Response surface model building and multidisciplinary optimization using D-optimal designs

Unal, Resit; Lepsch, Roger A.; McMillin, Mark L.

doi:10.2514/6.1998-4759

Cited by 55 publications

(47 citation statements)

References 7 publications

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“…We adopt the well-known response surface methodology (RSM) [12], [22], [23], [13]. Broadly speaking, one identifies first a desired scalar response variable (in our case: the satisfaction rate) and a number of so-called factors, i.e.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Mutual interference in large populations of co-located IEEE 802.15.4 body sensor networks—A sensitivity analysis

Moravejosharieh

Willig

2016

Computer Communications

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We consider scenarios where a large number of wireless body sensor networks (WBSN) meets at the same location, as can happen for example at sports events, and assess the impact of their mutual interference on their achievable transmission reliability. In particular, we consider several of MAC-and application parameters for a range of static and dynamic schemes for allocating WBSNs to frequencies, and determine their relative impacts on achievable performance. Our results indicate that parameters related to the MAC backoff scheme have by far the largest impact on performance, and that frequency adaptation can provide substantial performance benefits.

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Section: Sensitivity Analysismentioning

confidence: 99%

Mutual interference in large populations of co-located IEEE 802.15.4 body sensor networks—A sensitivity analysis

Moravejosharieh

Willig

2016

Computer Communications

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“…There are different types of design of experiments, such as full factorial, central composite design (CCD), Box Behnken designs (BBD) and computer generated designs, such as D-optimal design [20]. Because D-optimal DOE explores design parameters space efficiently with minimum number of run that enable model construction with good accuracy [21], it has be used for the study in this paper. The algorithm of D-optimal criterion optimise the feasible potential design points to form a subset of D-optimal points that will be used in simulation runs.…”

Section: B D-optimal Experimental Designmentioning

confidence: 99%

“…The algorithm of D-optimal criterion optimise the feasible potential design points to form a subset of D-optimal points that will be used in simulation runs. This optimisation is based on maximizing the determinant of XX', where XX' is called information matrix [21].…”

Section: B D-optimal Experimental Designmentioning

confidence: 99%

Energy Efficient Sensor Nodes Powered by Kinetic Energy Harvesters – Design for Optimum Performance

Kázmierski¹,

Wang²,

Aloufi³

2012

ELS

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Abstract-In an energy harvester powered wireless sensor node system, as the energy harvester is the only energy source, it is crucial to configure the microcontroller and the sensor node so that the harvested energy is used efficiently. This paper outlines modelling, performance optimisation and design exploration of the complete, complex system which includes the analogue mechanical model of a tunable kinetic microgenerator, its magnetic coupling with the electrical blocks, electrical power storage and processing parts, the digital control of the microgenerator tuning system, as well as the power consumption models of sensor node. Therefore not only the energy harvester design parameters but also the sensor node operation parameters can be optimised in order to achieve the best system performance. The power consumption models of the microcontroller and the sensor node are built based on their operation scenarios so that the parameters of the digital algorithms can be optimised to achieve the best energy efficiency. In the proposed approach, two Hardware Description Languages, VHDL-AMS and SystemC-A is used to model the system's analogue components as well as the digital control algorithms which are implemented in the microcontroller and the sensor node. Simulation and performance optimisation results are verified experimentally. In the development of the fast design exploration tool based on the response surface technique, the response surface model (RSM) is constructed by carrying out a series of simulations. The RSM is then optimised using MATLAB's optimisation toolbox and the optimisation results are presented.

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“…There are different types of design of experiments, such as full factorial, central composite design (CCD), Box Behnken designs (BBD) and computer generated designs, such as Doptimal design [10]. Because D-optimal DOE explores design parameters space efficiently with minimum number of run that enable model construction with good accuracy [11], it has be used for the study in this paper. The algorithm of D-optimal criterion optimise the feasible potential design points to form a subset of D-optimal points that will be used in simulation runs.…”

Section: B D-optimal Experimental Designmentioning

confidence: 99%

“…The algorithm of D-optimal criterion optimise the feasible potential design points to form a subset of D-optimal points that will be used in simulation runs. This optimisation is based on maximizing the determinant of XX , where XX is called information matrix [11].…”

Section: B D-optimal Experimental Designmentioning

confidence: 99%

Response-surface-based design space exploration and optimisation of wireless sensor nodes with tunable energy harvesters

Wang¹,

Kazmierski²,

Al-Hashimi³

et al. 2012

2012 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Abstract-In an energy harvester powered wireless sensor node, the energy harvester is often the only energy source, therefore it is crucial to configure the microcontroller and the sensor node so that the harvested energy is used efficiently. This paper presents a response surface model (RSM) based design space exploration and optimisation of a complete wireless sensor node system. In our work the power consumption models of the microcontroller and the sensor node are defined based on their digital operations so that the parameters of the digital algorithms can be optimised to achieve the best energy efficiency. In the proposed technique, SystemC-A is used to model the system's analogue components as well as the digital control algorithms implemented in the microcontroller and the sensor node. A series of simulations are carried out and a response surface model is constructed from the simulation results. The RSM is then optimised using MATLAB's optimisation toolbox and the results show that the optimised system configuration can double the total number of wireless transmissions with fixed amount of harvested energy. The great improvement in the system performance validates the efficiency of our technique.

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Response surface model building and multidisciplinary optimization using D-optimal designs

Cited by 55 publications

References 7 publications

Mutual interference in large populations of co-located IEEE 802.15.4 body sensor networks—A sensitivity analysis

Mutual interference in large populations of co-located IEEE 802.15.4 body sensor networks—A sensitivity analysis

Energy Efficient Sensor Nodes Powered by Kinetic Energy Harvesters – Design for Optimum Performance

Response-surface-based design space exploration and optimisation of wireless sensor nodes with tunable energy harvesters

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