WSN and RFID integration to support intelligent monitoring in smart buildings using hybrid intelligent decision support systems

The real time monitoring of environment context aware activities is becoming a standard in the service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt reaction to potential hazards identified at an early stage to engage appropriate control actions. This requires capturing real time data to process locally at the device level or communicate to backend systems for real time decision making. This research examines the wireless sensor network and radio frequency identification technology integration in smart homes to support advanced safety systems deployed upstream to safety and emergency response. These systems are based on the use of hybrid intelligent decision support systems configured in a multi-distributed architecture enabled by the wireless communication of detection and tracking data to support intelligent real-time monitoring in smart buildings. This paper introduces first the concept of wireless sensor network and radio frequency identification technology integration showing the various options for the task distribution between radio frequency identification and hybrid intelligent decision support systems. This integration is then illustrated in a multi-distributed system architecture to identify motion and control access in a smart building using a room capacity model for occupancy and evacuation, access rights and a navigation map automatically generated by the system. The solution shown in the case study is based on a virtual layout of the smart building which is implemented using the capabilities of the building information model and hybrid intelligent decision support system.The Saudi High Education Ministry and Brunel University (UK

Intelligent decision support systems for interative decision making in complex environments applied to regional planning

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN018121 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

An economic order quantity model for an imperfect production process with entropy cost

Among the assumptions of the classical economic order quantity (EOQ) model is that all units that are purchased (or produced) are of perfect quality. However, this is frequently unrealistic since production processes deteriorate resulting in the production of defective products requiring rework. Some recent studies suggest that production systems performance might be improved by applying the first and second laws of thermodynamics to reduce system entropy (or disorder). This paper applies the concept of entropy cost to extend the classical EOQ model under the assumptions of perfect and imperfect quality. Mathematical models are developed and numerical examples illustrating the solution procedure are provided. Accounting for entropy cost suggests that order quantities should be larger than the figures derived from the classical EOQ model.Lot sizing Defects Rework Entropy cost

Design and Implementation of a Robust 6-DOF Quadrotor Controller Based on Kalman Filter for Position Control

The objective of this chapter is to develop quadcopter flight control algorithms using a PID controller enhanced by a Kalman Filter (KF) using an experimental approach to extract the physical and aerodynamic settings of the quadcopter. It is first necessary to present the current state of the quadcopter analytical dynamics model in order to achieve an effective design. A second step involves the development of the quadcopter’s hardware and software, as well as the development of a full thrust test rig to extract the parameters of the propulsion system and the linearisation approximations between the different variables. Using the quadcopter’s 6-DOF analytical dynamic model, the controller’s control parameters are determined using a PID design enhanced with KF. Test results were assessed using dynamic response curves and 3D Matlab visualisations. In order to evaluate the performance of the PID controllers, we measured the time response, overshoot, and settling time with and without the KF. After the SIMULINK model’s results for the drone were accepted, a C++ code was produced. Uploading the generated code into the Pixhawk autopilot was accomplished through a Simulink application in the autopilot firmware. Based on the Pixhawk autopilot, we present a quick and real-time test solution for drone controllers. Further enhancements are provided by near-real-time tuning of the control settings. This research uses the Embedded Coder Tool to develop SIMULINK-generated code for the Pixhawk autopilot board