Random two-frame phase-shifting interferometry via minimization of coefficient of variation

Cheng, Zhongtao; Liu, Dong; Zhang, Lei

doi:10.1063/1.5118296

Cited by 16 publications

(4 citation statements)

References 26 publications

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“…For instance, high-resolution imaging techniques, such as phase-shifting interferometry (PSI), laser speckle angular measurement, and functional scanning force microscopy, provide detailed insights into the surface characteristics and dimensions of nanostructures and materials. Additionally, non-contact surface measurement methods, including infrared (IR) imaging and optical interferometric measurement, offer the advantage of capturing surface information without physically touching the sample, making them suitable for delicate or sensitive materials (Cheng et al, 2019;Wang et al, 2021;Chen et al, 2018;Tofail et al, 2018;Wu, 2020).…”

Section: Advanced Metrology Techniquesmentioning

confidence: 99%

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Adeniyi Kehinde Adeleke,

Emmanuel Chigozie Ani,

Kehinde Andrew Olu-lawal

et al. 2024

Int. J. Sci. Res. Arch.

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Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

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Section: Advanced Metrology Techniquesmentioning

confidence: 99%

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Adeniyi Kehinde Adeleke,

Emmanuel Chigozie Ani,

Kehinde Andrew Olu-lawal

et al. 2024

Int. J. Sci. Res. Arch.

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“…( 1), two-frame interferograms with a size of 640×640 are created. For more generality, the background and the amplitude of two-frame interferograms are set as non-uniform terms, For illustrating the performance of ours, it is compared with the results of the PCA&LSI method [9] and CV method [10] in terms of phase reconstruction performance. The phase unwrapping method in this paper is referred to literature [11].…”

Section: Simulationmentioning

confidence: 99%

Two-frame Phase-shifting Interferometry by Minimizing the Residual-background

Shan

et al. 2023

J. Phys.: Conf. Ser.

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Demodulation of two random phase shift fringe patterns is an ill-posed problem. Existing methods often need to remove the fringe background by pre filtering in the frequency, which takes more time, and have a great influence on the phase reconstruction because of the frequency aliasing, these limits the application of two frame phase-shift interferometry in actual production. In this case, this paper based on the idea of Statistical Average, proposes a Statistical Average technology for phase extraction of two frame randomly phase-shifted interferograms from rough to precision without pre filtering. The proposed methods based on minimizing the residual background, firstly estimates the average background by calculating the average for all pixels, and then adding and subtracting the two frames with the background removed. The phase shift and phase are solved by using the properties of trigonometric functions. Then the amplitude is solved using the obtained phase and phase shift. Finally, the background is solved and the original light intensity is updated. Compared with existing methods, our method has the advantages such as rapid convergence and quick calculation by offering the right initial value. The accuracy of my proposed method is approximately double that of existing methods. Simulation and experimental results show that our method is a practical method of two frame phase shift interferences, both simple and reliable.

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“…To better understand the relationships among the collected data, a descriptive statistical analysis was carried out, by computing several summarization metrics such as basic statistics, the coefficient of variation [64], Pearson correlation [65], and energy load histogram Q-Q plot [66].…”

Section: Knowledge Data Discovery (Kdd Analysis)mentioning

confidence: 99%

Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

et al. 2020

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The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios.Techniques devoted to forecasting electricity demand aim at estimating the amount of energy needed, over a historical time series, for transmission and later consumption by others. Despite the adaptation of several machine learning models to properly address the problem [5-9], tracking the progressive use of the electricity is not a straightforward task in practice. In fact, the electricity dispatch is intrinsically related to the internal operations of the power systems such as the periodic scheduling of power generation in hydroelectric plants, the preventive maintenance of the generators, the reliability evaluation of power systems, etc. [10]. Moreover, the problem becomes even more challenging and especially interesting when one has to deal with the highly nonlinear tendency of power data, as it is mathematically modeled by highly-oscillating time series whose parameters can be affected by exogenous variables such as weather/ambient conditions [11] and economy-related factors [12].Formally, the problem of predicting the power demand on time series can be described as follows: given a time series of electric load X 1 , X 2 , . . . , X t , in which X i accounts for the historical energy load at the instant i, i = 1, 2, ..., t, the goal is to predict the quantity X (t+h) , where h establishes the forecast horizon [13,14]. Taxonomically speaking, this kind of prediction usually comprises three categories of planning horizons: (i) long-term (years/months); (ii) regular-term (days/weeks); and (iii) short-term (minutes/hours). Since estimating the electricity demand becomes harder as the planning horizon increases, the predictions can be strongly influenced by several nonuniform variables such as electric consumption, temperature, air humidity, and socioeconomic aspects. Moreover, long-and regular-term time series make the problem more difficult to be technically manag...

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Random two-frame phase-shifting interferometry via minimization of coefficient of variation

Cited by 16 publications

References 26 publications

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Two-frame Phase-shifting Interferometry by Minimizing the Residual-background

Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

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