Nowadays, despite a negative impact on the natural environment, coal combustion is still a significant energy source. One way to minimize the adverse side effects is sophisticated combustion technologies, such as, e.g., staged combustion, co-combustion with biomass, and oxy-combustion. Maintaining the combustion process at its optimal state, considering the emission of harmful substances, safe operation, and costs requires immediate information about the process. Flame image is a primary source of data which proper processing make keeping the combustion at desired conditions, possible. The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states. The method is based on the adaptive selection of the gamma correction coefficient (G) in the flame segmentation process. It uses the empirically determined relationship between the G coefficient and the average intensity of the R image component. The pre-trained VGG16 model for classification was used. It provided accuracy in detecting particular combustion states on the ranging from 82 to 98%. High accuracy and fast processing time make the proposed method possible to apply in the real systems.

The paper considers the usage of non-standard analysis mathematical apparatus to solve some non-trivial problems of electrical engineering theory. The axiomatics of non-standard analysis makes it possible to simplify the transient analysis in the 1st order electrical circuits in violation of the commutation laws. Examples of solving such problems are given

Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allows the emission of pollutants into the atmosphere to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that will not exceed these standards. A preliminary analysis of the measurement signals was carried out, and signal predictions of flame intensity changes were determined using the autoregressive moving average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary and multi-class classifications of flame states were conducted. The best results were obtained from the ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08% for multi-class classification. The accuracy of the recognition of flame states and the high convergence of the determined predictions with measurement signals validate the application of the proposed approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures with biomass. Expert decisions determine the range of acceptable states.

Balance disorders are the main concern for patients after an ischemic stroke. They are caused by an abnormal force on the affected side or paresis, which causes uneven loading and visuospatial disorders. Minimizing the effects of stroke is possible through properly conducted rehabilitation. One of the known ways to achieve this objective is biological feedback. The lack of proper muscle tone on one side of the body is manifested by the uneven pressure of the lower extremities on the ground. The study and control groups were composed of two equal groups of 92 people each, in which the same set of kinesiotherapeutic exercises were applied. Patients in the study group, in addition to standard medical procedures, exercised five days a week on a Balance Trainer for four weeks. The examination and training with the device were recorded on the first day of rehabilitation, as well as after two and four weeks of training. The assessment was performed using the following functional tests and scales: Brunnström, Rankin, Barthel, Ashworth, and VAS. Patients in the control group started exercising on the Balance Trainer two weeks after the first day of rehabilitation using traditional methods. The study results reveal statistically significant reductions in the time the body’s center of gravity (COG) spent in the tacks, outside the tracks and in the COG distance, lower COG excursions in all directions. Post-stroke patients that received biofeedback training presented significantly better results than patients that did not receive such training.

The paper presents the application of high-speed flame imaging combined with convolutional neural networks (CNNs) for determining different states of biomass–coal co-combustion in terms of thermal power and excess air coefficient. The experimental setup and methodology used in a laboratory-scale co-combustion system are described, highlighting tests conducted across nine defined operational variants. The performance of several state-of-the-art CNN architectures was examined, focusing particularly on those achieving the highest classification metrics and exploring the dependence of input image resolution and applying a transfer learning paradigm. By benchmarking various CNNs on a large, diverse image dataset without preprocessing, the research advances intelligent, automated control systems for improved stability, efficiency, and emissions control, bridging advanced visual diagnostics with real-time industrial applications. The summary includes recommendations and potential directions for further research related to the use of image data and machine learning techniques in industry.