arXiv subjects

Aristides T. Hatjimihail

Publications and source records attributed to Aristides T. Hatjimihail.

4 recordsLinked to original sources

arXiv2026

Design of statistical quality control procedures using genetic algorithms

In general, we can not use algebraic or enumerative methods to optimize a quality control (QC) procedure so as to detect the critical random and systematic analytical errors with stated probabilities, while the probability for false rejection is minimum. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and search through large parameter spaces quickly. To explore the application of GAs in statistical QC, we have developed an interactive GAs based computer program that designs a novel near optimal QC procedure, given an analytical process. The program uses the deterministic crowding algorithm. An illustrative application of the program suggests that it has the potential to design QC procedures that are significantly better than 45 alternative ones that are used in the clinical laboratories.

cs.NE↗

arXiv2026

A nonlinear component of the analytical error

In clinical chemistry, a number of studies shows that the probability of very large errors is much greater than expected from the Gaussian distribution. In addition, it has been empirically found that the behavior of nonlinear complex systems is often asymptotically exponential. Consequently, we may assume that the error of some analytical systems may be approximated by the sum of a linear component of error with Gaussian distribution and a nonlinear component with Laplacian. Then, the probability density function (pdf) of the total error is approximated by the convolution integral of the Gaussian and the Laplacian pdf. To explore the hypothesis of a nonlinear component of the analytical error I have evaluated this distribution and calculated various quality control related statistics with numerical methods. Large errors are much more probable with the proposed distribution than with the Gaussian. Simulated series of measurements with the proposed distribution often meet the criteria for normality. The critical errors and the probabilities for critical error detection are less than the respective ones of the Gaussian distribution. The probabilities for false rejection are greater. Therefore, to optimize the quality control planning process, we should explore the possibility that there exists a nonlinear component of the analytical error.

nlin.AO↗

arXiv2023

Calculation of the confidence bounds for the fraction nonconforming of normal populations of measurements in clinical laboratory medicine

The fraction nonconforming is a key quality measure used in statistical quality control design in clinical laboratory medicine. The confidence bounds of normal populations of measurements for the fraction nonconforming each of the lower and upper quality specification limits when both the random and the systematic error are unknown can be calculated using the noncentral t-distribution, as it is described in detail and illustrated with examples.

cs.CE↗

arXiv2023

Quality control using convolutional neural networks applied to samples of very small size

Although there is extensive literature on the application of artificial neural networks (NNs) in quality control (QC), to monitor the conformity of a process to quality specifications, at least five QC measurements are required, increasing the related cost. To explore the application of neural networks to samples of QC measurements of very small size, four one-dimensional (1-D) convolutional neural networks (CNNs) were designed, trained, and tested with datasets of $ n $-tuples of simulated standardized normally distributed QC measurements, for $ 1 \leq n \leq 4$. The designed neural networks were compared to statistical QC functions with equal probabilities for false rejection, applied to samples of the same size. When the $ n $-tuples included at least two QC measurements distributed as $ \mathcal{N}(μ, σ^2) $, where $ 0.2 < |μ| \leq 6.0 $, and $ 1.0 < σ\leq 7.0 $, the designed neural networks outperformed the respective statistical QC functions. Therefore, 1-D CNNs applied to samples of 2-4 quality control measurements can be used to increase the probability of detection of the nonconformity of a process to the quality specifications, with lower cost.

cs.NE↗