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Journal articles on the topic 'Event-based models'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

GUO, Zheng-hu, Zhong-yu CHEN, and Ji-chang ZHANG. "Event-based merging of partial behavior models." Journal of Computer Applications 30, no. 1 (2010): 266–69. http://dx.doi.org/10.3724/sp.j.1087.2010.00266.

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2

Padoan, Simone A. "Extreme dependence models based on event magnitude." Journal of Multivariate Analysis 122 (November 2013): 1–19. http://dx.doi.org/10.1016/j.jmva.2013.07.009.

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3

Kim, Soo-Jong, and Yong-Joo Chung. "Sound Event Detection based on Segmental Models." Journal of Korean Institute of Information Technology 21, no. 6 (2023): 93–100. http://dx.doi.org/10.14801/jkiit.2023.21.6.93.

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4

Lu, Jing, and Vincent Ng. "Span-Based Event Coreference Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (2021): 13489–97. http://dx.doi.org/10.1609/aaai.v35i15.17591.

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Motivated by the recent successful application of span-based models to entity-based information extraction tasks, we investigate span-based models for event coreference resolution, focusing on determining (1) whether the successes of span-based models of entity coreference can be extended to event coreference; (2) whether exploiting the dependency between event coreference and the related subtask of trigger detection; and (3) whether automatically computed entity coreference information can benefit span-based event coreference resolution. Empirical results on the standard evaluation dataset pr

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5

XIA, Wei, Yi-Ping YAO, Xiao-Dong MU, and Lin LIU. "Parallel Model Checking for Discrete Event Simulation Models Based on Event Graphs." Journal of Software 23, no. 6 (2012): 1429–43. http://dx.doi.org/10.3724/sp.j.1001.2012.04047.

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6

Correcher, A., E. García, F. Morant, and E. Quiles. "Intermittent failure diagnosis based on discrete event models." IFAC Proceedings Volumes 37, no. 18 (2004): 147–52. http://dx.doi.org/10.1016/s1474-6670(17)30737-1.

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7

Moreira, Marcos V., and Jean-Jacques Lesage. "Fault diagnosis based on identified discrete-event models." Control Engineering Practice 91 (October 2019): 104101. http://dx.doi.org/10.1016/j.conengprac.2019.07.019.

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8

Trenouth, William R., and Bahram Gharabaghi. "Event-based soil loss models for construction sites." Journal of Hydrology 524 (May 2015): 780–88. http://dx.doi.org/10.1016/j.jhydrol.2015.03.010.

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9

Jansen, Aren, and Partha Niyogi. "Point process models for event-based speech recognition." Speech Communication 51, no. 12 (2009): 1155–68. http://dx.doi.org/10.1016/j.specom.2009.05.008.

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10

Delignières, Didier, Kjerstin Torre, and Loïc Lemoine. "Fractal models for event-based and dynamical timers." Acta Psychologica 127, no. 2 (2008): 382–97. http://dx.doi.org/10.1016/j.actpsy.2007.07.007.

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11

Luh, C. J., and B. P. Zeigler. "Abstracting event-based control models for high autonomy systems." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 1 (1993): 42–54. http://dx.doi.org/10.1109/21.214766.

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12

Letier, Emmanuel, Jeff Kramer, Jeff Magee, and Sebastian Uchitel. "Fluent temporal logic for discrete-time event-based models." ACM SIGSOFT Software Engineering Notes 30, no. 5 (2005): 70–79. http://dx.doi.org/10.1145/1095430.1081719.

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13

Philips, P. P. H. H., W. P. M. H. Heemels, H. A. Preisig, and P. P. J. Van Den Bosch. "Control of quantized systems based on discrete event models." International Journal of Control 76, no. 3 (2003): 277–94. http://dx.doi.org/10.1080/0020717031000067402.

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14

Zhang, Yang, and Victor S. Sheng. "Fog-enabled Event Processing Based on IoT Resource Models." IEEE Transactions on Knowledge and Data Engineering 31, no. 9 (2019): 1707–21. http://dx.doi.org/10.1109/tkde.2018.2867504.

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15

Cook, Jonathan E., and Alexander L. Wolf. "Discovering models of software processes from event-based data." ACM Transactions on Software Engineering and Methodology 7, no. 3 (1998): 215–49. http://dx.doi.org/10.1145/287000.287001.

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16

Chaudhari, Dipak L., and Om P. Damani. "Generating Hierarchical State Based Representation From Event-B Models." Electronic Notes in Theoretical Computer Science 280 (December 2011): 35–46. http://dx.doi.org/10.1016/j.entcs.2011.11.016.

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17

Danzl, Per, João Hespanha, and Jeff Moehlis. "Event-based minimum-time control of oscillatory neuron models." Biological Cybernetics 101, no. 5-6 (2009): 387–99. http://dx.doi.org/10.1007/s00422-009-0344-3.

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18

Ajmal, Muhammad, Muhammad Waseem, Jae-Hyun Ahn, and Tae-Woong Kim. "Improved Runoff Estimation Using Event-Based Rainfall-Runoff Models." Water Resources Management 29, no. 6 (2015): 1995–2010. http://dx.doi.org/10.1007/s11269-015-0924-z.

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19

Peng, Yansong, Yueyi Zhang, Peilin Xiao, Xiaoyan Sun, and Feng Wu. "Better and Faster: Adaptive Event Conversion for Event-Based Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2056–64. http://dx.doi.org/10.1609/aaai.v37i2.25298.

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Event cameras are a kind of bio-inspired imaging sensor, which asynchronously collect sparse event streams with many advantages. In this paper, we focus on building better and faster event-based object detectors. To this end, we first propose a computationally efficient event representation Hyper Histogram, which adequately preserves both the polarity and temporal information of events. Then we devise an Adaptive Event Conversion module, which converts events into Hyper Histograms according to event density via an adaptive queue. Moreover, we introduce a novel event-based augmentation method S

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20

Pinna, G. Michele, and Andrea Saba. "An Event Based Semantics of P Systems." Scientific Annals of Computer Science XVIII (June 5, 2008): 99–127. https://doi.org/10.5281/zenodo.12719941.

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Membrane systems have many similarities with classical concurrency models. In particular notions like parallelism, causality and concurrency seem to belong to membrane computing, though they are not yet regarded as central or cornerstone notions. Recently the interest in comparing membrane systems and other models for concurrency has grown. In this paper we propose a translation of membrane system into zero safe nets and then we show how to associate an event automaton to the 1-unfolding of these nets. Thus we propose an event based view of computations of a membrane system.

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21

Li, Yanhao, and Wei Liu. "Sudden Event Prediction Based on Event Knowledge Graph." Applied Sciences 12, no. 21 (2022): 11195. http://dx.doi.org/10.3390/app122111195.

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Event prediction is a knowledge inference problem that predicts the consequences or effects of an event based on existing information. Early work on event prediction typically modeled the event context to predict what would happen next. Moreover, the predicted outcome was often singular. These studies had difficulty coping with both the problems of predicting sudden events in unknown contexts and predicting outcomes consisting of multiple events. To address these two problems better, we present the heterogeneous graph event prediction model (HGEP), which is based on an event knowledge graph. T

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22

Bhattacharjya, Debarun, Tian Gao, Dharmashankar Subramanian, and Xiao Shou. "Score-Based Learning of Graphical Event Models with Background Knowledge Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 12189–97. http://dx.doi.org/10.1609/aaai.v37i10.26437.

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Graphical event models (GEMs) are representations of temporal point process dynamics between different event types. Many real-world applications however involve limited event stream data, making it challenging to learn GEMs from data alone. In this paper, we introduce approaches that can work together in a score-based learning paradigm, to augment data with potentially different types of background knowledge. We propose novel scores for learning an important parametric class of GEMs; in particular, we propose a Bayesian score for leveraging prior information as well as a more practical simplif

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23

Weijters, A. J. M. M., and W. M. P. van der Aalst. "Rediscovering workflow models from event-based data using little thumb." Integrated Computer-Aided Engineering 10, no. 2 (2003): 151–62. http://dx.doi.org/10.3233/ica-2003-10205.

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24

Kotevski, Zoran. "SIMULATING FSPN MODELS USING PROCESS-BASED DISCRETE -EVENT SIMULATION LANGUAGE." Acta Simulatio 4, no. 2 (2018): 1–11. http://dx.doi.org/10.22306/asim.v4i2.42.

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25

Bates, Peter C. "Debugging heterogeneous distributed systems using event-based models of behavior." ACM Transactions on Computer Systems 13, no. 1 (1995): 1–31. http://dx.doi.org/10.1145/200912.200913.

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26

Shi, Dawei, Robert J. Elliott, and Tongwen Chen. "Event-based state estimation of discrete-state hidden Markov models." Automatica 65 (March 2016): 12–26. http://dx.doi.org/10.1016/j.automatica.2015.11.023.

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27

Huang, Jiarao, Dawei Shi, and Tongwen Chen. "Energy-based event-triggered state estimation for hidden Markov models." Automatica 79 (May 2017): 256–64. http://dx.doi.org/10.1016/j.automatica.2017.02.012.

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28

Letier, Emmanuel, Jeff Kramer, Jeff Magee, and Sebastian Uchitel. "Deriving event-based transition systems from goal-oriented requirements models." Automated Software Engineering 15, no. 2 (2008): 175–206. http://dx.doi.org/10.1007/s10515-008-0027-7.

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29

Nguyen, Hoang, Marlon Dumas, Arthur H. M. ter Hofstede, Marcello La Rosa, and Fabrizio Maria Maggi. "Stage-based discovery of business process models from event logs." Information Systems 84 (September 2019): 214–37. http://dx.doi.org/10.1016/j.is.2019.05.002.

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30

Bates, Peter. "Debugging heterogeneous distributed systems using event-based models of behavior." ACM SIGPLAN Notices 24, no. 1 (1989): 11–22. http://dx.doi.org/10.1145/69215.69217.

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31

Chen, Meirong, Chaoyong Peng, and Zhenpeng Cheng. "Earthquake Event Recognition on Smartphones Based on Neural Network Models." Sensors 22, no. 22 (2022): 8769. http://dx.doi.org/10.3390/s22228769.

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Using sensors embedded in smartphones to study earthquake early warning (EEW) technology can effectively reduce the high construction and maintenance costs of traditional EEW systems. However, due to the impact of human activities, it is very difficult to accurately detect seismic events recorded on mobile phones. In this paper, to improve the detection accuracy of earthquakes on mobile phones, we investigated the suitability of different types of neural network models in seismic event detection. Firstly, we collected three-component acceleration records corresponding to human activities in va

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32

De Landtsheer, Renaud, Emmanuel Letier, and Axel van�Lamsweerde. "Deriving tabular event-based specifications from goal-oriented requirements models." Requirements Engineering 9, no. 2 (2004): 104–20. http://dx.doi.org/10.1007/s00766-004-0189-1.

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33

Stephens, C. M., F. M. Johnson, and L. A. Marshall. "Implications of future climate change for event-based hydrologic models." Advances in Water Resources 119 (September 2018): 95–110. http://dx.doi.org/10.1016/j.advwatres.2018.07.004.

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34

Koné, Oumar, Christian Artigues, Pierre Lopez, and Marcel Mongeau. "Event-based MILP models for resource-constrained project scheduling problems." Computers & Operations Research 38, no. 1 (2011): 3–13. http://dx.doi.org/10.1016/j.cor.2009.12.011.

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35

Wang, Jianhua, Junhe Liu, Feng Lin, Jing Zhao, Yongbing Long, and Yubin Lan. "A multiple-pattern complex event matching model based on merge sharing for massive event streams." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 04 (2021): 2150025. http://dx.doi.org/10.1142/s1793962321500252.

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Quickly matching the related primitive events for multiple complex events from the massive event streams usually faces with a great challenge due to the single-pattern characteristics of the existing complex event matching models. Aiming to solve the problem, a multiple-pattern complex event matching model based on merge sharing is proposed in this paper. The achievement of the paper lies in the fact that a multiple-pattern complex event matching model based on merge sharing is presented to successfully realize the quick matching of related primitive events for multiple complex events from the

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36

Wu, Chun-Yi, and Sheng-Yu Lin. "Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan." Water 14, no. 5 (2022): 717. http://dx.doi.org/10.3390/w14050717.

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While multi-year and event-based landslide inventories are both commonly used in landslide susceptibility analysis, most areas lack multi-year landslide inventories, and the analysis results obtained from the use of event-based landslide inventories are very sensitive to the choice of event. Based on 24 event-based landslide inventories for the Shihmen watershed from 1996 to 2015, this study established five event-based single landslide susceptibility models employing logistic regression, random forest, support vector machine, kernel logistic regression, and gradient boosting decision tree met

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37

Wang, Mingming, Jiangshui Zhang, Yibing Cao, Shenghui Li, and Minjie Chen. "A Study on a Spatiotemporal Entity-Based Event Data Model." ISPRS International Journal of Geo-Information 13, no. 10 (2024): 360. http://dx.doi.org/10.3390/ijgi13100360.

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An event is an important medium for recording, expressing, and understanding the real world. Additionally, a data model can provide a digital and structured description method for the real world. Therefore, studying event data models is highly important for describing the real world. By analyzing the representational categories of the existing event data models, the representation of existing event models was found to have different emphases and not be sufficiently balanced, and the universality and comprehensiveness need to be improved. Therefore, based on the advantages of the ontological ev

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38

Blas, Maria J., Mateo Toniolo, and Silvio Gonnet. "Enhancing Routed DEVS Models with Event Tracking." Journal of the Brazilian Computer Society 31, no. 1 (2025): 412–25. https://doi.org/10.5753/jbcs.2025.4613.

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The Routed Discrete Event System Specification (RDEVS) is a modular and hierarchical Modeling and Simulation (M&S) formalism based on the Discrete Event System Specification (DEVS) formalism that provides a set of design models for dealing with routing problems over DEVS. At the formal level, RDEVS models (as DEVS models themselves) are defined mathematically. However, software implementations of both formalisms are based on an object-oriented paradigm. Furthermore, at the implementation design level, the RDEVS formalism is represented by a conceptual model that uses DEVS simulators as exe

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39

Kemp, David, and Guna Hewa Alankarage. "Benchmarking Three Event-Based Rainfall-Runoff Routing Models on Australian Catchments." Hydrology 10, no. 6 (2023): 131. http://dx.doi.org/10.3390/hydrology10060131.

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In the field of hydrology, event-based models are commonly used for flood-flow prediction in catchments, for use in flood forecasting, flood risk assessment, and infrastructure design. The models are simplistic, as they do not consider longer-term catchment processes such as evaporation and transpiration. This paper examines the relative performance of two widely used models, the American HEC-HMS model, the Australian RORB model, and a newer model, the RRR model. The evaluation is conducted on four case study catchments in Australia. The first two models, HEC-HMS and RORB, do not include basef

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40

Brzychczy, Edyta. "Process Modelling Based on Event Logs." Multidisciplinary Aspects of Production Engineering 1, no. 1 (2018): 385–92. http://dx.doi.org/10.2478/mape-2018-0049.

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Abstract Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, E

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41

Chen, Jianxun, Peng Chen, and Xuxu Wu. "Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning." Applied Sciences 13, no. 17 (2023): 9500. http://dx.doi.org/10.3390/app13179500.

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Regarding the scarcity of annotated data for existing event extraction tasks and the insufficient semantic mining of event extraction models in the Chinese domain, this paper proposes a generative joint event extraction model to improve existing models in two aspects. Firstly, it utilizes the content generation capability of ChatGPT to generate annotated data corpora for event extraction tasks and trains the model using supervised learning methods adapted to downstream tasks. Secondly, explicit entity markers and event knowledge are added to the text to construct generative input templates, en

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42

Ali, Sabrina, Ataur Rahman, and Rehana Shaik. "A Review of Event-Based Conceptual Rainfall-Runoff Models: A Case for Australia." Encyclopedia 4, no. 2 (2024): 966–83. http://dx.doi.org/10.3390/encyclopedia4020062.

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Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Att

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43

Joya, Michael. "An Event-Based Framework for Process Inference." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1796–97. http://dx.doi.org/10.1609/aaai.v25i1.8064.

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We focus on a class of models used for representing the dynamics between a discrete set of probabilistic events in a continuous-time setting. The proposed framework offers tractable learning and inference procedures and provides compact state representations for processes which exhibit variable delays between events. The approach is applied to a heart sound labeling task that exhibits long-range dependencies on previous events, and in which explicit modeling of the rhythm timings is justifiable by cardiological principles.

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44

Wagner, Gerd. "Information and Process Modeling for Simulation – Part I: Objects and Events." Journal of Simulation Engineering 1 (June 28, 2018): 1:1–1:26. https://doi.org/10.5281/zenodo.11110129.

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In simulation engineering, a system model mainly consists of an <em>information model </em>describing a system's state structure and a <em>process model</em> describing its dynamics. In the fields of <em>Information Systems</em> and <em>Software Engineering</em>, there are widely used standards such as the <em>Class Diagrams</em> of the <em>Unified Modeling Language (UML)</em> for making information models, and the <em>Business Process Modeling Notation (BPMN)</em> for making process models. This tutorial presents a general <em>Object Event Modeling</em><em> (OEM)</em> approach

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45

Cucinotta, F. A., I. Plante, A. L. Ponomarev, and M. H. Y. Kim. "Nuclear interactions in heavy ion transport and event-based risk models." Radiation Protection Dosimetry 143, no. 2-4 (2011): 384–90. http://dx.doi.org/10.1093/rpd/ncq512.

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46

Kuljis, Jasna, and Ray J. Paul. "Web-based discrete event simulation models: Current states and possible futures." Simulation & Gaming 34, no. 1 (2003): 39–53. http://dx.doi.org/10.1177/1046878102250484.

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47

Canovi, Noemi, Benjamin A. Ellis, Tonje K. Sørdalen, et al. "Trajectory-based fish event classification through pre-training with diffusion models." Ecological Informatics 82 (September 2024): 102733. http://dx.doi.org/10.1016/j.ecoinf.2024.102733.

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48

Stallhofer, Verena, and Sophie N. Parragh. "Event-based models for the electric autonomous dial-a-ride problem." Transportation Research Part C: Emerging Technologies 171 (February 2025): 104896. https://doi.org/10.1016/j.trc.2024.104896.

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49

Tayfur, Gokmen, and Vijay P. Singh. "ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff." Journal of Hydraulic Engineering 132, no. 12 (2006): 1321–30. http://dx.doi.org/10.1061/(asce)0733-9429(2006)132:12(1321).

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50

Islo, H. E., and G. Sohlenius. "A Computer Support System for Manufacturing Based on Discrete Event Models." CIRP Annals 40, no. 1 (1991): 459–62. http://dx.doi.org/10.1016/s0007-8506(07)62029-0.

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