M
Muhammad Waqar Akram
Researcher at University of Science and Technology of China
Publications - 11
Citations - 499
Muhammad Waqar Akram is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Convolutional neural network & Renewable energy. The author has an hindex of 7, co-authored 11 publications receiving 285 citations. Previous affiliations of Muhammad Waqar Akram include University of Agriculture, Faisalabad.
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Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review
TL;DR: A comprehensive review of the bio-inspired algorithms used for global maximum power point tracking and the modified and combined forms of these methods found to have better performance than original algorithms.
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Research and current status of the solar photovoltaic water pumping system – A review
TL;DR: In this article, solar photovoltaic water pumping system (SPVWPS) is proposed as a promising alternative to the conventional pumping systems and a cost-effective application especially in remote off-grid areas of developing countries.
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Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy
TL;DR: New face cropping and rotation strategies and simplification of the convolutional neural network (CNN) to make data more abundant and only useful facial features can be extracted and compete with existing methods in terms of training time, testing time, and recognition accuracy.
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Building integrated solar concentrating systems: A review
Guiqiang Li,Qingdong Xuan,Muhammad Waqar Akram,Yousef Golizadeh Akhlaghi,Haowen Liu,Samson Shittu +5 more
TL;DR: A concise review on the building integrated concentrating devices, that have their own characteristics and multiple functions, and an elaborate introduction of the demands, types and applications and prospects/ directions/ policies about these technologies around the world.
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An Adaptive Anti-Noise Neural Network for Bearing Fault Diagnosis Under Noise and Varying Load Conditions
TL;DR: An end-to-end adaptive anti-noise neural network framework is proposed to solve the bearing fault diagnosis problem under heavy noise and varying load conditions, which takes the raw signal as input without requiring manual feature selection or denoising procedures.