Circuit Structure and Control Method to Reduce Size and Harmonic Distortion of Interleaved Dual Buck Inverter

A new circuit structure and control method for a high power interleaved dual-buck inverter are proposed. The proposed inverter consists of six switches, four diodes and two inductors, uses a dual-buck structure to eliminate zero-cross distortion, and operates in an interleaved mode to reduce the current stress of switch. To reduce the total harmonic distortion at low output power, the inverter is controlled using discontinuous-current-mode control combined with continuous-current-mode control. The experimental inverter had a power-conversion efficiency of 98.5% at output power = 1300 W and 98.3% at output power = 2 kW, when the inverter was operated at an input voltage of 400 V-DC, output voltage of 220 V-AC/60 Hz, and switching frequency of 20 kHz. The total harmonic distortion was < 0.66%, which demonstrates that the inverter is suitable for high-power dc-ac power conversion.11Ysciescopu

Tau functions as Widom constants

We define a tau function for a generic Riemann-Hilbert problem posed on a union of non-intersecting smooth closed curves with jump matrices analytic in their neighborhood. The tau function depends on parameters of the jumps and is expressed as the Fredholm determinant of an integral operator with block integrable kernel constructed in terms of elementary parametrices. Its logarithmic derivatives with respect to parameters are given by contour integrals involving these parametrices and the solution of the Riemann-Hilbert problem. In the case of one circle, the tau function coincides with Widom's determinant arising in the asymptotics of block Toeplitz matrices. Our construction gives the Jimbo-Miwa-Ueno tau function for Riemann-Hilbert problems of isomonodromic origin (Painlev\'e VI, V, III, Garnier system, etc) and the Sato-Segal-Wilson tau function for integrable hierarchies such as Gelfand-Dickey and Drinfeld-Sokolov.Comment: 26 pages, 6 figure

Learning Speed Enhancement of Iterative Learning Control with Advanced Output Data based on Parameter Estimation

Learning speed enhancement is one of the most important issues in learning control. If we can improve both learning speed and tracking performance, it will be helpful to the applicability of learning control. Considering these facts, in this paper, we propose a learning speed enhancement scheme for iterative learning control with advanced output data (ADILC) based on parameter estimation. We consider linear discrete-time non-minimum phase (NMP) systems, whose model is unknown, except for the relative degree and the number of NMP zeros. In each iteration, estimates of the impulse response are obtained from input-output relationship. Then, learning gain matrix is calculated from the estimates, and by using new learning gain matrix, learning speed can be enhanced. Simulation results show that the learning speed has been enhanced by applying the proposed method

Return and Volatility Transmission Between Oil Prices and Emerging Asian Markets

We investigated return and volatility transmission between oil futures prices and ten Asian emerging indices using a VAR-bivariate GARCH model. We also analyzed the optimal weights and hedge ratios for optimizing portfolios to minimize the exposure to risk associated with oil futures price changes. We found no significant influence of oil futures price returns on Asian stock returns. However, strong volatility spillover was observed from oil futures price shocks and volatility to counterpart volatilities. In addition, optimal weights and hedge ratios suggested that incorporating the oil asset in a well-diversified portfolio effectively hedged the risks associated with oil price volatility

Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

Human Activity Recognition (HAR) is a challenging task in the field of human-related signal processing. Owing to the development of wearable sensing technology, an emerging research approach in HAR is to identify user-performed tasks by using data collected from wearable sensors. In this paper, we propose a novel system for monitoring and recognizing daily living activities using an off-the-shelf smart band and two smart shoes. The system aims at providing a useful tool for solving problems regarding body part placement, fusion of multimodal sensors and feature selection for a specific set of activities. The system collects inertial and plantar pressure data at wrist and foot to analyze and then, extract, select important features for recognition. We construct and compare two predictive models of classifying activities from the reduced feature set. A comparison of the classification for each wearable device and a fusion scheme is provided to identify the best body part for activity recognition: either the wrist or the feet. This comparison also demonstrated the effective HAR performance of the proposed system