Component Outage Estimation based on Support Vector Machine

Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events

Exciton energy transfer in nanotube bundles

Photoluminescence is commonly used to identify the electronic structure of individual nanotubes. But, nanotubes naturally occur in bundles. Thus, we investigate photoluminescence of nanotube bundles. We show that their complex spectra are simply explained by exciton energy transfer between adjacent tubes, whereby excitation of large gap tubes induces emission from smaller gap ones via Forster interaction between excitons. The consequent relaxation rate is faster than non-radiative recombination, leading to enhanced photoluminescence of acceptor tubes. This fingerprints bundles with different compositions and opens opportunities to optimize them for opto-electronics.Comment: 5 pages, 5 figure

Estimating phase with a random generator: strategies and resources in multiparameter quantum metrology

Quantum metrology aims to exploit quantum phenomena to overcome classical limitations in the estimation of relevant parameters. We consider a probe undergoing a phase shift φ whose generator is randomly sampled according to a distribution with unknown concentration κ, which introduces a physical source of noise. We then investigate strategies for the joint estimation of the two parameters φ and κ given a finite number N of interactions with the phase imprinting channel. We consider both single qubit and multipartite entangled probes, and identify regions of the parameters where simultaneous estimation is advantageous, resulting in up to a twofold reduction in resources. Quantum enhanced precision is achievable at moderate N, while for sufficiently large N classical strategies take over and the precision follows the standard quantum limit. We show that full-scale entanglement is not needed to reach such an enhancement, as efficient strategies using significantly fewer qubits in a scheme interpolating between the conventional sequential and parallel metrological schemes yield the same effective performance. These results may have relevant applications in optimization of sensing technologies

Power Grid Management in Response to Extreme Events

Power system management in response to extreme events is one the most important operational aspects of power systems. In this thesis, a novel Event-driven Security Constrained Unit Commitment (E-SCUC) model and a statistical method, based on regression and data mining to estimate the system components outages, are proposed. The proposed models help consider the simultaneous outage of several system components represented by an N-1-m reliability criterion and accordingly determine the proper system response. In addition, an optimal microgrid placement model with the objective of minimizing the cost of unserved energy to enhance power system resilience is proposed. The numerical simulations on the standard IEEE 30-bus and IEEE 118-bus test systems exhibit the merits and applicability of the proposed E-SCUC model, as well as the advantages of the data mining approach in estimating component outage, and the effectiveness of the optimal microgrid placement in ensuring an economic operation under normal conditions and a resilient operation under contingency cases

Autocatalytic Biochemical Networks and Their Fundamental Limits

In the present work, we study autocatalytic pathways which contain reactions that need the use of one of their own productions. These pathways are common in biology; one of the simplest and widely studied autocatalytic pathways is Glycolysis. This pathway produces energy by breaking down Glucose. It is shown that this pathway can be simplified as a network of three biochemical reactions. We first revisit some conditions on the underlying structure of the autocatalytic network, which guarantee the existence of fundamental limits on the output energy of such networks. Then we focus on autocatalytic pathways with several biochemical reactions. Our aim is to characterize the zero-dynamics for a class of autocatalytic networks and then study the fundamental limitations of feedback control laws, using their associated zero-dynamics. For this aim, it is shown that the zero-dynamics of autocatalytic networks play an important role in studying the fundamental limits on performance. Zero-dynamics is defined as the dynamics of a system restricted to the control input and initial conditions such that the output of the system remains zero for all future time instances. We characterize the zero-dynamics for a class of unperturbed autocatalytic networks based on the structure of the original network. It is well-known that by knowing the zero-dynamics of a specific class of systems, one can obtain lower bounds on the best achievable performance (L2-norm of the output) for the system. For a specific class of autocatalytic networks, we characterize their zero-dynamics in terms of the state-space matrices of the underlying network. This can be utilized to quantify inherent fundamental limits on performance (the level of disturbance attenuation) for this class of network. In general, one should apply numerical algorithms to obtain such fundamental limits. We explain our method using a simple but illustrative example

A comparison of singlet oxygen explicit dosimetry (SOED) and singlet oxygen luminescence dosimetry (SOLD) for photofrin-mediated photodynamic therapy

Accurate photodynamic therapy (PDT) dosimetry is critical for the use of PDT in the treatment of malignant and nonmalignant localized diseases. A singlet oxygen explicit dosimetry (SOED) model has been developed for in vivo purposes. It involves the measurement of the key components in PDT—light fluence (rate), photosensitizer concentration, and ground-state oxygen concentration ([3O2])—to calculate the amount of reacted singlet oxygen ([1O2]rx), the main cytotoxic component in type II PDT. Experiments were performed in phantoms with the photosensitizer Photofrin and in solution using phosphorescence-based singlet oxygen luminescence dosimetry (SOLD) to validate the SOED model. Oxygen concentration and photosensitizer photobleaching versus time were measured during PDT, along with direct SOLD measurements of singlet oxygen and triplet state lifetime (τΔ and τt), for various photosensitizer concentrations to determine necessary photophysical parameters. SOLD-determined cumulative [1O2]rx was compared to SOED-calculated [1O2]rx for various photosensitizer concentrations to show a clear correlation between the two methods. This illustrates that explicit dosimetry can be used when phosphorescence-based dosimetry is not feasible. Using SOED modeling, we have also shown evidence that SOLD-measured [1O2]rx using a 523 nm pulsed laser can be used to correlate to singlet oxygen generated by a 630 nm laser during a clinical malignant pleural mesothelioma (MPM) PDT protocol by using a conversion formul