Condition assessment of timber utility poles based on a hierarchical data fusion model

This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.

Control and performance in IS projects: a meta-analysis of hierarchical and market-based control relationships

Literature on IS project control distinguishes between hierarchical and market-based control relationships. Prior studies typically investigate one of these two forms of control relationships in isolation. Hence, little is known about the differences between hierarchical and market-based control relationships. Responding to this gap, we analyze how the effects of control modes on IS project performance differ in hierarchical compared with market-based control relationships. Specifically, we conduct a metaanalysis to compare the effects of control modes on IS project performance reported in research on hierarchical and market-based control relationships. The results suggest that the effects of behavior and self-control on performance differ between these two forms of control relationships. Based on our results, we derive implications for complementary and substitutive effects between control modes, and for interrelations among hierarchical and market-based control relationships.

Leveraging visual features and hierarchical dependencies for conference information extraction

Traditional information extraction methods mainly rely on visual feature assisted techniques; but without considering the hierarchical dependencies within the paragraph structure, some important information is missing. This paper proposes an integrated approach for extracting academic information from conference Web pages. Firstly, Web pages are segmented into text blocks by applying a new hybrid page segmentation algorithm which combines visual feature and DOM structure together. Then, these text blocks are labeled by a Tree-structured Random Fields model, and the block functions are differentiated using various features such as visual features, semantic features and hierarchical dependencies. Finally, an additional post-processing is introduced to tune the initial annotation results. Our experimental results on real-world data sets demonstrated that the proposed method is able to effectively and accurately extract the needed academic information from conference Web pages. © 2013 Springer-Verlag.

Hierarchical Nafion enhanced carbon aerogels for sensing applications

This work describes the fabrication of hierarchical 3D Nafion enhanced carbon aerogels (NECAGs) for sensing applications via a fast freeze drying method. Graphene oxide, multiwalled carbon nanotubes and Nafion were mixed and extruded into liquid nitrogen followed by the removal of ice crystals by freeze drying. The addition of Nafion enhanced the mechanical strength of NECAGs and effective control of the cellular morphology and pore size was achieved. The resultant NECAGs demonstrated high strength, low density, and high specific surface area and can achieve a modulus of 20 kPa, an electrical conductivity of 140 S m(-1), and a specific capacity of 136.8 F g(-1) after reduction. Therefore, NECAG monoliths performed well as a gas sensor and as a biosensor with high sensitivity and selectivity. The remarkable sensitivity of 8.52 × 10(3)μA mM(-1) cm(-2) was obtained in dopamine (DA) detection, which is two orders of magnitude better than the literature reported values using graphene aerogel electrodes made from a porous Ni template. These outstanding properties make the NECAG a promising electrode candidate for a wide range of applications. Further in-depth investigations are being undertaken to probe the structure-property relationship of NECAG monoliths prepared under various conditions.

Hydrangea-like multi-scale carbon hollow submicron spheres with hierarchical pores for high performance supercapacitor electrodes

Uniform hydrangea-like multi-scale carbon hollow submicron spheres (HCSSg) are fabricated by a simple hydrothermal method using glucose as carbon source and fibrous silicon dioxides spheres as shape guide. Structure characterization suggests that petal-like partially graphitized carbon nanosheets with the thickness of about 10 nm arranged in three dimensions (3D) to form the hydrangea-like hollow spheres (size ranging from 250 to 500 nm) with mesoporous channels, which can be conducive to be a high specific surface area (934 m2 g-1) and bulk density (0.87 cm g-3), hierarchical pores structure with good conductivity. As a result, the HCSSg has been demonstrated to be a supercapacitor electrode material with high gravimetric (386 F g-1 at 0.2 A g-1) and outstanding volumetric (335 F cm-3) capacitance, good rate capability and cycling stability with 94% capacitance retention after 5000 cycles in aqueous electrolytes, thus suggesting its application potential.

Hierarchical composites from carbon fibres electrosprayed with carbon nanotubes

 This research developed a novel method for coating carbon nanotubes onto carbon fibre surfaces. It provided valuable guidance for producing various CNT morphologies on fibre surfaces. More importantly, the produced hybrid structures improved interfacial bonding in composites significantly, and this research will explore more potential applications of new generation composites.

Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes

Hierarchical Dirichlet processes (HDP) was originally designed and experimented for a single data channel. In this paper we enhanced its ability to model heterogeneous data using a richer structure for the base measure being a product-space. The enhanced model, called Product Space HDP (PS-HDP), can (1) simultaneously model heterogeneous data from multiple sources in a Bayesian nonparametric framework and (2) discover multilevel latent structures from data to result in different types of topics/latent structures that can be explained jointly. We experimented with the MDC dataset, a large and real-world data collected from mobile phones. Our goal was to discover identity–location– time (a.k.a who-where-when) patterns at different levels (globally for all groups and locally for each group). We provided analysis on the activities and patterns learned from our model, visualized, compared and contrasted with the ground-truth to demonstrate the merit of the proposed framework. We further quantitatively evaluated and reported its performance using standard metrics including F1-score, NMI, RI, and purity. We also compared the performance of the PS-HDP model with those of popular existing clustering methods (including K-Means, NNMF, GMM, DP-Means, and AP). Lastly, we demonstrate the ability of the model in learning activities with missing data, a common problem encountered in pervasive and ubiquitous computing applications.

Shared services and outsourcing : a contemporary outlook:10th Global Sourcing Workshop 2016, Val d'Isère, France, February 16-19, 2016, revised selected papers

This book constitutes the revised selected papers from the 10th Global Sourcing Workshop held in Val d’Isère, France, in February 2016. The 11 papers presented in this volume were carefully reviewed and selected from 47 submissions. The book offers a review of the key topics in outsourcing and offshoring of information technology and business services offering practical frameworks that serve as a tool kit to students and managers. The range of topics covered is wide and diverse, but predominately focused on how to achieve success in shared services and outsourcing. More specifically, the book examines outsourcing decisions and management practices, giving specific attention to shared services that have become one of the dominant sourcing models. The topics discussed combine theoretical and practical insights regarding challenges that industry leaders, policy makers, and professionals face or should be concerned with. Case studies from various organizations, industries and countries such as UK, Italy, The Netherlands, Canada, Australia and Denmark complete the book.

Achieving simple, secure and efficient hierarchical access control in cloud computing

Access control is an indispensable security component of cloud computing, and hierarchical access control is of particular interest since in practice one is entitled to different access privileges. This paper presents a hierarchical key assignment scheme based on linear-geometry as the solution of flexible and fine-grained hierarchical access control in cloud computing. In our scheme, the encryption key of each class in the hierarchy is associated with a private vector and a public vector, and the inner product of the private vector of an ancestor class and the public vector of its descendant class can be used to derive the encryption key of that descendant class. The proposed scheme belongs to direct access schemes on hierarchical access control, namely each class at a higher level in the hierarchy can directly derive the encryption key of its descendant class without the need of iterative computation. In addition to this basic hierarchical key derivation, we also give a dynamic key management mechanism to efficiently address potential changes in the hierarchy. Our scheme only needs light computations over finite field and provides strong key indistinguishability under the assumption of pseudorandom functions. Furthermore, the simulation shows that our scheme has an optimized trade-off between computation consumption and storage space.

A Generalized hierarchical transactive energy management framework for residential microgrids

This work develops a transactive energy management system in order to automate the operation and efficiently utilize the energy generated from the solar PV unit and BESS in a single house as well as in the microgrid and provides cost-benefit analysis.

A market-oriented hierarchical scheduling strategy in cloud workflow systems

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.

South Carolina economic outlook

Since 2008, the South Carolina Department of Commerce has published the Economic Outlook, a monthly snapshot of key state economic indicators on income, employment, and real estate.

South Carolina economic outlook

Since 2008, the South Carolina Department of Commerce has published the Economic Outlook, a monthly snapshot of key state economic indicators on income, employment, and real estate.

South Carolina economic outlook

Since 2008, the South Carolina Department of Commerce has published the Economic Outlook, a monthly snapshot of key state economic indicators on income, employment, and real estate.