Browsing by Keyword "industrial systems"
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Item Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM(2019-12) Ullah, Amin; Muhammad, Khan; Del Ser, Javier; Baik, Sung Wook; De Albuquerque, Victor Hugo C.; IANowadays digital surveillance systems are universally installed for continuously collecting enormous amounts of data, thereby requiring human monitoring for the identification of different activities and events. Smarter surveillance is the need of this era through which normal and abnormal activities can be automatically identified using artificial intelligence and computer vision technology. In this paper, we propose a framework for activity recognition in surveillance videos captured over industrial systems. The continuous surveillance video stream is first divided into important shots, where shots are selected using the proposed convolutional neural network (CNN) based human saliency features. Next, temporal features of an activity in the sequence of frames are extracted by utilizing the convolutional layers of a FlowNet2 CNN model. Finally, a multilayer long short-term memory is presented for learning long-term sequences in the temporal optical flow features for activity recognition. Experiments11https://github.com/Aminullah6264/Activity-Rec-ML-LSTM. are conducted using different benchmark action and activity recognition datasets, and the results reveal the effectiveness of the proposed method for activity recognition in industrial settings compared with state-of-the-art methods.Item Information Security Risk Assessment Methodology for Industrial Systems Supporting ISA/IEC 62443 Compliance(Institute of Electrical and Electronics Engineers Inc., 2023) Iturbe, Eider; Rios, Erkuden; Mansell, Jason; Toledo, Nerea; CIBERSEC&DLTIn the context of Industry 4.0, digitalization is one of the key ingredients to foster economic growth and competitiveness of the industrial sector. But the speed in which digitalization is coming into play as well as the growing use of novel technologies such as Cyber Physical Systems (CPSs), Industrial Internet of Things (IIoT) and artificial intelligence techniques, comes hand by hand, with the increase in the attack vectors to these industries. So now, more than ever, there is a need for clear and reusable methodologies that support security experts in identifying the threats as well as the required measures to secure next-generation industrial infrastructures and solutions. This paper presents a risk assessment methodology for security and privacy of industrial solutions which systematises the activities to be carried out in a technology-, system-, and domain-agnostic manner and, thus, it can be reused in multiple types of systems. The methodology supports the compliance with the industrial cybersecurity standard ISA/IEC 62443.