A Robust Control for Five-level Inverter Based on Integral Sliding Mode Control

This paper presents a new control strategy for cascaded H-bridge five-level inverter (CHB-5LI) based on the novel sliding mode control (NSMC). The proposed method can generate pulse-width modulation (PWM) without using conventional modulation techniques based on carrier waves. With the proposed NSMC technique, the PWM pulses can be obtained by the control signal u(t) from the output of the sliding mode controller and the levels of comparison. To eliminate the chattering and increase the speed convergence of the controller, the integral sliding-mode surface combined with a first-order low-pass filter (LPF) is used. The stability of the control system is validated by Lyapunov theory. The simulation and experimental results show that the proposed NSMC method has strong robustness, and better performance for multi-level inverter control systems with low total harmonic distortion, Common-Mode (CM) voltage reduction, switching frequency diminution, and less switching loss

A Robust Control for Five-level Inverter Based on Integral Sliding Mode Control

This paper presents a new control strategy for cascaded H-bridge five-level inverter (CHB-5LI) based on the novel sliding mode control (NSMC). The proposed method can generate pulse-width modulation (PWM) without using conventional modulation techniques based on carrier waves. With the proposed NSMC technique, the PWM pulses can be obtained by the control signal u(t) from the output of the sliding mode controller and the levels of comparison. To eliminate the chattering and increase the speed convergence of the controller, the integral sliding-mode surface combined with a first-order low-pass filter (LPF) is used. The stability of the control system is validated by Lyapunov theory. The simulation and experimental results show that the proposed NSMC method has strong robustness, and better performance for multi-level inverter control systems with low total harmonic distortion, Common-Mode (CM) voltage reduction, switching frequency diminution, and less switching loss

Atomically controlled processing for dopant segregation in CVD silicon and germanium epitaxial growth

Atomically controlled processing has become indispensable for the fabrication of Si-based ultra-small devices and heterodevices for ultra-large scale integration. This is because high performance devices require atomicorder abrupt heterostructures and doping profiles as well as strain engineering which is obtained by the introduction of Ge into Si. Our concept of atomically controlled processing is based on atomic-order surface reaction control in Si and Ge-based CVD growth [1-4]. The fabrication of atomic-level steep doping profiles requires the suppression of dopant segregation during epitaxial growth [5,6]. In this work, P and B impurity segregation during in-situ doping in Si and Ge CVD epitaxial growth is reviewed. Please click Additional Files below to see the full abstract

Strained and Relaxed Semiconducting Silicide Layers Heteroepitaxially Grown on Silicon

The semiconducting silicide ß-FeSi2, which can be grown epitaxially on silicon, is potentially an interesting material for integrated optoelectronic devices. Its semiconducting state stabilised by a solid state Jahn Teller effect is very unusual. Indeed the epitaxial growth of FeSi2 on silicon (111) in a Molecular Beam Epitaxy (MBE) chamber has revealed the existence of a metallic strained FeSi2 phase which is the result of a simultaneous electronic and structural transition. The stability and the relaxation of this strained phase which is specifically due to the epitaxy of FeSi2 on the silicon (111) face will be detailed in this paper. Furthermore, depending on the kinetics of the growth, we shall show that it is possible to epitaxially grow, on silicon, any silicide existing at low temperature (bcc Fe, FeSi, ß-FeSi2) and to observe dynamical transitions from the strained FeSi2 phase toward epitaxial ß-FeSi2 and FeSi

Architecture Parallel for the Renewable Energy System

This chapter present one possible evolution is the parallel topology on the high-voltage bus for the renewable energy system. The system is not connected to a chain of photovoltaic (PV) modules and the different sources renewable. This evolution retains all the advantages of this system, while increasing the level of discretization of the Maximum Power Point Tracker (MPPT). So it is no longer a chain of PV modules that works at its MPPT but each PV module. In addition, this greater discretization allows a finer control and monitoring of operation and a faster detection of defects. The main interest of parallel step-up voltage systems, in this case, lies in the fact that the use of relatively high DC voltages is possible in these architectures distributed

An Efficient Method for Generating Synthetic Data for Low-Resource Machine Translation – An empirical study of Chinese, Japanese to Vietnamese Neural Machine Translation

Data sparsity is one of the challenges for low-resource language pairs in Neural Machine Translation (NMT). Previous works have presented different approaches for data augmentation, but they mostly require additional resources and obtain low-quality dummy data in the low-resource issue. This paper proposes a simple and effective novel for generating synthetic bilingual data without using external resources as in previous approaches. Moreover, some works recently have shown that multilingual translation or transfer learning can boost the translation quality in low-resource situations. However, for logographic languages such as Chinese or Japanese, this approach is still limited due to the differences in translation units in the vocabularies. Although Japanese texts contain Kanji characters that are derived from Chinese characters, and they are quite homologous in sharp and meaning, the word orders in the sentences of these languages have a big divergence. Our study will investigate these impacts in machine translation. In addition, a combined pre-trained model is also leveraged to demonstrate the efficacy of translation tasks in the more high-resource scenario. Our experiments present performance improvements up to +6.2 and +7.8 BLEU scores over bilingual baseline systems on two low-resource translation tasks from Chinese to Vietnamese and Japanese to Vietnamese

Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells

Artificial Intelligence (AI) in healthcare, especially in white blood cell cancer diagnosis, is hindered by two primary challenges: the lack of large-scale labeled datasets for white blood cell (WBC) segmentation and outdated segmentation methods. These challenges inhibit the development of more accurate and modern techniques to diagnose cancer relating to white blood cells. To address the first challenge, a semi-supervised learning framework should be devised to efficiently capitalize on the scarcity of the dataset available. In this work, we address this issue by proposing a novel self-training pipeline with the incorporation of FixMatch. Self-training is a technique that utilizes the model trained on labeled data to generate pseudo-labels for the unlabeled data and then re-train on both of them. FixMatch is a consistency-regularization algorithm to enforce the model's robustness against variations in the input image. We discover that by incorporating FixMatch in the self-training pipeline, the performance improves in the majority of cases. Our performance achieved the best performance with the self-training scheme with consistency on DeepLab-V3 architecture and ResNet-50, reaching 90.69%, 87.37%, and 76.49% on Zheng 1, Zheng 2, and LISC datasets, respectively