Application Of Pattern Recognition Techniques Research Articles

Applications of Pattern Recognition Techniques in Social Science

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition algorithms can be effectively applied for analysis of big data obtained from society to predict possible chaos in future or to identify required changes needed for society or changing the public policies. This technical note reviews some method of pattern recognition in society.

An Analytic Approach for Acoustic Material Testing

Acoustic material testing is well known for quality inspection of metal and composite parts in serial production with short cycle times and a robust performance. It is a referencing as well as a comparative testing method. Preclassified reference parts are required to teach the testing algorithm. The teaching process of an acoustic material testing system (ART) for suitable test criteria has to be done iterative by re-assessment and optimisation to improve the testing characteristics and the hit rate. Common acoustic testing systems are using conventional characteristics like frequency position, damping factor and amplitude monitoring or amplitude attenuation. But this is very often not enough for a reliable testing in consideration of batch or production tolerances. Modern ART systems offer already a choice of intelligent algorithms in combination with characteristics based on pattern recognition like frequency split up, frequency distance relations, multiple frequency peak detection, compensation and more. An additional approach is the evaluation of a complete frequency range instead of individual frequencies. Such characteristic attributes are more known for NVH testing like cumulative or average level, square sum and flatness tolerance. Self-learning test system is taken care with the aid of mathematical pattern recognition processes and automatic re-assessment feedback which will increase the acceptance and reliability of such acoustic material testing systems. Innovative aspects The innovative aspect is the application of pattern recognition techniques which leads to the future implementation of artificial intelligence technologies. With these new approaches, the acoustic testing will become much more usable for e.g. composite materials.

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

Big-Data Analytics for Production-Data Classification Using Feature Detection: Application to Restimulation-Candidate Selection

Summary In recent years, there has been a proliferation of massive subsurface data sets from sources such as instrumented wells. This places significant challenges on traditional production-data-analysis methods for extracting useful information in support of reservoir management and decision making. In addition, with increased exploration interest in unconventional-shale-gas reservoirs, there is a heightened need for improved techniques and technologies to enhance the understanding of induced- and natural-fracture characteristics in the subsurface, as well as their associated effects on fluid flow and well performance. These challenges have the potential to be addressed by developing big-data-analytics tools that focus on uncovering masked trends related to fracture properties from large volumes of subsurface data through the application of pattern-recognition techniques. We present a new framework for fast and robust production-data classification, which is adapted from a real-time face-detection algorithm. This is achieved by generalizing production data as vectorized 1D images with pixel values proportional to rate magnitudes. Using simulated shale-gas-production data, we train a cascade of boosted binary classification models that are capable of providing probabilistic predictions. We demonstrate the viability of this approach for identifying hydraulically fractured wells that have the potential to benefit from restimulation treatment. The results show significant improvements over existing type-curve-based approaches for recognizing favorable-candidate wells, using only gas-rate profiles.

Scandium, yttrium, and lanthanide contents in soil from Serbia and their accumulation in the mushroom Macrolepiota procera (Scop.) Singer.

The mobility (fractionation) of rare earth elements (REEs) and their possible impacts on ecosystems are still relatively unknown. Soil samples were collected from two sites in central Serbia, an unpolluted mountain region (site 1) and a forest near a city (site 2). In order to investigate REE fractions (acid-soluble/exchangeable, reducible, oxidizable, and residual) in soils, BCR sequential extraction was performed. Additionally, the content of REEs was also determined in stipes and caps of the mushroom Macrolepiota procera, growing in the observed sites. Sc, Y, and lanthanide contents were determined by inductively coupled plasma mass spectrometry (ICP-MS), and results were subjected to multivariate data analysis. Application of pattern recognition technique revealed the existence of two distinguished clusters belonging to different geographical sites and determined by greater levels of Sc, Y, and lanthanides in Goč soil compared to Trstenik soil. Additionally, PCA analysis showed that REEs in soil were concentrated in two groups: the first consisted of elements belonging to light REEs and the second contained heavy REEs. These results suggest that the distribution of REEs in soils could indicate the geographical origin and type of soil. The bioconcentration factors and translocation factors for each REE were also calculated. This study provides baseline data on the rare earth element levels in the wild edible mushroom M. procera, growing in Serbia. In terms of bioconcentration and bioexclusion concept, Sc, Y, and REEs were bioexcluded in M. procera for both studied sites.

Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1H‐MR spectroscopy—A multi‐center study

Purpose3T magnetic resonance scanners have boosted clinical application of 1H‐MR spectroscopy (MRS) by offering an improved signal‐to‐noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic information. Spectroscopic data from clinical 1.5T MR scanners has been shown to discriminate between pediatric brain tumors by applying machine learning techniques to further aid diagnosis. The purpose of this multi‐center study was to investigate the discriminative potential of metabolite profiles obtained from 3T scanners in classifying pediatric brain tumors.MethodsA total of 41 pediatric patients with brain tumors (17 medulloblastomas, 20 pilocytic astrocytomas, and 4 ependymomas) were scanned across four different hospitals. Raw spectroscopy data were processed using TARQUIN. Borderline synthetic minority oversampling technique was used to correct for the data skewness. Different classifiers were trained using linear discriminative analysis, support vector machine, and random forest techniques.ResultsSupport vector machine had the highest balanced accuracy for discriminating the three tumor types. The balanced accuracy achieved was higher than the balanced accuracy previously reported for similar multi‐center dataset from 1.5T magnets with echo time 20 to 32 ms alone.ConclusionThis study showed that 3T MRS can detect key differences in metabolite profiles for the main types of childhood tumors. Magn Reson Med 79:2359–2366, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics.

BackgroundThe emergence of antibiotic resistant pathogenic bacteria has reduced our ability to combat infectious diseases. At the same time the numbers of new antibiotics reaching the market have decreased. This situation has created an urgent need to discover novel antibiotic scaffolds. Recently, the application of pattern recognition techniques to identify molecular fingerprints in ‘omics’ studies, has emerged as an important tool in biomedical research and laboratory medicine to identify pathogens, to monitor therapeutic treatments or to develop drugs with improved metabolic stability, toxicological profile and efficacy. Here, we hypothesize that a combination of metabolic intracellular fingerprints and extracellular footprints would provide a more comprehensive picture about the mechanism of action of novel antibiotics in drug discovery programs.ResultsIn an attempt to integrate the metabolomics approach as a classification tool in the drug discovery processes, we have used quantitative 1H NMR spectroscopy to study the metabolic response of Escherichia coli cultures to different antibiotics. Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis. The metabolic fingerprints and footprints of bacterial cultures were affected in a distinct manner and provided complementary information regarding intracellular and extracellular targets such as protein synthesis, DNA and cell wall. While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied. In addition, using a training set of E. coli fingerprints extracted after treatment with different antibiotic classes, the mode of action of streptomycin, tetracycline and carbenicillin could be correctly predicted.ConclusionThe metabolic profiles of E. coli treated with antibiotics with intracellular and extracellular targets could be separated in fingerprint and footprint analysis, respectively and provided complementary information. Based on the specific fingerprints obtained for different classes of antibiotics, the mode of action of several antibiotics could be predicted. The same classification approach should be applicable to studies of other pathogenic bacteria.

Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry

Scientific and clinical advances in perinatology and neonatology have enhanced the chances of survival of preterm and very low weight neonates. Infant cry analysis is a suitable noninvasive complementary tool to assess the neurologic state of infants particularly important in the case of preterm neonates. This article aims at exploiting differences between full-term and preterm infant cry with robust automatic acoustical analysis and data mining techniques. Twenty-two acoustical parameters are estimated in more than 3000 cry units from cry recordings of 28 full-term and 10 preterm newborns. Feature extraction is performed through the BioVoice dedicated software tool, developed at the Biomedical Engineering Lab, University of Firenze, Italy. Classification and pattern recognition is based on genetic algorithms for the selection of the best attributes. Training is performed comparing four classifiers: Logistic Curve, Multilayer Perceptron, Support Vector Machine, and Random Forest and three different testing options: full training set, 10-fold cross-validation, and 66% split. Results show that the best feature set is made up by 10 parameters capable to assess differences between preterm and full-term newborns with about 87% of accuracy. Best results are obtained with the Random Forest method (receiver operating characteristic area, 0.94). These 10 cry features might convey important additional information to assist the clinical specialist in the diagnosis and follow-up of possible delays or disorders in the neurologic development due to premature birth in this extremely vulnerable population of patients. The proposed approach is a first step toward an automatic infant cry recognition system for fast and proper identification of risk in preterm babies.

The Ninapro database: A resource for sEMG naturally controlled robotic hand prosthetics.

The dexterous natural control of robotic prosthetic hands with non-invasive techniques is still a challenge: surface electromyography gives some control capabilities but these are limited, often not natural and require long training times; the application of pattern recognition techniques recently started to be applied in practice. While results in the scientific literature are promising they have to be improved to reach the real needs. The Ninapro database aims to improve the field of naturally controlled robotic hand prosthetics by permitting to worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark database. Currently, the Ninapro database includes data from 67 intact subjects and 11 amputated subject performing approximately 50 different movements. The data are aimed at permitting the study of the relationships between surface electromyography, kinematics and dynamics. The Ninapro acquisition protocol was created in order to be easy to be reproduced. Currently, the number of datasets included in the database is increasing thanks to the collaboration of several research groups.

Multivariate Classification of Blood Oxygen Level-Dependent fMRI Data with Diagnostic Intention: A Clinical Perspective

There has been a recent upsurge of reports about applications of pattern-recognition techniques from the field of machine learning to functional MR imaging data as a diagnostic tool for systemic brain disease or psychiatric disorders. Entities studied include depression, schizophrenia, attention deficit hyperactivity disorder, and neurodegenerative disorders like Alzheimer dementia. We review these recent studies which-despite the optimism from some articles-predominantly constitute explorative efforts at the proof-of-concept level. There is some evidence that, in particular, support vector machines seem to be promising. However, the field is still far from real clinical application, and much work has to be done regarding data preprocessing, model optimization, and validation. Reporting standards are proposed to facilitate future meta-analyses or systematic reviews.

Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data

Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data. Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip [Formula: see text] Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Naïve-Bayes, Support Vector Machine (SVM) and [Formula: see text]-nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics. Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions ([Formula: see text]). SVM achieved the highest overall performance (accuracy 98%) for both the intratumoral and peritumoral areas. Naïve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology. The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

Application of Pattern Recognition Techniques to Predict Severe Thunderstorms

Thunderstorm forecasting is a challenging job. Machine learning techniques are being applied nowadays in meteorological fields for prediction purpose. This study presents the application of different machine learning tools based on multiple correlation, Multi-layer Perceptron (MLP), K-nearest neighbor (K-nn) method, and modified K-nn method to predict seasonal severe thunderstorms associated with squall occurring in Kolkata, North-East India. The models are trained and tested with the radiosonde data recorded in the early morning at 00:00UTC. The predictors are moisture difference and dry adiabatic lapse rate at different geopotential heights of the atmosphere. Our aim in this paper is to find how much correctly one can nowcast 10 to 14 hours before the 'occurrence'/ 'no occurrence' of evening squall-storms by using a few upper air diagnostic predictors. Modified K-nn method is found to yield very promising prognostic information with high prediction accuracy. The results indicate that forecasting can be done correctly up to 82.02% both for 'squall-storm/no storm' events, and up to 91.11% for 'squall-storm' events using modified K-nn based approach. In this article, modified K-nn method is proved as the best method in comparison with the other methods for the squall-storm prediction.

Characterising the Energy Consumption of Machine Tool Actuator Components Using Pattern Recognition

The efficiency of energy usage, coupled with the cost and availability of energy, have a substantial impact on the competitiveness and economic viability of manufacturing enterprises. The current uncertainty associated with energy prices has a significant impact on the manufacturing sector as this industry is exposed to the cost of energy at every level of the supply chain. Energy effi ciency varies dramatically between different industries, processes, and even between different facilities manufacturing similar products. In terms of machine tool energy efficiency, the design, age, maintenance strategy, and operating parameters all impact on the ov erall efficiency. Developing a complete understanding of the energy consumed by individual machine components during machining facilitates the optimisation of machine tool design and also NC programs. This paper examines the application of pattern recognition techniques to the area of machine component recognition. Initially, an overview of commonly used pattern recognition systems is outlined before the design and application of a customised solution, applied to a complex manufacturing problem is described. The system design, implementation, and results are discussed before the paper concludes with some of the limitations associated with the implemented methodology.

Emotion and mental state recognition from speech

As research in speech processing has matured, attention has gradually shifted from linguistic-related applications such as speech recognition towards paralinguistic speech processing problems, in particular the recognition of speaker identity, language, emotion, gender, and age. Determination of a speaker’s emotion or mental state is a particularly challenging problem, in view of the significant variability in its expression posed by linguistic, contextual, and speaker-specific characteristics within speech. In response, a range of signal processing and pattern recognition methods have been developed in recent years. Recognition of emotion and mental state from speech is a fundamentally multidisciplinary field, comprising contributions from psychology, speech science, linguistics, (cooccurring) nonverbal communication, machine learning, artificial intelligence and signal processing, among others. Some of the key research problems addressed to date include isolating sources of emotion-specific information in the speech signal, extracting suitable features, forming reduced-dimension feature sets, developing machine learning methods applicable to the task, reducing feature variability due to speaker and linguistic content, comparing and evaluating diverse methods, robustness, and constructing suitable databases. Studies examining the relationships between the psychological basis of emotion, the effect of emotion on speech production, and the measurable differences in the speech signal due to emotion have helped to shed light on these problems; however, substantial research is still required. Taking a broader view of emotion as a mental state, signal processing researchers have also explored the possibilities of automatically detecting other types of mental state which share some characteristics with emotion, for example stress, depression, cognitive load, and ‘cognitive epistemic’ states such as interest, scepticism, etc. The recent interest in emotion recognition research has seen applications in call centre analytics, human-machine and humanrobot interfaces, multimedia retrieval, surveillance tasks, behavioural health informatics, and improved speech recognition. This special issue comprises nine articles covering a range of topics in signal processing methods for vocal source and acoustic feature extraction, robustness issues, novel applications of pattern recognition techniques, methods for detecting mental states and recognition of non-prototypical spontaneous and naturalistic emotion in speech. These articles were accepted following peer review, and each submission was handled by an editor who was independent from all authors listed in that manuscript. Herein, we briefly introduce the articles comprising this special issue. Trevino, Quatieri and Malyska bring a new level of sophistication to an old problem, detecting signs of depressive disorders in speech. Their measures of depression come from standard psychiatric instruments, Quick Inventory of Depressive Symptomatology and Hamilton Depression rating scales. These are linked to measures of speech timing that are much richer than the traditional global measures of speech rate. Results indicate that different speech sounds and sound types behave differently in depression, and may relate to different aspects of depression. Caponetti, Buscicchio and Castellano propose the use of a more detailed auditory model than that embodied in the widely employed mel frequency cepstral coefficients, for extracting detailed spectral features during emotion recognition. Working from the Lyon cochlear model, the authors demonstrate improvements on a five-class problem from the speech under simulated and actual stress database. Their study also further validates the applicability of long short-term memory recurrent neural networks for classification in emotion and mental state recognition problems. Callejas, Griol and Lopez-Cozar propose a mental state prediction approach that considers both speaker * Correspondence: j.epps@unsw.edu.au School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia Full list of author information is available at the end of the article Epps et al. EURASIP Journal on Advances in Signal Processing 2012, 2012:15 http://asp.eurasipjournals.com/content/2012/1/15

The application of pattern recognition techniques in metabolite fingerprinting of six different Phyllanthus spp.

FTIR spectroscopy was used together with multivariate analysis to distinguish six different species of Phyllanthus. Among these species P. niruri, P. debilis and P. urinaria are morphologically similar whereas P. acidus, P. emblica and P. myr- tifolius are different. The FTIR spectrometer was used to obtain the mid-infrared spectra of the dried powdered leaves in the region of 400-4000 cm −1 . The region of 400-2000 cm −1 was analyzed with four different pattern recognition methods. Ini- tially, principal component analysis (PCA) was used to reduce the spectra to six principal components and these variables were used for linear discriminant analysis (LDA). The second technique used LDA on most discriminating wavenumber variables as searched by genetic algorithm using canonical variate approach for either 30 or 60 generations. SIMCA, which consisted of constructing an enclosure for each species using separate principal component models, was the third technique. Finally, multi-layer neural network with batch mode of backpropagation learning was used to classify the samples. The best results were obtained with GA of 60 gens. When LDA was run with the six wavenumbers chosen (1151, 1578, 1134, 609, 876 and 1227), 100% of the calibration spectra and 96.3% of the validation spectra were correctly assigned.

The application of pattern recognition techniques in metabolite fingerprinting of six differentPhyllanthusspp.

FTIR spectroscopy was used together with multivariate analysis to distinguish six different species ofPhyllanthus. Among these speciesP. niruri,P. debilisandP. urinariaare morphologically similar whereasP. acidus,P. emblicaandP. myrtifoliusare different. The FTIR spectrometer was used to obtain the mid-infrared spectra of the dried powdered leaves in the region of 400–4000 cm−1. The region of 400–2000 cm−1was analyzed with four different pattern recognition methods. Initially, principal component analysis (PCA) was used to reduce the spectra to six principal components and these variables were used for linear discriminant analysis (LDA). The second technique used LDA on most discriminating wavenumber variables as searched by genetic algorithm using canonical variate approach for either 30 or 60 generations. SIMCA, which consisted of constructing an enclosure for each species using separate principal component models, was the third technique. Finally, multi-layer neural network with batch mode of backpropagation learning was used to classify the samples. The best results were obtained with GA of 60 gens. When LDA was run with the six wavenumbers chosen (1151, 1578, 1134, 609, 876 and 1227), 100% of the calibration spectra and 96.3% of the validation spectra were correctly assigned.

Shape analysis for power signal cryptanalysis on secure components

Abstract This paper presents an application of pattern recognition techniques in reverse engineering for smart cards. The aim of the study is to design algorithms based on shape classification and to determine instructions executed on a chip as well as processed data sets. Information is extracted from the power consumption in order to recover secret information. Then geometrical features are determined and a syntactic analysis is achieved in order to recover secret algorithms and data. Some examples are given showing how code execution can be reversed on a recent secure component. These examples are essentially focused on instruction recovery but the algorithms also work on data recovery or on a combination of both instruction and data recovery.

Classification of the impact parameter in nucleus–nucleus collisions by a support vector machine method

One of the problems in the analysis of nucleus–nucleus collisions is to get information on the value of the impact parameter b. This work consists in the application of pattern recognition techniques aimed at associating values of b to groups of events. To this end, a support vector machine (SVM) classifier is adopted to analyse multifragmentation reactions. This method allows us to backtrace the values of b through a particular multidimensional analysis. The SVM classification consists of two main phases. In the first one, known as the training phase, the classifier learns to discriminate events that are generated by a model. In this case we used a classical molecular dynamics (CMD) model for the reaction: 58Ni + 48Ca at 25 A MeV. To check the classification of events in the second one, known as the test phase, what has been learned is tested on new events generated by the same model. These new results have been compared to those obtained through others techniques of backtracing the impact parameter (estimate function of b and PCA analysis). This approach better classifies central and peripheral collisions with respect to other techniques. We have finally performed the SVM classification on the experimental data measured by the NUCL-EX Collaboration with CHIMERA apparatus for the previous reaction and we show some results of the method.

Low-resolution pattern recognition—sorting triples in the FIRST database

The application of pattern recognition techniques to the data from the Faint Images of the Radio Sky at Twenty Centimeters Survey (FIRST) is explored. The methodology developed for the automatic classification of a particular type of radio-source morphology, the so-called "bent doubles" is described. While the procedures and techniques for doing supervised pattern recognition using accurately classified training sets, high-resolution images, and determinate shapes are well established, there exists little guidance for dealing with more ambiguous populations. The implications of low resolution for pattern recognition are discussed and methods for dealing with more ambiguous populations are presented. Bent doubles with three components are considered. With approximately 6% of the catalog sources being a component of a triple, visual classification suggests that approximately 6% of triples are bent. In addition to the problems associated with low resolution, the classification task is complicated by the lack of scale and orientation information, the chance superposition of sources, the very ambiguous nature of the visual classifications, and the considerable variation in bent morphology. For a bent or ambiguous or nonbent visual classification, classifiers are presented that produce results having up to an 80% recognition rate when looking at the top-ranked 16% of sources. The associated nonambiguous false positive rate varies between 14 and 18%. Given that recognition rates cannot be expected to be better than the accuracy of the training set, this is believed to be within a few percent of the maximum recognition rates attainable without an observationally verified training set. Decision tree and neural network results are compared and show, within associated errors, substantially equivalent results. Ranking of sources is demonstrated and the highest-ranked sources are in good agreement with visual classifications. In addition to providing determinate expressions for characterizing the target class, the resulting distributions suggest that the automated classifications constrain the target class in a better manner than the visual classifications.

AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing

The application of pattern recognition techniques, expert systems, artificial neural networks, fuzzy systems and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The paper outlines the most important steps of this process and introduces some new results with special emphasis on hybrid AI and multistrategy machine learning approaches. Agent-based (holonic) systems are highlighted as promising tools for managing complexity, changes and disturbances in production systems. Further integration of approaches is predicted.