Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

Multielement simultaneous quantitative analysis of trace elements in stainless steel via full spectrum laser-induced breakdown spectroscopy

Laser-Induced Breakdown Spectroscopy (LIBS) instruments are increasingly recognized as valuable tools for detecting trace metal elements due to their simplicity, rapid detection, and ability to perform simultaneous multi-element analysis. Traditional LIBS modeling often relies on empirical or machine learning-based feature band selection to establish quantitative models. In this study, we introduce a novel approach-simultaneous multi-element quantitative analysis based on the entire spectrum, which enhances model establishment efficiency and leverages the advantages of LIBS. By logarithmically processing the spectra and quantifying the cognitive uncertainty of the model, we achieved remarkable predictive performance (R2) for trace elements Mn, Mo, Cr, and Cu (0.9876, 0.9879, 0.9891, and 0.9841, respectively) in stainless steel. Our multi-element model shares features and parameters during the learning process, effectively mitigating the impact of matrix effects and self-absorption. Additionally, we introduce a cognitive error term to quantify the cognitive uncertainty of the model. The results suggest that our approach has significant potential in the quantitative analysis of trace elements, providing a reliable data processing method for efficient and accurate multi-task analysis in LIBS. This methodology holds promising applications in the field of LIBS quantitative analysis.

Refractory residues classification strategy using emission spectroscopy of laser-induced plasmas in tandem with a decision tree-based algorithm

The recycling of refractory scraps began to be forged just over a decade ago. Until then, virtually all refractory scraps were disposed off in landfill sites without any application. Over these past few years, a growing interest and a gain steady momentum of the circular economy, the emergent framing around waste and resource management that promotes the notions of their productive cycling, has been the driving force towards the "zero waste" culture across the spectrum of refractory users and producers. In this way, the circular economy, operated following strategies such as, but not limited to, reusing, recycling, and remanufacturing, has played the pillar role in the different essential value chains of the refractory industry to the entering the new era of secondary raw material supply. In any case, prior to starting any sustainable process, it is really necessary to know the wastes and to classify them. In this context, the present research focused on a refractory residue-classification strategy based on combined laser-induced breakdown spectroscopy (LIBS) and a decision tree algorithm for a qualitative analytical performance. This tandem approach allowed the categorization of a rich set of residues in up to 10 different refractory groups. By choosing original LIBS emission intensities and intensity ratios involving the most relevant constituent elements (Al, Mg, C ‒through its related-species CN‒, Si and Zr) of various refractory wastes, a decision tree with multiple nodes that decided how to classify inputs was designed and trained. Categorization performed from LIBS emission spectra of "blind" refractory residues showed that LIBS data combined with this supervised machine learning algorithm provided good refractory scraps-classification performance, with a classification accuracy of up to 75%. However, some more than justified decisions of the algorithm on allegedly misclassified residues showed that scores for the decision tree could found to be far superior to those obtained. The results achieved support the strategy designed for its industrial implementation, either directly in the iron and steel industry, as the major end-user of refractories, in the refractory waste management industry, or in both.

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.

Interpol review of questioned documents 2016-2019

This review paper covers the forensic-relevant literature in questioned documents from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol Review Papers 2019.pdf.

Forensic Analysis of Commercial Inks by Laser-Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) was tested for all of the relevant issues in forensic examinations of commercial inks, including classification of pen inks on one paper type and on different paper types, determination of the deposition order of layered inks, and analysis of signatures and toners on one questioned document. The scope of this work was to determine the potential of a single LIBS setup that is compatible with portable instruments for different types of ink analysis, rather than building a very large database for inks and papers. We identified up to seven metals characteristic for the examined inks, which allowed to fully discriminate all eight black inks on one type of printing paper. When the inks were tested on ten different papers, the correct classification rates for some of them were reduced for reasons thoroughly studied and explained. The replicated tests on three crossing points, each one involving a pair of blue or black inks, were successful in five cases out of six. In the test simulating documents of forensic interest (questioned documents), LIBS was able to correctly identify the differences in three inks used for signatures on one of the three pages and the use of different printing inks on each page of the document.

Automated Parkinson's disease recognition based on statistical pooling method using acoustic features

Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods have been proposed to automatically diagnose Parkinson's disease using acoustic, hand writing and gaits. In this study, a statistical pooling method is proposed to recognize Parkinson's disease using the vowels. The used Parkinson's disease dataset contains the features of vowels. In the proposed method, the features of dataset are increased by applying statistical pooling method. Then, the most weighted features are selected from increased feature vector by using ReliefF. The classification is applied using the most weighted feature vector obtained. In the proposed method, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are used. The success rate was calculated as 91.25% and 91.23% with by using SVM and KNN respectively. The proposed method has two main contributions. The first is to obtain new features from the Parkinson's acoustic dataset using the statistical pooling method. The second one is the selection of the most significant features from the many feature vectors obtained. Thus, successful results were obtained for both KNN and SVM algorithms. The comparatively results clearly show that the proposed method achieved the best success rate among the selected state-of-art methods. Considering the proposed method and the results obtained, it proposed method is successful for Parkinson's disease recognition.

High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks

Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate foreign object intrusion detection is particularly important. As a result of the lack of intruding foreign object samples during the operational period, artificially generated ones will greatly benefit the development of the detection methods. In this paper, we propose a novel method to generate railway intruding object images based on an improved conditional deep convolutional generative adversarial network (C-DCGAN). It consists of a generator and multi-scale discriminators. Loss function is also improved so as to generate samples with a high quality and authenticity. The generator is extracted in order to generate foreign object images from input semantic labels. We synthesize the generated objects to the railway scene. To make the generated objects more similar to real objects, on scale in different positions of a railway scene, a scale estimation algorithm based on the gauge constant is proposed. The experimental results on the railway intruding object dataset show that the proposed C-DCGAN model outperforms several state-of-the-art methods and achieves a higher quality (the pixel-wise accuracy, mean intersection-over-union (mIoU), and mean average precision (mAP) are 80.46%, 0.65, and 0.69, respectively) and diversity (the Fréchet-Inception Distance (FID) score is 26.87) of generated samples. The mIoU of the real-generated pedestrian pairs reaches 0.85, and indicates a higher scale of accuracy for the generated intruding objects in the railway scene.

Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study

Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications.