Search Results (1,663)

Industrial-grade printed circuit boards (PCBs) exhibit high structural order and inherent geometric symmetry, where minute surface defects essentially constitute symmetry-breaking anomalies that disrupt topological integrity. Detecting these anomalies is quite challenging due to issues like scale variation and low contrast. Therefore, this paper proposes a symmetry-aware object detection framework, DAS-YOLO, based on an improved YOLOv11. The U-shaped adaptive feature extraction module (Def-UAD) reconstructs the C3K2 unit, overcoming the geometric limitations of standard convolutions through a deformation adaptation mechanism. This significantly enhances feature extraction capabilities for irregular defect topologies. A semantic-aware module (SADRM) is introduced at the backbone and neck regions. The lightweight and efficient ESSAttn improves the distinguishability of small or weak targets. At the same time, to address information asymmetry between deep and shallow features, an iterative attention feature fusion module (IAFF) is designed. By dynamically weighting and calibrating feature biases, it achieves structured coordination and balanced multi-scale representation. To evaluate the validity of the proposed method, we carried out comprehensive experiments using publicly accessible datasets focused on PCB defects. The results show that the Recall, mAP@50, and mAP@50-95 of DAS-YOLO reached 82.60%, 89.50%, and 46.60%, respectively, which are 3.7%, 1.8%, and 2.9% higher than those of the baseline model, YOLOv11n. Comparisons with mainstream detectors such as GD-YOLO and SRN further demonstrate a significant advantage in detection accuracy. These results confirm that the proposed framework offers a solution that strikes a balance between accuracy and practicality in addressing the key challenges in PCB surface defect detection. Full article

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management. Full article

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analyzing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and analyzing the surges of these pollutants well before the recycling process is needed to make intelligent decisions for water cleaning. The dynamic changes in pollutants need constant monitoring and effective planning with appropriate treatment strategies. We propose an edge-computing-based smart framework that captures data from sensors, including ultraviolet, electrochemical, and microfluidic, along with other significant sensor streams. The edge devices send the data from the cluster of sensors to a centralized server that segments anomalies, analyzes the data and suggests the treatment plan that is required, which includes aeration, dosing adjustments, and other treatment plans. A logic layer is designed at the server level to process the real-time data from the sensor clusters and identify the discharge of nutrients, metals, and emerging contaminants in the water that affect the quality. The platform can make decisions on water treatments using its monitoring, prediction, diagnosis, and mitigation measures in a feedback loop. A rule-based Large Language Model (LLM) agent is attached to the server to evaluate data and trigger required actions. A streamlined data pipeline is used to harmonize sensor intervals, flag calibration drift, and store curated features in a local time-series database to run ad hoc analyses even during critical conditions. A user dashboard has also been designed as part of the system to show the recommendations and actions taken. The proposed system acts as an AI-enabled system that makes smart decisions on water treatment, providing an effective cleaning process to improve sustainability. Full article

We present a combined ML/NLP (Machine Learning, Natural Language Processing) pipeline for protecting cloud-based APIs (Application Programming Interfaces), which works both at the level of individual HTTP (Hypertext Transfer Protocol) requests and at the access log file reading mode, linking explicitly technical anomalies with business risks. The system processes each event/access log through parallel numerical and textual branches: a set of anomaly detectors trained on traffic engineering characteristics and a hybrid NLP stack that combines rules, TF-IDF (Term Frequency-Inverse Document Frequency), and character-level models trained on enriched security datasets. Their results are integrated using a risk-aware policy that takes into account endpoint type, data sensitivity, exposure, and authentication status, and creates a discrete risk level with human-readable explanations and recommended SOC (Security Operations Center) actions. We implement this design as a containerized microservice pipeline (input, preprocessing, ML, NLP, merging, alerting, and retraining services), orchestrated using Docker Compose and instrumented using OpenSearch Dashboards. Experiments with OWASP-like (Open Worldwide Application Security Project) attack scenarios show a high detection rate for injections, SSRF (Server-Side Request Forgery), Data Exposure, and Business Logic Abuse, while the processing time for each request remains within real-time limits even in sequential testing mode. Thus, the pipeline bridges the gap between ML/NLP research for security and practical API protection channels that can evolve over time through feedback and retraining. Full article

To achieve energy savings, reduce consumption, and support the “dual-carbon” strategy in China, this study applies digital twin technology to investigate the centralized air-conditioning water system of a metro-station HVAC installation and develops a high-fidelity digital twin model to reveal the thermal delay and thermal attenuation characteristics of the pipeline network. Using the noncausal modeling approach of the Modelica language, a full digital twin representation of the centralized air-conditioning water network is constructed by covering chillers, cooling towers, pumps, terminal units, the pipeline network, etc. The model is validated against real operation data to ensure high fidelity. Validation shows the predicted chilled water flow rate of the digital twin model agrees well with the measured chilled water flow rate with an RMSE of 0.27 kg/s. Validation also shows the difference is about 0.3 °C between the digital twin prediction and the measurement in the main pipe. Based on the validation digital twin model, the thermal delay and thermal attenuation characteristics of the centralized air-conditioning water system are seriously evaluated. The results indicate that branch K3, due to its longest transport distance, exhibits a delay of 227 s. The overall thermal delay of the system reaches 7.5 min. The temperature attenuation of this water system is about 0.2 °C due to heat loss through pipe walls. The findings may offer theoretical support for the optimal regulation and control, fault detection, and anomaly identification of this centralized air-conditioning water system. Full article

The increasing application of lithium-ion batteries in manufacturing and energy storage systems necessitates high-precision screening of abnormal cells during manufacturing, so as to ensure safety and performance. Existing methods struggle to break down the barrier between prior knowledge and data, suffering from limitations such as insufficient detection accuracy and poor interpretability. This becomes even more prominent when facing distributional shifts in data. In this study, we propose a knowledge-enhanced anomaly detection framework for cell screening. This framework integrates domain knowledge, such as electrochemical principles, expert heuristic rules, and manufacturing constraints, into data-driven models. By combining features extracted from charging/discharging curves with rule-based prior knowledge, the proposed framework not only improves detection accuracy but also enables a traceable reasoning process behind anomaly identification. Experiments based on real-world battery production data demonstrate that the proposed framework outperforms baseline models in both precision and recall, making it a promising preferred solution for quality control in intelligent battery manufacturing. Full article

This paper presents the design and optimisation of a low-power embedded sensor-node architecture for real-time environmental monitoring with on-board machine-learning inference. The proposed system integrates heterogeneous sensing elements for air quality and ambient parameters (temperature, humidity, gas concentration, and particulate matter) into a modular embedded platform based on a low-power microcontroller coupled with an energy-efficient neural inference accelerator. The design emphasises end-to-end energy optimisation through adaptive duty-cycling, hierarchical power domains, and edge-level data reduction. The embedded machine-learning layer performs lightweight event/anomaly detection via on-device multi-class classification (normal/anomalous/critical) using quantised neural models in fixed-point arithmetic. A comprehensive system-level analysis, performed via MATLAB Simulink simulations, evaluates inference accuracy, latency, and energy consumption under realistic environmental conditions. Results indicate that the proposed node achieves 94% inference accuracy, 0.87 ms latency, and an average power consumption of approximately 2.9 mWh, enabling energy-autonomous operation with hybrid solar–battery harvesting. The adaptive LoRaWAN communication strategy further reduces data transmissions by ≈88% relative to periodic reporting. The results indicate that on-device inference can reduce network traffic while maintaining reliable event detection under the evaluated operating conditions. The proposed architecture is intended to support energy-efficient environmental sensing deployments in smart-city and climate-monitoring contexts. Full article

Detecting anomalous events in high-dimensional behavioral data is a fundamental challenge in modern cybersecurity, particularly in scenarios involving stealthy advanced persistent threats (APTs). Traditional anomaly detection techniques rely on heuristic notions of distance or density yet rarely offer a mathematically coherent description of how sparse events can be formally empirically separated from the dominant behavioral structure. This study introduces a density–metric geometric space framework that unifies geometric, topological, and density-based perspectives into a single analytical model. Behavioral events are embedded in a five-dimensional Euclidean geometric space equipped with a neighborhood-based density operator. Anomalies are formally defined as points whose local density falls below a fixed threshold, and we show that such points occupy empirically distinct low-density regions of the induced metric space. The theoretical foundations are supported by experiments conducted on openly available cybersecurity datasets, including ADFA-LD and UNSW-NB15, where we demonstrate that low-density behavioral patterns correspond to structurally rare attack configurations. The proposed framework provides a mathematically grounded framework with empirical validation for why APT-like behaviors naturally emerge as sparse and weakly coherent regions in high-dimensional space. These results offer a principled basis for high-dimensional anomaly detection and open new directions for leveraging geometric learning in cybersecurity. Full article

Unsupervised anomaly detection has been extensively studied. However, most existing methods are designed for either numerical or nominal data, which struggle to detect anomalies effectively in real-world mixed-type datasets. Fuzzy information granulation is a key concept in granular computing, which offers a potent framework for managing uncertainty in mixed-type data and provides a viable pathway for unsupervised anomaly detection. Nevertheless, conventional fuzzy information granulation-based detection methods often model only simple, linear fuzzy relations between samples. This limitation prevents them from capturing the complex, nonlinear structures inherent in the data, leading to a degradation in detection performance. To address these shortcomings, we propose a Hybrid Kernelized Fuzzy Density-based anomaly detector (HKFD). HKFD pioneers a hybrid kernelized fuzzy relation by integrating a hybrid distance metric with kernel methods. This new relation allows us to define a hybrid kernelized fuzzy density for each sample within every feature subspace, effectively capturing the local data dispersion. Crucially, we introduce an information-theoretic weighting mechanism. By calculating the fuzzy information entropy of each feature’s distribution, HKFD automatically assigns higher weights to more informative feature subspaces that contribute more to identifying anomalies. The final anomaly factor is then calculated by the weighted fusion of these densities. Comprehensive experiments on 20 datasets demonstrate that HKFD significantly outperforms state-of-the-art methods, achieving superior anomaly detection performance. Full article

Determining long-term changes in global ice and water storage from satellite gravimetry remains challenging due to the limited temporal coverage of high-resolution missions. Here, we combine Satellite Laser Ranging (SLR) and Gravity Recovery and Climate Experiment (GRACE) data to reconstruct large-scale, non-linear mass variations from 1995 to 2024, extending gravity-based observations into the pre-GRACE era while preserving spatial detail through backward extrapolation. The combined model reveals widespread and statistically significant accelerations in global water and ice mass changes and enables the identification of key turning points in their temporal evolution. Results indicate that in Svalbard, a non-linear transition in ice mass balance occurred in late 2004, followed by a pronounced acceleration of mass loss due to climate warming. Glaciers in the Gulf of Alaska exhibit persistent mass loss with a marked intensification after 2012, while in the Antarctic Peninsula, ice mass loss substantially slowed and a potential trend reversal emerged around 2021. The reconstructed mass anomalies show strong consistency with independent satellite altimetry and climate indicators, including a clear response to the 1997/1998 El Niño event prior to the GRACE mission. These findings demonstrate that integrating SLR with GRACE enables robust detection of non-linear, climate-driven mass redistribution on a global scale and provides a physically consistent extension of satellite gravimetry records beyond the GRACE era. Full article

Acoustic-based anomaly detection in elevators is crucial for predictive maintenance and operational safety, yet it faces significant challenges in real-world settings, including pervasive multi-source acoustic interference within confined spaces and severe class imbalance in collected data, which critically degrades the detection performance for minority yet critical acoustic events. To address these issues, this study proposes a novel hierarchical self-distillation framework. The method embeds auxiliary classifiers into the intermediate layers of a backbone network, creating a deep teacher–shallow student knowledge transfer paradigm optimized jointly via Kullback–Leibler divergence and feature alignment losses. A self-attentive temporal pooling layer is introduced to adaptively weigh discriminative time-frequency features, thereby mitigating temporal overlap interference, while a focal loss function is employed specifically in the teacher model to recalibrate the learning focus towards hard-to-classify minority samples. Extensive evaluations on the public UrbanSound8K dataset and a proprietary industrial elevator audio dataset demonstrate that the proposed model achieves superior performance, exceeding 90% in both accuracy and F1-score. Notably, it yields substantial improvements in recognizing rare events, validating its robustness for elevator acoustic monitoring. Full article

Currently, the manual delineation of landslide anomalies from Interferometric Synthetic Aperture Radar(InSAR )deformation data is labor-intensive and time-consuming, creating a major bottleneck for operational large-scale landslide mapping. This study proposes an automated approach for large-scale landslide identification by integrating InSAR technology with an improved Faster Regional Convolutional Neural Network (Faster R-CNN). First, surface deformation over the study area was obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. An enhanced CRF-Faster R-CNN model was then developed by incorporating a Residual Network with 50 layers (ResNet-50)-based backbone, strengthened with a Convolutional Block Attention Module (CBAM), within a Feature Pyramid Network (FPN) framework. This model was applied to deformation velocity maps for the automated detection of landslide-prone areas. Preliminary results were subsequently validated and refined using optical images to produce a final landslide inventory. The proposed method was evaluated in the Ahai Reservoir area of the Jinsha River Basin using 248 ascending and descending Sentinel-1A images acquired between January 2019 and December 2021. Its performance was compared with that of the standard Faster R-CNN model. The results indicate that the CRF-Faster R-CNN model outperforms the conventional approach in terms of landslide anomaly detection, convergence speed, and overall accuracy. A total of 38 potential landslide hazards were identified in the Ahai Reservoir area, with an 84% validation accuracy confirmed through field investigations. This study provides crucial technical support for the rapid identification and operational application of large-scale potential landslide hazards. Full article

Detecting fraudulent and anomalous transactions in large-scale digital payment systems is significantly challenging due to severe class imbalance and the fact that transactional risk is tightly coupled to the historical interactions and behaviors of transacting parties. In this study, a scalable Behavioral Interaction-Aware Gradient Boosting (BI-GBDT) framework is proposed for anomaly detection in tabular transaction data to overcome these challenges. The methodology models sending and receiving behaviors separately through direction-specific clustering based on transaction frequency and amount. Each transaction is characterized by cluster-pair prevalence ratios, which capture the population-level prevalence of sender–receiver interaction patterns. To handle extreme class imbalance, all transactions are clustered, and a cluster-level risk score is computed as the ratio of anomalous transactions to the total number of transactions within each cluster. This score is incorporated as a feature, serving as a behavioral risk prior highlighting concentrated anomaly. These interaction-aware features are integrated into a GBDT in a big data environment. Experiments were conducted on a large masked real-world payment dataset spanning six months and containing more than 456 million transactions, with the prediction task defined as binary classification between fraudulent and non-fraudulent transactions. Unlike standard GBDT models trained only on transactional attributes and graph-based approaches, BI-GBDT captures sender–receiver interaction patterns in a graph-free manner and outperforms a baseline GBDT, reducing the false positive rate from 37.0% to 4.3%, increasing recall from 52.3% to 72.0%, and improving accuracy from 63.0% to 95.7%. Full article

Monitoring marine litter in the Arctic is crucial for environmental assessment, yet automated methods are needed to process large volumes of visual data. This study develops and compares two distinct machine learning approaches to automatically detect floating marine litter, birds, and other anomalies from ship-based optical imagery captured in the Barents and Kara seas. We evaluated a supervised Visual Object Detection (VOD) model (YOLOv11) against a self-supervised classification approach that combines a Momentum Contrast (MoCo) framework with a ResNet50 backbone and a CatBoost classifier. Both methods were trained and tested on a dataset of approximately 10,000 manually annotated sea surface images. Our findings reveal a significant performance trade-off between the two techniques. The YOLOv11 model excelled in detecting clearly visible objects like birds with an F1-score of 73%, compared to 67% for the classification method. However, for the primary and more challenging task of identifying marine litter, which demonstrates less clear visual representation in optical imagery, the self-supervised approach was substantially more effective, achieving a 40% F1-score, versus the 10% obtained for the VOD model. This study demonstrates that, while standard object detectors are effective for distinct objects, self-supervised learning strategies can offer a more robust solution for detecting less-defined targets like marine litter in complex sea-surface imagery. Full article

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. Full article