Object recognition and classification as well as obstacle distance calculation are of the utmost importance in today’s autonomous driving systems. One such system designed to detect obstacle and track intrusion in railways is considered in this paper. The heart of this system is the decision support system (DSS), which is in charge of making complex decisions, important for a safe and efficient autonomous train drive based on the information obtained from various sensors. DSS determines the object class and its distance from a running train by analyzing sensor images using machine learning algorithms. For the quality training of these machine learning models, it is necessary to provide training sets with images of adequate quality, which is often not the case in real-world railway applications. Furthermore, the images of insufficient quality should not be processed at all in order to save computational time. One of the most common types of distortion which occurs in real-world conditions (train movement and vibrations, movement of other objects, bad weather conditions, and day and night image differences) is blur. This paper presents an improved edge-detection method for the automatic detection and rejection of images of inadequate quality regarding the blur level. The proposed method, with its improvements convenient for railway application, is compared with several other state-of-the-art methods for blur detection, and its superior overall performance is demonstrated.

Rail transport plays a crucial role in promoting sustainability and reducing the environmental impact of transport. Ongoing efforts to improve the sustainability of rail transport through technological advancements and operational improvements are further enhancing its reputation as a sustainable mode of transport. Autonomous obstacle detection in railways is a critical aspect of railway safety and operation. While the widespread deployment of autonomous obstacle detection systems is still under consideration, the ongoing advancements in technology and infrastructure are paving the way for their full implementation. The SMART2 project developed a holistic obstacle detection (OD) system consisting of three sub-systems: long-range on-board, trackside (TS), and Unmanned Aerial Vehicle (UAV)-based OD sub-systems. All three sub-systems are integrated into a holistic OD system via interfaces to a central Decision Support System (DSS) that analyzes the inputs of all three sub-systems and makes decision about locations of possible hazardous obstacles with respect to trains. A holistic approach to autonomous obstacle detection for railways increases the detection area, including areas behind a curve, a slope, tunnels, and other elements blocking the train’s view on the rail tracks, in addition to providing long-range straight rail track OD. This paper presents a demonstration of the SMART2 holistic OD performed during the operational cargo haul with in-service trains. This paper defines the demonstration setup and scenario and shows the performance of the developed holistic OD system in a real environment.

In this paper a mono-objective optimum design procedure for parallel robot is outlined by using optimality criterion of workspace and numerical aspects. A mono-objective optimization problem is formulated by referring to a basic performance of parallel robots. Additional objective functions can be used to extend the proposed design procedure to more general but specific design problems. A kinematic optimization was performed to maximize the workspace of the mini parallel robot. Optimization was performed using genetic algorithms

Osteoporosis constitutes a significant public health concern necessitating proactive prevention, treatment, and monitoring efforts. Timely identification holds paramount importance in averting fractures and alleviating the overall disease burden. The realm of osteoporosis diagnosis has witnessed a surge in interest in machine learning applications. This burgeoning technology excels at recognizing patterns and forecasting the onset of osteoporosis, paving the way for more efficacious preventive and therapeutic interventions. Smart walkers emerge as valuable tools in this context, serving as data acquisition platforms for datasets tailored to machine learning techniques. These datasets, trained to discern patterns indicative of osteoporosis, play a pivotal role in enhancing diagnostic accuracy. In this study, encompassing 40 participants—20 exhibiting robust health and 20 diagnosed with osteoporosis—data from force sensors embedded in the handlebars of conventional walkers were gathered. A windowing action was used to increase the size of the dataset. The data were normalized, and k-fold cross-validation was applied to assess how well our model performs on untrained data. We used multiple machine learning algorithms to create an accurate model for automatic monitoring of users’ gait, with the Random Forest classifier performing the best with 95.40% accuracy. To achieve the best classification accuracy on the validation dataset, the hyperparameters of the Random Forest classifier were further adjusted on the training data. The results suggest that machine learning-based automatic monitoring of gait parameters could lead to accurate, non-laborious, cost-effective, and efficient diagnostic tools for osteoporosis and other musculoskeletal disorders. Further research is needed to validate these findings.

In this article, experiments are realized in the flexible manufacturing system ICIM 3000 (FESTO, Germany), and its assembly system, located at the Institute of production technologies, Faculty of Material Sciences and Technologies, Slovak University of Technology. The assembly system is the final product assembled, and this process consists of five components. Unwanted inaccuracies in the assembly process of the elements, such as the insertion of thermometers and hygrometers into the base plate, usually arise. Based on these inaccuracies, we realize some experiments by the camera system SBOC-Q-R3-WB. This deals with the method of image processing. The camera system parameters are set-up. At the end of this contribution, a base of evaluated results is suggested and some minor design changes are realized in the assembly station. The goal of these changes is the higher reliability of the assembly process.

This paper is aimed at presenting a study on the optimization of the Biglide micro parallel robot, which comprises two-degree-of-freedom (DOF) micro parallel robot with constant struts. The robot workspace is characterized and the inverse kinematics equation is obtained. In the paper, design optimization is implemented with Genetic Algorithms (GA) for optimization considering transmission quality index, design space and workspace. Here, intended to show the advantages of using the GA, we applied it to a multicriteria optimization problem of 2 DOF micro parallel robot. Genetic algorithms (GA) are so far generally the best and most robust kind of evolutionary algorithms. A GA has a number of advantages. It can quickly scan a vast solution set. Bad proposals do not affect the end solution negatively as they are simply discarded. The obtained results have shown that the use of GA in such kind of optimization problem enhances the quality of the optimization outcome, providing a better and more realistic support for the decision maker.