Integrated Pattern Recognition Research Articles

Observation weights matching approach for causal inference

This study introduces a novel method integrating pattern recognition models with causal inference methodologies to adeptly identify and manage overlapping regions between treatment and control groups. Our approach, Observation Weights Matching (OWM), addresses the intrinsic challenges in observational studies—specifically, the fixed sample size and the lack of complete overlap in pretreatment variables. Through ensemble learning, OWM effectively retains examples within these critical overlapping regions, systematically generating weighted data distributions that aid in the precise identification of these instances. By prioritizing hard-to-classify observations and employing a novel metric of critical values for matched samples, our approach optimizes matching performance and provides greater robustness in causal analysis. Through empirical and simulation studies, we demonstrate OWM's notable advantage over traditional matching methods, enhancing causal inference in observational research. Furthermore, we show that OWM provides richer balance scores than propensity scores, ensuring unbiased estimations and advancing the field significantly.

Advancements towards the development artificial intelligence for sensory analysis: Integrating pattern recognition and signal processing in ATR-FTIR analysis of spirits

Spirits have been gaining acceptance by international consumers due to its distinct organoleptic profile, which provide each beverage with a characteristic sensation of the product (taste, smell, and color). However, it is important to establish differences by an analytical technique supported by artificial intelligence to demonstrate with mathematical solutions rather of being subjective such as human senses. To reduce the uncertainty associated to the sensory analysis, the present study shows as strategy the recognition of patterns of ATR-FTIR spectra of a variety of distilled beverages: Tequila 100% agave (n = 20), Mezcal (n = 20), Raicilla (n = 20), Aguardiente (n = 20), and Vodka (n = 20). All ATR-FTIR spectra present seven characteristic bands associated with methanol, ethanol, isoamyl alcohol, butanol, ethyl acetate, acetaldehyde, and furfural in beverages. Based on the analysis of the peak signals obtained from the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectra, the implementation of an Artificial Neural Network was proposed with the objective to classify each alcoholic beverage in its corresponding groups. Results showed that the artificial intelligence algorithm proposed permits to correct classify 100% of the samples being a great alternative strategy being innovative, fast, and of low-cost strengthening food quality analyses by machine learning decision-making.

Community engagement and data quality: best practices and lessons learned from a citizen science project on birdsong

Citizen Science (CS) is a research approach that has become popular in recent years and offers innovative potential for dialect research in ornithology. As the scepticism about CS data is still widespread, we analysed the development of a 3-year CS project based on the song of the Common Nightingale (Luscinia megarhynchos) to share best practices and lessons learned. We focused on the data scope, individual engagement, spatial distribution and species misidentifications from recordings generated before (2018, 2019) and during the COVID-19 outbreak (2020) with a smartphone using the ‘Naturblick’ app. The number of nightingale song recordings and individual engagement increased steadily and peaked in the season during the pandemic. 13,991 nightingale song recordings were generated by anonymous (64%) and non-anonymous participants (36%). As the project developed, the spatial distribution of recordings expanded (from Berlin based to nationwide). The rates of species misidentifications were low, decreased in the course of the project (10–1%) and were mainly affected by vocal similarities with other bird species. This study further showed that community engagement and data quality were not directly affected by dissemination activities, but that the former was influenced by external factors and the latter benefited from the app. We conclude that CS projects using smartphone apps with an integrated pattern recognition algorithm are well suited to support bioacoustic research in ornithology. Based on our findings, we recommend setting up CS projects over the long term to build an engaged community which generates high data quality for robust scientific conclusions.

Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating

Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model.

Performance Study of the First 2-D Prototype of Vertically Integrated Pattern Recognition Associative Memory

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high-luminosity Large Hadron Collider (HL-LHC) running conditions. Associative memory (AM)-based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available, and the speed performance must be improved over previous systems while maintaining a comparable number of patterns. The vertically integrated pattern recognition AM (VIPRAM) project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2-D prototype ( protoVIPRAM00 ) in which the AM building blocks were designed to be compatible with the 3-D integration. In this article, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3-D stacking. We also present performance boundary characterization of the chip under extreme conditions.

The landing safety prediction model by integrating pattern recognition and Markov chain with flight data

This paper aims to predict the landing state during the landing phase to ensure landing safety and reduce the accidents loss. Some past researches have demonstrated the landing phase is the most da...

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.

Design and testing of the first 2D Prototype Vertically Integrated Pattern Recognition Associative Memory

An associative memory-based track finding approach has been proposed for a Level 1 tracking trigger to cope with increasing luminosities at the LHC. The associative memory uses a massively parallel architecture to tackle the intrinsically complex combinatorics of track finding algorithms, thus avoiding the typical power law dependence of execution time on occupancy and solving the pattern recognition in times roughly proportional to the number of hits. This is of crucial importance given the large occupancies typical of hadronic collisions. The design of an associative memory system capable of dealing with the complexity of HL-LHC collisions and with the short latency required by Level 1 triggering poses significant, as yet unsolved, technical challenges. For this reason, an aggressive R&D program has been launched at Fermilab to advance state of-the-art associative memory technology, the so called VIPRAM (Vertically Integrated Pattern Recognition Associative Memory) project. The VIPRAM leverages emerging 3D vertical integration technology to build faster and denser Associative Memory devices. The first step is to implement in conventional VLSI the associative memory building blocks that can be used in 3D stacking; in other words, the building blocks are laid out as if it is a 3D design. In this paper, we report on the first successful implementation of a 2D VIPRAM demonstrator chip (protoVIPRAM00). The results show that these building blocks are ready for 3D stacking.

K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis

K-local hyperplane distance nearest neighbor (HKNN) classifier is an improved K-nearest neighbor (KNN) algorithm that has been successfully applied to pattern classification. This paper embeds the decision rule of HKNN classifier into the discriminant analysis model to develop a new feature extractor. The obtained feature extractor is called K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis (HOLDA), in which a regularization item is imposed on the original HKNN algorithm to obtain a more reliable distance metric. Based on this distance metric, the homo-class and hetero-class local scatters are characterized in HOLDA. By maximizing the ratio of the hetero-class local scatter to the homo-class local scatter, we obtain a subspace which is suitable for feature extraction and classification. In general, this paper provides a framework for building a feature extractor from the decision rule of a classifier. By this means, the feature extractor and classifier can be seamlessly integrated. Experimental results on four databases demonstrate that the integrated pattern recognition system is effective.

A New Concept of Vertically Integrated Pattern Recognition Associative Memory

Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990’s, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing fast pattern recognition for a track trigger, requiring about three orders of magnitude more associative memory patterns than what was used in the original CDF SVT. Scaling of current technologies is unlikely to satisfy the scientific needs of the future, and investments in transformational new technologies need to be made. In this paper, we will discuss a new concept of using the emerging 3D vertical integration technology to significantly advance the state-of-the-art for fast pattern recognition within and outside HEP. A generic R&D proposal [1] based on this new concept, with a few institutions involved, has recently been submitted to DOE with the goal to design and perform the ASIC engineering necessary to realize a prototype device. The progress of this R&D project will be reported in the future. Here we will only focus on the concept of this new approach. © 2011 Elsevier BV. Selection and/or peer-review under responsibility of the organizing committee for TIPP 2011.

IPRA-ENHANCING THE SENSING ABILITIES OF AMBIENT INTELLIGENCE

The ability of ambient intelligence to serve a person could be considerably enhanced, if intelligent devices would take into account the person's current cognitive and affective state. One primary source for state information are physiological measures. However, obtaining state information requires concertedly employing a multitude of different techniques. To facilitate the use of physiological signals as sources of state information and thereby allow improving ambient intelligence, we developed Ipra, an integrated pattern recognition approach which (a) employs all techniques necessary to appropriately analyze physiological data, (b) is broadly applicable, and (c) is easy to use even for nonexperts. This approach together with results of its first application are presented in this contribution.

Vibration based Fault Diagnosis of induction Motor

All types of rotating machines even new ones generate some level of vibrations. Small level of machine vibration is acceptable, however higher level and increasing trends are symptoms of abnormal machine performance. Therefore, machine vibration analysis becomes one of the important tools for machine fault identification. This paper presents an integrated pattern recognition method based on Euclidean distance measure for vibration diagnostic of machine health. The overall process is subdivided into stages of data acquisition, preprocessing, feature extraction and calculation of distance index. A computerized on-line monitoring system has been developed to record the frame vibrations, temperature, speed, line voltages and line currents from a three-phase induction motor for different operating conditions. The proposed method is suitable for both off-line and online applications. The method has been successfully tested for a large number of vibration data collected from the set of five identical test motors under different operating conditions. The test results establish the accuracy, effectiveness and reliability of the method.

A cellular automaton for the propagation of circular fronts and its applications

Artificial intelligence techniques are widely used to solve complicated manufacturing and fabrication process problems in today's industrial world. Cellular automata (CAs) have been applied numerous times as an evolvable technique to the solution of some of the aforementioned complicated problems with great success due to their inherent parallelism, structural locality, regularity and modularity. One of the most difficult problems that CAs dealt with in several cases (i.e. integrated circuit fabrication, pattern recognition and classification, computer aided design, machine vision, etc.) is the identification and the reproduction of circular fronts and shapes. In this paper a CA is used to propagate circular fronts. This CA has an extended Moore neighborhood and a relatively simple local rule based on Boolean operators. Simulation results of an integrated circuit fabrication process, namely chemical vapor deposition based on the proposed CA are also presented. These results were found to be in very good qualitative agreement with the experimental results published in the literature. Moreover, because of the CA's binary states and its local rule simplicity, the VLSI implementation of the proposed CA is straightforward.

Computer-aided interpretation of ECG for diagnostics

The paper presents an integrated pattern recognition method for the computer-aided interpretation of the ECG for diagnostic purposes. The overall process of computer-aided interpretation has been subdivided into the stages of preprocessing, feature extraction, parameter measurement, frontal plane axis (FPA) angle calculation and diagnostics. A novel method has been developed for the delineation of peaks with boundary detection up to an accuracy of 99·98%. Accuracies have been obtained up to 99·83% in QRS identification and 96% in P and T wave identification, using fuzzy criteria, respectively. A fast and efficient algorithm has been implemented for FPA calculation. A point scoring scheme for left and right ventricular hypertrophy has been used for the diagnostics. Through this integrated approach the output results are available in a concise tabular form for easy inspection by the cardiologists. The method is suitable for both off-line and on-line applications in cardiac care units. The method has been successfully tested for a large number of ECG waveforms. The test results establish the accuracy, effectiveness and reliability of the method.