scholarly journals Analysis and Design of Broadband Simultaneous Wireless Information and Power Transfer (SWIPT) System Considering Rectifier Effect

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2387 ◽  
Author(s):  
Mehdi Shirichian ◽  
Somayyeh Chamaani ◽  
Alireza Akbarpour ◽  
Giovanni Del Galdo
Keyword(s):  
System Performance ◽  
Systems Design ◽  
Realistic Model ◽  
Power Delivery ◽  
Center Frequency ◽  
Power Transfer ◽  
Broadband Antennas ◽  
Analysis And Design ◽  
Iot Devices ◽  
Pareto Fronts

The deployment of internet of things (IOT) devices in several applications is limited by their need of having batteries as a power source. This has led many researchers to make efforts on simultaneous wireless information and power transfer (SWIPT) systems design. Increasing the bandwidth provides higher capacity; however, due to the narrowband response of conventional power transfer subsystems, power delivery is decreased. In order to design an optimum wideband SWIPT system, first, a realistic model of the system, including antennas and rectifier, should be developed. Then, proper methods to increase the bandwidth of subsystems for optimum power delivery can be proposed. In this paper, a wideband SWIPT system (300 MHz bandwidth at the center frequency of 1.44 GHz) while considering realistic limitations of antennas and rectifiers is designed. To optimize the system performance, a novel power allocation method is proposed. Using this algorithm, Pareto fronts of Shannon channel capacity versus power delivery in three scenarios (broadband antennas without considering rectifier, broadband antennas with narrowband rectifier and broadband antennas with broadband rectifier) are compared. The results show that, without considering the realistic behaviour of the subsystems, the performance is largely overestimated. Furthermore, this model allows for designers to optimize each subsystem directly and assess its effect on the overall SWIPT system performance.

Analysis and Design of EIT-Like Magnetic Coupling Wireless Power Transfer Systems

2021 ◽  
pp. 1-11
Author(s):  
Zhi-Juan Liao ◽  
Qi-Kai Feng ◽  
Chen-Hui Jiang ◽  
Fan Wu ◽  
Chen-Yang Xia ◽  
...  
Keyword(s):  
Magnetic Coupling ◽  
Wireless Power Transfer ◽  
Power Transfer ◽  
Wireless Power ◽  
Analysis And Design

A design approach for process-based knowledge management systems

2017 ◽  
Vol 21 (4) ◽  
pp. 693-717 ◽  
Author(s):  
Surendra Sarnikar ◽  
Amit V. Deokar
Keyword(s):  
Knowledge Management ◽  
Feasibility Study ◽  
Systems Analysis ◽  
Design Theory ◽  
Systems Design ◽  
Design Approach ◽  
Content Type ◽  
Analysis And Design ◽  
Systems Analysis And Design ◽  
Design Techniques

Purpose This paper presents a design approach for process-based knowledge management (PKM) systems that can support knowledge-intensive processes where effective task execution is highly reliant on the knowledge and expertise of participants executing the tasks. The proposed design approach includes design methods and kernel theories governing the design of PKM systems and can also be easily integrated with existing systems analysis and design techniques. Design/methodology/approach The design science research methodology is used to design and develop the artifact which includes the overall PKM design approach. Information systems design theory is used as a high-level framework to develop and structure the design approach. Relevant design methods and behavioral theories are reviewed to identify kernel theories that guide the design and development of PKM systems. The design approach consists of meta-requirements for PKM systems and design processes to achieve the meta-requirements. A feasibility study is conducted to evaluate the feasibility of the proposed approach. Findings The design approach presented in this paper can guide system analysts and system developers in the design of knowledge management systems for supporting knowledge-intensive processes. The paper also includes a comprehensive design theory for PKM systems consisting of meta-requirements and a synthesis of various kernel theories into actionable design procedures. The proposed procedures include knowledge requirements modeling, knowledge flows modeling and knowledge and process performance modeling procedures. The feasibility study indicates that the PKM approach can be more useful and effective than solely using unified modeling language (UML)-based systems analysis and design techniques for the design of PKM systems. Research limitations/implications An implication to information systems design research is the feasibility of developing a specialized design approach that incorporates significant domain knowledge to solve complex information system design problems. An implication to practice is the significant potential to improve productivity and effectiveness of systems analysts and designers in developing PKM systems. A limitation is the small sample size of the feasibility study used to evaluate the ease of use and utility of the design approach. Originality/value The study makes a unique contribution by proposing a design approach that integrates business process and knowledge management considerations. The approach is particularly valuable because of the focus on integration with existing systems analysis and design techniques, thus allowing for easier adoption.

SAMS: Stochastic Analysis With Minimal Sampling—A Fast Algorithm for Analysis and Design Under Uncertainty

2004 ◽  
Vol 127 (4) ◽  
pp. 558-571 ◽  
Author(s):  
A. Mawardi ◽  
R. Pitchumani
Keyword(s):  
System Performance ◽  
Stochastic Analysis ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Analysis And Design ◽  
Process Outcomes ◽  
Input Parameters ◽  
Computationally Intensive ◽  
Uncertain Input ◽  
Reliability And Robustness

Design of processes and devices under uncertainty calls for stochastic analysis of the effects of uncertain input parameters on the system performance and process outcomes. The stochastic analysis is often carried out based on sampling from the uncertain input parameters space, and using a physical model of the system to generate distributions of the outcomes. In many engineering applications, a large number of samples—on the order of thousands or more—is needed for an accurate convergence of the output distributions, which renders a stochastic analysis computationally intensive. Toward addressing the computational challenge, this article presents a methodology of S̱tochastic A̱nalysis with M̱inimal S̱ampling (SAMS). The SAMS approach is based on approximating an output distribution by an analytical function, whose parameters are estimated using a few samples, constituting an orthogonal Taguchi array, from the input distributions. The analytical output distributions are, in turn, used to extract the reliability and robustness measures of the system. The methodology is applied to stochastic analysis of a composite materials manufacturing process under uncertainty, and the results are shown to compare closely to those from a Latin hypercube sampling method. The SAMS technique is also demonstrated to yield computational savings of up to 90% relative to the sampling-based method.

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