Optimization of ADC Channels of A Smart Energy Meter Including Random Noise Effects

Adamo, Francesco; Berni, Rossella; Nisio, Attilio Di; Scarano, Valeria L.; Spadavecchia, Maurizio

doi:10.1002/qre.1838

Cited by 2 publications

(2 citation statements)

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“…In this direction, wireless communications and transmission are studied through a full factorial design in [22], where the received signal strength is analyzed. Similarly, in [23], an Analog to Digital Converter (ADC) channel optimization is carried out by considering random noise effects and the multi-response case; in [24], a statistical analysis is applied in order to study and to remove noises by the spectral domain. When considering the application of the well-known full factorial experimental design, as in [25], it must be noted that this design is an efficient statistical tool when fractionated and applied in an RSM context; and also, as basic design for the split-plot, as in [26].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

Bertocci

Fort

Mugnaini

et al. 2017

Sensors

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Eight different types of nanostructured perovskites based on YCoO3 with different chemical compositions are prepared as gas sensor materials, and they are studied with two target gases NO2 and CO. Moreover, a statistical approach is adopted to optimize their performance. The innovative contribution is carried out through a split-plot design planning and modeling, also involving random effects, for studying Metal Oxide Semiconductors (MOX) sensors in a robust design context. The statistical results prove the validity of the proposed approach; in fact, for each material type, the variation of the electrical resistance achieves a satisfactory optimized value conditional to the working temperature and by controlling for the gas concentration variability. Just to mention some results, the sensing material YCo0.9Pd0.1O3 (Mt1) achieved excellent solutions during the optimization procedure. In particular, Mt1 resulted in being useful and feasible for the detection of both gases, with optimal response equal to +10.23% and working temperature at 312∘C for CO (284 ppm, from design) and response equal to −14.17% at 185∘C for NO2 (16 ppm, from design). Analogously, for NO2 (16 ppm, from design), the material type YCo0.9O2.85+1%Pd (Mt8) allows for optimizing the response value at −15.39% with a working temperature at 181.0∘C, whereas for YCo0.95Pd0.05O3 (Mt3), the best response value is achieved at −15.40% with the temperature equal to 204∘C.

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Section: Introductionmentioning

confidence: 99%

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

Bertocci

Fort

Mugnaini

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Two papers address the topic of process optimization in multiresponse contexts. Francesco Adamo et al explore a robust approach based on a mixed‐response surface model and apply it to a smart grid infrastructure, while Nikolaus Rudak et al extend the joint optimization method – a method devoted to finding the process settings leading to pre‐specified targets in the responses – to a case in which the quality characteristics are correlated and then apply it to a thermal spraying process.…”

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confidence: 99%

The ENBIS‐14 Quality and Reliability Engineering International Special Issue

Ograjenšek

Reis

2015

Quality & Reliability Eng

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Special Issue highlights the application of statistical thinking and methods in several key areas of interest to the ENBIS community. One of them has traditionally been healthcare. In this area, Shuki Dror and Dina Margol 1 show an interesting application of the Pareto principle and quality function deployment by developing and critically evaluating a framework for analysing malpractice cases in healthcare with the ultimate goal of improving the healthcare system. Rosa Falotico et al., 2 on the other hand, strive to prevent malpractice cases by proposing the use of text mining as a tool for identifying oncological patient communication needs with the goal of improving e-health communication. Service improvement is also at the core of the paper written by Alex Kuiper and Michel Mandjes, 3 who discuss practical principles in appointment scheduling. Finally, occupational health and safety are dealt with by Aliye Ayca Supciller and Nilsu Abali 4 within the scope of risk analysis.Another key area is manufacturing. Manoj Dora and Xavier Gellynck 5 present an interesting case study in the SME framework: an application of Lean Six Sigma -an effective hybrid method that combines the variability elimination tools from Six Sigma with the waste elimination practices from lean manufacturing -to reduce variability and waste in the production process of a food processing company. The challenges of manufacturing are also dealt with by Alberto Pasanisi et al. 6 who present a discrete modelling formalism for describing the reliability of equipment for which the relevant information is the number of cycles or solicitations it can endure. The proposed approach is particularly useful for low (decelerated) ageing conditions.The third key area is transportation. Pasquale Erto et al. 7 outline a procedure for predicting and controlling the ship fuel consumption and, consequently, the ship energy efficiency. At the core of the procedure is a multiple linear regression model exploiting the navigation information, which is usually available on modern ships to support managerial decision-making.Design of experiments has always been a much discussed topic at ENBIS conferences. In her paper, Ulrike Grömping 8 first evaluates the existing methods for assessing the effect significance in half-normal effects plots from fractional factorial two-level experiments and then proposes a new one. Peter Zemroch, 9 in his contribution, deals with the computerized generation of fractional-replicate designs that allow for more freedom in the definition of the number of experiments to conduct.Two papers address the topic of process optimization in multiresponse contexts. Francesco Adamo et al. 10 explore a robust approach based on a mixed-response surface model and apply it to a smart grid infrastructure, while Nikolaus Rudak et al. 11 extend the joint optimization method -a method devoted to finding the process settings leading to pre-specified targets in the responsesto a case in which the quality characteristics are correlated and then apply it to a the...

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Optimization of ADC Channels of A Smart Energy Meter Including Random Noise Effects

Cited by 2 publications

References 87 publications

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

The ENBIS‐14 Quality and Reliability Engineering International Special Issue

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