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Soil and rock strength
Published in Burt G. Look, Earthworks, 2023
Industry requirements for quality control (QC) are driven by historical associations rather than current technology, as is discussed in later chapters. Figure 6.8-1 is a diagrammatic representation of where each test sits when its significant components of precision, accuracy, cost of test, ease of use, time to conduct test and report on results, etc are combined and assessed (Look and Lacey, 2018 presentation; Look et al., 2020). A survey of industry showed the preferred features of a test for attributes. The ranking order is shown. PLT and density testing are the QC gold standards – they represent the most accurate and most precise tests, respectively, but neither test represents the state-of-the-art technology. In any measurement, accuracy refers to the closeness of the measurement to a “true” value, while precision refers to the repeatability of the test itself.
Motivation for Computing First- and Second-Order Sensitivities of System Responses to the System’s Parameters
Published in Dan Gabriel Cacuci, The Second-Order Adjoint Sensitivity Analysis Methodology, 2018
Experience shows that it is practically impossible to measure exactly the true value of a physical quantity. This is because of various imperfections that occur at various stages involved in a measurement, including uncontrollable experimental errors, inaccurate standards, and other uncertainties arising in the data measurement and interpretation (reduction) process. Therefore, around any reported experimental value, there always exist a certain ranges of plausible values that may also be true. A “measurable” or “physical” quantity is a property of phenomena, bodies, or substances that can be defined qualitatively and can be expressed quantitatively. Measurement is the process of finding experimentally the value of a physical quantity, with the help of devices called measuring instruments. A measurement has three features:
General introduction
Published in Adedeji B. Badiru, Handbook of Industrial and Systems Engineering, 2013
Two categories of research and applications have been developed independently to achieve process control. Engineering process control (EPC) uses measurements to prescribe changes and adjust the process inputs with the intention of bringing the process outputs closer to targets. It employs feedback/feedforward controllers for process regulation and has gained a lot of popularity in continuous process industries. Statistical process control (SPC) uses measurements to monitor a process and look for major changes in order to eliminate the root causes of the changes. It has found widespread applications in discrete parts industries for process improvement, process parameter estimation, and process capability determination. Successful projects have also been developed in other industries such as hospital service, business marketing, and financial management for detecting important process changes to support decision making. Although both techniques aim at the same objective of reducing process variation, they have different origins and have used different implementation strategies for decades.
Comparative Performance Study of Different Filtering Techniques with LSTM for the Prediction of Power Consumption in Smart Grid
Published in IETE Journal of Research, 2023
Sanju Kumari, Neeraj Kumar, Prashant Singh Rana
The swept-frequency signal is also a very effective filter. Here, the authors elaborated on frequency response. They worked over full-waveform inversion (FWI), which uses the high-resolution velocity models. It can minimize the differences between actual versus predicted waveforms [18]. The frequency-swept signal approach measures the source calibration. Mainly, calibration is a documented comparison of the measurement devices compared against a traceable reference standard or device. The reference standard should be more accurate than the device to be calibrated. The frequencies are delivered as 1.5 Hz. The inversion of a mixed substances dataset provides no better results than this technology. Noha et.al discussed data from a decentralized smart grid data system through three different machine learning methods. They analyzed random forest tree model and shown a better accuracy [19]. A detailed comparison of the related research is outlined in Table 1.
Research challenges and future directions towards medical data processing
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Anusha Ampavathi, Vijaya Saradhi T
The performance measures considered for the analysis of medical storage systems from existing studies are given in Table 1. Diverse measures have been used for the evaluating the performance. Here, accuracy is defined as the closeness of the measurements to a particular value, whereas precision is described as the closeness of the measurements to each other. The time taken for considering a functional unit or model for reaction to a specified input is called as response time. F1- score is also used for testing the accuracy and is the mean of recall and precision. Entropy measures the spatial observations in terms of local vs global entropy. Signal-to-Noise Ratio (SNR) is used for characterising the image quality, which is also said as relative to signal. The number of bit errors per unit time is considered as Bit Error Rate (BER), while the probabilities of receiving a symbol and bit in error are indicated as Symbol Error Rate (SER).
Assessment of countermovement jump with and without arm swing using a single inertial measurement unit
Published in Sports Biomechanics, 2022
Ramin Fathian, Aminreza Khandan, Loren Z. F. Chiu, Hossein Rouhani
Accuracy is the degree of closeness of a measured quantity to the quantity’s actual, and precision is the closeness of repeated measurements of the same quantity (Taylor et al., 1983). Therefore, we consider the mean error as a parameter defining the accuracy of the measurement and the standard deviation of the error as a parameter defining the precision of the measurement. In addition, Pearson’s correlation coefficient and standard errors of estimate (SEE) were presented to present a comprehensive insight into the effect of random and systematic errors in the estimated values and assess the validity, accuracy and precision of the sacrum-mounted IMU (Atkinson & Nevill, 1998; Chiu & Dæhlin, 2020; Chiu & Salem, 2010; Hopkins, 2015; Hopkins et al., 2009; Picerno et al., 2011; Requena et al., 2012). More specifically, to assess the accuracy and precision of the detected temporal parameters, the flight time (tto to ttd) and countermovement jump duration (tin to tla) were obtained from both IMU and force-plate and compared together. The mean error for each duration was calculated as the difference between the IMU and force-plate measurements.