Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

Two dynamic discrete choice estimation problems and simulation method solutions

This paper considers two problems that frequently arise in dynamic discrete choice problems but have not received much attention with regard to simulation methods. The first problem is how to simulate unbiased simulators of probabilities conditional on past history. The second is simulating a discrete transition probability model when the underlying dependent variable is really continuous. Both methods work well relative to reasonable alternatives in the application discussed. However, in both cases, for this application, simpler methods also provide reasonably good results.

Approximate solutions to stochastic dynamic programs

This paper examines the properties of various approximation methods for solving stochastic dynamic programs in structural estimation problems. The problem addressed is evaluating the expected value of the maximum of available choices. The paper shows that approximating this by the maximum of expected values frequently has poor properties. It also shows that choosing a convenient distributional assumptions for the errors and then solving exactly conditional on the distributional assumption leads to small approximation errors even if the distribution is misspecified. © 1997 Cambridge University Press.

A Combined Renormalization Group-Multiple Scale Method for Singularly Perturbed Problems

This paper introduces a straightforward method to asymptotically solve a variety of initial and boundary value problems for singularly perturbed ordinary differential equations whose solution structure can be anticipated. The approach is simpler than conventional methods, including those based on asymptotic matching or on eliminating secular terms. © 2010 by the Massachusetts Institute of Technology.

Some solutions for a compressible isotropic elastic material

In this article we obtain closed-form solutions for the combined inflation and axial shear of an elastic tube in respect of the compressible Isotropic elastic material introduced by Levinson and Burgess. Several other boundary-value problems are also examined, including the bending of a rectangular block and straightening of a cylindrical sector, both coupled with stretching and shearing, and an axially varying twist deformation. Some of the solutions appear in closed form, others are expressible in terms of elliptic functions.

Multiple modular additions and crossword puzzle attack on NLSv2

NLS is a stream cipher which was submitted to the eSTREAM project. A linear distinguishing attack against NLS was presented by Cho and Pieprzyk, which was called Crossword Puzzle (CP) attack. NLSv2 is a tweak version of NLS which aims mainly at avoiding the CP attack. In this paper, a new distinguishing attack against NLSv2 is presented. The attack exploits high correlation amongst neighboring bits of the cipher. The paper first shows that the modular addition preserves pairwise correlations as demonstrated by existence of linear approximations with large biases. Next, it shows how to combine these results with the existence of high correlation between bits 29 and 30 of the S-box to obtain a distinguisher whose bias is around 2^−37. Consequently, we claim that NLSv2 is distinguishable from a random cipher after observing around 2^74 keystream words.

Towards a multiple-goal pursuit and multilevel view of continuing information technology usage : a process model integration and a research agenda

The value of information technology (IT) is often realized when continuously being used after users’ initial acceptance. However, previous research on continuing IT usage is limited for dismissing the importance of mental goals in directing users’ behaviors and for inadequately accommodating the group context of users. This in-progress paper offers a synthesis of several literature to conceptualize continuing IT usage as multilevel constructs and to view IT usage behavior as directed and energized by a set of mental goals. Drawing from the self-regulation theory in the social psychology, this paper proposes a process model, positioning continuing IT usage as multiple-goal pursuit. An agent-based modeling approach is suggested to further explore causal and analytical implications of the proposed process model.

Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

Using enterprise information architecture methods to model wicked problems in information systems design research

Design Science is the process of solving ‘wicked problems’ through designing, developing, instantiating, and evaluating novel solutions (Hevner, March, Park and Ram, 2004). Wicked problems are described as agent finitude in combination with problem complexity and normative constraint (Farrell and Hooker, 2013). In Information Systems Design Science, determining that problems are ‘wicked’ differentiates Design Science research from Solutions Engineering (Winter, 2008) and is a necessary part of proving the relevance to Information Systems Design Science research (Hevner, 2007; Iivari, 2007). Problem complexity is characterised as many problem components with nested, dependent and co-dependent relationships interacting through multiple feedback and feed-forward loops. Farrell and Hooker (2013) specifically state for wicked problems “it will often be impossible to disentangle the consequences of specific actions from those of other co-occurring interactions”. This paper discusses the application of an Enterprise Information Architecture modelling technique to disentangle the wicked problem complexity for one case. It proposes that such a modelling technique can be applied to other wicked problems and can lay the foundations for proving relevancy to DSR, provide solution pathways for artefact development, and aid to substantiate those elements required to produce Design Theory.

Simultaneous localization and planning on multiple map hypotheses

This paper presents a novel method to rank map hypotheses by the quality of localization they afford. The highest ranked hypothesis at any moment becomes the active representation that is used to guide the robot to its goal location. A single static representation is insufficient for navigation in dynamic environments where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners. In our approach we simultaneously rank multiple map hypotheses by the influence that localization in each of them has on locally accurate odometry. This is done online for the current locally accurate window by formulating a factor graph of odometry relaxed by localization constraints. Comparison of the resulting perturbed odometry of each hypothesis with the original odometry yields a score that can be used to rank map hypotheses by their utility. We deploy the proposed approach on a real robot navigating a structurally noisy office environment. The configuration of the environment is physically altered outside the robots sensory horizon during navigation tasks to demonstrate the proposed approach of hypothesis selection.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Emerging Opportunities for "Design Thinking" to Deliver Sustainable Solutions in the Built Environment

In the built environment sector, a range of innovations are delivering environmental improvements with mixed success worldwide. The authors of this paper argue that a more “disruptive” form of innovation is needed to bring about significant and systemic change within the sector. Critical to this transition is the development of new behaviours and values. In particular, built environment professionals need to become active change agents in cultivating these new behaviours and values through the development of collaborative visions, scenarios, practices, and ideas. This paper identifies and discusses the critical role that design (in its broadest sense) can play in this process. Drawing on a comprehensive review of literature, the authors highlight a number of transformational opportunities for cross professional learning and sharing between design and built environment disciplines in achieving environmental innovation (eco-innovation). The paper also considers several design-based concepts that have a potential application in the built environment sector including: design thinking, social innovation (human-centered), and disruptive innovation (transformational) approaches. The research findings will assist in building the capabilities of designers and innovators to create sustainable solutions to global problems, and in supporting the social diffusion of systems-changing ideas in the built environment sector.

Data fusion and tracking with multiple UAVs

This chapter describes decentralized data fusion algorithms for a team of multiple autonomous platforms. Decentralized data fusion (DDF) provides a useful basis with which to build upon for cooperative information gathering tasks for robotic teams operating in outdoor environments. Through the DDF algorithms, each platform can maintain a consistent global solution from which decisions may then be made. Comparisons will be made between the implementation of DDF using two probabilistic representations. The first, Gaussian estimates and the second Gaussian mixtures are compared using a common data set. The overall system design is detailed, providing insight into the overall complexity of implementing a robust DDF system for use in information gathering tasks in outdoor UAV applications.

The topography of multiple drug use among adolescent Australians : findings from the National Drug Strategy Household Survey

Introduction and aims: Despite evidence that many Australian adolescents have considerable experience with various drug types, little is known about the extent to which adolescents use multiple substances. The aim of this study was to examine the degree of clustering of drug types within individuals, and the extent to which demographic and psychosocial predictors are related to cluster membership. Design and method: A sample of 1402 adolescents aged 12-17. years were extracted from the Australian 2007 National Drug Strategy Household Survey. Extracted data included lifetime use of 10 substances, gender, psychological distress, physical health, perceived peer substance use, socioeconomic disadvantage, and regionality. Latent class analysis was used to determine clusters, and multinomial logistic regression employed to examine predictors of cluster membership. Result: There were 3 latent classes. The great majority (79.6%) of adolescents used alcohol only, 18.3% were limited range multidrug users (encompassing alcohol, tobacco, and marijuana), and 2% were extended range multidrug users. Perceived peer drug use and psychological distress predicted limited and extended multiple drug use. Psychological distress was a more significant predictor of extended multidrug use compared to limited multidrug use. Discussion and conclusion: In the Australian school-based prevention setting, a very strong focus on alcohol use and the linkages between alcohol, tobacco and marijuana are warranted. Psychological distress may be an important target for screening and early intervention for adolescents who use multiple drugs.

Making optimal and justifiable asset maintenance decisions

Maintenance decisions for large-scale asset systems are often beyond an asset manager's capacity to handle. The presence of a number of possibly conflicting decision criteria, the large number of possible maintenance policies, and the reality of budget constraints often produce complex problems, where the underlying trade-offs are not apparent to the asset manager. This paper presents the decision support tool "JOB" (Justification and Optimisation of Budgets), which has been designed to help asset managers of large systems assess, select, interpret and optimise the effects of their maintenance policies in the presence of limited budgets. This decision support capability is realized through an efficient, scalable backtracking- based algorithm for the optimisation of maintenance policies, while enabling the user to view a number of solutions near this optimum and explore tradeoffs with other decision criteria. To assist the asset manager in selecting between various policies, JOB also provides the capability of Multiple Criteria Decision Making. In this paper, the JOB tool is presented and its applicability for the maintenance of a complex power plant system.