A family of DC-DC converters deduced from impedance source DC-DC converters for high step-up conversion

This paper introduces a family of DC-DC converters based on impedance source DC-DC converters. The derived topology is suitable for high voltage step-up ratios. Compared to the typical impedance source DC-DC converters, the proposed topology dramatically reduces the voltage stresses on the power semiconductor devices. In order to suppress the voltage spikes and recycle the leakage inductance energy, the passive-lossless clamp scheme is designed in this paper. The paper presents an analysis of the converter and results from a prototype converter to validate the topology’s performance

ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis

We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images without any auxiliary information. The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss. The ALR module aligns the layout structure (which refers to locations of objects and background) of a synthesized image with that of its corresponding real image. In ALR module, we proposed an Adaptive Layout Refinement (ALR) loss to balance the matching of hard and easy features, for more efficient layout structure matching. Based on the refined layout structure, the LVR loss further refines the visual representation within the layout area. Experimental results on two widely-used datasets show that ALR-GAN performs competitively at the Text-to-Image generation task.Comment: Accepted by TM

Study on Nonlinear Phenomena in Buck-Boost Converter with Switched-Inductor Structure

The switched-inductor structure can be inserted into a traditional Buck-Boost converter to get a high voltage conversion ratio. Nonlinear phenomena may occur in this new converter, which might well lead the system to be unstable. In this paper, a discrete iterated mapping model is established when the new Buck-Boost converter is working at continuous conduction current-controlled mode. On the basis of the discrete model, the bifurcation diagrams and Poincare sections are drawn and then used to analyze the effects of the circuit parameters on the performances. It can be seen clearly that various kinds of nonlinear phenomena are easy to occur in this new converter, including period-doubling bifurcation, border collision bifurcation, tangent bifurcation, and intermittent chaos. Value range of the circuit parameters that may cause bifurcations and chaos are also discussed. Finally, the time-domain waveforms, phase portraits, and power spectrum are obtained by using Matlab/Simulink, which validates the theoretical analysis results

MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification

This paper presents a novel person re-identification model, named Multi-Head Self-Attention Network (MHSA-Net), to prune unimportant information and capture key local information from person images. MHSA-Net contains two main novel components: Multi-Head Self-Attention Branch (MHSAB) and Attention Competition Mechanism (ACM). The MHSAM adaptively captures key local person information, and then produces effective diversity embeddings of an image for the person matching. The ACM further helps filter out attention noise and non-key information. Through extensive ablation studies, we verified that the Structured Self-Attention Branch and Attention Competition Mechanism both contribute to the performance improvement of the MHSA-Net. Our MHSA-Net achieves state-of-the-art performance especially on images with occlusions. We have released our models (and will release the source codes after the paper is accepted) on https://github.com/hongchenphd/MHSA-Net.Comment: Submitted to IEEE Transactions on Image Processing (TIP

Z-source matrix rectifier

This paper presents a novel Z-source matrix rectifier(ZSMR). To overcome the inherent disadvantage that the voltage transfer ratio for traditional matrix rectifier cannot be more than 0.866, a Z-source network has been combined with the matrix rectifier. The proposed rectifier realizes a voltage-boost function and the Z-source network also serves as power storage and guarantees double filtration grade at the output of the rectifier. The open-circuit zero state is required to obtain the voltage-boost function and ensure the output angle of the current vector to be invariant to obtain the expected power factor. In addition, to widely extend the voltage transfer ratio of the proposed rectifier, this paper presents the switched-inductor matrix rectifier(SL-ZSMR) and tapped-inductor matrix rectifier(TL-ZSMR). The corresponding circuit topologies, control strategies and operating principles are introduced. Both simulation and experiment results are shown to verify the theoretical analysis

BMP signaling in the development and regeneration of tooth roots: from mechanisms to applications

Short root anomaly (SRA), along with caries, periodontitis, and trauma, can cause tooth loss, affecting the physical and mental health of patients. Dental implants have become widely utilized for tooth restoration; however, they exhibit certain limitations compared to natural tooth roots. Tissue engineering-mediated root regeneration offers a strategy to sustain a tooth with a physiologically more natural function by regenerating the bioengineered tooth root (bio-root) based on the bionic principle. While the process of tooth root development has been reported in previous studies, the specific molecular mechanisms remain unclear. The Bone Morphogenetic Proteins (BMPs) family is an essential factor regulating cellular activities and is involved in almost all tissue development. Recent studies have focused on exploring the mechanism of BMP signaling in tooth root development by using transgenic animal models and developing better tissue engineering strategies for bio-root regeneration. This article reviews the unique roles of BMP signaling in tooth root development and regeneration