High-efficiency voltage source converters with silicon super-junction MOSFETs

High-efficiency power converters have the benefits of minimising energy consumption, reducing costs, and realising high power densities. The silicon super-junction (SJ) MOSFET is an attractive device for high-efficiency applications. However, its highly non-linear output capacitance and the reverse recovery properties of its intrinsic diode must be addressed when used in voltage source converters (VSCs). The research in this thesis aims at addressing these two problems and realising high efficiency. Initially, state-of-art techniques in the literature are reviewed. In order to develop a solution with simple hardware, no major auxiliary magnetic components, and no onerous timing requirements, a dual-mode switching technique is proposed. The technique is demonstrated using a SJ MOSFET based bridge-leg circuit. The hardware performance is then experimentally investigated with different power semiconductor device permutations. The transition conditions between the two switching modes do not have to be tightly set in order to maintain a high efficiency. The dual-mode switching technique is then further investigated with a current transformer (CT) arrangement embedded in the MOSFET’s gate driver circuit in order to control the profile of the MOSFET’s incoming drain current at turn on. The dual-mode switching technique, with or without a CT scheme, is shown to achieve high efficiency with minimal additional hardware.High-efficiency power converters have the benefits of minimising energy consumption, reducing costs, and realising high power densities. The silicon super-junction (SJ) MOSFET is an attractive device for high-efficiency applications. However, its highly non-linear output capacitance and the reverse recovery properties of its intrinsic diode must be addressed when used in voltage source converters (VSCs). The research in this thesis aims at addressing these two problems and realising high efficiency. Initially, state-of-art techniques in the literature are reviewed. In order to develop a solution with simple hardware, no major auxiliary magnetic components, and no onerous timing requirements, a dual-mode switching technique is proposed. The technique is demonstrated using a SJ MOSFET based bridge-leg circuit. The hardware performance is then experimentally investigated with different power semiconductor device permutations. The transition conditions between the two switching modes do not have to be tightly set in order to maintain a high efficiency. The dual-mode switching technique is then further investigated with a current transformer (CT) arrangement embedded in the MOSFET’s gate driver circuit in order to control the profile of the MOSFET’s incoming drain current at turn on. The dual-mode switching technique, with or without a CT scheme, is shown to achieve high efficiency with minimal additional hardware

A high-efficiency super-junction MOSFET based inverter-leg configuration using a dual-mode switching technique

High-efficiency power converters have benefits of minimizing energy consumption, reducing costs, and realizing high power densities. The silicon super-junction MOSFET is an attractive device for high-efficiency applications. However, its highly non-linear output capacitance and the reverse recovery properties of its intrinsic diode must be addressed when used in voltage source converters. A dual-mode switching technique operating in conjunction with intrinsic diode deactivation circuitry is proposed in this paper. The technique is demonstrated in an 800-W inverter-leg configuration operating from a 400-V DC voltage rail and switching at 20 kHz. Intended applications include machine drives. The full-load efficiency reaches approximately 98.7% and no forced cooling is needed

Control of incoming drain currents drawn by super-junction MOSFETs in voltage source bridge-legs

To increase the efficiency of renewable energy power conversion systems, traditional silicon IGBTs can be replaced with silicon super-junction MOSFETs. However, the poor performance of the MOSFET's intrinsic diode and the output capacitance present difficulties in voltage source converter bridge-legs. When a MOSFET in this circuit turns on, a charging current has to be sourced into the output capacitance of the complementary freewheeling MOSFET, even if the diode has been deactivated. The peak incoming drain current into the MOSFET turning on can be limited by using a large resistance in series with its gate. However, this increases MOSFET power dissipation. Also, the turn-on propagation delay time is increased. This paper presents a gate driver circuit for profiling the MOSFET's incoming drain current to provide an improved trade-off between incoming peak current, turn-on power dissipation, and delay time

Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Many adversarial-based UDA methods involve high-instability training and have to carefully tune the optimization procedure. Some non-adversarial UDA methods employ a consistency regularization on the target predictions of a student model and a teacher model under different perturbations, where the teacher shares the same architecture with the student and is updated by the exponential moving average of the student. However, these methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model. In this paper, we propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation. By exploiting the latent uncertainty information of the target samples, more meaningful and reliable knowledge from the teacher model can be transferred to the student model. In addition, we further reveal the reason why the current consistency regularization is often unstable in minimizing the distribution discrepancy. We also show that our method can effectively ease this issue by mining the most reliable and meaningful samples with a dynamic weighting scheme of consistency loss. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods on two domain adaptation benchmarks, $i.e.,$ GTAV $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes

Context-Aware Mixup for Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always reduce the domain shifts in pixel level, feature level, and output level. However, almost all of them largely neglect the contextual dependency, which is generally shared across different domains, leading to less-desired performance. In this paper, we propose a novel Context-Aware Mixup (CAMix) framework for domain adaptive semantic segmentation, which exploits this important clue of context-dependency as explicit prior knowledge in a fully end-to-end trainable manner for enhancing the adaptability toward the target domain. Firstly, we present a contextual mask generation strategy by leveraging the accumulated spatial distributions and prior contextual relationships. The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels. Besides, provided the context knowledge, we introduce a significance-reweighted consistency loss to penalize the inconsistency between the mixed student prediction and the mixed teacher prediction, which alleviates the negative transfer of the adaptation, e.g., early performance degradation. Extensive experiments and analysis demonstrate the effectiveness of our method against the state-of-the-art approaches on widely-used UDA benchmarks.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

DMT: Dynamic Mutual Training for Semi-Supervised Learning

Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels. However, pseudo labels are unreliable. Self-training methods usually rely on single model prediction confidence to filter low-confidence pseudo labels, thus remaining high-confidence errors and wasting many low-confidence correct labels. In this paper, we point out it is difficult for a model to counter its own errors. Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors. With this new viewpoint, we propose mutual training between two different models by a dynamically re-weighted loss function, called Dynamic Mutual Training (DMT). We quantify inter-model disagreement by comparing predictions from two different models to dynamically re-weight loss in training, where a larger disagreement indicates a possible error and corresponds to a lower loss value. Extensive experiments show that DMT achieves state-of-the-art performance in both image classification and semantic segmentation. Our codes are released at https://github.com/voldemortX/DST-CBC .Comment: Reformatte

Development of dynamical network biomarkers for regulation in Epstein-Barr virus positive peripheral T cell lymphoma unspecified type

Background: This study was performed to identify key regulatory network biomarkers including transcription factors (TFs), miRNAs and lncRNAs that may affect the oncogenesis of EBV positive PTCL-U.Methods: GSE34143 dataset was downloaded and analyzed to identify differentially expressed genes (DEGs) between EBV positive PTCL-U and normal samples. Gene ontology and pathway enrichment analyses were performed to illustrate the potential function of the DEGs. Then, key regulators including TFs, miRNAs and lncRNAs involved in EBV positive PTCL-U were identified by constructing TF–mRNA, lncRNA–miRNA–mRNA, and EBV encoded miRNA–mRNA regulatory networks.Results: A total of 96 DEGs were identified between EBV positive PTCL-U and normal tissues, which were related to immune responses, B cell receptor signaling pathway, chemokine activity. Pathway analysis indicated that the DEGs were mainly enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. Based on the TF network, hub TFs were identified regulate the target DEGs. Afterwards, a ceRNA network was constructed, in which miR-181(a/b/c/d) and lncRNA LINC01744 were found. According to the EBV-related miRNA regulatory network, CXCL10 and CXCL11 were found to be regulated by EBV-miR-BART1-3p and EBV-miR-BHRF1-3, respectively. By integrating the three networks, some key regulators were found and may serve as potential network biomarkers in the regulation of EBV positive PTCL-U.Conclusion: The network-based approach of the present study identified potential biomarkers including transcription factors, miRNAs, lncRNAs and EBV-related miRNAs involved in EBV positive PTCL-U, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of EBV positive PTCL-U