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This research aims to provide a fault diagnosis approach for Hybrid Dynamic (SDHs), Systems and FaultTolerant Control synthesis, while also ensuring the smooth operation of industrial settings. This study is a part of the larger topic of Hybrid Dynamic System control and fault diagnosis. The primary focus is on modelling strategies designed expressly for Hybrid Dynamic Systems, with a concentration on combining continuous and event-driven components. Much work is devoted to developing a model that can incorporate both kinds of elements. A system model that can track several modes without explicit identification can be created by utilizing Neuro-Fuzzy Networks, providing a thorough overview. On the basis of this synthesized model, an AI-based fault diagnosis method is subsequently developed. [ABSTRACT FROM AUTHOR]

In order to increase the security of medical image sharing and transfer, we present in this work a watermarking approach to protect medical images. This approach consists of inserting hospital signature information and patient data into the medical image. The goal of our work is to integrate the watermark with as little distortion as possible to retain the medical information in the image. In this approach DWT decomposition is applied to the image which allows a very fine adjustment during the insertion. An SVD is then applied to the three subbands LL, LH and HL, which allows storing the maximum energy of the image in a minimum of singular values. A combination of the three resulting singular value matrices is then performed for watermark integration. The proposed approach ensures data integrity, confidentiality when sharing data, and robustness to several conventional attacks. [ABSTRACT FROM AUTHOR]

This research provides a method that accelerates learning and avoids local minima to improve the policy gradient algorithm’s learning process. Reinforcement learning has the advantage of not requiring a model. Consequently, it can improve control performance, mainly when a model is generally unavailable, such as when an error occurs. The proposed method efficiently and expeditiously investigates the action space. First, it quantifies the resemblance between agents’ and traditional controllers’ actions. Then, the principal reward function is modified to reflect this similarity. This reward-shaping mechanism guides the agent to maximize its return via an attractive force during the gradient ascent. To validate our concept, we establish a satellite attitude control environment with a similarity subsystem. The outcomes demonstrate the effectiveness and robustness of our method. [ABSTRACT FROM AUTHOR]