development are linked to TMS through the group identification mechanism of a shared common team identity. Informed by social identity theory, we also differentiate between intragroup and intergroup contexts and outline how, in multidisciplinary teams, professional identification and perceived equality of status among professional subgroups have a role to play in TMS development. We provide a theoretical discussion of future research directions aimed at testing and extending our model.

Structural identification of hindered amine light stabilisers in coil coatings using electrospray ionisation tandem mass spectrometry

Hindered amine light stabilisers (HALS) are the most effective antioxidants currently available for polymer systems in post-production, in-service applications, yet the mechanism of their action is still not fully understood. Structural characterisation of HALS in polymer matrices, particularly the identification of structural modifications brought about by oxidative conditions, is critical to aid mechanistic understanding of the prophylactic effects of these molecules. In this work, electrospray ionisation tandem mass spectrometry (ESI-MS/MS) was applied to the analysis of a suite of commercially available 2,2,6,6-tetramethylpiperidine-based HALS. Fragmentation mechanisms for the \[M + H](+) ions are proposed, which provide a rationale for the product ions observed in the MS/MS and MS(3) mass spectra of N-H, N-CH(3), N-C(O)CH(3) and N-OR containing HALS (where R is an alkyl substituent). A common product ion at m/z 123 was identified for the group of antioxidants containing N-H, N-CH3 or N-C(0)CH3 functionality, and this product ion was employed in precursor ion scans on a triple quadrupole mass spectrometer to identify the HALS species present in a crude extract from of a polyester-based coil coating. Using MS/MS, two degradation products were unambiguously identified. This technique provides a simple and selective approach to monitoring HALS structures within complex matrices. Copyright (C) 2010 John Wiley & Sons, Ltd.

Methodology for developing real-time motorway traffic risk identification models using individual-vehicle data

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

Исследования и испытания алгоритмов автоматической пилотажно-навигационной системы малогабаритного беспилотного летательного аппарата = Research and testing of automatic navigation management system’s algorithms for small unmanned aerial vehicle

В статье представлено развитие принципа построения автоматической пилотажно-навигационной системы (АПНС) для беспилотного летательного аппарата (БЛА). Принцип заключается в синтезе комплексных систем управления БПЛА не только на основе использования алгоритмов БИНС, но и алгоритмов, объединяющих в себе решение задач формирования и отработки сформированной траектории резервированной системой управления и навигации. Приведены результаты аналитического исследования и данные летных экспериментов разработанных алгоритмов АПНС БЛА, обеспечивающих дополнительное резервирование алгоритмов навигации и наделяющих БЛА новым функциональной способностью по выходу в заданную точку пространства с заданной скоростью в заданный момент времени с учетом атмосферных ветровых возмущений. Предложена и испытана методика идентификации параметров воздушной атмосферы: направления и скорости W ветра. Данные летных испытаний полученного решения задачи терминальной навигации демонстрируют устойчивую работу синтезированных алгоритмов управления в различных метеоусловиях. The article presents a progress in principle of development of automatic navigation management system (ANMS) for small unmanned aerial vehicle (UAV). The principle defines a development of integrated control systems for UAV based on tight coupling of strap down inertial navigation system algorithms and algorithms of redundant flight management system to form and control flight trajectory. The results of the research and flight testing of the developed ANMS UAV algorithms are presented. The system demonstrates advanced functional redundancy of UAV guidance. The system enables new UAV capability to perform autonomous multidimensional navigation along waypoints with controlled speed and time of arrival taking into account wind. The paper describes the technique for real-time identification of atmosphere parameters such as wind direction and wind speed. The flight test results demonstrate robustness of the algorithms in diverse meteorological conditions.

A robust methodology for developing an operational systems view of mine water interactions

The Minerals Council of Australia’s (MCA) Water Accounting Framework (WAF) is an industry lead initiative to enable cross company communication and comparisons of water management performance. The WAF consists of two models, the Input-Output Model that represents water interactions between an operation and its surrounding environment and the Operational Model that represents water interactions within an operation. Recently, MCA member companies have agreed to use the Input-Output Model to report on their external water interactions in Australian operations, with some adopting it globally. The next step will be to adopt the Operational Model. This will expand the functionality of the WAF from corporate reporting to allowing widespread identification of inefficiencies and to connect internal and external interactions. Implementing the WAF, particularly the Operational Model, is non-trivial. It can be particularly difficult for operations that are unfamiliar with the WAF definitions and methodology, lack information pertaining to flow volumes or contain unusual configurations. Therefore, there is a need to help industry with its implementation. This work presents a step-by-step guide to producing the Operational Model. It begins by describing a methodology for implementing the Operational Model by describing the identification of pertinent objects (stores, tasks and treatments), quantification of flows, aggregation of objects and production of reports. It then discusses how the Operational Model can represent a series of challenging scenarios and how it can be connected with Input-Output Model to improve water management.

Mandatory Reporting Laws and Identification of Child Abuse and Neglect: Consideration of Differential Maltreatment Types, and a Cross-Jurisdictional Analysis of Child Sexual Abuse Reports

Mandatory reporting laws have been created in many jurisdictions as a way of identifying cases of severe child maltreatment on the basis that cases will otherwise remain hidden. These laws usually apply to all four maltreatment types. Other jurisdictions have narrower approaches supplemented by differential response systems, and others still have chosen not to enact mandatory reporting laws for any type of maltreatment. In scholarly research and normative debates about mandatory reporting laws and their effects, the four major forms of child maltreatment—physical abuse, sexual abuse, emotional abuse, and neglect—are often grouped together as if they are homogenous in nature, cause, and consequence. Yet, the heterogeneity of maltreatment types, and different reporting practices regarding them, must be acknowledged and explored when considering what legal and policy frameworks are best suited to identify and respond to cases. A related question which is often conjectured upon but seldom empirically explored, is whether reporting laws make a difference in case identification. This article first considers different types of child abuse and neglect, before exploring the nature and operation of mandatory reporting laws in different contexts. It then posits a differentiation thesis, arguing that different patterns of reporting between both reporter groups and maltreatment types must be acknowledged and analysed, and should inform discussions and assessments of optimal approaches in law, policy and practice. Finally, to contribute to the evidence base required to inform discussion, this article conducts an empirical cross-jurisdictional comparison of the reporting and identification of child sexual abuse in jurisdictions with and withoutmandatory reporting, and concludes that mandatory reporting laws appear to be associated with better case identification.

Predicting transactive memory systems in multidisciplinary teams : The interplay between team and professional identities

In explaining how communication quality predicts TMS in multidisciplinary teams, we drew on the social identity approach to investigate the mediating role of team identification and the moderating role of professional identification. Recognizing that professional identification could trigger intergroup biases among professional subgroups, or alternatively, could bring resources to the team, we explored the potential moderating role of professional identification in the relationship between team identification and TMS. Using data collected from 882 healthcare personnel working in 126 multidisciplinary hospital teams, results supported our hypothesis that perceived communication quality predicted TMS through team identification. Furthermore, findings provided support for a resource view of professional subgroup identities with results indicating that high levels of professional identification compensated for low levels of team identification in predicting TMS. We provide recommendations on how social identities may be used to promote TMS in multidisciplinary teams.

MIMO and SISO identification of radiation force terms for models of marine structures in waves

This paper presents a discussion on the use of MIMO and SISO techniques for identification of the radiation force terms in models for surface vessels. We compare and discuss two techniques recently proposed in literature for this application: time domain identification and frequency domain identification. We compare the methods in terms of estimates model order, accuracy of the fit, use of the available information, and ease of use and implementation.

Resilience of water supply systems in meeting the challenges posed by climate change and population growth

This research project provides a scientifically robust approach for assessing the resilience of water supply systems, which are critical infrastructure, to impacts of climate change and population growth. An approach for the identification of trigger points that allows timely and appropriate management actions to be taken to avoid catastrophic system failure is an important outcome of this project. In the current absence of a formal method to evaluate the resilience of a water supply system, the approach developed in this study was based on the characterisation of resilience of a water supply system to a range of surrogate measures. Accordingly, a set of indicators are proposed to evaluate system behaviour and logistic regression analysis was used to assess system behaviour under predicted rainfall, storage and demand conditions.