The acceleration of a vehicle is important information in vehicle states. The vehicle acceleration is measured by an inertial measurement unit (IMU). However, gravity affects the IMU when there is a transition in vehicle attitude; thus, the IMU produces an incorrect signal output. Therefore, vehicle attitude information is essential for obtaining correct acceleration information. This paper proposes a convolutional neural network (CNN) for attitude estimation. Using sequential data of a vehicle’s chassis sensor signal, the roll and pitch angles of a vehicle can be estimated without using a high-cost sensor such as a global positioning system or a six-dimensional IMU. This paper also proposes a dual-extended Kalman filter (DEKF), which can accurately estimate acceleration/angular velocity based on the estimated roll/pitch information. The proposed method is validated by real-car experiment data and CarSim, a vehicle simulator. It accurately estimates the attitude estimation with limited sensors, and the exact acceleration/angular velocity is estimated considering the roll and pitch angle with de-noising effect. In addition, the DEKF can improve the modeling accuracy and can estimate the roll and pitch rates.
The function of the Supervisory Control and Data Acquisition (SCADA) System is to monitor and control physical processes in real time in a geographically spread environment. SCADA system is applied in supervision and control of devices action in electricity distribution, transmission; oil and gas pipelines, water distribution, and traffic lights among other critical infrastructure. Deregulation of electricity sector in Nigeria provides private independent power producers' access to the Transmission Company of Nigeria network and hence transforms the closed (isolated) SCADA System of the TCN to an open architecture SCADA System. An open architecture SCADA System is susceptible to threats and attacks within and without with catastrophic impact on the efficiency of the critical infrastructure it is designed to monitor and control. Using empirical method, the type of threats and level of exposure of the TCN SCADA System were examined. The investigation revealed that TCN SCADA System is majorly protected against internal threats. Hence security enhancement through Defense-in-depth strategies that would provide wide arrays of security were proposed and briefly elaborated on for successful implementation.
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