Event-based Classification Research Articles

Spatial variability of rainfall: deciphering flood characteristics and model precision

ABSTRACT This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogeneous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR’s role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy.

DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding.

Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of pedestrian spaces. In response, we introduce the DELTA dataset, designed to improve the analysis and mapping of pedestrian zones, thereby filling the critical need for sidewalk-centric multimodal datasets. The DELTA dataset was collected in a single urban setting using a custom-designed modular multi-sensing e-scooter platform encompassing high-resolution and synchronized audio, visual, LiDAR, and GNSS/IMU data. This assembly provides a detailed, contextually varied view of urban pedestrian environments. We developed three distinct pedestrian route segmentation models for various sensors-the 4K camera, stereocamera, and LiDAR-each optimized to capitalize on the unique strengths and characteristics of the respective sensor. These models have demonstrated strong performance, with Mean Intersection over Union (IoU) values of 0.84 for the reflectivity channel, 0.96 for the 4K camera, and 0.92 for the stereocamera, underscoring their effectiveness in ensuring precise pedestrian route identification across different resolutions and sensor types. Further, we explored audio event-based classification to connect unique soundscapes with specific geolocations, enriching the spatial understanding of urban environments by associating distinctive auditory signatures with their precise geographical origins. We also discuss potential use cases for the DELTA dataset and the limitations and future possibilities of our research, aiming to expand our understanding of pedestrian environments.

A monitoring-based classification system for risk management of bridge scour

Flood-induced scour is the principal cause of bridge failure worldwide. Nevertheless, bridge scour risk assessment is still based on visual inspections, which may be affected by human errors and cannot be performed during flood peaks. This problem, together with the simplifications in scour estimation, might cause misclassification of the bridge scour risk, unnecessary bridge closures or recourse to avoidable scour mitigation measures. Structural-health-monitoring (SHM) systems allow overcoming these issues, providing bridge managers with more accurate information about scour, thus supporting them in taking optimal management decisions. This paper illustrates the development of an SHM- and event-based classification system for bridge scour management, which extends and complements current risk-rating procedures by incorporating the various sources of uncertainty characterising the scour estimation and information from different sensors. The proposed system is based on a probabilistic framework for scour risk estimation and can be used to provide transport agencies with real-time scour risk classification of bridges under a heavy-flood event. The system is applied to a bridge network located in south-west Scotland in a heavy-flood scenario, and information from heterogeneous sources is considered for updating the knowledge of scour. It is shown that integrating scour-monitoring data leads to an overall uncertainty reduction that is reflected in more accurate scour risk classification, thus helping transport agencies in prioritising bridge inspections and risk mitigation actions.

VMV-GCN: Volumetric Multi-View Based Graph CNN for Event Stream Classification

Event cameras can perceive pixel-level brightness changes to output asynchronous event streams, and have notable advantages in high temporal resolution, high dynamic range and low power consumption for challenging vision tasks. To apply existing learning models on event data, many researchers integrate sparse events into dense frame-based representations which can work with convolutional neural networks directly. Although these works achieve high performance on event-based classification, their models need lots of parameters to process dense event frames which do not fit with the sparsity of event data. To utilize the sparse nature of events, we propose a voxel-wise graph learning model ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VMV-GCN</i> ) for spatio-temporal feature learning on event streams. Specifically, we design the volumetric multi-view fusion module ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VMVF</i> ) to extract spatial and temporal information from views of voxelized event data. Then we take representative event voxels as vertices and use a novel dual-graph construction strategy to connect them. By aggregating neighborhood information based on relationships of vertices, the proposed dynamic neighborhood feature learning module ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DNFL</i> ) can capture discriminative spatio-temporal features on dynamically updated graphs. Experiments show that our method achieves state-of-the-art performance with low model complexity on event-based classification tasks, such as object classification and action recognition.

Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning.

Ca2+ sparks are the elementary Ca2+ release events in cardiomyocytes, altered properties of which lead to impaired Ca2+ handling and finally contribute to cardiac pathology under various diseases. Despite increasing use of machine-learning algorithms in deciphering the content of biological and medical data, Ca2+ spark images and data are yet to be deeply learnt and analyzed. In the present study, we developed a deep residual convolutional neural network method to detect Ca2+ sparks. Compared to traditional detection methods with arbitrarily defined thresholds to distinguish signals from noises, our new method detected more Ca2+ sparks with lower amplitudes but similar spatiotemporal distributions, thereby indicating that our new algorithm detected many very weak events that are usually omitted when using traditional detection methods. Furthermore, we proposed an event-based logistic regression and binary classification model to classify single cardiomyocytes using Ca2+ spark characteristics, which to date have generally been used only for simple statistical analyses and comparison between normal and diseased groups. Using this new detection algorithm and classification model, we succeeded in distinguishing wild type (WT) vs RyR2-R2474S± cardiomyocytes with 100% accuracy, and vehicle vs isoprenaline-insulted WT cardiomyocytes with 95.6% accuracy. The model can be extended to judge whether a small number of cardiomyocytes (and so the whole heart) are under a specific cardiac disease. Thus, this study provides a novel and powerful approach for the research and application of calcium signaling in cardiac diseases.

Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data

Forestry is a complex economic sector which is relying on resource and process monitoring data. Most of the forest operations such as planting and harvesting are supported by the use of tools and machines, and their monitoring has been traditionally done by the use of pen-and-paper time studies. Nevertheless, modern data collection and analysis methods involving different kinds of platforms and machine learning techniques have been studied lately with the aim of easing the data management process. By their outcomes, improvements are still needed to reach a close to 100% activity recognition, which may depend on several factors such as the type of monitored process and the characteristics of the signals used as inputs. In this paper, we test, thought a case study on mechanized pit-drilling operations, the potential of digital signal processing techniques combined with Artificial Neural Networks (ANNs) in improving the event-based classification accuracy in the time domain. Signal processing was implemented by the means of median filtering of triaxial accelerometer data (window sizes of 3, 5, and up to 21 observations collected at 1 Hz) while the ANNs were subjected to the regularization hyperparameter’s tunning. An acceleration signal processed by a median filter with a window size of 3 observations and fed into an ANN set to learn and generalize by a regularization parameter of α = 0.01 has been found to be the best strategy in improving the event-based classification accuracy (improvements of 1% to 8% in classification accuracy depending on the type of event in question). Improvement of classification accuracy by signal filtering and ANN tuning may depend largely on the type of monitored process and its outcomes in terms of event duration; therefore, other monitoring applications may need particular designs of signal processing and ANN tuning.

AMAE: Adaptive Motion-Agnostic Encoder for Event-Based Object Classification

Event cameras, with low power consumption, high temporal resolution, and high dynamic range, have been used increasingly in computer vision. These superior characteristics enable event cameras to perform low-energy and high-response object classification tasks in challenging scenarios. Nevertheless, specific encoding methods for event-based classification are required owing to the unconventional output of event cameras. Existing event-based encoding methods have focused on extracting semantic and motion information in event signals. However, two main problems exist in these methods: (i) the motion information of event signals leads to mispredictions by the classifiers. (ii) effective evaluation methods to validate the motion robustness of event-based classification models have yet to be proposed. In this work, we introduce an adaptive motion-agnostic encoder for event streams to address the first problem. The proposed encoder would allow us to extract indistinguishable semantic information from an object in different motion circumstances. In addition, we propose a novel motion inconsistency evaluation method to assess the motion robustness of the classification models. We apply our method to several benchmark datasets and evaluate it using motion consistency and inconsistency testing methods. Classification performance shows that our proposed encoder outperforms state-of-the-art methods by a large margin.

Skill of a Ceiling and Visibility Local Ensemble Prediction System (LEPS) according to Fog-Type Prediction at Paris-Charles de Gaulle Airport

Abstract A specific event, called a low-visibility procedure (LVP), has been defined when visibility is under 600 m and/or the ceiling is under 60 m at Paris-Charles de Gaulle Airport, Paris, France, to ensure air traffic safety and to reduce the economic issues related to poor visibility conditions. The Local Ensemble Prediction System (LEPS) has been designed to estimate LVP likelihood in order to help forecasters in their tasks. This work evaluates the skill of LEPS for each type of LVP that takes place at the airport area during five winter seasons from 2002 to 2007. An event-based classification reveals that stratus base lowering, advection, and radiation fogs make up for 78% of the LVP cases that occurred near the airport during this period. This study also demonstrates that LEPS is skillful on these types of event for short-term forecasts. When the ensemble runs start with initialized LVP events, the prediction of advection fogs is as skillful as the prediction of radiation fog events and stratus base lowering. At 3 and 6 h before the runs where LVP events were initialized, LEPS still shows positive skill for radiation fog events and stratus base lowering cases.

Pattern-Based Opinion Mining for Stock Market Trend Prediction

Stock market reports in on-line news are widely used by amateurs to make quick investment decisions. Financial analysts often give opinions about trends of stock markets based on past and present economic event indicators. These opinions commonly appear in text form and are abundant over the Internet. It is tedious and time consuming for users to browse through such text manually let alone to understand the embedded opinions. To overcome this shortcoming, automatic trend predication methods have been proposed. Under conventional methods, reports are represented using bag of words and trend prediction is treated as a 3-way trend classification problem, i.e. trend as 'up', 'down' or 'stable'. In this paper, we propose a new pattern-based opinion mining method for market trend predication. Experiments show that (1) pattern-based classification is more effective than its word-based counterpart for feature representation; and (2) opinion mining outperforms event-based classification for trend predication. The task of opinion mining gets more difficult when the users are exposed to opinions from more than one analyst. The question becomes whose opinions should he/she trust? This lays down our second research objective, i.e. to study different opinion incorporation strategies. Intuitively, one would trust the opinion supported by the majority. However, we show that on the contrary, the user is better off trusting the most credible analyst.

Gain-Related Recovery

With his ‘event-based classification’, the late Peter Birks provided a coherent and principled framework for the civil law of obligations. In this article, ‘restitution’ is considered in the context of his taxonomy and two main propositions are advanced. The first proposition is that ‘gain-related recovery’ is the most adequate description of restitution as a legal response. The second proposition is that the term ‘restitution’ qualifies the mechanism of taking away the defendant's benefit without any direct reference to the immediate purpose of the award and therefore has both a ‘giving up’ and a ‘giving back’ function. Those theories which distinguish these two functions, it is argued, confuse the mechanism of the right realized with the aim of the response awarded. Restitution as a legal response can be applied to reach different aims, such as giving back wealth mistakenly transferred by the claimant to the defendant or surrendering profits wrongfully obtained by the defendant. Yet, the right to restitution which is realized in court operates always in the same fashion: it transfers to the claimant wealth up to the level of the defendant's benefit. This interpretation tackles the criticism of those favourable to ‘disgorgement’, that is ‘giving up’, as an independent legal response and shows that their concerns are justified, but do not affect the consistency of the event-based classification.

Expert system for classification and analysis of power system events

This paper presents an expert system that is able to classify different types of power system events to the underlying causes (i.e., events) and offer useful information in terms of power quality. The expert system uses the voltage waveforms and distinguishes the different types of voltage dips (fault-induced, transformer saturation, induction motor starting) as well as interruptions (nonfault, fault-induced). A method for event-based classification is used, where a segmentation algorithm is first applied to divide waveforms into several possible events. The expert system is tested using real measurements and the results show that the system enables fast and accurate analysis of data from power quality monitors.

Expert System for Classification and Analysis of Power System Events

This paper presents an expert system that is able to classify different types of power system events to the underlying causes (i.e., events) and offer useful information in terms of power quality. The expert system uses the voltage waveforms and distinguishes the different types of voltage dips (fault-induced, transformer saturation, induction motor starting), as well as interruptions (nonfault, fault induced). A method for event-based classification is used, where a segmentation algorithm is first applied to divide waveforms into several possible events. The expert system is tested using real measurements and the results show that the system enables fast and accurate analysis of data from power quality monitors.