Development of an integrated low-power RF partial discharge detector

This paper presents the results from integrating a low-power partial discharge detector with a wireless sensor node designed for operating as part of an IEEE 802.15.4 sensor network, and applying an on-line classifier capable of classifying partial discharges in real-time. Such a system is of benefit to monitoring engineers as it provides a means to exploit the RF technique using a low-cost device while circumventing the need for any additional cabling associated with new condition monitoring systems. The detector uses a frequency-based technique to differentiate between multiple defects, and has been integrated with a SunSPOT wireless sensor node hosting an agent-based monitoring platform, which includes a data capture agent and rule induction agent trained using experimental data. The results of laboratory system verification are discussed, and the requirements for a fully robust and flexible system are outlined

A frequency-based RF partial discharge detector for low-power wireless sensing

Partial discharge (PD) monitoring has been the subject of significant research in recent years, which has given rise to a range of well-established PD detection and measurement techniques, such as acoustic and RF, on which condition monitoring systems for highvoltage equipment have been based. This paper presents a novel approach to partial discharge monitoring by using a low-cost, low-power RF detector. The detector employs a frequency-based technique that can distinguish between multiple partial discharge events and other impulsive noise sources within a substation, tracking defect severity over time and providing information pertaining to plant health. The detector is designed to operate as part of a wireless condition monitoring network, removing the need for additional wiring to be installed into substations whilst still gaining the benefits of the RF technique. This novel approach to PD detection not only provides a low-cost solution to on-line partial discharge monitoring, but also presents a means to deploy wide-scale RF monitoring without the associated costs of wide-band monitoring systems

Radiation content of Conformally flat initial data

We study the radiation of energy and linear momentum emitted to infinity by the headon collision of binary black holes, starting from rest at a finite initial separation, in the extreme mass ratio limit. For these configurations we identify the radiation produced by the initially conformally flat choice of the three geometry. This identification suggests that the radiated energy and momentum of headon collisions will not be dominated by the details of the initial data for evolution of holes from initial proper separations $L_0\geq7M$. For non-headon orbits, where the amount of radiation is orders of magnitude larger, the conformally flat initial data may provide a relative even better approximation.Comment: 4 pages, 4 figure

Photodynamics of potent antioxidants: ferulic and caffeic acids

The dynamics of ferulic acid (3-(4-hydroxy-3-methoxyphenyl)-2-propenoic acid) and caffeic acid (3-(3,4-dihydroxyphenyl)-2-propenoic acid) in acetonitrile, dioxane and water at pH 2.2 following photoexcitation to the first excited singlet state are reported. These hydroxycinnamic acids display both strong ultraviolet absorption and potent antioxidant activity, making them promising sunscreen components. Ferulic and caffeic acids have previously been shown to undergo trans–cis photoisomerization via irradiation studies, yet time-resolved measurements were unable to observe formation of the cis-isomer. In the present study, we are able to observe the formation of the cis-isomer as well as provide timescales of relaxation following initial photoexcitation

Binary black hole initial data for numerical general relativity based on post-Newtonian data

With the goal of taking a step toward the construction of astrophysically realistic initial data for numerical simulations of black holes, we for the first time derive a family of fully general relativistic initial data based on post-2-Newtonian expansions of the 3-metric and extrinsic curvature without spin. It is expected that such initial data provide a direct connection with the early inspiral phase of the binary system. We discuss a straightforward numerical implementation, which is based on a generalized puncture method. Furthermore, we suggest a method to address some of the inherent ambiguity in mapping post-Newtonian data onto a solution of the general relativistic constraints.Comment: 13 pages, 8 figures, RevTex

Conformal-thin-sandwich initial data for a single boosted or spinning black hole puncture

Sequences of initial-data sets representing binary black holes in quasi-circular orbits have been used to calculate what may be interpreted as the innermost stable circular orbit. These sequences have been computed with two approaches. One method is based on the traditional conformal-transverse-traceless decomposition and locates quasi-circular orbits from the turning points in an effective potential. The second method uses a conformal-thin-sandwich decomposition and determines quasi-circular orbits by requiring the existence of an approximate helical Killing vector. Although the parameters defining the innermost stable circular orbit obtained from these two methods differ significantly, both approaches yield approximately the same initial data, as the separation of the binary system increases. To help understanding this agreement between data sets, we consider the case of initial data representing a single boosted or spinning black hole puncture of the Bowen-York type and show that the conformal-transverse-traceless and conformal-thin-sandwich methods yield identical data, both satisfying the conditions for the existence of an approximate Killing vector.Comment: 13 pages, 2 figure

Bayesian Nonparametric Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given the transition function and a set of observed demonstrations in the form of state-action pairs. Current IRL algorithms attempt to find a single reward function which explains the entire observation set. In practice, this leads to a computationally-costly search over a large (typically infinite) space of complex reward functions. This paper proposes the notion that if the observations can be partitioned into smaller groups, a class of much simpler reward functions can be used to explain each group. The proposed method uses a Bayesian nonparametric mixture model to automatically partition the data and find a set of simple reward functions corresponding to each partition. The simple rewards are interpreted intuitively as subgoals, which can be used to predict actions or analyze which states are important to the demonstrator. Experimental results are given for simple examples showing comparable performance to other IRL algorithms in nominal situations. Moreover, the proposed method handles cyclic tasks (where the agent begins and ends in the same state) that would break existing algorithms without modification. Finally, the new algorithm has a fundamentally different structure than previous methods, making it more computationally efficient in a real-world learning scenario where the state space is large but the demonstration set is small

Incorporating expression data in metabolic modeling: a case study of lactate dehydrogenase

Integrating biological information from different sources to understand cellular processes is an important problem in systems biology. We use data from mRNA expression arrays and chemical kinetics to formulate a metabolic model relevant to K562 erythroleukemia cells. MAP kinase pathway activation alters the expression of metabolic enzymes in K562 cells. Our array data show changes in expression of lactate dehydrogenase (LDH) isoforms after treatment with phorbol 12-myristate 13-acetate (PMA), which activates MAP kinase signaling. We model the change in lactate production which occurs when the MAP kinase pathway is activated, using a non-equilibrium, chemical-kinetic model of homolactic fermentation. In particular, we examine the role of LDH isoforms, which catalyze the conversion of pyruvate to lactate. Changes in the isoform ratio are not the primary determinant of the production of lactate. Rather, the total concentration of LDH controls the lactate concentration.Comment: In press, Journal of Theoretical Biology. 27 pages, 9 figure

Evidence of Final-State Suppression of High-p_T Hadrons in Au + Au Collisions Using d + Au Measurements at RHIC

Transverse momentum spectra of charged hadrons with ${p_{T} <}$ 6 GeV/c have been measured near mid-rapidity (0.2 $< \eta <$ 1.4) by the PHOBOS experiment at RHIC in Au + Au and d + Au collisions at ${\sqrt{s_{_{NN}}} = \rm {200 GeV}}$. The spectra for different collision centralities are compared to ${p + \bar{p}}$ collisions at the same energy. The resulting nuclear modification factor for central Au + Au collisions shows evidence of strong suppression of charged hadrons in the high-$p_{T}$ region (${>2}$ GeV/c). In contrast, the d + Au nuclear modification factor exhibits no suppression of the high-$p_{T}$ yields. These measurements suggest a large energy loss of the high-$p_{T}$ particles in the highly interacting medium created in the central Au + Au collisions. The lack of suppression in d + Au collisions suggests that it is unlikely that initial state effects can explain the suppression in the central Au + Au collisions.Comment: 3 pages, 4 figures, International Europhysics Conference on High Energy Physics EPS (July 17th-23rd 2003) in Aachen, German

Structural and biophysical characterization of bacillus thuringiensis insecticidal proteins Cry34Ab1 and Cry35Ab1

Bacillus thuringiensis strains are well known for the production of insecticidal proteins upon sporulation and these proteins are deposited in parasporal crystalline inclusions. The majority of these insect-specific toxins exhibit three domains in the mature toxin sequence. However, other Cry toxins are structurally and evolutionarily unrelated to this three-domain family and little is known of their three dimensional structures, limiting our understanding of their mechanisms of action and our ability to engineer the proteins to enhance their function. Among the non-three domain Cry toxins, the Cry34Ab1 and Cry35Ab1 proteins from B. thuringiensis strain PS149B1 are required to act together to produce toxicity to the western corn rootworm (WCR) Diabrotica virgifera virgifera Le Conte via a pore forming mechanism of action. Cry34Ab1 is a protein of ∼14 kDa with features of the aegerolysin family (Pfam06355) of proteins that have known membrane disrupting activity, while Cry35Ab1 is a ∼44 kDa member of the toxin_10 family (Pfam05431) that includes other insecticidal proteins such as the binary toxin BinA/BinB. The Cry34Ab1/Cry35Ab1 proteins represent an important seed trait technology having been developed as insect resistance traits in commercialized corn hybrids for control of WCR. The structures of Cry34Ab1 and Cry35Ab1 have been elucidated to 2.15 Å and 1.80 Å resolution, respectively. The solution structures of the toxins were further studied by small angle X-ray scattering and native electrospray ion mobility mass spectrometry. We present here the first published structure from the aegerolysin protein domain family and the structural comparisons of Cry34Ab1 and Cry35Ab1 with other pore forming toxins