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Journal articles on the topic 'Group event detection'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Weiyao Lin, Ming-Ting Sun, R. Poovendran, and Zhengyou Zhang. "Group Event Detection With a Varying Number of Group Members for Video Surveillance." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 8 (2010): 1057–67. http://dx.doi.org/10.1109/tcsvt.2010.2057013.

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Achim, André. "Statistical detection of between-group differences in event-related potentials." Clinical Neurophysiology 112, no. 6 (2001): 1023–34. http://dx.doi.org/10.1016/s1388-2457(01)00519-3.

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Benz, Susanne A., and Philipp Blum. "Global detection of rainfall-triggered landslide clusters." Natural Hazards and Earth System Sciences 19, no. 7 (2019): 1433–44. http://dx.doi.org/10.5194/nhess-19-1433-2019.

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Abstract. An increasing awareness of the cost of landslides on the global economy and of the associated loss of human life has led to the development of various global landslide databases. However, these databases typically report landslide events instead of individual landslides, i.e., a group of landslides with a common trigger and reported by media, citizens and/or government officials as a single unit. The latter results in significant cataloging and reporting biases. To counteract these biases, this study aims to identify clusters of landslide events that were triggered by the same rainfa

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Del Giudice, Ennio, Angela Francesca Crisanti, and Alfonso Romano. "Short duration outpatient video electroencephalographic monitoring: The experience of a southern‐Italian general pediatric department." Epileptic Disorders 4, no. 3 (2002): 197–202. http://dx.doi.org/10.1684/j.1950-6945.2002.tb00493.x.

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ABSTRACT The authors assessed the event detection rate and clinical usefulness of short duration, outpatient video electroencephalographic monitoring (VEM), in the pediatric age group. The duration of monitoring was set at a two‐hour period. One hundred consecutive patients aged 0‐18 years were enrolled in the study. Patients belonged to one of the following groups: A) patients evaluated to differentiate between true epileptic seizures and nonepileptic events; B) patients with known epilepsy evaluated for a better definition of their seizure type; C) patients with isolated EEG abnormalities ev

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Manisha, Samanta, Kumar Meena Yogesh, Prokash Mazumdar Arka, Singh Girdhari, and Gopalani Dinesh. "Meliorating usable document density for online event detection." International Journal of Informatics and Communication Technology 11, no. 2 (2022): 85–95. https://doi.org/10.11591/ijict.v11i2.pp85-95.

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Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are at times unu

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Samanta, Manisha, Yogesh Kumar Meena, Arka Prokash Mazumdar, Girdhari Singh, and Dinesh Gopalani. "Meliorating usable document density for online event detection." International Journal of Informatics and Communication Technology (IJ-ICT) 11, no. 2 (2022): 85. http://dx.doi.org/10.11591/ijict.v11i2.pp85-95.

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<span>Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are

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Yang, Nachuan, Yongjun Zhao, Fuqiang Wang, and Jinyang Chen. "Using Phase-Sensitive Optical Time Domain Reflectometers to Develop an Alignment-Free End-to-End Multitarget Recognition Model." Electronics 12, no. 7 (2023): 1617. http://dx.doi.org/10.3390/electronics12071617.

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This pattern recognition method can effectively identify vibration signals collected by a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and improve the accuracy of alarms. An alignment-free end-to-end multi-vibration event detection method based on Φ-OTDR is proposed, effectively detecting different vibration events in different frequency bands. The pulse accumulation and pulse cancellers determine the location of vibration events. The local differential detection method demodulates the vibration event time-domain variation signals. After the extraction of the signal time-frequenc

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Yang, Sumin, Bummo Koo, Seunghee Lee, et al. "Determination of Gait Events and Temporal Gait Parameters for Persons with a Knee–Ankle–Foot Orthosis." Sensors 24, no. 3 (2024): 964. http://dx.doi.org/10.3390/s24030964.

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Gait event detection is essential for controlling an orthosis and assessing the patient’s gait. In this study, patients wearing an electromechanical (EM) knee–ankle–foot orthosis (KAFO) with a single IMU embedded in the thigh were subjected to gait event detection. The algorithm detected four essential gait events (initial contact (IC), toe off (TO), opposite initial contact (OIC), and opposite toe off (OTO)) and determined important temporal gait parameters such as stance/swing time, symmetry, and single/double limb support. These gait events were evaluated through gait experiments using four

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Greenberg, Jeff, Louis Tijerina, Reates Curry, et al. "Driver Distraction: Evaluation with Event Detection Paradigm." Transportation Research Record: Journal of the Transportation Research Board 1843, no. 1 (2003): 1–9. http://dx.doi.org/10.3141/1843-01.

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The effects of eight in-vehicle tasks on driver distraction were measured in a large, moving-base driving simulator. Forty-eight adults, ranging in age from 35 to 66, and 15 teenagers participated in the simulated drive. Hand-held and hands-free versions of phone dialing, voicemail retrieval, and incoming calls represented six of the eight tasks. Manual radio tuning and climate control adjustment were also included to allow comparison with tasks that have traditionally been present in vehicles. During the drive the participants were asked to respond to sudden movements in surrounding traffic.

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Miranda, Julián, Angélica Flórez, Gustavo Ospina, Ciro Gamboa, Carlos Flórez, and Miguel Altuve. "Proposal for a System Model for Offline Seismic Event Detection in Colombia." Future Internet 12, no. 12 (2020): 231. http://dx.doi.org/10.3390/fi12120231.

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This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendatio

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Yuan, Shuang, Lidong Yang, and Yong Guo. "Sound Event Detection with Perturbed Residual Recurrent Neural Network." Electronics 12, no. 18 (2023): 3836. http://dx.doi.org/10.3390/electronics12183836.

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Sound event detection (SED) is of great practical and research significance owing to its wide range of applications. However, due to the heavy reliance on dataset size for task performance, there is often a severe lack of data in real-world scenarios. In this study, an improved mean teacher model is utilized to carry out semi-supervised SED, and a perturbed residual recurrent neural network (P-RRNN) is proposed as the SED network. The residual structure is employed to alleviate the problem of network degradation, and pre-training the improved model on the ImageNet dataset enables it to learn i

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Wang, Ziru, Hao Xu, Fei Guan, and Zhihui Chen. "Real-Time Wild Horse Crossing Event Detection Using Roadside LiDAR." Electronics 13, no. 19 (2024): 3796. http://dx.doi.org/10.3390/electronics13193796.

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Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, object tracking, and object classification. Considering that the background information collected by LiDAR may change over time, an automatic background filtering method that updates the background in real-time has bee

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Eichele, Tom, Srinivas Rachakonda, Brage Brakedal, Rune Eikeland, and Vince D. Calhoun. "EEGIFT: Group Independent Component Analysis for Event-Related EEG Data." Computational Intelligence and Neuroscience 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/129365.

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Independent component analysis (ICA) is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series a

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Ahmadi, Parvin, Mahmoud Tabandeh, and Iman Gholampour. "Abnormal event detection and localisation in traffic videos based on group sparse topical coding." IET Image Processing 10, no. 3 (2016): 235–46. http://dx.doi.org/10.1049/iet-ipr.2015.0399.

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Song, Doohwan, Ikjun Yeom, and Honguk Woo. "Event-Triggered Ephemeral Group Communication and Coordination over Sound for Smart Consumer Devices." Sensors 19, no. 8 (2019): 1883. http://dx.doi.org/10.3390/s19081883.

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Voice-based interfaces have become one of the most popular device capabilities, recently being regarded as one flagship user experience of smart consumer devices. However, the lack of common coordination mechanisms might often degrade the user experience, especially when interacting with multiple voice-enabled devices located closely. For example, a hotword or wake-up utterance such as “hi Bixby” or “ok Google” frequently triggers redundant responses by several nearby smartphones. Motivated by the problem of uncoordinated react of voice-enabled devices especially in a multiple device environme

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Koh, Keng Tat, Wan Chung Law, Win Moe Zaw, et al. "Smartphone electrocardiogram for detecting atrial fibrillation after a cerebral ischaemic event: a multicentre randomized controlled trial." EP Europace 23, no. 7 (2021): 1016–23. http://dx.doi.org/10.1093/europace/euab036.

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Abstract Aims Atrial fibrillation (AF) is a preventable cause of ischaemic stroke but it is often undiagnosed and undertreated. The utility of smartphone electrocardiogram (ECG) for the detection of AF after ischaemic stroke is unknown. The aim of this study is to determine the diagnostic yield of 30-day smartphone ECG recording compared with 24-h Holter monitoring for detecting AF ≥30 s. Methods and results In this multicentre, open-label study, we randomly assigned 203 participants to undergo one additional 24-h Holter monitoring (control group, n = 98) vs. 30-day smartphone ECG monitoring (

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Mahy, Mahy, A. F. Elgamal, I. Elmenshawi, and Hanan E. Abdelkader. "Automated validated tool for epileptic seizure detection using deep learning." Journal of Intelligent Systems and Internet of Things 15, no. 1 (2025): 74–90. https://doi.org/10.54216/jisiot.150107.

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This paper explores an innovative approach for the automatic detection of epileptic seizures from audio recordings and Heart Rate Variability (HRV) using Convolutional Neural Networks (CNNs). In medical settings, accurately labeling seizure events is critical for patient monitoring. However, manual annotation by experts is not only time-intensive but also highly repetitive. To address this challenge, we developed a structured questionnaire for patients and eyewitnesses, concentrating on observable characteristics during typical seizure events. This questionnaire was used to prospectively study

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Wang, Xuehui, Yong Zhang, Hao Liu, Yang Wang, Lichun Wang, and Baocai Yin. "An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow." Journal of Advanced Transportation 2018 (August 14, 2018): 1–12. http://dx.doi.org/10.1155/2018/7191549.

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Subway is an important transportation means for residents, since it is always on schedule. However, some temporal management policies or unpredicted events may change passenger flow and then affect passengers requirement for punctuality. Thus, detecting anomaly event, mining its propagation law, and revealing its potential impact are important and helpful for improving management strategy; e.g., subway emergency management can predict flow change under the condition of knowing specific policy and estimate traffic impact brought by some big events such as vocal concerts and ball games. In this

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Yu, Jinying, Yuchen Gao, Yuxin Wu, Dian Jiao, Chang Su, and Xin Wu. "Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration." Applied Sciences 9, no. 17 (2019): 3558. http://dx.doi.org/10.3390/app9173558.

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Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on mul

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Gan, Linhai, та Gang Wang. "Tracking Split Group with δ-Generalized Labeled Multi-Bernoulli Filter". Journal of Sensors 2019 (19 травня 2019): 1–12. http://dx.doi.org/10.1155/2019/9278725.

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As target splitting is not considered in the initial development of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Gaussian inverse Wishart (GGIW) distribution and derive a δ-GLMB filter based on the group splitting model, in which the target splitting event is investigated. Two simplifications of the approach are presented to improve the computing efficiency, where with splitting detection, we need not to predict

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U.Saravanakumar. "Clustering Textures with EHG Algorithm for Modelling Video." International Journal of Computer Science and Engineering Communications 1, no. 1 (2013): 42–46. https://doi.org/10.5281/zenodo.821755.

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In this paper we present a novel approach for common recognition of group activities for video surveillance applications. We propose a Energetic-based approach for detecting abnormal events in surveillance video. It requires the appropriate definition of similarity between events. Human pose estimation via motion tracking systems can be considered as a regression problem within a discriminative framework. We defined the overfitting problem was handled by Hidden Markov Model based similarity. We propose in this paper a multi model-based similarity measure. In this measure, the Hidden Markov Mod

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BENSON, NOAH C., and VALERIE DAGGETT. "A CHEMICAL GROUP GRAPH REPRESENTATION FOR EFFICIENT HIGH-THROUGHPUT ANALYSIS OF ATOMISTIC PROTEIN SIMULATIONS." Journal of Bioinformatics and Computational Biology 10, no. 04 (2012): 1250008. http://dx.doi.org/10.1142/s0219720012500084.

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Graphs are rapidly becoming a powerful and ubiquitous tool for the analysis of protein structure and for event detection in dynamical protein systems. Despite their rise in popularity, however, the graph representations employed to date have shared certain features and parameters that have not been thoroughly investigated. Here, we examine and compare variations on the construction of graph nodes and graph edges. We propose a graph representation based on chemical groups of similar atoms within a protein rather than residues or secondary structure and find that even very simple analyses using

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Bowden, Katie A., Pamela L. Heinselman, Darrel M. Kingfield, and Rick P. Thomas. "Impacts of Phased-Array Radar Data on Forecaster Performance during Severe Hail and Wind Events." Weather and Forecasting 30, no. 2 (2015): 389–404. http://dx.doi.org/10.1175/waf-d-14-00101.1.

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Abstract The ongoing Phased Array Radar Innovative Sensing Experiment (PARISE) investigates the impacts of higher-temporal-resolution radar data on the warning decision process of NWS forecasters. Twelve NWS forecasters participated in the 2013 PARISE and were assigned to either a control (5-min updates) or an experimental (1-min updates) group. Participants worked two case studies in simulated real time. The first case presented a marginally severe hail event, and the second case presented a severe hail and wind event. While working each event, participants made decisions regarding the detect

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Cai, Jun, Xin Xu, Hongpeng Zhu, and Jian Cheng. "An Efficient Compressive Sensing Event-Detection Scheme for Internet of Things System Based on Sparse-Graph Codes." Sensors 23, no. 10 (2023): 4620. http://dx.doi.org/10.3390/s23104620.

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This work studied the event-detection problem in an Internet of Things (IoT) system, where a group of sensor nodes are placed in the region of interest to capture sparse active event sources. Using compressive sensing (CS), the event-detection problem is modeled as recovering the high-dimensional integer-valued sparse signal from incomplete linear measurements. We show that the sensing process in IoT system produces an equivalent integer CS using sparse graph codes at the sink node, for which one can devise a simple deterministic construction of a sparse measurement matrix and an efficient int

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Do, Hien, Hien T. Ho, Phu D. Tran, et al. "Building the hospital event-based surveillance system in Viet Nam: a qualitative study to identify potential facilitators and barriers for event reporting." Western Pacific Surveillance and Response Journal 11, no. 3 (2020): 10–20. http://dx.doi.org/10.5365/wpsar.2019.10.1.009.

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Introduction: Hospitals are a key source of information for the early identification of emerging disease outbreaks and acute public health events for risk assessment, decision-making and public health response. The objective of this study was to identify potential facilitators and barriers for event reporting from the curative sector to the preventive medicine sector in Viet Nam. Methods: In 2016, we conducted 18 semi-structured, in-depth interviews, as well as nine focus group discussions, with representatives from the curative and preventive medicine sectors in four provinces. We transcribed

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Tavakoli, Paniz, Addo Boafo, Emily Jerome, and Kenneth Campbell. "Active and Passive Attentional Processing in Adolescent Suicide Attempters: An Event-Related Potential Study." Clinical EEG and Neuroscience 52, no. 1 (2020): 29–37. http://dx.doi.org/10.1177/1550059420933086.

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Introduction: Suicide is the second leading cause of death among adolescents. Suicidal behavior is associated with impairments in attention. Attention can be directed toward relevant events in the environment either actively, under voluntary control, or passively, by external salient events. The extent to which the risk for suicidal behavior affects active and passive attention is largely unknown. Methods: Event-related potentials (ERPs) were recorded while 14 adolescents with acute suicidal behavior and 14 healthy controls performed an auditory 3-stimulus oddball task. The task consisted of s

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Tomc, Matej, and Zlatko Matjačić. "Real-Time Gait Event Detection with Adaptive Frequency Oscillators From a Single Head-Mounted IMU." Sensors 23, no. 12 (2023): 5500. http://dx.doi.org/10.3390/s23125500.

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Accurate real-time gait event detection is the basis for the development of new gait rehabilitation techniques, especially when utilizing robotics or virtual reality (VR). The recent emergence of affordable wearable technologies, especially inertial measurement units (IMUs), has brought forth various new methods and algorithms for gait analysis. In this paper, we highlight some advantages of using adaptive frequency oscillators (AFOs) over traditional gait event detection algorithms, implemented a real-time AFO-based algorithm that estimates the gait phase from a single head-mounted IMU, and v

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Chen, Ke Wei, and Hau Tieng Wu. "0324 Localizing Respiratory Events in Short Signal Segment to Improve Respiratory Event Counting." SLEEP 47, Supplement_1 (2024): A139. http://dx.doi.org/10.1093/sleep/zsae067.0324.

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Abstract Introduction When predicting respiratory events over a short segment (60 seconds) as input to a machine learning model, it is common to divide the full night recording into multiple non- overlapping short segments and count segments containing respiratory events. However, underestimate happens when multiple events exist within a segment, which influences the event counting performance. Methods The Stanford Technology Analytics and Genomics in Sleep (STAGES) database was used. Recordings from 332 nights were randomly selected for training and 100 nights for testing SpO2, Instantaneous

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Selvam, Sheba, Ramadoss Balakrishnan, and Balasundaram Sadhu Ramakrishnan. "Ontology With Hybrid Clustering Approach for Improving the Retrieval Relevancy in Social Event Detection." International Journal on Semantic Web and Information Systems 14, no. 4 (2018): 33–56. http://dx.doi.org/10.4018/ijswis.2018100102.

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Progression in digital technology and the fame of social media sites such as Facebook, YouTube, Flickr etc., necessitate sharing memories. This results in a colossal amount of multimedia content such as text, audio, photographs and video on the web. Retrieving photographs exclusively from web in the large collection is a challenging task. One way to retrieve photographs is by identifying them as events. The automatic organization of a multimedia collection into groups of items, where each group corresponds to a distinct event is described as Social Event Detection (SED). Contextual information

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Qu, Shan, Zhe Guan, Eric Verschuur, and Yangkang Chen. "Automatic high-resolution microseismic event detection via supervised machine learning." Geophysical Journal International 222, no. 3 (2020): 1881–95. http://dx.doi.org/10.1093/gji/ggaa193.

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SUMMARY Microseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-to-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2*wavelength is used to divide the data into segments. Later on, 191 feat

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Luchik, T. S., and W. G. Tiederman. "Timescale and structure of ejections and bursts in turbulent channel flows." Journal of Fluid Mechanics 174 (January 1987): 529–52. http://dx.doi.org/10.1017/s0022112087000235.

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Burst structures in the near wall region of turbulent flows are associated with a large portion of the turbulent momentum transport from the wall. However, quantitative measures of the timescales associated with the burst event are not well defined, largely due to ambiguities associated with the methods used to detect a burst.In the present study, Eulerian burst-detection schemes were developed through extensions of the uv quadrant 2, VITA, and u-level techniques. Each of the basic techniques detects ejections. One or more ejections are contained in each burst and hence the key idea is to iden

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Sirikongtham, Puwadol, Worapat Paireekreng, and Suwannit Chareen Chit. "Brainwave Detection Model for Panic Attacks Based on Event-related Potential." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (2019): 333–44. http://dx.doi.org/10.29196/jubpas.v27i1.2168.

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Panic attacks could adversely affect a patient’s daily life and can pose risks to others. The symptoms of panic attacks can be timely observed by detecting the brainwave. This research presents a model that can evaluate the level of panic attack symptoms using the brainwaves detection during (or before) the symptom occurs. It helps monitor the patient’s brainwave based on Event-related potential (ERP). The model is derived from the simulation with horror pictures and frightening sound on the experimental group of 30 people. The survey related to symptoms has been used regarding to the criteria

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Bayram, Barış, and Gökhan İnce. "An Incremental Class-Learning Approach with Acoustic Novelty Detection for Acoustic Event Recognition." Sensors 21, no. 19 (2021): 6622. http://dx.doi.org/10.3390/s21196622.

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Acoustic scene analysis (ASA) relies on the dynamic sensing and understanding of stationary and non-stationary sounds from various events, background noises and human actions with objects. However, the spatio-temporal nature of the sound signals may not be stationary, and novel events may exist that eventually deteriorate the performance of the analysis. In this study, a self-learning-based ASA for acoustic event recognition (AER) is presented to detect and incrementally learn novel acoustic events by tackling catastrophic forgetting. The proposed ASA framework comprises six elements: (1) raw

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Liu, Yu, Yang Yang, Xiaopeng Lv, and Lifeng Wang. "A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/712028.

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Many applications based on Internet of Things (IoT) technology have recently founded in industry monitoring area. Thousands of sensors with different types work together in an industry monitoring system. Sensors at different locations can generate streaming data, which can be analyzed in the data center. In this paper, we propose a framework for online sensor fault detection. We motivate our technique in the context of the problem of the data value fault detection and event detection. We use the Statistics Sliding Windows (SSW) to contain the recent sensor data and regress each window by Gauss

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Mu, Bingxian, Kunwu Zhang, Feng Xiao, and Yang Shi. "Event-Based Rendezvous Control for a Group of Robots With Asynchronous Periodic Detection and Communication Time Delays." IEEE Transactions on Cybernetics 49, no. 7 (2019): 2642–51. http://dx.doi.org/10.1109/tcyb.2018.2831684.

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Zahradka, Nicole, Khushboo Verma, Ahad Behboodi, Barry Bodt, Henry Wright, and Samuel C. K. Lee. "An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’." Sensors 20, no. 18 (2020): 5272. http://dx.doi.org/10.3390/s20185272.

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Video- and sensor-based gait analysis systems are rapidly emerging for use in ‘real world’ scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coor

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Ronchieri, Elisabetta, Luca Giommi, Luigi Benedettto Scarponi, et al. "Anomaly Detection in Data Center IT & Physical Infrastructure." EPJ Web of Conferences 295 (2024): 07004. http://dx.doi.org/10.1051/epjconf/202429507004.

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Anomaly detection in data center IT and physical infrastructure is challenging due to the amount of heterogeneous data to be analyzed. Defining a solution that early identifies unexpected anomalies is particularly important to prevent data losses, breakdown of the system, and any other event considered to be critical for the activity of the data center. In the context of the INFN CNAF data center, one of the WLCG Tier-1s, we have performed a study based on monitored cooling, electrical, and IT hardware and software metrics to identify anomalies. In the present work, we aim to explore statistic

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Chun, Chanjun, Hyung Jin Park, and Myoung Bae Seo. "Static Sound Event Localization and Detection Using Bipartite Matching Loss for Emergency Monitoring." Applied Sciences 14, no. 4 (2024): 1539. http://dx.doi.org/10.3390/app14041539.

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In this paper, we propose a method for estimating the classes and directions of static audio objects using stereo microphones in a drone environment. Drones are being increasingly used across various fields, with the integration of sensors such as cameras and microphones, broadening their scope of application. Therefore, we suggest a method that attaches stereo microphones to drones for the detection and direction estimation of specific emergency monitoring. Specifically, the proposed neural network is configured to estimate fixed-size audio predictions and employs bipartite matching loss for

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Li, Jiada, Daniyal Hassan, Simon Brewer, and Robert Sitzenfrei. "Is Clustering Time-Series Water Depth Useful? An Exploratory Study for Flooding Detection in Urban Drainage Systems." Water 12, no. 9 (2020): 2433. http://dx.doi.org/10.3390/w12092433.

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As sensor measurements emerge in urban water systems, data-driven unsupervised machine learning algorithms have drawn tremendous interest in event detection and hydraulic water level and flow prediction recently. However, most of them are applied in water distribution systems and few studies consider using unsupervised cluster analysis to group the time-series hydraulic-hydrologic data in stormwater urban drainage systems. To improve the understanding of how cluster analysis contributes to flooding location detection, this study compared the performance of K-means clustering, agglomerative clu

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Morel, Maryan, Emmanuel Bacry, Stéphane Gaïffas, Agathe Guilloux, and Fanny Leroy. "ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection." Biostatistics 21, no. 4 (2019): 758–74. http://dx.doi.org/10.1093/biostatistics/kxz003.

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Summary With the increased availability of large electronic health records databases comes the chance of enhancing health risks screening. Most post-marketing detection of adverse drug reaction (ADR) relies on physicians’ spontaneous reports, leading to under-reporting. To take up this challenge, we develop a scalable model to estimate the effect of multiple longitudinal features (drug exposures) on a rare longitudinal outcome. Our procedure is based on a conditional Poisson regression model also known as self-controlled case series (SCCS). To overcome the need of precise risk periods specific

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Williams, DJ, S. Olsen, W. Crichton, et al. "Detection of Adverse Events in a Scottish Hospital Using a Consensus-based Methodology." Scottish Medical Journal 53, no. 4 (2008): 26–30. http://dx.doi.org/10.1258/rsmsmj.53.4.26.

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Objective To determine, using a consensus based methodology, the rate and nature of adverse events (AEs) among patients admitted to acute medicine, acute surgery and obstetrics in a large teaching hospital in Scotland. Methods Retrospective case-note review of 450 medical, nursing and medication records to identify and classify adverse events. Results For 354 patients whose length of stay was greater than 24 hours, the overall adverse event rate was 7.9% which ranged from 0% in obstetrics, 7.2% in acute medicine to 13% in acute surgery. Among all AEs, 43% were deemed preventable by a consensus

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Shaheer, Rizana, and Malu U. "Real-Time Video Violence Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2586–90. http://dx.doi.org/10.22214/ijraset.2023.52182.

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Abstract: In order to effectively enforce the law and keep cities secure, monitoring technologies that detect violent events are becoming increasingly important. In computer vision, the practice of action recognition has gained popularity. In the field of computer vision, action recognition has gained popularity. The action recognition group, however, has mainly concentrated on straightforward activities like clapping, walking, jogging, etc. Comparatively little study has been done on identifying specific occurrences that have immediate practical applications, like fighting or violent behavior

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Vilajosana, I., E. Suriñach, A. Abellán, G. Khazaradze, D. Garcia, and J. Llosa. "Rockfall induced seismic signals: case study in Montserrat, Catalonia." Natural Hazards and Earth System Sciences 8, no. 4 (2008): 805–12. http://dx.doi.org/10.5194/nhess-8-805-2008.

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Abstract. After a rockfall event, a usual post event survey includes qualitative volume estimation, trajectory mapping and determination of departing zones. However, quantitative measurements are not usually made. Additional relevant quantitative information could be useful in determining the spatial occurrence of rockfall events and help us in quantifying their size. Seismic measurements could be suitable for detection purposes since they are non invasive methods and are relatively inexpensive. Moreover, seismic techniques could provide important information on rockfall size and location of i

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Ciaraglia, Angelo, Victor A. Convertino, Hanzhang Wang, et al. "Intraoperative Use of Compensatory Reserve Measurement in Orthotopic Liver Transplant: Improved Sensitivity for the Prediction of Hypovolemic Events." Military Medicine 188, Supplement_6 (2023): 322–27. http://dx.doi.org/10.1093/milmed/usad130.

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ABSTRACT Introduction The compensatory reserve measurement (CRM) is a continuous non-invasive monitoring technology that measures the summation of all physiological mechanisms involved in the compensatory response to central hypovolemia. The CRM is displayed on a 0% to 100% scale. The objective of this study is to characterize the use of CRM in the operative setting and determine its ability to predict hypovolemic events compared to standard vital signs. Orthotopic liver transplant was used as the reference procedure because of the predictable occurrence of significant hemodynamic shifts. Meth

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Akkemik, Ozlem, Hakkı Kazaz, Sadik Tamsel, Nesrin Dündar, Sahin Sahinalp, and Hulya Ellidokuz. "A 5 years follow-up for ischemic cardiac outcomes in patients with carotid artery calcification on panoramic radiographs confirmed by doppler ultrasonography in Turkish population." Dentomaxillofacial Radiology 49, no. 4 (2020): 20190440. http://dx.doi.org/10.1259/dmfr.20190440.

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Objective: To evaluate the diagnostic accuracy of digital panoramic radiograph (DPR) for detection of carotid artery calcification (CAC) confirmed by Doppler Ultrasonography (DUSG) and to clarify the relationship between between CAC identified by DPR and cardiovascular events through a 5 year follow-up period. Methods: Of 3600 consecutive patients examined, 158 patients presented with CAC as detected by DPR. The final study group was composed of 96 patients who had CAC confirmed by DUSG or CT angiogram. The control group was composed of 62 patients who has normal DUSG. The end point of the stu

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Zhang, Xi Zhu. "A Study on the Campus Public Safety Monitoring System Based on Intelligent Vision." Advanced Materials Research 1028 (September 2014): 257–61. http://dx.doi.org/10.4028/www.scientific.net/amr.1028.257.

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With the continuous development of video detection technology, the video analysis technology based on campus security has become an important part of the construction of safe campus. As the college students still are a group that has poor ability of security protection, campus security issue is closely related to the stability of society and family happiness, and has become a topic of concern to the whole society. The intelligent vision-based campus public safety monitoring system is an important means to achieve security monitoring, it can automatically analyze the video image sequence, and d

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Yao, Xu, Yu Yang, Yi Jing Fu, Yu Lin Li, and Guo Shi Wu. "Hot Issues Detection on Weibo Based on Social Network Analysis." Advanced Materials Research 846-847 (November 2013): 1818–25. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1818.

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Weibo is a leading twitter-like microblog service in China, acting as the key barometer of social changes. This paper proposes an innovative model, which automatically detects hot issues on Weibo based on social network analysis instead of search-based approaches. Three stages are consecutively collaborated to discover the hot issues and each issue was presented by a group of distinguished keywords as outcome of the model, i.e., firstly self-revised opinion leaders list construction, secondly keywords selection according to a weighting criterion, and finally keyword co-occurrence network build

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Deng, Qian, Yuqing Chen, Xin Wang, Wenjuan Cai, Yanping Han, and Juanjuan Wang. "Comparison of the Efficacy of Different Insulin Administration and Blood Glucose Monitoring Methods in the Treatment of Type 1 Diabetes Mellitus in Children." Evidence-Based Complementary and Alternative Medicine 2022 (September 14, 2022): 1–5. http://dx.doi.org/10.1155/2022/2862682.

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Objective. To compare the clinical efficacy of different insulin administration methods and blood glucose monitoring methods in treating type 1 diabetes mellitus in children. Methods. Patients were divided into four groups: multiple daily injection (MDI) + fingertip blood glucose detection, continuous subcutaneous insulin infusion (CSII) + fingertip blood glucose detection, MDI + continuous glucose monitoring system (CGMS), and CSII + CGMS. After six months of treatment, followed by telephone and at least once a month in an outpatient clinic, insulin doses were adjusted according to the childr

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Jung, Sung Won, In Ho Moh, Hana Yoo, et al. "Effect of Coffee Added to a Polyethylene glycol plus Ascorbic acid Solution for Bowel Preparation prior to Colonoscopy." Journal of Gastrointestinal and Liver Diseases 25, no. 1 (2016): 63–69. http://dx.doi.org/10.15403/jgld.2014.1121.251.cff.

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Background & Aims: Conventional bowel cleansers for colonoscopy have an unpleasant taste and a large volume of solution must be ingested. Coffee increases bowel motility and has an intense flavor. The addition of coffee to a polyethylene glycol+ascorbic acid solution reduces the volume of the solution to be consumed without reducing efficacy, improves the taste of the solution and enhances patient comfort. Methods: Outpatients with clinical indication or people who wanted screening for cancer were considered eligible. Control group (PEGAS group) consumed a 1-L solution of polyethylen

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Woźniak, Kinga Anna, Olmo Cerri, Javier M. Duarte, et al. "New Physics Agnostic Selections For New Physics Searches." EPJ Web of Conferences 245 (2020): 06039. http://dx.doi.org/10.1051/epjconf/202024506039.

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We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an e

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