Wildfire monitoring systems increasingly rely on satellite-derived risk surfaces to support resource-constrained prioritization. However, less attention has been paid to how spatial aggregation interacts with alarm sparsity in shaping event-level wil...
Multivariate time-series anomaly detection is often evaluated with point-adjusted metrics, which can overstate practical performance when alarms are judged at the event level. Explanation results are also frequently reported as descriptive attributio...
Automatic Drum Transcription (ADT) remains challenging due to overlapping spectral content among drum instruments, dense transient activity, and the increased difficulty associated with fine-grained full-kit transcription. This study investigates whe...
Multivariate time-series anomaly detection is important for server monitoring, industrial systems, and intelligent operation and maintenance. Existing point-wise and point-adjusted metrics often fail to show whether an anomalous segment is detected e...
Falls require rapid assistance, but fall-like activities and domain shift complicate alarm control. We present a cross-dataset fall-detection framework combining red–green–blue (RGB) appearance, pose evidence and sequential decisions. A f...
Errors in temporal behavior annotations can change decisions about when behavioral events occur. This study examined how six types of annotation errors affect downstream event decisions. A temporal event decision model (TEDM) was evaluated using 100...
Acoustic sensing is a valuable approach for non-destructive fruit-quality evaluation. For coconut maturity determination, acoustic responses elicited by tapping may convey relevant information; however, recording-level analyses may not fully exploit...
Sleep apnea, characterized by its high prevalence and serious health consequences, faces a critical bottleneck in diagnosis. Polysomnography (PSG), the gold standard, is costly and cumbersome, while wearable devices struggle with quality control and...
The railway track system is a crucial infrastructure for the transportation of people and goods in modern societies. With the increase in railway traffic, the availability of the track for monitoring and maintenance purposes is becoming significantly...
Internal reconnection events (IREs) are rapid magnetohydrodynamic phenomena that play an important role in the confinement and stability of spherical tokamak plasmas. Reliable identification of IREs in experimental data is challenging due to short di...
This study addresses cooperative radio-frequency (RF) identification of low-altitude unmanned aerial vehicles (UAVs) under dynamic receiver availability, where single-receiver observations are susceptible to spatial variability and distributed sensin...
Wrong-way driving (WWD) is a rare but highly dangerous traffic event that can lead to severe head-on collisions. This study proposes a LiDAR-based infrastructure system for early WWD detection using roadside sensing and high-definition map semantics....
Interpersonal synchrony is a time-dependent coordination pattern in which interacting partners’ body movements become temporally aligned. This study frames interpersonal synchrony as a human motion analysis problem and presents Synchrony Vision...
Magnitude-threshold classifiers trained on multiple station records from the same earthquake are vulnerable to event-level data leakage, and comparisons across magnitude boundaries can be misleading because the class definition and prevalence change....
Ship collisions emerge from interactions among human, vessel, environmental, and management factors. Aggregate accident networks can, however, combine relations across events and evidence levels, obscuring whether recurrent topology represents event-...
The increasing volume and dimensionality of electrical monitoring data in healthcare facilities create challenges for identifying unusual operating behavior without verified fault labels. This study presents a reproducible unsupervised framework for...
Understanding complex vessel behaviors in port waters is important for maritime traffic supervision, navigation safety management, and intelligent maritime decision-making. However, existing AIS-based studies often focus on isolated behavior recognit...
Urban inundation forecasting in high-density cities is complicated by sparse incident labels, nonstationary rainfall, and changing sensing systems. Using Taipei as a case study, we develop a building-scale GeoAI decision-support prototype that integr...
Regenerative braking recovers vehicle kinetic energy during deceleration, but realised battery-side recovery depends on braking demand and battery charge-acceptance conditions. This study characterises real-world regenerative braking behaviour and ev...
Unmanned aerial vehicles (UAVs) are increasingly being used for early wildfire monitoring in remote areas, but UAV endurance is fundamentally constrained by the battery capacity. This work presents an energy-aware edge-vision framework for UAV fire d...
Real-time management of short-term ozone peaks during arc welding remains challenging because ventilation- and enclosure-defined transport boundaries can create strong position-dependent peak behavior, even under fixed process settings. This study es...
25 August 2023
Aviation turbulence is a major concern for flight safety. Detecting and nowcasting upper-level turbulence is usually associated with known sources of turbulence, such as convective clouds and transverse cirrus bands. However, in extended clear-air co...
Important changes are underway in the U.S. power industry in the way that electricity is sourced, transported, and utilized. Disruption from extreme weather events and cybersecurity events is bringing new scrutiny to power-system resilience. Recogniz...
In automotive crash testing, trustworthy crash-test curves are essential for reliable crashworthiness assessment, yet automated anomaly detection is difficult due to limited labeled abnormal cases, event-level data scarcity, and distribution shifts a...
With the development of social media, social communication has changed. While this facilitates people’s communication and access to information, it also provides an ideal platform for spreading rumors. In normal or critical situations, rumors c...
This retrospective multicenter study integrated individual- and event-level records from 190 captive giant pandas managed at four study sites of the China Conservation and Research Center for the Giant Panda (CCRCGP) during 2024 to characterize patte...
Multimodal sensing provides a mature foundation for elevator monitoring, while visual records, equipment-state records, and operational responses still need to be connected in many deployed community systems. This study develops an event-level closed...
Accurate prediction of football pass outcomes is important for tactical analysis, decision evaluation, and skill-oriented feedback in student football training and physical education. However, event-level pass outcome prediction remains challenging b...
Electroencephalogram (EEG)-based biometric sensing provides a promising pathway for secure and user-specific human authentication, but practical deployment remains limited by cross-session non-stationarity and potentially optimistic evaluation protoc...
Post-disaster debris detection is important for rapid damage assessment, emergency response, and recovery planning. However, debris objects in aerial imagery are often fragmented, irregularly shaped, partially occluded, and visually confused with sha...
Online platforms need scalable risk-screening methods for rapidly spreading rumors, misleading content, and large-scale user responses. Platform-side content governance often needs to rank suspicious events when only partial propagation evidence is v...
Overlapping forecasting windows can conceal the limited independence of event archives. We evaluated seven models using 2039 records from 41 events at Hongjiata Hydrological Station during 2016–2025. Telemetry elevations were harmonized by addi...
In this paper we present a hybrid convolutional neural network–hidden Markov model framework for detecting solar flare events of intensity greater than or equal to M1.0 from very low frequency signals via their induced sudden ionospheric distur...
Event-level segmentation of small-scale unmanned aerial vehicles (UAVs) is challenging because target-generated events are sparse and are easily mixed with background motion and sensor noise. This study extends EV-SpSegNet with Adaptive Temporal Real...
Large distributed sensor arrays require repeated recalibration as radiation damage, material aging, gain variation, and readout drift alter channel responses. We studied a high-granularity calorimeter as a large sensor array and addressed unsupervise...
This paper presents a digitally controlled current-mode synaptic weighting cell in 28 nm FD-SOI CMOS for low-complexity Multi-Layer Perceptron (MLP) inference in event-based Non-Intrusive Load Monitoring (NILM). A compact bias-free [16,16] MLP is tra...
Dairy foods—particularly cheeses produced from raw or minimally processed milk—remain vulnerable to hazards such as Listeria monocytogenes, where delayed laboratory confirmation can expand recalls, increase food waste, and delay outbreak...
This paper presents a modular evaluation questionnaire designed to assess Knowledge and Technology Transfer (KT) events in Higher Education Institutions (HEIs). KT is central to the HEI’s third mission, contributing to societal and economic pro...
Tropical cyclones reduce port capacity and leave heterogeneous congestion. Yet empirical measurements of port resilience are rarely connected to operational strategy evaluation on the same observed event baseline. This study develops a dual-layer fra...
Airport low visibility is spatially heterogeneous, whereas point observations of meteorological optical range (MOR) and runway visual range (RVR) provide certified surface visibility observations but limited information on low-level dynamics and the...
Storm surge current-time estimations are strongly influenced by recent water-level conditions, while the temporal dependence among samples from the same typhoon event can complicate the assessment of model generalization to unseen events. This study...
An effective flood early warning system depends on accurate forecasting, calibrated uncertainty estimates, robustness to missing data, and effective risk assessment. This study evaluates and compares different techniques using the Fish River catchmen...
In Non-Intrusive Load Monitoring (NILM), adaptive segmentation of electricity consumption time series is critical for appliance recognition. However, prevailing methods face challenges including heuristic parameter tuning, boundary sensitivity, and m...
Deep-learning research on flood inundation mapping is driven largely by architectural innovation and single-run reporting. We ask whether the segmentation architecture or the input representation contributes more to accuracy in flood-event surface-wa...
Tunnel boring machine (TBM) excavation generates continuous mechanical vibration that can obscure weak, short-duration propagating responses related to structural-plane disturbance. This study develops a Signal-Constrained Activation Detection and Lo...
Tele-oncology expanded rapidly during the COVID-19 pandemic and remains central to cancer care, yet evidence on guideline adherence in tele-oncology is limited, particularly in Latin America. In Chile, the Explicit Health Guarantees (GES) program man...
Large wildfires cause casualties, ecosystem damage, and property loss. Reducing them requires predicting spread; validating a spread model requires pixel-level time of arrival (ToA) from real fires. Few regions have both such data and an independent...
To address low efficiency in manual counting and high costs of data collection and labeling in Landes goose farming, this study developed a simulation-driven video detection and counting method. A three-dimensional simulation environment generated go...
Accurate binarization of phase-detection probe signals (gas vs. liquid) is necessary for the estimation of local void fraction, interfacial velocity, and bubble statistics in gas–liquid flows, particularly in nuclear thermal–hydraulic exp...
Rainfall-based models are widely used to support territorial landslide early warning systems, in which the occurrence of one or more landslides is assessed over predefined warning areas rather than at individual, known slopes. Their development, howe...
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