Nuclear spectral analysis via artificial neural networks for waste handling

Keller, Paul E.; Kangas, Lars J.; Troyer, G.L.; Hashem, Sherif; Kouzes, Richard T.

doi:10.1109/23.467888

Cited by 37 publications

(15 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this case, the standard deviation is given by ⌬y(i) ϭ ͱy(i) (7) Relative variations are preserved by the normalisation step in Eq. (6), and therefore the corresponding standard deviation for z(i) is given by…”

Section: Fitting Gaussian Peaksmentioning

confidence: 99%

“…Using the model to process new spectra becomes a simple and speedy operation which can be implemented in real time using compact and portable apparatus. These are important requirements in applications where speed and simplicity are more important than high accuracy, such as in determining contaminants in waste sites [6]. With the spectrum shape as input, the problem is reduced to that of automatic pattern recognition which has been applied in many disciplines [7].…”

Section: Introductionmentioning

confidence: 99%

“…A mathematical description of the foundations of AIM is given elsewhere [13]. Neural network techniques have been proposed as a new approach to automate radiation spectrum analysis [6,14,15]. Trained on a set of reference spectra for isotopes expected to be present in the samples of interest, the network can identify and quantify those isotopes in the spectrum of an unknown mixture.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks

Abdel-Aal

2002

Neural Comput Applic

View full text Add to dashboard Cite

Fitting Gaussian peaks to experimental data is important in many disciplines, including nuclear spectroscopy. Nonlinear least squares fitting methods have been in use for a long time, but these are iterative, computationally intensive, and require user intervention. Machine learning approaches automate and speed up the fitting procedure. However, for a single pure Gaussian, there exists a simple and automatic analytical approach based on linearisation followed by a weighted linear Least Squares (LS) fit. This paper compares this algorithmic method with an abductive machine learning approach based on AIM 1 (Abductory Induction Mechanism). Both techniques are briefly described and their performance compared for analysing simulated and actual spectral peaks. Evaluated on 500 peaks with statistical uncertainties corresponding to a peak count of 100, average absolute errors for the peak height, position and width are 4.9%, 2.9% and 4.2% for AIM, versus 3.3%, 0.5% and 7.7% for the LS. AIM is better for the width, while LS is more accurate for the position. LS errors are more biased, under-estimating the peak position and overestimating the peak width. Tentative CPU time comparison indicates a five-fold speed advantage for AIM, which also has a constant execution time, while LS time depends upon the peak width.

show abstract

Section: Fitting Gaussian Peaksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks

Abdel-Aal

2002

Neural Comput Applic

View full text Add to dashboard Cite

show abstract

“…ANN eliminates the limitations of classical approaches by extracting the desired information from the input data. Applying ANN to a spectrometry system needs sufficient input and output data instead of mathematical equations for performing the fit to nuclear spectra, including X-, gamma-ray and alphaparticles spectra (Baeza et al, 2011;Basheer and Hajmeer, 2000;Keller et al, 1995;Yoshida et al,2002;Kangas et al 2008;Chen and Wei, 2009;Medhat, 2012;Miranda et al, 2009;Doostmohammadi et al, 2010). For each nuclear spectrum, such as alpha spectrum, up to 2048 data points are selected as inputs.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the suitability of neural network method for prediction of uranium activity ratio in environmental alpha spectra

Einian

Aghamiri

Ghaderi

2015

Applied Radiation and Isotopes

View full text Add to dashboard Cite

“…Also the same team has done an identification of the nuclear spectrum for waste water handling [7]. Olmos has also suggested an automation analysis of radiation spectrum using ANNs [4] and [5].…”

Section: Introductionmentioning

confidence: 99%

Identification of spectral lines of elements using artificial neural networks

Saritha

Nampoori

2009

Microchemical Journal

View full text Add to dashboard Cite

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. This paper describes how an ANN can be used to identify the spectral lines of elements. The spectral lines of Cadmium (Cd), Calcium (Ca), Iron (Fe), Lithium (Li), Mercury (Hg), Potassium (K) and Strontium (Sr) in the visible range are chosen for the investigation. One of the unique features of this technique is that it uses the whole spectrum in the visible range instead of individual spectral lines. The spectrum of a sample taken with a spectrometer contains both original peaks and spurious peaks. It is a tedious task to identify these peaks to determine the elements present in the sample. ANNs capability of retrieving original data from noisy spectrum is also explored in this paper. The importance of the need of sufficient data for training ANNs to get accurate results is also emphasized. Two networks are examined: one trained in all spectral lines and other with the persistent lines only. The network trained in all spectral lines is found to be superior in analyzing the spectrum even in a noisy environment.

show abstract

Nuclear spectral analysis via artificial neural networks for waste handling

Cited by 37 publications

References 9 publications

Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks

Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks

Evaluation of the suitability of neural network method for prediction of uranium activity ratio in environmental alpha spectra

Identification of spectral lines of elements using artificial neural networks

Contact Info

Product

Resources

About