dr hab. inż. Michał Grochowski
Employment
- Associate professor at Department of Intelligent and Decision Support Systems
- Head of Department at Department of Intelligent and Decision Support Systems
Social media
Contact
- michal.grochowski@pg.edu.pl
Associate professor
- Department of Intelligent and Decision Support Systems
- Faculty of Electrical and Control Engineering
- Workplace
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Budynek Wydz. Elektr.
room 7 open in new tab - Phone
- +48583472357
Publication showcase
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Data augmentation for improving deep learning in image classification problem
These days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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Hierarchical predictive control of integrated wastewater treatment systems
The paper proposes an approach to designing the control structure and algorithms for optimising control of integrated wastewater treatment plant-sewer systems (IWWTS) under a full range of disturbance inputs. The optimised control of IWWTS allows for significant cost savings, fulfilling the effluent discharge limits over a long period and maintaining the system in sustainable operation. Due to the specific features of a wastewater...
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Neural Architecture Search for Skin Lesion Classification
Deep neural networks have achieved great success in many domains. However, successful deployment of such systems is determined by proper manual selection of the neural architecture. This is a tedious and time-consuming process that requires expert knowledge. Different tasks need very different architectures to obtain satisfactory results. The group of methods called the neural architecture search (NAS) helps to find effective architecture...
Obtained scientific degrees/titles
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2020-04-14
Obtained science degree
dr hab. inż. automation, electronics, electrical engineering and space technologies (Engineering and Technology)
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