Research Interests

Comparing ML OCR Engines on Texts from 19^th Century Written in the Romanian Transitional Script

• Marc Frîncu^†, Marius E. Penteliuc, Simina Frîncu, Gheorhe Bran, and Manuela Zanescu^‡
• Department of Computer Science
• West University of Timișoara, Romania
• {marius.penteliuc, ioana.frincu, gheorghe.bran, manuela.zanescu}@e-uvt.ro
• †School of Science and Technology
• Nottingham Trent University, United Kingdom
• marc.frincu@ntu.ac.uk
• ‡Central University Library Timișoara, Romania

Abstract — Many 19^th century Romanian texts are written in the Transitional Script (RTS) combining Cyrillic and Latin characters in variable proportions depending on specific factors (period, region, publishing house, literary trends, and personal beliefs). Thousands of heritage documents in numerous libraries across Romania have yet to be digitized. Reading texts written in RTS is challenging for modern scholars, today’s students and OCR engines which due to specific particularities have difficulties in processing them. Transfer learning can be used to train state-of-the-art tools such as Tesseract or Transkribus, but their efficiency varies according to the underlying deep neural network, the training parameters, and the particularities of the scanned images and used scripts. In this paper, we compare the performance of two architectures on 180 OCR models for 5 scenarios (best CER <4.6% for Tesseract) and discuss OCR challenges on RTS texts.

Keywords — training; optical character recognition; transfer learning; training data; phonetics; data models.

DOI — 10.1109/BigData59044.2023.10386851

Challenges and Solutions in Transliterating 19^th Century Romanian Texts from the Transitional to the Latin Script

• Marc Frîncu^†, Simina Frîncu^‡, and Marius E. Penteliuc^‡
• †School of Science and Technology
• Nottingham Trent University, United Kingdom
• marc.frincu@ntu.ac.uk
• ‡Faculty of Mathematics and Computer Science
• Department of Computer Science
• West University of Timișoara, Romania
• {ioana.frincu, marius.penteliuc}@e-uvt.ro

Abstract — During the 19^th century, the Romanian script has undergone a massive yet uneven transition from the Cyrillic to the current Latin alphabet. The amount of existing literature written in that script as well as the problems it poses for OCR and transliteration engines make the problem highly challenging from a Big Data perspective. In this paper, we discuss the issues and propose and test a machine-learning solution trained on small datasets using either transfer learning from Latin/Cyrillic or from scratch.

A Solar Radiation Forecast Platform Spanning over the Edge-Cloud Continuum

• Marc Frîncu^†, Marius E. Penteliuc^‡, and Adrian Spătaru^‡
• †School of Science and Technology
• Nottingham Trent University, United Kingdom
• marc.frincu@ntu.ac.uk
• ‡Faculty of Mathematics and Computer Science
• Department of Computer Science
• West University of Timișoara, Romania
• {marius.penteliuc, adrian.spataru}@e-uvt.ro

Abstract — The prediction of PV output represents an important task for PV farm operators as it enables them to forecast the energy they will produce and sell on the energy market. Existing approaches rely on a combination of satellite/all-sky images and numerical methods which for high spatial resolutions require considerable processing time and resources. In this paper, we propose a hybrid egde–cloud platform that leverages the performance of edge devices to perform time-critical computations locally, while delegating the rest to the remote cloud infrastructure. The proposed platform relies on novel metaheuristics algorithms for cloud dynamics detection and proposes to forecast irradiance by analyzing pixel values taken with various filters/bands. The results demonstrate the scalability improvement when using GPU-enabled devices and the potential of using pixel information instead of cloud types to infer irradiance.

Keywords — solar irradiance; cloud dynamics; cloud type; metaheuristics; cloud–edge platform; smart grid.

DOI — 10.3390/electronics11172756

Processing Large Satellite Imagery to Estimate Solar Irradiance

• Marius E. Penteliuc
• Faculty of Mathematics and Computer Science
• Department of Computer Science
• West University of Timișoara, Romania
• marius.penteliuc@e-uvt.ro

Abstract — We use satellite imagery to identify cloud types and correlate with irradiance data from a solar platform. Processing the large images takes several minutes. Integrating into a web service would enable multiple users to estimate solar irradiance using just satellite imagery. It would also allow for the processing of many images at once as more remote sensing satellites are launched and data is easily accessible. The Sentinel 2 Cirrus Band shows an 81% correlation with the diffuse solar irradiance values measured at the platform.

Keywords — satellite imagery; irradiance; cloud types.

DOI — 10.1109/BigData52589.2021.9671916

Short Term Cloud Motion Forecast based on Boid's Algorithm for use in PV Output Prediction

• Marius E. Penteliuc* and Marc Frîncu^†
• *Faculty of Mathematics and Computer Science
• Department of Computer Science
• West University of Timișoara, Romania
• marius.penteliuc@e-uvt.ro
• †School of Science and Technology
• Nottingham Trent University, United Kingdom
• marc.frincu@ntu.ac.uk

Abstract — Forecasting cloud motion and dynamics is crucial for many areas of study. Solar energy production depends on the cloud coverage over the area which impacts PV output, by clouds limiting the incoming solar irradiance. In this paper we propose an adaptation of a nature-inspired technique called the Flocking Behavior Algorithm (or Boids Algorithm) for the problem of cloud motion forecasting, for nowcasting and intra-day prediction windows. Without limiting the application of the algorithm we validate it on a sequence of satellite images and show its efficiency for short term forecasting.

Keywords — cloud motion forecast; nature inspired algorithm; Boids Algorithm.

DOI — 10.1109/ISGTEurope52324.2021.9640027

Parallel Cloud Movement Forecasting based on a Modified Boids Flocking Algorithm

• Adrian Spătaru*, Larisa Tranca*, Marius E. Penteliuc*, and Marc Frîncu^†
• *Faculty of Mathematics and Computer Science
• Department of Computer Science
• West University of Timișoara, Romania
• {adrian.spataru, larisa.tranca96, marius.penteliuc}@e-uvt.ro
• †School of Science and Technology
• Nottingham Trent University, United Kingdom
• marc.frincu@ntu.ac.uk

Abstract — Nowcasting is vital for PV farms in order to match supply and demand on energy markets. The accuracy of existing forecasting methods relies on short term modelling of cloud movement and dynamics by combining satellite images with ground based allsky observations. Cloud movement is modelled based on complex nonlinear numerical models which on the short term are outperformed by image analysis techniques. In this research we propose a simplified nature inspired model for nowcasting which scales with the data. The model is based on forecasting wind direction extracted from motion vector fields by modelling wind using a modified Boids Flocking algorithm. We show that the model is accurate in predicting the cloud coverage up to several hours ahead and that it scales with the number of particles on shared memory systems suited for commodity machines available to PV farm operators.

Keywords — Boids flocking algorithm; Cloud movement; OpenMP; CUDA.

DOI — 10.1109/ISPDC52870.2021.9521639

Prediction of Cloud Movement from Satellite Images using Neural Networks

• Marius E. Penteliuc and Marc Frîncu
• West University of Timișoara
• Faculty of Mathematics and Computer Science
• Department of Computer Science
• Timișoara, Romania
• {marius.penteliuc, marc.frincu}@e-uvt.ro

Abstract — Predicting cloud movement and dynamics is an important aspect in several areas, including prediction of solar energy generation. Knowing where a cloud will be or how it evolves over a given geographical area can help energy providers to better estimate their production levels. In this paper we propose a novel approach to predicting cloud movement based on satellite imagery. It combines techniques of generating motion vectors from sequential images with neural networks. First, the images are masked to isolate cloud pixels, then Farneback’s version of the Optical Flow algorithm is used to detect motion from one image to the next and generate motion vector flow for each pair of images. After that, a feed forward back propagation neural network is trained with the vector data derived from the dataset imagery. Different parameters for the duration of the training, size of the input, and the neighborhood radius of one point in the scene are used. Promising results are presented and discussed to weight the potential of the proposed algorithm for forecasting cloud cover and cloud position in a scene.

Keywords — cloud motion; forecasting; neural networks; satellite imagery.

DOI — 10.1109/SYNASC49474.2019.00038