steering geometry.pptx
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The document discusses several key specifications and angles related to vehicle suspension geometry: Caster angle is the angle between the vertical line and the kingpin center line and provides self-centering action and straight line stability. Camber angle is the inward or outward tilt of the wheel and lowering steering effort. Toe angle is the symmetric angle between the wheels and vehicle axis and provides straight line stability while increasing tire wear with more toe. Thrust angle occurs when the rear wheel has improper alignment and can cause the vehicle to pull in the direction away from the thrust line. Turning radius is determined by track, wheelbase, and average steering angle and describes the circle made by the outside wheels when turning fully.
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The document discusses the steering system of vehicles. It describes the steering system as the interface between the driver and vehicle that allows the driver to guide the vehicle by controlling the path. It converts rotational motion of the steering wheel into angular turns of the wheels. Factors like steering ratio, geometry, power steering determine the steering effort required and turning radius. Proper alignment of components like caster, camber, toe is important for steering stability and reducing tire wear.
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International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
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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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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- 1. ◾CASTER ANGLE ◾CAMBER ANGLE ◾STEERING AXIS INCLINATION ◾TOE ◾THRUST ANGLE ◾TURNING RADIUS ◾SUSPENSION HEIGHT
- 2. CASTER ANGLE Angle between vertical line and centre line of king pin FUNCTIONS ◾ SELF CENTRING ACTION ◾ STRAIGHT LINE STABILITY ◾ EXCESSIVE CASTER MAKES STEERING HEAVIER, LESS RESPONSIVE AND INCREASED EFFORT
- 3. FUNCTIONS ◾ +VE CAMER LOWER STEERING EFFORT ◾ -VE CAMBER IMPROVES GRIP ◾ EXCESSIVE CAMBER CAUSES INCREASED TIRE WEAR, REDUCES STRAIGT LINE ACCLERATION Inward and outward lilt of wheel (from F.V.) CAMBER ANGLE
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