Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Renewable energy harvesting for low power wireless monitoring networks
    (IACSIT Press, 1/11/2017) Rehman Z; Al-Bahadly IH; Mukhopadhyay S; Amir, HFA
    —Energy Harvesting Technologies for wireless electronics networks have undergone a tremendous development in the recent past. Several micro level energy generating units have been developed to convert variety of renewable energy sources to useable electrical energy. In order to integrate and exploit maximum benefits from renewable sources, an intelligent power electronics interface is mandatory. This paper presents a multiport power electronics circuitry to extract maximum energy from renewable energy sources and route it to power up wireless electronics networks. This new topology has ability to cope with different voltage level requirements and is capable of integrating several energy sources to satisfy the variable load demands. The sources can be utilized independently or concurrently. Surplus energy can also be stored and made available in case of absence of renewable energy sources. Analytical and simulation results in Continuous Conduction mode are presented and are validated by experimental results on a prototype model
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    Solving fuzzy multi-objective linear programming problems using deviation degree measures and weighted max-min method
    (Elsevier, 1/06/2013) Cheng HF; Huang WL; Zhou Q; Cai JH
    This paper proposes a method for solving fuzzy multi-objective linear programming (FMOLP) problems where all the coefficients are triangular fuzzy numbers and all the constraints are fuzzy equality or inequality. Using the deviation degree measures and weighted max–min method, the FMOLP problem is transformed into crisp linear programming (CLP) problem. If decision makers fix the values of deviation degrees of two side fuzzy numbers in each constraint, then the δ-pareto-optimal solution of the FMOLP problems can be obtained by solving the CLP problem. The bigger the values of the deviation degrees are, the better the objectives function values will be. So we also propose an algorithm to find a balance-pareto-optimal solution between two goals in conflict: to improve the objectives function values and to decrease the values of the deviation degrees. Finally, to illustrate our method, we solve a numerical example.