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

Assisting V2V failure recovery using Device-to-Device Communications

This paper aims to propose a new solution for failure recovery (dead-ends) in Vehicle to Vehicle (V2V) communications through LTE-assisted Device-to-Device communications (D2D). Based on the enhanced networking capabilities offered by Intelligent Transportation Systems (ITS) architecture, our solution can efficiently assist V2V communications in failure recovery situations. We also derive an analytical model to evaluate generic V2V routing recovery failures. Moreover, the proposed hybrid model is simulated and compared to the generic model under different constrains of worst and best cases of D2D discovery and communication. According to our comparison and simulation results, the hybrid model decreases the delay for alarm message propagation to the destination (typically the Traffic Control Center TCC) through the Road Side Unit (RSU)Comment: 3 page

Using CONFIG for Simulation of Operation of Water Recovery Subsystems for Advanced Control Software Evaluation

A hybrid discrete/continuous simulation tool, CONFIG, has been developed to support evaluation of the operability life support systems. CON FIG simulates operations scenarios in which flows and pressures change continuously while system reconfigurations occur as discrete events. In simulations, intelligent control software can interact dynamically with hardware system models. CONFIG simulations have been used to evaluate control software and intelligent agents for automating life support systems operations. A CON FIG model of an advanced biological water recovery system has been developed to interact with intelligent control software that is being used in a water system test at NASA Johnson Space Cente

Nano-satellite attitude control system based on adaptive neuro-controller

The current research focuses on designing of an intelligent controller for attitude control system (ACS) of nano-satellite. The nanosatellite namely Innovative Satellite (InnoSAT) was organized by Agensi Angkasa Negara (ANGKASA) to attract the interest of Malaysian universities in satellite development.In this study, an intelligent controller based on Hybrid Multi Layered Perceptron (HMLP) network was developed. The network used model reference adaptive control (MRAC) system as a control scheme to control a time varying systems where the performance specifications are given in terms of a reference model.The Weighted Recursive Least Square (WRLS) algorithm will adjust the controller parameters to minimize error between the plant output and the model reference output.The objective of this paper is to analyze the tracking performance of ANC based on HMLP network and ANC based on standard MLP network for controlling a satellite attitude. The simulation results indicate that ANC based on HMLP network gave better performance than ANC based on standard MLP network

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Fuzzy-neuro-genetic aerofin control

U ovom radu razmatrano je fazi-genetsko-neuro upravljanje elektromehaničkog aktuatora aerokrila za kontrolu leta projektila, pokretanim motorom jednosmerne struje sa četkicama i permanentnim magnetom koji je pogonjen drajverom sa konstantnom strujom. U našim prethodnim radovima, na osnovu razvijenog nelinearnog modela sistema, razvijena su i testirana različta konvencionalna i hibridna konvencionalno-inteligentna upravljanja. U ovom radu predloženo je fazi i neuro-fazi upravljanje sa genetskom optimizacijom. Predloženi inteligentni upravljački sistemi, koji obezbeđuju dobro ponašanje sistema, su verifikovani numeričkim simulacijima i upoređeni sa prethodnim rezultatima. .In this paper fuzzy-neuro-genetic control of an electromechanical actuator (EMA) system for aerofin control (AFC), with permanent magnet brush DC motor driven by a constant current driver, is investigated. In our previous papers, nonlinear model of the EMA-AFC system and different classical and hybrid classicalcomputationally intelligent control systems have been designed and tested. In this paper we have proposed fuzzy and neuro-fuzzy control with genetic optimization. Proposed intelligent control systems, providing good transient response and system behaviour, have been validated by various numerical experiments and compared to previous results.

Hybrid systems: a real-time interface to control engineering

This paper gives an introduction to a general hybrid systems model for definition of system requirements and a corresponding software architecture together with an example of their specialization for use in implementing a modeswitching controller for a hydraulic cylinder. 1. Introduction An important application area for real-time computing is embedded systems where the computing system provides intelligent control of a mechanical, chemical etc. plant or device. The software requirements for such applications depend heavily on the properties of the plant. These properties are usually investigated by control engineers that base their work on the theory of dynamic systems [3, 9]. The mathematical tool for this work is thus mathematical analysis, in particular the theory of differential equations. If the models are linear differential equations, there are standard techniques for implementing a corresponding discrete, sampled system in the form of a computer program, and the work for the s..

Design and development of a hybrid control system for flexible manufacturing : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Manufacturing and Industrial Technology at Massey University

Irregular Pagination MisnumberedFlexible Manufacturing Systems (FMS) appeared upon the manufacturing scene in the early 1970s, installations presently number in the thousands. However, many current installations in fact lack flexibility, do not operate in real-time and are prohibitively expensive. Therefore there are obvious benefits to be gained from making improvements to existing flexible manufacturing systems. Research conducted for this thesis focused on two major areas. The implementation of the FMS control system on a SCADA package and the development of an auction based scheduling system. This entailed the development of a hybrid control model composed of three distinct layers; factory, cell and intelligent entity. Key portions of both the factory and cell controllers were then implemented so as to create a minimal system. This has been completed to the point where the auction algorithm has been implemented and tested in an appropriate framework. In achieving the goals mentioned above a number of novel design concepts have been utilised. There are two which are most important, these are the use of low cost modules for the construction of a flexible co-operative manufacturing system, and the ability of this system to operate in a physically distributed area via a Local Area Network. Meaning it is inherently adaptable and resistant to failure. These novel design concepts were ingrained throughout the entire three layered control model. It is felt that this research has succeeded in demonstrating the possibility of implementing a FMS control system on a low cost SCADA package using low cost software and computing elements. The ability of the distributed, auction-based approach to operate successfully within this system, has also been demonstrated through simulation

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer