Smart FRP Composite Sandwich Bridge Decks in Cold Regions

INE/AUTC 12.0

Synchronization of fractional chaotic complex networks with delays

summary:The synchronization of fractional-order complex networks with delay is investigated in this paper. By constructing a novel Lyapunov-Krasovskii function $V$ and taking integer derivative instead of fractional derivative of the function, a sufficient criterion is obtained in the form of linear matrix inequalities to realize synchronizing complex dynamical networks. Finally, a numerical example is shown to illustrate the feasibility and effectiveness of the proposed method

Efficiency characterization of a large neuronal network: a causal information approach

When inhibitory neurons constitute about 40% of neurons they could have an important antinociceptive role, as they would easily regulate the level of activity of other neurons. We consider a simple network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticity, representative of a cortical column or hypercolumn with large proportion of inhibitory neurons. Each neuron fires following a Hodgkin-Huxley like dynamics and it is interconnected randomly to other neurons. The network dynamics is investigated estimating Bandt and Pompe probability distribution function associated to the interspike intervals and taking different degrees of inter-connectivity across neurons. More specifically we take into account the fine temporal ``structures'' of the complex neuronal signals not just by using the probability distributions associated to the inter spike intervals, but instead considering much more subtle measures accounting for their causal information: the Shannon permutation entropy, Fisher permutation information and permutation statistical complexity. This allows us to investigate how the information of the system might saturate to a finite value as the degree of inter-connectivity across neurons grows, inferring the emergent dynamical properties of the system.Comment: 26 pages, 3 Figures; Physica A, in pres

Revealing networks from dynamics: an introduction

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.Comment: Topical review, 48 pages, 7 figure

UNDERWATER COMMUNICATIONS WITH ACOUSTIC STEGANOGRAPHY: RECOVERY ANALYSIS AND MODELING

In the modern warfare environment, communication is a cornerstone of combat competence. However, the increasing threat of communications-denied environments highlights the need for communications systems with low probability of intercept and detection. This is doubly true in the subsurface environment, where communications and sonar systems can reveal the tactical location of platforms and capabilities, subverting their covert mission set. A steganographic communication scheme that leverages existing technologies and unexpected data carriers is a feasible means of increasing assurance of communications, even in denied environments. This research works toward a covert communication system by determining and comparing novel symbol recovery schemes to extract data from a signal transmitted under a steganographic technique and interfered with by a simulated underwater acoustic channel. We apply techniques for reliably extracting imperceptible information from unremarkable acoustic events robust to the variability of the hostile operating environment. The system is evaluated based on performance metrics, such as transmission rate and bit error rate, and we show that our scheme is sufficient to conduct covert communications through acoustic transmissions, though we do not solve the problems of synchronization or equalization.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Flexible operation of grid-interfacing converters in distribution networks : bottom-up solutions to voltage quality enhancement

Due to the emerging application of distributed generation (DG), large numbers of DG systems are expected to deliver electricity into the distribution network in the near future. For the most part these systems are not ready for riding through grid disturbances and cannot mitigate unwanted influences on the grid. On the one hand, with the increasing use of sensitive and critical equipment by customers, the electricity network is required to serve high voltage quality. On the other hand, more and more unbalanced and nonlinear equipment, including DG units, is negatively affecting the power quality of distribution networks. To adapt to the future distribution network, the tendency for grid-interfacing converters will be to integrate voltage quality enhancement with DG functionality. In this thesis, the flexible operation of grid-interfacing converters in distribution networks is investigated for the purpose of voltage quality enhancement at both the grid and user sides. The research is carried out in a bottom-up fashion, from the low-level power electronics control, through the realization of individual system functionality, finally arriving at system-level concepts and implementation. Being essential to the control of grid-interfacing converters, both stationaryframe techniques for voltage detection and synchronization in disturbed grids, and asymmetrical current regulation are investigated. Firstly, a group of high performance filters for the detection of fundamental symmetrical sequences and harmonics under various grid conditions is proposed. The robustness of the proposed filters to small grid-frequency variation and their adaptability to large frequency change are discussed. Secondly, multiple reference frame current regulation is explored for dealing with unbalanced grid conditions. As a complement to the existing proportional resonant (PR) controllers, sequence-decoupled resonant (SDR) controllers are proposed for regulating individual symmetric sequences. Based on the modeling of a four-leg grid-connected system in different reference frames, three types of controllers, i.e. PI, PR, and proportional plus SDR controllers are compared. Grid-interactive control of distributed power generation, i.e. voltage unbalance compensation, grid-fault ride-through control and flexible power transfer, as well as the modeling of harmonic interaction, are all investigated. The in-depth study and analysis of these grid interactions show the grid-support possibilities and potential negative impact on the grid of inverter-based DG units, beyond their primary goal of power delivery. In order to achieve a co-operative voltage unbalance compensation based on distributed DG systems, two control schemes, namely voltage unbalance factor based control and negative-sequence admittance control, are proposed. The negativesequence voltages at the grid connection point can be compensated and mitigated by regulating the negative-sequence currents flowing between the grid and DG converters. Flexible active and reactive power control during unbalanced voltage dips is proposed that enables DG systems to enhance grid-fault ride-through capability and to adapt to various requirements for grid voltage support. By changing adaptable weighting factors, the compensation of oscillating power and the regulation of grid currents can be easily implemented. Two joint strategies for the simultaneous control of active and reactive power are derived, which maintain the adaptive controllability that can cope with multiple constraints in practical applications. The contribution of zero-sequence currents to active power control is also analyzed as a complement to the proposed control, which is based on positive- and negative-sequence components. Harmonic interaction between DG inverters and the grid is modeled and analyzed with an impedance-based approach. In order to mitigate the harmonic distortion in a polluted grid, it is proposed to specify output impedance limits as a design constraint for DG inverters. Results obtained from modeling, analysis, and simulations of a distribution network with aggregated DG inverters, show that the proposed method is a simple and effective way for estimating harmonic quasi-resonance problems. By integrating these proposed control strategies in a modified conventional series-parallel structure, we arrived at a group of grid-interfacing system topologies that is suitable for DG applications, voltage quality improvement, and flexible power transfer. A concrete laboratory system details the proposed concepts and specifies the practical problems related to control design. The introduction of multi-level control objectives illustrates that the proposed system can ride through voltage disturbances, can enhance the grid locally, and can continue the power transfer to and from the grid while high voltage quality is maintained for the local loads within the system module. A dual-converter laboratory set-up was built, with which the proposed concepts and practical implementation have been fully demonstrated

On-the-fly tracing for data-centric computing : parallelization, workflow and applications

As data-centric computing becomes the trend in science and engineering, more and more hardware systems, as well as middleware frameworks, are emerging to handle the intensive computations associated with big data. At the programming level, it is crucial to have corresponding programming paradigms for dealing with big data. Although MapReduce is now a known programming model for data-centric computing where parallelization is completely replaced by partitioning the computing task through data, not all programs particularly those using statistical computing and data mining algorithms with interdependence can be re-factorized in such a fashion. On the other hand, many traditional automatic parallelization methods put an emphasis on formalism and may not achieve optimal performance with the given limited computing resources. In this work we propose a cross-platform programming paradigm, called on-the-fly data tracing , to provide source-to-source transformation where the same framework also provides the functionality of workflow optimization on larger applications. Using a big-data approximation computations related to large-scale data input are identified in the code and workflow and a simplified core dependence graph is built based on the computational load taking in to account big data. The code can then be partitioned into sections for efficient parallelization; and at the workflow level, optimization can be performed by adjusting the scheduling for big-data considerations, including the I/O performance of the machine. Regarding each unit in both source code and workflow as a model, this framework enables model-based parallel programming that matches the available computing resources. The techniques used in model-based parallel programming as well as the design of the software framework for both parallelization and workflow optimization as well as its implementations with multiple programming languages are presented in the dissertation. Then, the following experiments are performed to validate the framework: i) the benchmarking of parallelization speed-up using typical examples in data analysis and machine learning (e.g. naive Bayes, k-means) and ii) three real-world applications in data-centric computing with the framework are also described to illustrate the efficiency: pattern detection from hurricane and storm surge simulations, road traffic flow prediction and text mining from social media data. In the applications, it illustrates how to build scalable workflows with the framework along with performance enhancements