Unit 5---bearings
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This document discusses different types of bearings, including radial bearings which support radial loads, thrust bearings which support axial loads, and journal bearings which include full, partial, and fitted types to support shafts and rotational movement. It also mentions the design of journal bearings.
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We are using springs in our daily life without noticing them. Types of springs & their uses are here.
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This document discusses different types of springs, including helical springs (tension, compression, torsion, and spiral) and leaf springs. It provides details on their construction, materials used, and applications. Helical springs store energy through twisting or stretching and release it when unloaded. Leaf springs are arc-shaped lengths of steel that provide dampening and spring functions in vehicles. Springs are commonly used to apply and control forces, measure weights, store potential energy, and reduce shocks and vibrations.
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Springs are elastic bodies that can be twisted, pulled, or stretched by an applied force and return to their original shape when the force is released. There are several types of springs including helical springs, leaf springs, and spiral springs. Helical springs are further classified as tension springs, compression springs, torsion springs, and spiral springs. Springs are used in many applications to apply forces, store energy, reduce shocks and vibrations, and measure forces. Common spring materials include steel alloys and metals like titanium.
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IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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