Mass Metrology

Gupta, S. V.

doi:10.1007/978-3-642-23412-5

Cited by 7 publications

(6 citation statements)

References 37 publications

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“…[13,14] for more thorough historical overviews of mass metrology). In 1791, a new system of units was recommended to the French Academy of Sciences, including a unit that was the mass of a declared volume of distilled water in vacuo at the freezing point (which was soon succeeded by a cubic decimetre of water at 4 C À at which temperature water is most dense).…”

Section: Massmentioning

confidence: 99%

Some Basics of Measurement

Leach¹

2014

Fundamental Principles of Engineering Nanometrology

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Section: Massmentioning

confidence: 99%

Some Basics of Measurement

Leach¹

2014

Fundamental Principles of Engineering Nanometrology

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“…According the principle of the resistance strain gauge [25], the resistance of the ith strain gauge R i is acquired by…”

Section: A Ideal Weighing Model Of a Load Cellmentioning

confidence: 99%

“…Step 2: Training the NN, i.e., solving the augmented performance index described in (19) using the (k − 1)th parameters ω(k − 1), b (1) (k − 1), v(k − 1), and b (2) (k − 1) and the algorithms described in (24) and (25). If the mean square error of training NN is less than Err_MSE or Train_num is more than Train_Max, stop training the NN.…”

Section: Algorithm Implementation Of Almnnmentioning

confidence: 99%

“…Second, we initialize the parameters, and then train the NN using (19)- (25). The initial values of these parameters are, respectively, written as follows: η = 0. errors of ALMNN are smaller than those of DINN, and the prior knowledge is beneficial for improving the NN's generalization ability regardless of the number of training samples.…”

Section: A Training For the Almnn-based Nnmentioning

confidence: 99%

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Nonlinear Error Compensation for Load Cells Based on the Optimal Neural Network With an Augmented Lagrange Multiplier

Lin

Wang

et al. 2015

IEEE Trans. Instrum. Meas.

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This paper presents a new approach for compensating nonlinear errors of a load cell, based on the optimal neural network with an augmented Lagrange multiplier (ALMNN). The load cell has serious nonlinear errors, which lower the accuracy of the weighing results. In this proposed approach, first, we construct the constraints for training the neural network (NN) using the load cell's prior knowledge, i.e., the monotonic increasing property of a load cell's input-output function. Second, we create the augmented Lagrange function with a multiplier and a penalty factor to serve as an NN's performance index, and then deduce the ALMNN's detailed training algorithm. The penalty factor plays an important role in ALMNN, thus the reasonable range of numerical value is discussed in this paper. Meanwhile, the weighing principle of a strain gauge load cell is introduced and its nonlinear error model is established. Finally, the experimental results demonstrate the effectiveness of ALMNN. By comparing with the conventional data induction method (DINN, i.e., the training of NN not with the prior knowledge but only the data samples), ALMNN significantly improves the NN's generalization ability, especially in the case of lacking training samples. In addition, the applicability of ALMNN is verified by three additional experiments.Index Terms-First-order derivative constraint, Lagrange multiplier, load cell, monotonicity, neural networks (NNs), nonlinear error compensation.

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“…This environmental control covers mainly the temperature, humidity and vibrations [1]. For this reason, running costs of the laboratory are significant.…”

Section: Introductionmentioning

confidence: 99%

Determination of Parameters of Second Order Integration Model for Weighing Scales

Ugodziński

Szewczyk

2014

Mechatronics 2013

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Abstract. Paper presents physical background of the second order integration model for laboratory weighing scales. Moreover, method of identification of the model's parameters is described. This method enables determination of parameters for both static and dynamic characteristics of the object. On the base of the determined model, the weighing scale control algorithm may be optimised. As a result, due to this optimisation, the weighing process may be significantly shortened, what is especially important from the point of view of the laboratory equipment's functional parameters. IntroductionLaboratory weighing scales operate in a strictly controlled environment. This environmental control covers mainly the temperature, humidity and vibrations [1]. For this reason, running costs of the laboratory are significant. As a result, it is highly required to shorten the time of weight measurements in order to increase the efficiency of operation of the weighing laboratory. Time of single mass measurement on laboratory weighing scale is connected with its dynamic characteristic. This characteristic is determined by the mechanical construction of the weighing scale as well as by its control algorithm. Whereas optimisation of the mechanical construction is problematic, due to stiffness and robustness requirements [2], the possibilities of optimisation of the weighing scale's control algorithm still seem to be significant.For optimisation process oriented on the control of laboratory weighing scale, the most important issue is the proper determination of both static and dynamic transfer functions, as well as function's parameters. Moreover, especially in the case of high resolution weighing scales, nonlinearities of these characteristics can't be neglected. As a result, development of the mathematical model for dynamic of a weighing scale requires both detailed analyses of physical processes and careful identification of the model's parameters on the base of experiments.

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Mass Metrology

Cited by 7 publications

References 37 publications

Some Basics of Measurement

Some Basics of Measurement

Nonlinear Error Compensation for Load Cells Based on the Optimal Neural Network With an Augmented Lagrange Multiplier

Determination of Parameters of Second Order Integration Model for Weighing Scales

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