Unified model of voltage/current mode control to predict saddle-node bifurcation

A unified model of voltage mode control (VMC) and current mode control (CMC) is proposed to predict the saddle-node bifurcation (SNB). Exact SNB boundary conditions are derived, and can be further simplified in various forms for design purpose. Many approaches, including steady-state, sampled-data, average, harmonic balance, and loop gain analyses are applied to predict SNB. Each approach has its own merits and complement the other approaches.Comment: Submitted to International Journal of Circuit Theory and Applications on December 23, 2010; Manuscript ID: CTA-10-025

Bifurcation Boundary Conditions for Switching DC-DC Converters Under Constant On-Time Control

Sampled-data analysis and harmonic balance analysis are applied to analyze switching DC-DC converters under constant on-time control. Design-oriented boundary conditions for the period-doubling bifurcation and the saddle-node bifurcation are derived. The required ramp slope to avoid the bifurcations and the assigned pole locations associated with the ramp are also derived. The derived boundary conditions are more general and accurate than those recently obtained. Those recently obtained boundary conditions become special cases under the general modeling approach presented in this paper. Different analyses give different perspectives on the system dynamics and complement each other. Under the sampled-data analysis, the boundary conditions are expressed in terms of signal slopes and the ramp slope. Under the harmonic balance analysis, the boundary conditions are expressed in terms of signal harmonics. The derived boundary conditions are useful for a designer to design a converter to avoid the occurrence of the period-doubling bifurcation and the saddle-node bifurcation.Comment: Submitted to International Journal of Circuit Theory and Applications on August 10, 2011; Manuscript ID: CTA-11-016

Closed-Form Critical Conditions of Saddle-Node Bifurcations for Buck Converters

A general and exact critical condition of saddle-node bifurcation is derived in closed form for the buck converter. The critical condition is helpful for the converter designers to predict or prevent some jump instabilities or coexistence of multiple solutions associated with the saddle-node bifurcation. Some previously known critical conditions become special cases in this generalized framework. Given an arbitrary control scheme, a systematic procedure is proposed to derive the critical condition for that control scheme.Comment: Submitted to IEEE Transactions on Automatic Control on Jan. 9, 2012. Seven of my arXiv manuscripts have a common reviewe

Ascertaining price formation in cryptocurrency markets with machine learning

The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars

Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

<p>Abstract</p> <p>Background</p> <p>The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.</p> <p>Results and discussion</p> <p>To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis.</p> <p>Conclusion</p> <p>Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.</p

Effect of Turbulent Uniform Flow past a Two- Dimensional Square Cylinder

Turbulent uniform flows past a two-dimensional square cylinder are investigated numerically. By varying the turbulence intensity and turbulence length scale of the approaching flow, the flow effect of the cylinder are compared to that in a laminar approaching-flow case. In addition, the variations of drag and lift coefficients with respect to the changes of turbulence intensity and turbulence length scale are analyzed on a systematic basis. In the large eddy simulations, the approaching-flow turbulence is generated by a spectral method according to Kármán spectrum. Two levels of turbulence intensities (5% and 10%) and three turbulence length scales (0.25, 0.50 and 1.0 times of the cylinder width) are selected in the study to examine the effect on the cylinder. Results show that the Strouhal number remains almost unchanged when the uniform approaching-flow changes from a laminar state to a turbulent one. The approaching-flow turbulence has noticeable effect in promoting the resulting drag and lift fluctuations. However, its effect on the mean drag appears negligible. In contrast, an increase of the approaching-flow turbulence length scale leads to mild increases of the mean and root-mean-square values of drag. On the other hand, the resulting lift fluctuation is insensitive to the change of the turbulence length scale

K^+ production in baryon-baryon and heavy-ion collisions

Kaon production cross sections in nucleon-nucleon, nucleon-delta and delta-delta interactions are studied in a boson exchange model. For the latter two interactions, the exchanged pion can be on-mass shell, only contributions due to a virtual pion are included via the Peierls method by taking into account the finite delta width. With these cross sections and also those for pion-baryon interactions, subthreshold kaon production from heavy ion collisions is studied in the relativistic transport model.Comment: to appear in Phys. Rev.

Wide-field extended-resolution fluorescence microscopy with standing surface-plasmon-resonance waves

The resolution of conventional SPR imaging has been limited by the diffraction nature of light. A wide-field extended-resolution optical imaging technique, standing-wave surface plasmon resonance fluorescence (SW-SPRF) microscopy, has been developed. Based on evanescent SPR standing waves, SW-SPRF provides lateral resolution approaching 100 nm and offers the advantages of significant signal enhancement and background noise reduction. SW-SPRF has the potential for sensitive biomolecular detection, nanoscale imaging, and lithographic applications

Kaon differential flow in relativistic heavy-ion collisions

Using a relativistic transport model, we study the azimuthal momentum asymmetry of kaons with fixed transverse momentum, i.e., the differential flow, in heavy-ion collisions at beam momentum of 6 GeV/c per nucleon, available from the Alternating Gradient Synchrotron (AGS) at the Brookhaven National Laboratory (BNL). We find that in the absence of kaon potential the kaon differential flow is positive and increases with transverse momentum as that of nucleons. The repulsive kaon potential as predicted by theoretical models, however, reduces the kaon differetnial flow, changing it to negative for kaons with low momenta. Cancellation between the negative differential flow at low mementa and the positive one at high momenta is then responsible for the experimentally observed nearly vanishing in-plane transverse flow of kaons in heavy ion experiments.Comment: Phys. Rev. C in pres