IFAC-PapersOnLine最新文献

A new approach for the online state estimation of discrete event systems (DESs) modeled by λ-free labeled Petri nets (λf-LPNs) is presented, wherein all events are observable. Instead of exhaustively enumerating all marking vectors consistent with any given event occurrence, we do a representation-based state estimation, where we compute a compact representation structure that characterizes these markings. Our representation structure is based on a representative Petri net (RPN), whose single initial marking represents all markings of the system λf-LPN consistent with the sequence. These representations can be directly used to solve other DES-related problems, such as fault diagnosis. Our approach can compute representations of any λf-LPNs. In addition, for a class of unbounded λf-LPNs, our approach can compute representations that do not grow indefinitely as more events occur.

We present a new algorithm to verify inference observability in supervisory control of decentralized discrete-event systems. The algorithm's success relies on the following idea. An inferencing solution exists only when a language contains no inferencing cycles. When there are no cycles, the algorithm computes the smallest upper bound for an inferencing solution.

In control engineering and computer science, different formalisms are used for the specification and subsequent analysis of timed extensions of discrete event systems. In this paper, the expressivity of timed discrete event systems and that of timed automata are compared for the purpose of modeling of discrete event systems involving time aspects. A behavior-preserving transformation from timed discrete event systems to timed automata is provided, and it is shown that a similar transformation from timed automata to timed discrete event systems is not possible under the assumption that the event sets of the system modeled remains the same.

STEM education aims to prepare students for their future jobs, providing authentic tasks and problems to solve. Usually, approaches to teaching STEM subjects are based on a constructivist learning theory that accentuates active, practical, and interactive learning approaches. Nowadays, the implementation of STEM education faces several logistical and pedagogical challenges, which can impact the effectiveness of STEM education programs. The conditions for applying information technologies in STEM education in Bulgarian schools and universities are presented. The paper proposes a conceptual model of the innovative STEM educational system, which includes personalization and optimization of the applied teaching methods.

Mobile robots are revolutionizing healthcare with their potential to streamline logistics, patient care, and facility maintenance in hospitals. This paper introduces a mobile robot designed for medicine distribution in patient rooms, detailing its mechanical design and working principle. A scenario demonstrating robot path planning for medicine distribution is presented. Future research directions are outlined.

This paper focuses on the implementation of recognition and detection algorithms for programmable logic controllers (PLCs) based on artificial intelligence (AI). AI methods based on deep neural networks (DNN) are characterized by a large number of hidden layers and neurons in hidden layers, which increases the computational complexity of computing the outputs of artificial neural networks (ANNs). Popular convolutional neural networks (CNN) are among such ResNet, AlexNet, GoogLeNet and others. A remote cloud solution is proposed in this paper to compute CNN outputs. The CNN implementation was tested on object recognition and detection applications. Training and testing of the CNN and YOLO detector was carried out in the Python environment.