Event Filtering Research Articles

Offline filter of data with abnormal high voltage at BESIII drift chamber

Stable operation of the detector is essential for high quality data taking in high energy physics experiments. But it is not easy to keep the detector always running stably, especially during data taking period in an environment with high beam-induced background. In the BESIII experiment, serious beam-related background can cause instability of the high voltages in the drift chamber, the innermost sub-detector. This could result in the decrease of gain and wrong dE/dx measurements. The relationship between the dE/dx measurement and the change in high voltage has been studied. To guarantee the data quality for physics study, an offline event filter algorithm has been developed to remove the data with abnormal high voltages of the drift chamber. After applying the event filter on the data set with serious high voltage problem, the events with wrong dE/dx measurement are removed effectively.

ELUQuant: event-level uncertainty quantification in deep inelastic scattering

We introduce a physics-informed Bayesian neural network with flow-approximated posteriors using multiplicative normalizing flows for detailed uncertainty quantification (UQ) at the physics event-level. Our method is capable of identifying both heteroskedastic aleatoric and epistemic uncertainties, providing granular physical insights. Applied to deep inelastic scattering (DIS) events, our model effectively extracts the kinematic variables x, Q 2, and y, matching the performance of recent deep learning regression techniques but with the critical enhancement of event-level UQ. This detailed description of the underlying uncertainty proves invaluable for decision-making, especially in tasks like event filtering. It also allows for the reduction of true inaccuracies without directly accessing the ground truth. A thorough DIS simulation using the H1 detector at HERA indicates possible applications for the future electron–ion collider. Additionally, this paves the way for related tasks such as data quality monitoring and anomaly detection. Remarkably, our approach effectively processes large samples at high rates.

Asynchronous adaptive event-triggered fault detection for delayed Markov jump neural networks: A delay-variation-dependent approach

This paper presents a delay-variation-dependent approach to fault detection of a discrete-time Markov jump neural network (MJNN) with a time-varying delay and mismatched modes. The goal is to detect the potential fault of delayed MJNNs by constructing an appropriate adaptive event-triggered and asynchronous H∞ filter. By choosing a delay-product-type Lyapunov–Krasovskii (L–K) functional with a delay-dependent matrix and exploiting some matrix polynomial inequalities, bounded real lemmas (BRLs) are obtained on the existence of suitable adaptive event generator and filters. These BRLs are dependent not only on the delay bounds but also on the delay variation rate. Simulation results are given to show the validity of the proposed theoretical method.

Denoising for Dynamic Vision Sensor Based on Augmented Spatiotemporal Correlation

To effectively denoise the output events of Dynamic Vision Sensor (DVS) in real-time, a denoising method based on augmented spatiotemporal correlation is proposed. In addition, this paper also presents a novel method to evaluate the performance of event denoising. By classifying the events and noise based on their spatiotemporal properties, we use the dynamic response characteristics of DVS under different cases to augment the spatiotemporal correlation, and construct two event filters to process different kinds of events. To benchmark event denoising, we exploit the low-latency of DVS to temporally synchronize raw event stream through motion compensation and obtain the ideal response probability of DVS to quantify the plausibility of events and measure the denoising accuracy. Experiments in various scenes validate that the denoising method is effective at reducing noise with high accuracy and little computational cost, which significantly outperforms the state-of-the-art methods. Moreover, the evaluation method can quantify the denoising accuracy and benchmark different denoising methods objectively and comprehensively.

Evaluation of accelerometer fitting position, sampling intervals and data editing techniques for measuring equine lying postures when stabled

The aim of this study was to determine whether a leg-mounted accelerometer could be used to reliably predict the time spent by horses in sternal and lateral recumbency. Six stabled horses were used for the study and each observation period lasted 11.5 hrs (2000–0730). Each evening, three horses were fitted with an accelerometer logger to the front-left (metacarpal) and back-left (metatarsal) legs, and each logger was configured to sample an acceleration value at the same rate (60 s, 20 s or 10 s) on all three axes (X, Y, Z). Observations were repeated for each horse at each sampling interval resulting in six datasets per horse. Ground-truth information was simultaneously recorded using a camera located in each stable and each accelerometer dataset was subsequently annotated with the ground-truth observations. Optimal thresholds for standing and lying (Y-axis) and variants of lying (sternal and lateral recumbency: Z-axis) were determined across all horses using the highest sum of sensitivity, specificity, positive predictive value, and negative predictive value across sampling intervals and accelerometer fitting sites. For standing and combined lying postures, mean predictive performance across all sampling intervals and fitting sites was > 98% for all performance measures. However, the best estimates of lying variants were achieved using data from the front leg where mean predictive performance for all measures was > 86% across all sampling intervals. The effect of short and potentially erroneous readings on the predictive ability of the thresholds was also examined using two event filters. The event filters converted predictions that occurred individually or in consecutive runs of ≤ 2 to the behaviour preceding them, and results were compared to unfiltered predictions. Linear regressions were used to evaluate predicted values with ground-truth observations for total lying time (overall lying time as well as total time in sternal and lateral recumbency), mean lying bout duration (combined lying bouts), and total lying bouts (switches between sternal and lateral recumbency). Herein we make recommendations on the use of event filters for generating reliable estimates (no statistical difference (P > 0.05) from 1 and 0 for slope and intercept, respectively, and R2 ≥ 0.90) of equine standing and lying postures across the tested sampling intervals and accelerometer fitting sites. This is the first study to demonstrate that leg-mounted accelerometers can be used to reliably predict the time spent by horses in sternal and lateral recumbency.

Event-based micro vibration measurement using phase correlation template matching with event filter optimization

Condition monitoring of mechanical systems based on vibration is one of the most effective and useful techniques for predictive maintenance. With advancements in image processing and artificial intelligence technologies, vision camera-based condition monitoring has come into the spotlight. An event camera, also called a dynamic vision sensor, operates on a principle different from that of traditional frame-based cameras. However, there is still no standardized method for event camera-based vibration measurement. This study proposes an event filter-based phase correlation template match (EF-PCTM) method, including its optimal design procedure, to measure micro-vibrations using an event camera. In this study, event filter is designed using an infinite impulse response filter and genetic algorithm, and a cost function is defined to improve the performance of EF-PCTM. The performance of the proposed method was verified by two experiments using a rotor system simulator: i) extraction of micro-vibration and ii) fault condition monitoring. The proposed image processing procedure using EF-PCTM makes it possible to measure micro-vibrations, which had been challenging to measure in the past, and calculate the operational deflection shape.

The light detection system of the ICARUS detector in the Short Baseline Neutrino program at Fermilab

The Short Baseline Neutrino (SBN) program at Fermilab is an extensive experimental programme aiming at searching for sterile neutrino(s) [1], whose existence is one of the fundamental open questions of neutrino physics. It employs three Liquid Argon Time Projection Chamber (LArTPC) detectors, called ICARUS, MicroBooNE and SBND, sampling the Booster Neutrino Beam (BNB) at different locations from its target. The SBN detectors, working near the Earth’s surface, are subjected to a substantial cosmic background, which can mimic genuine neutrino interactions. Thus, it is essential to distinguish the signals related to the neutrino beams from those induced by the cosmic rays. The light detection system is a vital part of LArTPCs, but its role is even more critical for surface-operating detectors like ICARUS. The ICARUS light detection system is based on 360 Photomultiplier Tubes (PMTs). Its main role is to provide an efficient trigger and contribute to the 3D reconstruction of detected events. The light detection system calibration and further trigger system improvements were performed for the final detector configuration during the detector commissioning at Fermilab. The trigger system effectively exploits the information given by the PMTs. To further increase that system’s efficiency in event filtering, an alternative method based on Convolutional Neural Network (CNN) has been developed. Simulation-based results show that this technique can reduce the cosmic background by up to 76% with a neutrino selection efficiency of 99%. However, to filter the real data cases, which are usually not identical to the simulated ones, this method was improved by applying Domain Adversarial Neural Network (DANN). The results prove that adversarial training through a DANN can alleviate the simulation bias, demonstrating the first successful application of DANN for CNN as an event classifier for a LArTPC.

Monte Carlo study of a 3D CZT spectroscopic-imager for scattering polarimetry

The measurement of the polarisation of the high-energy emission from cosmic sources has now become a key observational parameter for understanding the production mechanisms and the geometry of the regions involved. Therefore, a mandatory requirement for new instrumentation in this energy range will be to provide high sensitivity for polarimetric measurements. For several years our group has studied the performance of CdTe/CZT pixel spectrometers as scattering polarimeters, however in order to achieve the sensitivities required by the next generation of instrumentation in the 100 keV to MeV band, a promising solution is now offered by the development of spectrometers capable of three-dimensional spatial resolution (i.e. 3D spectroscopic imager) to be used as the focal plane of new high energy focusing optics. In the framework of the study of a new spaceborne telescope based on a broad-band Laue lens (AHEAD 2020), we report on the results of a Monte Carlo study devoted to optimise the configuration of a 3D CZT detector for scattering polarimetry, i.e. to optimise its modulation factor (Q). In particular, we present results on the dependence of Q on the detector geometrical configurations (thickness and pixel/voxel scales), and from various event filters that can be implemented.

Charged particle tracking in real-time using a full-mesh data delivery architecture and associative memory techniques

We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.

Suppression of top-up injection backgrounds with offline event filter in the BESIII experiment

Suppression of top-up injection backgrounds with offline event filter in the BESIII experiment

Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs

Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.

Hydraulic and nutrient removal performance of vegetated filter strips with engineered infiltration media for treatment of roadway runoff

As a new strategy for treating excess nutrients in roadway runoff, a self-filtering roadway could be accomplished by including engineered infiltration media within a vegetated filter strip (VFS) located in the roadway shoulder. However, nutrient removal performance will depend on the design to effectively infiltrate roadway runoff and the capacity of subsurface media to sequester or remove nutrients from infiltrated runoff. The objective of this study is to test hydraulic and nutrient removal performance of a roadside VFS over varied rainfall-runoff event sizes and filter widths. Two identical 1:1 scale physical models of roadway shoulders and embankments, one containing engineered media (Treatment model) and the other without (Control model), were tested with simulated rainfall and runoff from 1- and 2-lane roadways. Overall, 32 paired hydraulic experiments and 28 paired nutrient removal experiments were completed to assess performance across frequent and extreme rainfall-runoff events. The results indicate that scalability of performance with filter width varied by parameter. Runoff generation scaled predictably with filter width, as runoff generated close to the pavement and total infiltration increased with filter length. A 6 m-wide VFS containing the engineered media infiltrated all rainfall-runoff except during the most extreme storm events (1-h storms of 76.2 mm and 50.8 mm), where respectively 35% and 22% of rainfall-runoff did not infiltrate and left the system as surface runoff. A majority of phosphorus was retained within a 1.5 m filter while nitrate removal was not observed until 6 m. The Treatment model strongly outperformed the Control model with respect to nitrate (arithmetic mean ± standard deviation of 94 ± 6% reduction vs. 23 ± 64% increase, p < .001) and total nitrogen removal (80 ± 5% vs. 38 ± 23% reduction, p < .001) due to higher rates of microbially-mediated denitrification in the Treatment model. The two models performed comparably with regard to phosphorus reduction (84 ± 9% vs. 82 ± 12% reduction). A minimum 6 m filter width is recommended to ensure sufficient infiltration of runoff and nitrogen removal. Results of this study address uncertainty regarding nutrient removal performance of VFS in urban runoff applications and highlight a potential strategy for standardizing VFS performance across varied soil properties by including engineered media within the filter.

The marvellous life of a Haitian refugee: James Noël’s Belle merveille

This article examines the marvellous realism of two Haitian writers, past and present. Building on earlier schools of literary and socio-ethnographic thought, including Haitian indigenism, French surrealism and the Cuban writer Alejo Carpentier’s ‘marvellous real’, Jacques Stephen Alexis theorized marvellous realism at the first Congress of Black Writers and Artists in 1956. Some 60 years later, James Noël published Belle merveille, a novel that depicts a refugee who survives the earthquake of 2010 and embarks on a journey to understand his place among international aid groups that proliferate in the aftermath. The article suggests that Noël’s novel is both a tribute to and a creative rethinking of Alexis’s ideological commitment to the intersection of literary and social realism. It argues that by filtering events through the imaginary of the refugee, Noël interrogates the very categories of the marvellous and the real undergirding Alexis’s aesthetic and political project. After providing theoretical and historical context for Noël’s work, the article carries out close readings of Belle merveille to illuminate the ways in which its redeployment of marvellous realism delivers a critique of humanitarian aid.

The Event Detection System in the NEXT-White Detector.

This article describes the event detection system of the NEXT-White detector, a 5 kg high pressure xenon TPC with electroluminescent amplification, located in the Laboratorio Subterráneo de Canfranc (LSC), Spain. The detector is based on a plane of photomultipliers (PMTs) for energy measurements and a silicon photomultiplier (SiPM) tracking plane for offline topological event filtering. The event detection system, based on the SRS-ATCA data acquisition system developed in the framework of the CERN RD51 collaboration, has been designed to detect multiple events based on online PMT signal energy measurements and a coincidence-detection algorithm. Implemented on FPGA, the system has been successfully running and evolving during NEXT-White operation. The event detection system brings some relevant and new functionalities in the field. A distributed double event processor has been implemented to detect simultaneously two different types of events thus allowing simultaneous calibration and physics runs. This special feature provides constant monitoring of the detector conditions, being especially relevant to the lifetime and geometrical map computations which are needed to correct high-energy physics events. Other features, like primary scintillation event rejection, or a double buffer associated with the type of event being searched, help reduce the unnecessary data throughput thus minimizing dead time and improving trigger efficiency.

An automated fault detection system for communication networks and distributed systems

Automating fault detection in communication networks and distributed systems is a challenging process that usually requires the involvement of supporting tools and the expertise of system operators. Automated event monitoring and correlating systems produce event data that is forwarded to system operators for analyzing error events and creating fault reports. Machine learning methods help not only analyzing event data more precisely but also forecasting possible error events by learning from existing faults. This study introduces an automated fault detection system that assists system operators in detecting and forecasting faults. This system is characterized by the capability of exploiting bug knowledge resources at various online repositories, log events and status parameters from the monitored system; and applying bug analysis and event filtering methods for evaluating events and forecasting faults. The system contains a fault data model to collect bug reports, a feature and semantic filtering method to correlate log events, and machine learning methods to evaluate the severity, priority and relation of log events and forecast the forthcoming critical faults of the monitored system. We have evaluated the prototyping implementation of the proposed system on a high performance computing cluster system and provided analysis with lessons learned.

ACOUSTIC EMISSION TESTING OF FIBERGLASS PIPES AND FITTINGS

Experimental studies was carried out. The purpose was to develop a method to test fiberglass pipelines in operating mode. The acoustic emission method was chosen as the main method of nondestructive testing, and visual and dimensional inspection was chosen as an additional method. Acoustic parameters and acoustic emission properties of fiberglass pipes were determined. It was found that acoustic emission sensors can be installed at distances of up to 9-18 m from each other. A series of loading tests was carried out to refine the methodology. Every loading case was performed until leakage registration. In most cases, leakage occurred near the fillet at pressures of 2.2…3.0 from the working pressure, which indicates a large margin of safety for fiberglass pipes. It is confirmed that the acoustic emission method allows early defect detection. Based on the acoustic emission data, 4 main stages of fiberglass pipes degradation were identified. Visual and dimensional inspection was informative only at stages III – IV. Stage IV in most cases corresponds to the leakage. Even early stage of depressurization was registered as continuous acoustic emission. Signals with amplitudes exceeding 60…80 dB were registered at all loading stages. The location map became informative after filtering events by acoustic emission parameters. A methodology for the testing of fiberglass pipes and fittings in operating mode was developed. It contains, in contrast to the currently valid standards, specific numerical values of various quantities related to both the preparation and carrying out of acoustic emission testing and the classification of the identified sources of acoustic emission according to the degree of danger and allows to evaluate the residual life of fiberglass pipelines. The most informative parameter was the activity of acoustic emission; therefore, it is recommended to carry out loading without holding the pressure. It is planned to carry out additional experiments to clarify the mechanisms of fracture acting at each of the 4 identified stages of degradation.

Digital Transformation Enables Automated Real-Time Torque-and-Drag Modeling

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199670, “Digital Transformation Strategy Enables Automated Real-Time Torque-and-Drag Modeling,” by Dingzhou Cao, Occidental Petroleum; Don Hender, SPE, IPCOS; and Sam Ariabod, Apex Systems, et al., prepared for the 2020 IADC/SPE International Drilling Conference, Galveston, Texas, 3-5 March. The paper has not been peer reviewed. Automated real-time torque-and-drag (RT-T&amp;D) analysis compares real-time measurements with evergreen models to monitor and manage downhole wellbore friction, improving drilling performance and safety. Enabling RT-T&amp;D modeling with contextual well data, rig-state detection, and RT-interval event filters poses significant challenges. The complete paper presents a solution that integrates a physics-based T&amp;D stiff/soft string model with a real-time drilling (RTD) analytics system using a custom-built extract, transform, and load (ETL) translator and digital-transformation applications to automate the T&amp;D modeling work flow. Methodology A T&amp;D representational state transfer (REST) application program interface (API) was integrated with an RTD analytics system capable of receiving and processing both real-time (hookload, torque, and rig-state) and digitized (drillstring and casing components, trajectory profiles, and mud-property) well data across multiple platforms. This strategy consists of four parts: Digital transformation apps, ETL, and translator Physics-based stiff/soft string T&amp;D model API Pre-existing data infrastructure RTD analytics system The data-flow architecture reveals a flexible design in the sense that it can accommodate different types of T&amp;D models or any other physics-based REST API models (e.g., drillstring buckling or drilling hydraulics) and can be accessed offline for prejob/post-job planning. Drilling engineers can also leverage the RTD systems’ historical database to perform recalculations, comparative analysis, and friction calibrations. The RT-T&amp;D model also can be deployed in a cloud environment to ensure that horizontal scalability is achieved.

Event-Based Object Detection and Tracking for Space Situational Awareness

In this work, we present an optical space imaging dataset using a range of event-based neuromorphic vision sensors. The unique method of operation of event-based sensors makes them ideal for space situational awareness (SSA) applications due to the sparseness inherent in space imaging data. These sensors offer significantly lower bandwidth and power requirements making them particularly well suited for use in remote locations and space-based platforms. We present the first publicly-accessible event-based space imaging dataset including recordings using sensors from multiple providers, greatly lowering the barrier to entry for other researchers given the scarcity of such sensors and the expertise required to operate them for SSA applications. The dataset contains both day time and night time recordings, including simultaneous co-collections from different event-based sensors. Recorded at a remote site, and containing 572 labeled targets with a wide range of sizes, trajectories, and signal-to-noise ratios, this real-world event-based dataset represents a challenging detection and tracking task that is not readily solved using previously proposed methods. We propose a highly optimized and robust feature-based detection and tracking method, designed specifically for SSA applications, and implemented via a cascade of increasingly selective event filters. These filters rapidly isolate events associated with space objects, maintaining the high temporal resolution of the sensors. The results from this simple yet highly optimized algorithm on the space imaging dataset demonstrate robust high-speed event-based detection and tracking which can readily be implemented on sensor platforms in space as well as terrestrial environments.

OBSERVASI SELFIE PARA PEKERJA FAST MOVING CUNSUMER GOODS SAAT PANDEMI COVID-19

Taking selfies for documentation reasons mentioned by Rettberg (2014) and Chae (2017). Likewise, this study reports documentation and archiving, which is part of communicating self-motives. Additionally, this research draws attention to general digital effects such as special event filters and seasonal filters used with selfies to signal specific locations or events. In their research, Senft and Baym (2015) noted that selfies can be used as an empowering tool to overcome political problems. This research shows how selfies are used as an empowering tool to educate Saudi women, particularly about their legal rights. The use of selfies as an educational tool in the context of public problems has not been reported in previous research.

A crowdsourced set of curated structural variants for the human genome.

A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is more challenging. In this study, we manually curated 1235 SVs, which can ultimately be used to evaluate SV callers or train machine learning models. We developed a crowdsourcing app-SVCurator-to help GIAB curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy. SVCurator displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. 'Expert' curators were 93% concordant with each other, and 37 of the 61 curators had at least 78% concordance with a set of 'expert' curators. The curators were least concordant for complex SVs and SVs that had inaccurate breakpoints or size predictions. After filtering events with low concordance among curators, we produced high confidence labels for 935 events. The SVCurator crowdsourced labels were 94.5% concordant with the heuristic-based draft benchmark SV callset from GIAB. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies.