Search Results (105)

Accurate photovoltaic (PV) power forecasting degrades when models are deployed across sites or seasons, primarily due to distribution shift (amplitude bias and scale mismatch) and anomalous contamination—with pronounced amplitude–phase errors during rapidly changing cloud passages. To address this, we propose Res2-ELM-C, a lightweight Extreme Learning Machine framework featuring three-stage residual stacking—main fit, first-order residual, and near-orthogonal residual—fused via a non-negative ridge-gated mechanism learned on a time-delayed validation window. Robust scaling and a two-step linear calibration—constant de-biasing followed by per-hour gain alignment—mitigate out-of-distribution drift and enhance peak tracking under rapidly varying conditions. In a unified evaluation protocol, the proposed approach consistently reduces MAE/RMSE/MAPE relative to standard baselines while maintaining ELM-level training and inference complexity. These properties make Res2-ELM-C suitable for quasi-real-time day-ahead/intraday dispatch and distributed energy management system applications. Full article

In recent years, the number of satellites in space has experienced explosive growth, and the number of non-cooperative satellites requiring close attention and precise tracking has also increased rapidly. Despite this, the world’s satellite precision tracking equipment is constrained by factors such as a slower growth in numbers and a scarcity of available deployment sites. To rapidly and efficiently identify satellites with potential new anomalies among the large number of cataloged non-cooperative satellites currently transiting, we have constructed a Bi-Directional Minimal GRU deep learning network model incorporating an attention mechanism based on Minimal GRU. This model is termed the Attention-based Bi-Directional Minimal GRU model (ABMGRU). This model utilizes tracking data from relatively inexpensive satellite observation equipment such as phased array radars, along with catalog information for non-cooperative satellites. It rapidly detects anomalies in target satellites during the initial phase of their passes, providing decision support for the subsequent deployment, scheduling, and allocation of precision satellite tracking equipment. The satellite tracking observation data used to support model training is predicted through Satellite Tool Kit simulation based on existing catalog information of non-cooperative satellites, encompassing both anomaly free data and various types of data containing anomalies. Due to limitations imposed by relatively inexpensive observation equipment, satellite tracking data is restricted to the following categories: time, azimuth, elevation, distance, and Doppler shift, while incorporating realistic noise levels. Since subsequent precision tracking requires utilizing more satellite pass time, the duration of tracking data collected during this phase should not be excessively long. The tracking observation time in this study is limited to 1000 s. To enhance the efficiency and effectiveness of satellite anomaly detection, we have developed an Attention-based Bi-Directional Minimal GRU deep learning network model. Experimental results demonstrate that the proposed method can detect non-cooperative anomalous satellites more effectively and efficiently than existing lightweight intelligent algorithms, outperforming them in both completion efficiency and detection performance. It exhibits superiority across various non-cooperative satellite anomaly detection scenarios. Full article

The analysis of anomalous diffusion characteristics within single-particle tracking data is a key problem in several applied-science domains, including biosignal processing, bioinformatics, and biotechnology. This task becomes particularly challenging in the presence of short trajectories, localization errors, and non-ergodicity, features that are common in real experimental data. To address these limitations, this work proposes an approach that improves the robustness and accuracy of estimating the anomalous diffusion exponent α, even for very short trajectories of up to 10 points. The approach includes an ensemble-based variance estimation of the exponent α, along with a bias correction based on time–ensemble averaged mean squared displacement, which reduces the systematic bias. These components integrate well into neural network architectures and are suitable for analyzing experimental trajectories in biotechnology and bioprocess engineering applications. Full article

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work presents the comprehensive development of a thermal infrared monitoring network, detailing everything from the hardware schematics of the remote monitoring station (RMS) to the code for the final data processing software. The procedures implemented in the RMS for managing TIR sensor operations, acquiring environmental data, and transmitting data remotely are thoroughly discussed, along with the technical solutions adopted. The processing of TIR imagery is carried out using ASIRA (Automated System of InfraRed Analysis), a free software package, now developed for GNU Octave. ASIRA performs quality filtering and co-registration, and applies various seasonal correction methodologies to extract time series of deseasoned surface temperatures, estimate heat fluxes, and track variations in thermally anomalous areas. Processed outputs include binary, Excel, and CSV formats, with interactive HTML plots for visualization. The system’s effectiveness has been validated in active volcanic areas of southern Italy, demonstrating high reliability in detecting anomalous thermal behavior and distinguishing endogenous geophysical processes. The aim of this work is to enable readers to easily replicate and deploy this open-source, low-cost system for the continuous, automated thermal monitoring of active volcanic and geothermal areas and environmental pollution, thereby supporting hazard assessment and scientific research. Full article

The Western Hemisphere (WH) circulation pattern was discovered in recent years through Self-Organizing Maps (SOMs) clustering of the Northern Hemisphere 500 hPa geopotential height during winter. For example, the extremely cold wave that occurred in North America during 2013–14 is associated with WH circulation anomalies. We discussed the extremely cold weather conditions within the WH pattern during the winter season from 1979 to 2023. The variations of cold air in North America during the WH pattern have been demonstrated using the NCEP/NCAR reanalysis datasets. By defining WH events and North American extremely cold events, we have identified a connection between the two. In extremely cold events, linear winds are the key factor driving the temperature drop, as determined by calculating temperature advection. The ridge in the Gulf of Alaska serves as an early signal for this cold weather. The WH circulation anomaly triggers an anomalous ridge in the Gulf of Alaska region, leading to trough anomalies downstream over North America. This results in the southward movement of cold air from the polar regions, causing cooling in the mid-to-northern parts of North America. With the maintenance of the stationary wave in the North Pacific (NP), the anomalous trough over North America can be deepened, driving cold air into the continent. Influenced by the low pressure over Greenland and the storm track, the cold anomalies are concentrated in the central and northern parts of North America. This cold air situation persists for approximately two weeks. The high-level patterns of the WH pattern in both the 500 hPa height and the troposphere level have been identified using SOM. This cold weather is primarily a tropospheric phenomenon with limited correlation to stratospheric activities. Full article

In a 1975 review, red kangaroos in the arid rangelands of Australia were said to be favoured with an anomalous prosperity following the introduction of ruminant livestock. In the western and central locations reviewed, this was not sustained, but in the sheep rangelands of Southern Australia, it is often claimed that such prosperity continues. Here, as elsewhere, the marsupial herbivore guild (kangaroos, wallabies, bettongs and bandicoots) has been simplified by the extinction of the smaller species (the anomaly), while large kangaroos remain abundant. However, the mammalian herbivore guild has gained complexity with not only the introduction of managed ruminant livestock, some of which run wild, but also game like rabbits. We studied the population dynamics, habitat selection and individual mobility of red, western and eastern grey kangaroos, common wallaroos, Merino sheep, feral goats and European rabbits at Fowlers Gap Station in far northwestern New South Wales, Australia. This site is representative of the arid chenopod (Family: Chenopodiaceae) shrublands stocked with sheep, where sheep and red kangaroos dominate the mammalian herbivores by biomass. The study site comprised two contiguous pairs of stocked and unstocked paddocks: a sloping run-off zone and a flat run-on zone, covering a total area of 2158 ha. This three-year study included initial rain-deficient (drought) months followed by more regular rainfall. Red kangaroos showed avoidance of sheep when given the opportunity and heightened mobility in response to localized drought-breaking storms and dispersion of the sheep flock at lambing. Western grey kangaroos were sedentary and did not dissociate from sheep. These effects were demonstrated at the population level and the individual level through radio-tracking a small cohort of females. The other kangaroo species and goats were transient and preferred other habitats. Rabbits were persistent and localized without strong interactions with other species. The results are discussed with a focus on the red kangaroo and some causes for its resilience in the sheep rangelands. Full article

Seismic exploration is widely used in shallow engineering applications, yet extracting reflected wave information remains challenging due to contamination from Rayleigh waves. To overcome this, we propose a common shot point time-domain differential method that leverages the distinct velocity contrast between slow Rayleigh waves and faster P-wave reflections. These waves exhibit lower velocity and minimal dispersion in the radiation direction under the same seismic source excitation. This study establishes two closely spaced track records termed “far main and near slave” along the direction of the measurement line to counteract this interference. This method employs the difference in travel time between Rayleigh waves and subsurface interface reflection waves for time-domain differential analysis. The interference is minimized while preserving the reflected wave signal by conducting slight amplitude compensation on the far-field Rayleigh wave signal and subtracting the master and slave records. The application of time-domain differential detection technology in shallow engineering seismic exploration and marble plate thickness detection experiments demonstrated that this method effectively eliminates the influence of Rayleigh surface waves and enhances the resolution of reflection signals from anomalous bodies. Additionally, this study examines the impact of boundaries on time-domain differential technology. Without relying on long array shot records, this approach provides a promising result for Rayleigh wave suppression and offers broad potential in elastic wave exploration. Full article

Anomaly detection is essential in cybersecurity for identifying abnormal activities, a requirement that has grown increasingly critical with the complexity of cyberthreats. This study leverages the BPF-Extended Tracking Honeypot (BETH) dataset, a comprehensive resource designed to benchmark robustness in detecting anomalous behavior in kernel-level process and network logs. The symmetry of the proposed system lies in its ability to identify balanced and consistent patterns within kernel-level process logs, which form the foundation for accurately distinguishing anomalies. This study focuses on anomaly detection in kernel-level process logs by introducing an enhanced Isolation Forest (iForest) model, which is integrated into a structured framework that includes exploratory data analysis (EDA), data pre-processing, model training, validation, and evaluation. The proposed approach achieves a significant performance improvement in the anomaly detection results, with an area under the receiver operating characteristic curve (AUROC) score of 0.917—an approximate 7.88% increase over the baseline model’s AUROC of 0.850. Additionally, the model demonstrates high precision (99.57%), F1-score (91.69%), and accuracy (86.03%), effectively minimizing false positives while maintaining balanced detection capabilities. These results underscore the role of leveraging symmetry in designing advanced intrusion detection systems, offering a structured and efficient solution for identifying cyberthreats. Full article

This paper presents an innovative approach to wind energy forecasting through the implementation of an extended long short-term memory (xLSTM) model. This research addresses fundamental limitations in time-sequence forecasting for wind energy by introducing architectural enhancements to traditional LSTM networks. The xLSTM model incorporates two key innovations: exponential gating with memory mixing and a novel matrix memory structure. These improvements are realized through two variants, i.e., scalar LSTM and matrix LSTM, which are integrated into residual blocks to form comprehensive architectures. The xLSTM model was validated using SCADA data from wind turbines, with rigorous preprocessing to remove anomalous measurements. Performance evaluation across different wind speed regimes demonstrated robust predictive capabilities, with the xLSTM model achieving an overall coefficient of determination value of 0.923 and a mean absolute percentage error of 8.47%. Seasonal analysis revealed consistent prediction accuracy across varied meteorological patterns. The xLSTM model maintains linear computational complexity with respect to sequence length while offering enhanced capabilities in memory retention, state tracking, and long-range dependency modeling. These results demonstrate the potential of xLSTM for improving wind power forecasting accuracy, which is crucial for optimizing turbine operations and grid integration of renewable energy resources. Full article

To date, there is little publicly available scientific data on unidentified aerial phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal, multi-spectral ground-based observatory to continuously monitor the sky and collect data for UAP studies via a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave-infrared FLIR Boson 640 cameras. In addition to performing intrinsic and thermal calibrations, we implement a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance–Broadcast (ADS-B) data that we collect synchronously on site. Using a You Only Look Once (YOLO) machine learning model for object detection and the Simple Online and Realtime Tracking (SORT) algorithm for trajectory reconstruction, we establish a first baseline for the performance of the system over five months of field operation. Using an automatically generated real-world dataset derived from ADS-B data, a dataset of synthetic 3D trajectories, and a hand-labeled real-world dataset, we find an acceptance rate (fraction of in-range airplanes passing through the effective field of view of at least one camera that are recorded) of 41% for ADS-B-equipped aircraft, and a mean frame-by-frame aircraft detection efficiency (fraction of recorded airplanes in individual frames which are successfully detected) of 36%. The detection efficiency is heavily dependent on weather conditions, range, and aircraft size. Approximately 500,000 trajectories of various aerial objects are reconstructed from this five-month commissioning period. These trajectories are analyzed with a toy outlier search focused on the large sinuosity of apparent 2D reconstructed object trajectories. About 16% of the trajectories are flagged as outliers and manually examined in the IR images. From these ∼80,000 outliers and 144 trajectories remain ambiguous, which are likely mundane objects but cannot be further elucidated at this stage of development without information about distance and kinematics or other sensor modalities. We demonstrate the application of a likelihood-based statistical test to evaluate the significance of this toy outlier analysis. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers for the five-month interval at a 95% confidence level. This test is applicable to all of our future outlier searches. Full article

This paper presents a proof-of-concept of a versatile Power Quality (PQ) analyser for tracking the voltage supply in industrial and residential sectors. It implements 2D Higher-Order Statistics (HOS) to assess voltage quality, based more on the sinusoidal waveform than on power fluctuations. Beyond the second-order parameters and permissible deviations regulated by the norm, EN 50160, the two-dimensional traces and probability density functions, along with a previously studied differential index, manage to identify different states of the electrical grid. Waveforms were measured in the wall plugs of a public building. In regard to analysing reliability and voltage waveform, the results corroborate that incorporating skewness and kurtosis indicators improves the characterisation, as well as extracting the customers’ supply behaviour under normal and anomalous operations. The instrument showed good behaviour in site characterisation, and the implemented method was considered as a probabilistic approach for the risk assessment of an installation. The prototype was tested in the facilities of a public building of the university, being able to detect deviations in 10 s traces of 3.9% in variance and 0.6% in kurtosis. Full article

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and repetitive behaviors, often associated with atypical visual attention patterns. In this paper, the Gaze-Based Autism Classifier (GBAC) is proposed, which is a Deep Neural Network model that leverages both data distillation and data attribution techniques to enhance ASD classification accuracy and explainability. Using data sampled by eye tracking sensors, the model identifies unique gaze behaviors linked to ASD and applies an explainability technique called TracIn for data attribution by computing self-influence scores to filter out noisy or anomalous training samples. This refinement process significantly improves both accuracy and computational efficiency, achieving a test accuracy of 94.35% while using only 77% of the dataset, showing that the proposed GBAC outperforms the same model trained on the full dataset and random sample reductions, as well as the benchmarks. Additionally, the data attribution analysis provides insights into the most influential training examples, offering a deeper understanding of how gaze patterns correlate with ASD-specific characteristics. These results underscore the potential of integrating explainable artificial intelligence into neurodevelopmental disorder diagnostics, advancing clinical research by providing deeper insights into the visual attention patterns associated with ASD. Full article

In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series in video format using a multimodal approach. Through pattern recognition algorithms and segmentation, informative measures of the number of people and image occupancy are extracted at regular intervals, which are then analyzed to obtain trends and anomalous behaviors. Specifically, through temporal decomposition and residual analysis, intervals or specific situations of unusual behaviors are identified, which can be used in decision-making and the improvement of actions in sectors related to human movement such as tourism or security. This methodology introduces a novel, privacy-focused approach by analyzing anonymized metrics rather than tracking or recognizing individuals, setting a new standard for ethical crowd monitoring. Applied to the webcam of Turisme Comunitat Valenciana in the town of Morella (Comunitat Valenciana, Spain), this approach has shown excellent results, correctly detecting specific anomalous situations and unusual overall increases during the previous weekend and during the October 2023 festivities. These results have been obtained while preserving the confidentiality of individuals at all times by using measures that maximize anonymity, without trajectory recording or person recognition. Full article

This study explores the intersection of personality, attention and task performance in traditional 2D and immersive virtual reality (VR) environments. A visual search task was developed that required participants to find anomalous images embedded in normal background images in 3D space. Experiments were conducted with 30 subjects who performed the task in 2D and VR environments while their eye movements were tracked. Following an exploratory correlation analysis, we applied machine learning techniques to investigate the predictive power of gaze features on human data derived from different data collection methods. Our proposed methodology consists of a pipeline of steps for extracting fixation and saccade features from raw gaze data and training machine learning models to classify the Big Five personality traits and attention-related processing speed/accuracy levels computed from the Group Bourdon test. The models achieved above-chance predictive performance in both 2D and VR settings despite visually complex 3D stimuli. We also explored further relationships between task performance, personality traits and attention characteristics. Full article

Experiments to measure a single molecule/particle, i.e., an individual molecule/particle, at room temperature or under physiological conditions without immobilization—for example, on a surface or without significant hydrodynamic flow—have so far failed. This failure has given impetus to the underlying theory of Brownian molecular motion towards its stochastics due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion). Full article