Space vector PWM sequence and harmonic comparison for high power current source rectifier

The space vector PWM (SVPWM) schemes for high power current source drives normally produce low order harmonics due to low switching frequency. To provide a SVPWM with the best harmonic performance, different space vector sequences suitable for a current source rectifier (CSR) are investigated in this project. Details on how to achieve the waveform symmetries with minimum switching frequency for each sequence are discussed. A thorough comparison of the harmonic performance of different space vector sequences based on current source rectifier implementations is carried out. An optimum space vector modulation (SVM) method is proposed to achieve the best line current THD and reduced switching losses. The space vector sequence investigation has been verified in simulation and experimentally using a 10kVA GCT based CSR prototype.

Space vector PWM sequence and harmonic comparison for high power current source rectifier

The space vector PWM (SVPWM) schemes for high power current source drives normally produce low order harmonics due to low switching frequency. To provide a SVPWM with the best harmonic performance, different space vector sequences suitable for a current source rectifier (CSR) are investigated in this project. Details on how to achieve the waveform symmetries with minimum switching frequency for each sequence are discussed. A thorough comparison of the harmonic performance of different space vector sequences based on current source rectifier implementations is carried out. An optimum space vector modulation (SVM) method is proposed to achieve the best line current THD and reduced switching losses. The space vector sequence investigation has been verified in simulation and experimentally using a 10kVA GCT based CSR prototype.

A New Iterative Construction for Approximating Solutions of a Split Common Fixed Point Problem

In this paper, we aim to construct a new strong convergence algorithm for a split common fixed point problem involving the demicontractive operators. It is proved that the vector sequence generated via the Halpern-like algorithm converges to a solution of the split common fixed point problem in norm. The main convergence results presented in this paper extend and improve some corresponding results announced recently. The highlights of this paper shed on the novel algorithm and the new analysis techniques.

Genome targeting by hybrid Flp-TAL recombinases

Genome engineering is a rapidly evolving field that benefits from the availability of different tools that can be used to perform genome manipulation tasks. We describe here the development of the Flp-TAL recombinases that can target genomic FRT-like sequences in their native chromosomal locations. Flp-TAL recombinases are hybrid enzymes that are composed of two functional modules: a variant of site-specific tyrosine recombinase Flp, which can have either narrow or broad target specificity, and the DNA-binding domain of the transcription activator-like effector, TAL. In Flp-TAL, the TAL module is responsible for delivering and stabilizing the Flp module onto the desired genomic FRT-like sequence where the Flp module mediates recombination. We demonstrate the functionality of the Flp-TAL recombinases by performing integration and deletion experiments in human HEK-293 cells. In the integration experiments we targeted a vector to three genomic FRT-like sequences located in the β-globin locus. In the deletion experiments we excised ~ 15 kilobases of DNA that contained a fragment of the integrated vector sequence and the neighboring genome sequence. On average, the efficiency of the integration and deletion reactions was about 0.1% and 20%, respectively.

Recurrent Neural Network for Human Action Recognition using Star Skeletonization

Human Action Recognition has been an active research topic since early 1980s due to its promising applications in many domains like video indexing, surveillance, gesture recognition, video retrieval and human-computer interactions where the actions in the form of videos or sensor datas are recognized. The extraction of relevant features from the video streams is the most challenging part. With the emergence of advanced artificial intelligence techniques, deep learning methods are adopted to achieve the goal. The proposed system presents a Recurrent Neural Network (RNN) methodology for Human Action Recognition using star skeleton as a representative descriptor of human posture. Star skeleton is the process of jointing the gross contour extremes of a body to its centroid. To use star skeleton as feature for action recognition, the feature is defined as a five-dimensional vector in star fashion because the head and four limbs are usually local extremes of human body. In our project, we assumed an action is composed of a series of star skeletons overtime. Therefore, images expressing human action which are time-sequential are transformed into a feature vector sequence. Then the feature vector sequence must be transformed into symbol sequence so that RNN can model the action. RNN is used because the features extracted are time dependent

Different applications of RRE on nonsymmetric algebraic Riccati equation in transport theory

This paper proposes three different ways of applying a vector extrapolation method to a slow converging vector sequence to accelerate the convergence. The solution of this vector sequence is used to solve an algebraic Riccati equation in transport theory. A numerical example is presented to compare the three applications.