Self-tuning neurofuzzy control for nonlinear systems with offset

A self-tuning neurofuzzy controller with an ability to remove offsets is derived in this paper based on the self-tuning integrating controller derived for the local linear model. The training target for the proposed controllers is derived, and they can be trained by the simplified recursive least squares (RLS) method with a computing time that is linear instead of geometric in the number of weights in the network. Further, the simplified RLS method not only has the same convergence property as the RLS method, it also has a better ability in tracking varying parameters. The performance of the self-tuning neurofuzzy controller is illustrated by examples involving both linear and nonlinear systems.published_or_final_versio

A diagrammatic approach to search for minimum sampling frequency and quantization resolution for digital control of power converters

A diagrammatic approach to find out the minimum sampling frequency and quantization resolution for digital control of power converters is proposed. The proposed solution algorithm combines consideration on both time sampling and quantization resolution axes to search for the minimum required digital controller settings. Experiments results are presented to justify the proposed algorithm. © 2007 IEEE.published_or_final_versio

Model-reference adaptive control based on neurofuzzy networks

Model reference adaptive control (MRAC) is a popular approach to control linear systems, as it is relatively simple to implement. However, the performance of the linear MRAC deteriorates rapidly when the system becomes nonlinear. In this paper, a nonlinear MRAC based on neurofuzzy networks is derived. Neurofuzzy networks are chosen not only because they can approximate nonlinear functions with arbitrary accuracy, but also they are compact in their supports, and the weights of the network can be readily updated on-line. The implementation of the neurofuzzy network-based MRAC is discussed, and the local stability of the system controlled by the proposed controller is established. The performance of the neurofuzzy network-based MRAC is illustrated by examples involving both linear and nonlinear systems. © 2004 IEEE.published_or_final_versio

Feedback-induced nonlinearity and superconducting on-chip quantum optics

Quantum coherent feedback has been proven to be an efficient way to tune the dynamics of quantum optical systems and, recently, those of solid-state quantum circuits. Here, inspired by the recent progress of quantum feedback experiments, especially those in mesoscopic circuits, we prove that superconducting circuit QED systems, shunted with a coherent feedback loop, can change the dynamics of a superconducting transmission line resonator, i.e., a linear quantum cavity, and lead to strong on-chip nonlinear optical phenomena. We find that bistability can occur under the semiclassical approximation, and photon anti-bunching can be shown in the quantum regime. Our study presents new perspectives for engineering nonlinear quantum dynamics on a chip.Comment: 10 pages, 9 figure

Wilson's disease: A patient undiagnosed for 18 years

Wilson's disease, an autosomal recessive disorder of copper metabolism, is the most common inherited hepatic disease in Hong Kong. Diagnosis is based on the presence of Kayser-Fleischer rings, typical neurological symptoms, and/or a low serum ceruloplasmin concentration (<0.20 g/L). Early detection and treatment protect patients and their presymptomatic siblings from devastating organ damage. The diagnosis of Wilson's disease may nonetheless be overlooked if only established clinical and laboratory tests are used as diagnostic criteria. We report diagnosis of the disorder using genetic analysis of ATP7B in a presymptomatic sibling who escaped diagnosis during family screening 18 years previously. The patient was 11 months old when family screening was performed following diagnosis of Wilson's disease in an elder sister. The boy was considered to be unaffected on the basis of laboratory results in the expected range: serum copper level, 4.6 μmol/L; serum ceruloplasmin level, 0.16 g/L; and 24-hour urinary copper excretion, 0.14 μmol/day. Molecular analysis of ATP7B was performed; it revealed that the two siblings shared the same compound heterozygous mutations (G943D and 2299delC). We recommend that molecular diagnosis is the only definitive means of diagnosing Wilson's disease in children younger than 1 year.published_or_final_versio

[FeIII(TF4DMAP)OTf] catalysed anti-Markovnikov oxidation of terminal aryl alkenes to aldehydes and transformation of methyl aryl tertiary amines to formamides with H2O2 as a terminal oxidant

Anti-Markovnikov oxidation of terminal aryl alkenes to aldehydes and transformation of N-methyl aryl tertiary amines to formamides with H2O2 as a terminal oxidant under mild conditions have been achieved with moderate to good product yields using [FeIII(TF4DMAP)OTf] as catalyst.postprin

Melatonin Inhibits Androgen Receptor Splice Variant-7 (AR-V7)-Induced Nuclear Factor-Kappa B (NF-κB) Activation and NF-κB Activator-Induced AR-V7 Expression in Prostate Cancer Cells: Potential Implications for the Use of Melatonin in Castration-Resistant Prostate Cancer (CRPC) Therapy

A major current challenge in the treatment of advanced prostate cancer, which can be initially controlled by medical or surgical castration, is the development of effective, safe, and affordable therapies against progression of the disease to the stage of castration resistance. Here, we showed that in LNCaP and 22Rv1 prostate cancer cells transiently overexpressing androgen receptor splice variant-7 (AR-V7), nuclear factor-kappa B (NF-κB) was activated and could result in up-regulated interleukin (IL)-6 gene expression, indicating a positive interaction between AR-V7 expression and activated NF-κB/IL-6 signaling in castration-resistant prostate cancer (CRPC) pathogenesis. Importantly, both AR-V7-induced NF-κB activation and IL-6 gene transcription in LNCaP and 22Rv1 cells could be inhibited by melatonin. Furthermore, stimulation of AR-V7 mRNA expression in LNCaP cells by betulinic acid, a pharmacological NF-κB activator, was reduced by melatonin treatment. Our data support the presence of bi-directional positive interactions between AR-V7 expression and NF-κB activation in CRPC pathogenesis. Of note, melatonin, by inhibiting NF-κB activation via the previously-reported MT₁ receptor-mediated antiproliferative pathway, can disrupt these bi-directional positive interactions between AR-V7 and NF-κB and thereby delay the development of castration resistance in advanced prostate cancer. Apparently, this therapeutic potential of melatonin in advanced prostate cancer/CRPC management is worth translation in the clinic via combined androgen depletion and melatonin repletion.published_or_final_versio

Fully-Unintegrated Parton Distribution and Fragmentation Functions at Perturbative k_T

We define and study the properties of generalized beam functions (BFs) and fragmenting jet functions (FJFs), which are fully-unintegrated parton distribution functions (PDFs) and fragmentation functions (FFs) for perturbative k_T. We calculate at one loop the coefficients for matching them onto standard PDFs and FFs, correcting previous results for the BFs in the literature. Technical subtleties when measuring transverse momentum in dimensional regularization are clarified, and this enables us to renormalize in momentum space. Generalized BFs describe the distribution in the full four-momentum k_mu of a colliding parton taken out of an initial-state hadron, and therefore characterize the collinear initial-state radiation. We illustrate their importance through a factorization theorem for pp -> l^+ l^- + 0 jets, where the transverse momentum of the lepton pair is measured. Generalized FJFs are relevant for the analysis of semi-inclusive processes where the full momentum of a hadron, fragmenting from a jet with constrained invariant mass, is measured. Their significance is shown for the example of e^+ e^- -> dijet+h, where the perpendicular momentum of the fragmenting hadron with respect to the thrust axis is measured.Comment: Journal versio

Resting-state fMRI activity predicts unsupervised learning and memory in an immersive virtual reality environment

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment

Giant magnetoelectric effect of a hybrid of magnetostrictive and piezoelectric composites

Author name used in this publication: H. L. W. Chand2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe