947 resultados para Limit State Functions
Resumo:
This paper proposes a new approach for state estimation of angles and frequencies of equivalent areas in large power systems with synchronized phasor measurement units. Defining coherent generators and their correspondent areas, generators are aggregated and system reduction is performed in each area of inter-connected power systems. The structure of the reduced system is obtained based on the characteristics of the reduced linear model and measurement data to form the non-linear model of the reduced system. Then a Kalman estimator is designed for the reduced system to provide an equivalent dynamic system state estimation using the synchronized phasor measurement data. The method is simulated on two test systems to evaluate the feasibility of the proposed method.
Resumo:
Mandatory data breach notification laws are a novel statutory solution in relation to organizational protections of personal information. They require organizations which have suffered a breach of security involving personal information to notif'y those persons whose information may have been affected. These laws originated in the state based legislatures of the United States during the last decade and have subsequently garnered worldwide legislative interest. Despite their perceived utility, mandatory data breach notification laws have several conceptual and practical concems that limit the scope of their applicability, particularly in relation to existing information privacy law regimes. We outline these concerns, and in doing so, we contend that while mandatory data breach notification laws have many useful facets, their utility as an 'add-on' to enhance the failings of current information privacy law frameworks should not necessarily be taken for granted.
Resumo:
Proteoglycans (PGs) are crucial extracellular matrix (ECM) components that are present in all tissues and organs. Pathological remodeling of these macromolecules can lead to severe diseases such as osteoarthritis or rheumatoid arthritis. To date, PG-associated ECM alterations are routinely diagnosed by invasive analytical methods. Here, we employed Raman microspectroscopy, a laser-based, marker-free and non-destructive technique that allows the generation of spectra with peaks originating from molecular vibrations within a sample, to identify specific Raman bands that can be assigned to PGs within human and porcine cartilage samples and chondrocytes. Based on the non-invasively acquired Raman spectra, we further revealed that a prolonged in vitro culture leads to phenotypic alterations of chondrocytes, resulting in a decreased PG synthesis rate and loss of lipid contents. Our results are the first to demonstrate the applicability of Raman microspectroscopy as an analytical and potential diagnostic tool for non-invasive cell and tissue state monitoring of cartilage in biomedical research. ((c) 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
Resumo:
The Attentional Control Theory (ACT) proposes that high-anxious individuals maintain performance effectiveness (accuracy) at the expense of processing efficiency (response time), in particular, the two central executive functions of inhibition and shifting. In contrast, research has generally failed to consider the third executive function which relates to the function of updating. In the current study, seventy-five participants completed the Parametric Go/No-Go and n-back tasks, as well as the State-Trait Anxiety Inventory in order to explore the effects of anxiety on attention. Results indicated that anxiety lead to decay in processing efficiency, but not in performance effectiveness, across all three Central Executive functions (inhibition, set-shifting and updating). Interestingly, participants with high levels of trait anxiety also exhibited impaired performance effectiveness on the n-back task designed to measure the updating function. Findings are discussed in relation to developing a new model of ACT that also includes the role of preattentive processes and dual-task coordination when exploring the effects of anxiety on task performance.
Resumo:
The railway industry has been slow to adopt limit states principles in the structural design of concrete sleepers for its tracks, despite the global take up of this form of design for almost every other type of structural element. Concrete sleeper design is still based on limiting stresses but is widely perceived by track engineers to lead to untapped reserves of strength in the sleepers. Limit design is a more rational philosophy, especially where it is based on the ultimate dynamic capacity of the concrete sleepers. The paper describes the development of equations and factors for a limit design methodology for concrete sleepers in flexure using a probabilistic evaluation of sleeper loading. The new method will also permit a cogent, defensible means of establishing the true capacity of the billions of concrete sleepers that are currently in-track around the world, leading to better utilisation of track infrastructure. The paper demonstrates how significant cost savings may be achieved by track owners.
Resumo:
Recently updated information has raised a concern over not only the existing cost-ineffective design method but also the unrealistic analysis mode of railroad prestressed concrete sleepers. Because of the deficient knowledge in the past, railway civil engineers have been mostly aware of the over-conservative design methods for structural components in any railway track, which rely on allowable stresses and material strength reductions. Based on a number of proven experiments and field data, it is believed that the concrete sleepers which complied with the allowable stress concept possess unduly untapped fracture toughness. A collaborative research project run by the Australian Cooperative Research Centre for Railway Engineering and Technologies (RailCRC) was initiated to ascertain the reserved capacity of Australian railway prestressed concrete sleepers designed using the existing design code. The findings have led to the development of a new limit states design concept. This briefing highlights the conventional and the new limit states design philosophies and their implication to both the railway and the public community.
Resumo:
Mobile telecommunications have become a key lifestyle and technological trend of the twenty first century. In the context of increased urbanism and pressure on cites for citizen engagement for the purpose of creating good public places the potential of these technologies raises critical questions for planning professionals. Even though technology has become integral to all functions within our urban environment, little is known about perceptions and relationship between urban planners and the ubiquitous, ever-present digital layer of urban data and information. This paper explores this issue, via three focus groups and an additional follow-up interview with planners from local and state government, education and private sector. This paper explores the issues of integrating information and communication technologies into planning practice and the affordances that these technologies offer for community consultation and placemaking.
Resumo:
The internet has become important in political communication in Australia. Using Habermas' ideal types, it is argued that political blogs can be viewed as public spheres that might provide scope for the expansion of deliberative democratic discussion. This hypothesis is explored through analysis of the group political blog Pineapple Party Time. It is evident that the bloggers and those who commented on their posts were highly knowledgeable about and interested in politics. Form an examination of these posts and the comments on them, Pineapple Party Time did act as a public sphere to some degree, and did provide for the deliberative discussion essential for a democracy, but it was largely restricted to Crikey readers. For a deliberative public sphere and democratic discussion to function to any extent, the public sphere must be open to all citizens, who need to have the access and knowledge to engage in deliberative discussion.
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The research described in this paper forms part of an in-depth investigation of safety culture in one of Australia’s largest construction companies. The research builds on a previous qualitative study with organisational safety leaders and further investigates how safety culture is perceived and experienced by organisational members, as well as how this relates to their safety behaviour and related outcomes at work. Participants were 2273 employees of the case study organisation, with 689 from the Construction function and 1584 from the Resources function. The results of several analyses revealed some interesting organisational variance on key measures. Specifically, the Construction function scored significantly higher on all key measures: safety climate, safety motivation, safety compliance, and safety participation. The results are discussed in terms of relevance in an applied research context.
Resumo:
The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.
Resumo:
Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.
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The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.
Resumo:
This chapter is a tutorial that teaches you how to design extended finite state machine (EFSM) test models for a system that you want to test. EFSM models are more powerful and expressive than simple finite state machine (FSM) models, and are one of the most commonly used styles of models for model-based testing, especially for embedded systems. There are many languages and notations in use for writing EFSM models, but in this tutorial we write our EFSM models in the familiar Java programming language. To generate tests from these EFSM models we use ModelJUnit, which is an open-source tool that supports several stochastic test generation algorithms, and we also show how to write your own model-based testing tool. We show how EFSM models can be used for unit testing and system testing of embedded systems, and for offline testing as well as online testing.
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Capacity probability models of generating units are commonly used in many power system reliability studies, at hierarchical level one (HLI). Analytical modelling of a generating system with many units or generating units with many derated states in a system, can result in an extensive number of states in the capacity model. Limitations on available memory and computational time of present computer facilities can pose difficulties for assessment of such systems in many studies. A cluster procedure using the nearest centroid sorting method was used for IEEE-RTS load model. The application proved to be very effective in producing a highly similar model with substantially fewer states. This paper presents an extended application of the clustering method to include capacity probability representation. A series of sensitivity studies are illustrated using IEEE-RTS generating system and load models. The loss of load and energy expectations (LOLE, LOEE), are used as indicators to evaluate the application
Resumo:
Sfinks is a shift register based stream cipher designed for hardware implementation and submitted to the eSTREAM project. In this paper, we analyse the initialisation process of Sfinks. We demonstrate a slid property of the loaded state of the Sfinks cipher, where multiple key-IV pairs may produce phase shifted keystream sequences. The state update functions of both the initialisation process and keystream generation and also the pattern of the padding affect generation of the slid pairs.