Results 101 to 110 of about 18,710 (282)

Comparative Analysis of Input Power Factor Control Techniques in Matrix Converters Based on Model Predictive and Space Vector Control Schemes

IEEE Access, 2019
Model Predictive Control (MPC) with a finite control set and space vector modulation (SVM) are the most common control methods for the matrix converters (MCs).
Zahra Malekjamshidi, Mohammad Jafari, Jianguo Zhu, Dan Xiao   +3 more
doaj   +1 more source

Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Adeno‐associated viral (AAV) vectors for gene therapy are becoming integral to modern medicine, providing therapeutic options for diseases once deemed incurable. Currently, viral vector purification is a critical bottleneck in the gene therapy industry, impacting product efficacy and safety as well as accessibility and cost to patients ...
Kelvin P. Idanwekhai, Shriarjun Shastry, Morgan R. Hurst, Arianna Minzoni, Eduardo Barbieri, Luke Remmler, Eugene N. Muratov, Michael A. Daniele, Stefano Menegatti, Alexander Tropsha   +9 more
wiley   +1 more source

Algoritmo para modulación por vector espacial en 3D para un convertidor trifásico multinivel

Tecnura, 2012
This paper presents the simulation design and implementation of 3D space vector modulation (SVM) for multilevel converters. The algorithm easily calculates the appropriate switching sequence and switching frequency of power devices.
Alfonso Álzate Gómez, Jesser James Marulanda Durango   +1 more
doaj  

Direct Torque Control with Space Vector Modulation (DTC-SVM) with Adaptive Fractional-Order Sliding Mode: A Path Towards Improved Electric Vehicle Propulsion

World Electric Vehicle Journal
Electric vehicles demand efficient and robust motor control to maximize range and performance. This paper presents an innovative adaptive fractional-order sliding mode (FO-SM) control approach tailored for Direct Torque Control with Space Vector Modulation (DTC-SVM) applied to induction motor drives.
Fatma Ben Salem, Motab Turki Almousa, Nabil Derbel   +2 more
openaire   +2 more sources

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

A simplified space vector modulation for the single-phase active power decoupling quasi-Z-source inverter

International Journal of Electrical Power & Energy Systems
The double-line frequency ripple power of the single-phase quasi-Z source inverter (qZSI) will result in a large designed qZS impedance on the dc side, which can be greatly reduced by the coupled-type active power decoupling (APD) method.
Rui Zhang, Shunliang Wang, Yuan Li, Junpeng Ma, Peng Wang, Tianqi Liu   +5 more
doaj   +1 more source

Linear Model Predictive Control of Induction Machine [PDF]

, 2015
This article presents new control algorithm for induction machine based on linear model predictive control (MPC). Controller works in similar manners as field oriented control (FOC), but control is performed in stator coordinates.
Mynář, Z.
core  

High‐Adhesion Quadruple Hydrogen‐Bonded Ionogels: A Dual‐Function Platform for Gesture Recognition and Real‐Time NH3 Detection in Multiscenario

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Quadruple hydrogen‐bonded high‐adhesion ionogels for gesture recognition and real‐time NH3 sensing. Ionogels have garnered significant attention in flexible sensing due to their outstanding mechanical properties, conductivity, and stability. However, establishing a robust and stable adhesive interface with various substrates remains a significant ...
Haohao Lin, Xiaotong Ma, Zihao Chen, Long Yang, Zitong Kan, Ruochen An, Lin Xu, Zhiyong Chang   +7 more
wiley   +1 more source

Machine Learning‐Assisted Design of BaTiO3‐Based Superparaelectric High‐Entropy Ceramics with Superior Energy Storage

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study employed an adaptive iterative strategy combining machine learning algorithms, domain knowledge, experimental design, and experimental feedback to aim to precisely and quickly discover high‐entropy ceramics with excellent energy storage performance.
Haowen Liu, Xiaoyan Zhang, Zhiyuan Ma, Kailong Ma, Xiwei Qi   +4 more
wiley   +1 more source

A Hybrid Fuzzy–Support Vector Machine Framework for Real‐Time Dust Detection and Thermal Mapping in Photovoltaic Panels

Energy Science &Engineering, EarlyView.
A hybrid Fuzzy–SVM framework for real‐time dust detection and thermal mapping in PV panels. ABSTRACT Dust accumulation significantly degrades the energy output of photovoltaic (PV) panels, particularly in arid and semi‐arid regions. While existing studies have separately explored image‐based dust detection, environmental modeling, and machine learning (
Debasish Sarker, S. M. Rezaul Karim, MD. Shouquat Hossain, Md. Moshaddek Hossain, Md. Monirul Kabir   +4 more
wiley   +1 more source