Search Results (387)

Wireless pipeline robots often suffer from localization drift and position loss due to electromagnetic attenuation and shielding in complex pipeline configurations, which hinders accurate pipeline reconstruction. This paper proposes a trajectory reconstruction method based on Geometric Constraint–Long Short-Term Memory (GC-LSTM). First, a motor control system based on Field-Oriented Control (FOC) was developed for the proposed pipeline robot; second, trajectory errors are mitigated by exploiting pipeline geometric characteristics; third, a Long Short-Term Memory (LSTM) network is used to predict and compensate the robot’s velocity when odometer slip occurs; finally, multi-sensor fusion is employed to obtain the reconstructed trajectory. In straight-pipe tests, the GC-LSTM method reduced the maximum deviation and mean absolute deviation by 69.79% and 72.53%, respectively, compared with the Back Propagation (BP) method, resulting in a maximum deviation of 0.0933 m and a mean absolute deviation of 0.0351 m. In bend-pipe tests, GC-LSTM reduced the maximum deviation and the mean absolute deviation by 60.48% and 69.91%, respectively, compared with BP, yielding a maximum deviation of 0.2519 m and a mean absolute deviation of 0.0850 m. The proposed method significantly improves localization accuracy for wireless pipeline robots and enables more precise reconstruction of pipeline environments, providing a practical reference for accurate localization in pipeline inspection applications. Full article

Autonomous commercial vehicles often mount multiple LiDARs to enlarge their field of view, but conventional calibration is labor-intensive and prone to drift during long-term operation. We present an online self-calibration method that combines a ground plane motion constraint with a virtual RGB–D projection, mapping 3D point clouds to 2D feature/depth images to reduce feature extraction cost while preserving 3D structure. Motion consistency across consecutive frames enables a reduced-dimension hand–eye formulation. Within this formulation, the estimation integrates geometric constraints on $SE\left(3\right)$ using Lagrange multiplier aggregation and quasi-Newton refinement. This approach highlights key aspects of identifiability, conditioning, and convergence. An online monitor evaluates plane alignment and LiDAR–INS odometry consistency to detect degradation and trigger recalibration. Tests on a commercial vehicle with six LiDARs and on nuScenes demonstrate accuracy comparable to offline, target-based methods while supporting practical online use. On the vehicle, maximum errors are 6.058 cm (translation) and 4.768° (rotation); on nuScenes, 2.916 cm and 5.386°. The approach streamlines calibration, enables online monitoring, and remains robust in real-world settings. Full article

Accurate and reliable vehicle localization is essential for autonomous driving in complex outdoor environments. Traditional feature-based visual–inertial odometry (VIO) suffers from sparse features and sensitivity to illumination, limiting robustness in outdoor scenes. Deep learning-based optical flow offers dense and illumination-robust motion cues. However, existing methods rely on simple bidirectional consistency checks that yield unreliable flow in low-texture or ambiguous regions. Global navigation satellite system (GNSS) measurements can complement VIO, but often degrade in urban areas due to multipath interference. This paper proposes a multi-sensor fusion system that integrates monocular VIO with GNSS measurements to achieve robust and drift-free localization. The proposed approach employs a hybrid VIO framework that utilizes a deep learning-based optical flow network, with an enhanced consistency constraint that incorporates local structure and motion coherence to extract robust flow measurements. The extracted optical flow serves as visual measurements, which are then fused with inertial measurements to improve localization accuracy. GNSS updates further enhance global localization stability by mitigating long-term drift. The proposed method is evaluated on the publicly available KITTI dataset. Extensive experiments demonstrate its superior localization performance compared to previous similar methods. The results show that the filter-based multi-sensor fusion framework with optical flow refined by the enhanced consistency constraint ensures accurate and reliable localization in large-scale outdoor environments. Full article

Predicting early-age temperature evolution in mass concrete is crucial for controlling thermal cracks. This process involves two distinct physical stages: an initial, hydration-driven heating stage (Stage I) and a subsequent, environment-dominated cooling stage (Stage II). To address this challenge, we propose a novel two-stage hybrid modeling strategy that decouples the underlying physical processes. This approach was developed and validated using a 450-h on-site monitoring dataset. For the deterministic heating phase (Stage I), we employed polynomial regression. For the subsequent stochastic cooling phase (Stage II), a Random Forest algorithm was used to model the complex environmental interactions. The proposed hybrid model was benchmarked against several alternatives, including a physics-based finite element model (FEM) and a single Random Forest model. During the critical cooling stage, our approach demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of $0.24°\mathrm{C}$. This represents a 17.2% improvement over the best-performing single model. Furthermore, cumulative error analysis indicated that the hybrid model maintained a stable and unbiased prediction trend throughout the monitoring period. This addresses a key weakness in single-stage models, where underlying phase-specific errors can accumulate and lead to long-term drift. The proposed framework offers an accurate, robust, and transferable paradigm for modeling other complex engineering processes that exhibit distinct multi-stage characteristics. Full article

Accurate parameter estimation of solar cells is critical for early-stage fault diagnosis in photovoltaic (PV) power systems. A physical model based on three-diode configuration has been recently introduced to improve model accuracy. However, nonlinear and recursive relationships between internal parameters and PV output, along with parameter drift and PV degradation due to long-term operation, pose significant challenges. To address these issues, this study proposes a deep learning-based ensemble framework that integrates outputs from multiple optimization algorithms to improve estimation precision and robustness. The proposed method consists of three stages. First, the collected data were preprocessed using some data processing techniques. Second, a PV power generation system is modeled using the three-diode structure. Third, several optimization algorithms with distinct search behaviors are employed to produce diverse estimations. Finally, a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is used to learn from these results. Experimental validation on a 733 kW PV power generation system demonstrates that the proposed method outperforms individual optimization approaches in terms of prediction accuracy and stability. Full article

With the iterative evolution of SLAM (Simultaneous Localization and Mapping) technology in the robotics domain, the SLAM paradigm based on three-dimensional Gaussian distribution models has emerged as the current state-of-the-art technical approach. This research proposes a novel MSGS-SLAM system (Monocular Semantic Gaussian Splatting SLAM), which innovatively integrates monocular vision with three-dimensional Gaussian distribution models within a semantic SLAM framework. Our approach exploits the inherent spherical symmetries of isotropic Gaussian distributions, enabling symmetric optimization processes that maintain computational efficiency while preserving geometric consistency. Current mainstream three-dimensional Gaussian semantic SLAM systems typically rely on depth sensors for map reconstruction and semantic segmentation, which not only significantly increases hardware costs but also limits the deployment potential of systems in diverse scenarios. To overcome this limitation, this research introduces a depth estimation proxy framework based on Metric3D-V2, which effectively addresses the inherent deficiency of monocular vision systems in depth information acquisition. Additionally, our method leverages architectural symmetries in indoor environments to enhance semantic understanding through symmetric feature matching. Through this approach, the system achieves robust and efficient semantic feature integration and optimization without relying on dedicated depth sensors, thereby substantially reducing the dependency of three-dimensional Gaussian semantic SLAM systems on depth sensors and expanding their application scope. Furthermore, this research proposes a keyframe selection algorithm based on semantic guidance and proxy depth collaborative mechanisms, which effectively suppresses pose drift errors accumulated during long-term system operation, thereby achieving robust global loop closure correction. Through systematic evaluation on multiple standard datasets, MSGS-SLAM achieves comparable technical performance to existing three-dimensional Gaussian model-based semantic SLAM systems across multiple key performance metrics including ATE RMSE, PSNR, and mIoU. Full article

Wireless sensor networks (WSNs) are deployed in terrestrial environments. However, on the sea surface, sensor nodes can drift due to ocean currents and wind; thus, network topologies continuously evolve, and the communication between nodes is frequently disrupted. These unstable connections significantly degrade data transmission stability and overall network performance. These problems are particularly significant in maritime regions where the sea state changes rapidly, thus imposing stringent technical requirements on the design of long-range, reliable, low-latency, and persistent sensing systems. This study proposes a wireless sensor network architecture for sea surface drifting nodes, which is termed Drift-Aware Routing and Clustering with Recovery (DARCR). The proposed system consists of three major components: (1) an enhanced dynamic drift model that more accurately predicts node movement for realistic ocean conditions; (2) a cluster-based framework that prevents disconnection and minimizes delay, which improves cluster stability and adaptability to dynamic environments through refined clustering and route setup mechanisms; and (3) a self-recovery routing strategy for re-establishing communication after disconnection. The proposed method is evaluated using ocean current data from the Copernicus Ocean Data Center simulating a 60-h drifting scenario around the central Taiwan Strait. The experimental results show that the average hourly disconnection rate is maintained at 6.2%, with a variance of 0.31%, and the transmission of newly sensed data is completed within 3 to 5 s, with a maximum delay of approximately 10 s. These findings demonstrate the feasibility of maintaining communication stability and low-latency data transmission for sea surface WSNs that operate in highly dynamic marine conditions. Full article

Accurate long-term tracking of individual cattle is essential for precision livestock farming but remains challenging due to occlusions, posture variability, and identity drift in free-range environments. We propose a multi-camera tracking framework that combines bird’s-eye-view (BEV) trajectory matching with cattle face recognition to ensure identity preservation across long video sequences. A large-scale dataset was collected from five synchronized 4K cameras in a commercial barn, capturing both full-body movements and frontal facial views. The system employs center point detection and BEV projection for cross-view trajectory association, while periodic face recognition during feeding refreshes identity assignments and corrects errors. Evaluations on a two-day dataset of more than 600,000 images demonstrate robust performance, with an AssPr of 84.481% and a LocA score of 78.836%. The framework outperforms baseline trajectory matching methods, maintaining identity consistency under dense crowding and noisy labels. These results demonstrate a practical and scalable solution for automated cattle monitoring, advancing data-driven livestock management and welfare. Full article

The demand for low-latency computing from the Internet of Things (IoT) and emerging applications challenges traditional cloud computing. Mobile Edge Computing (MEC) offers a solution by deploying resources at the network edge, yet terrestrial deployments face limitations. Unmanned Aerial Vehicles (UAVs), leveraging their high mobility and flexibility, provide dynamic computation offloading for User Equipments (UEs), especially in areas with poor infrastructure or network congestion. However, UAV-assisted MEC confronts significant challenges, including time-varying wireless channels and the inherent energy constraints of UAVs. We put forward the Lyapunov-based Deep Deterministic Policy Gradient (LyDDPG), a novel computation offloading algorithm. This algorithm innovatively integrates Lyapunov optimization with the Deep Deterministic Policy Gradient (DDPG) method. Lyapunov optimization transforms the long-term, stochastic energy minimization problem into a series of tractable, per-timeslot deterministic subproblems. Subsequently, DDPG is utilized to solve these subproblems by learning a model-free policy through environmental interaction. This policy maps system states to optimal continuous offloading and resource allocation decisions, aiming to minimize the Lyapunov-derived “drift-plus-penalty” term. The simulation outcomes indicate that, compared to several baseline and leading algorithms, the proposed LyDDPG algorithm reduces the total system energy consumption by at least 16% while simultaneously maintaining low task latency and ensuring system stability. Full article

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries under complex operating conditions remains challenging, as the SOC signal combines a global linear (quasi-linear) trend with localized dynamic fluctuations driven by polarization, ion diffusion, temperature gradients, and load transients. In practice, open-circuit-voltage (OCV) approaches are affected by hysteresis and parameter drift, while high-fidelity electrochemical models require extensive parameterization and significant computational resources that hinder their real-time deployment in battery management systems (BMS). Purely data-driven methods capture temporal patterns but may under-represent abrupt local fluctuations and blur the distinction between trend and fluctuation, leading to biased SOC tracking when operating conditions change. To address these issues, LF-Net is proposed. The architecture decomposes battery time series into long-term trend and local fluctuation components. A linear branch models the quasi-linear SOC evolution. Multi-scale convolutional and differential branches enhance sensitivity to transient dynamics. An adaptive Fusion Module aggregates the representations, improving interpretability and stability, and keeps the parameter budget small for embedded hardware. Our experimental results demonstrate that the proposed model achieves a mean absolute error (MAE) of 0.0085 and a root-mean-square error (RMSE) of 0.0099 at 40 °C, surpassing mainstream models and confirming the method’s efficacy. Full article

The data collected from sensors are subject to the presence of anomaly data. These anomalies may stem from sensor malfunctions or poor communication. Prior to the processing of the data, it is imperative to detect and isolate the anomaly data from the substantial volume of normal data. The utilization of data-driven approaches for sensor anomaly detection and isolation frequently confronts the predicament of inadequately labeled data. In one aspect, the data obtained from sensors usually contain no or few examples of faults and those faults are difficult to identify manually from a large amount of raw data. Additionally, the operational states of a machine may undergo alterations during its functioning, potentially resulting in different sensor measurement behaviors. However, the operational states of a machine are not clearly labeled either. In order to address the challenges posed by the absence or lack of labeled data in both domains, a sensor anomaly detection and isolation method using LSTM (long short-term memory) networks is proposed in this paper. In order to predict sensor measurements at a subsequent timestep, behaviors in the preceding timesteps are utilized to consider the influence of the varying operational states. The inputs of the LSTM networks are selected based on prediction errors trained by a small dataset to increase the prediction accuracy and reduce the influence of redundant sensors. The residual between the predicted data and the measurement data is used to determine whether an anomaly has been identified. The proposed method is evaluated using a real dataset obtained from a truck operating in a mine. The results showed that the proposed network with the input-selection method demonstrated the ability to accurately detect drift and stall anomalies accurately in the experiments. Full article

Chemical sensors have undergone transformative advances in recent years, driven by the convergence of nanomaterials, advanced fabrication strategies, and state-of-the-art characterization methods. This review emphasizes recent developments, with particular attention to progress achieved over the past decade, and highlights the role of the United States as a major driver of global innovation in the field. Nanomaterials such as graphene derivatives, MXenes, carbon nanotubes, metal–organic frameworks (MOFs), and hybrid composites have enabled unprecedented analytical performance. Representative studies report detection limits down to the parts-per-billion (ppb) and even parts-per-trillion (ppt) level, with linear ranges typically spanning 10–500 ppb for volatile organic compounds (VOCs) and 0.1–100 μM for biomolecules. Response and recovery times are often below 10–30 s, while reproducibility frequently exceeds 90% across multiple sensing cycles. Stability has been demonstrated in platforms capable of continuous operation for weeks to months without significant drift. In parallel, additive manufacturing, device miniaturization, and flexible electronics have facilitated the integration of sensors into wearable, stretchable, and implantable platforms, extending their applications in healthcare diagnostics, environmental monitoring, food safety, and industrial process control. Advanced characterization techniques, including in situ Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS, Atomic Force Microscopy (AFM), and high-resolution electron microscopy, have elucidated interfacial charge-transfer mechanisms, guiding rational material design and improved selectivity. Despite these achievements, challenges remain in terms of scalability, reproducibility of nanomaterial synthesis, long-term stability, and regulatory validation. Data privacy and cybersecurity also emerge as critical issues for IoT-integrated sensing networks. Looking forward, promising future directions include the integration of artificial intelligence and machine learning for real-time data interpretation, the development of biodegradable and eco-friendly materials, and the convergence of multidisciplinary approaches to ensure robust, sustainable, and socially responsible sensing platforms. Overall, nanomaterial-enabled chemical sensors are poised to become indispensable tools for advancing public health, environmental sustainability, and industrial innovation, offering a pathway toward intelligent and adaptive sensing systems. Full article

Phylogeographic studies in Antarctica allow us to understand the demographic events of populations during glacial periods. In this study, the polyplacophoran Tonicina zschaui was analyzed in several localities on the West Antarctic Coast using the mitochondrial gene cytochrome oxidase subunit I (COI). Two genetically distinct populations were identified: one in the Weddell Sea and another across the Antarctic Peninsula and South Shetland Islands. Genetic diversity was generally low to moderate, suggesting limited gene flow and the influence of historical climatic events. Star-like haplotype networks and demographic analyses indicate population contractions during the Last Glaciation followed by postglacial expansion, especially in the Antarctic Peninsula–South Shetland Islands population. Several sites in this region were identified as potential glacial refugia, exhibiting proportionally elevated genetic diversity and exclusive haplotypes. Conversely, the small Weddell Sea population displayed signs of long-term isolation, limited expansion, and low diversity, likely due to stronger environmental constraints and genetic drift. Ocean currents such as the Antarctic Coastal Current, the Antarctic Peninsula Coastal Current and the Weddell Gyre appear to restrict larval dispersal, reinforcing genetic discontinuities. These findings support the hypothesis of glacial survival in localized refugia and postglacial recolonization, a pattern observed in other Antarctic marine invertebrates. Full article

Ensuring the reliability of dialysis machines and their components, such as sensors and actuators, is critical for maintaining continuous and safe dialysis treatment for patients with chronic kidney disease. This study investigates the application of Artificial Intelligence for detecting drift in dialysis machine components by comparing a Long Short-Term Memory (LSTM) neural network with a traditional linear regression model. Both models were trained to learn normal patterns from time-dependent signals monitoring the performance of specific components of a dialytic machine, such as a weight loss sensor in the present case, enabling the detection of anomalies related to sensor degradation or failure. Real-world data from multiple clinical cases were used to validate the approach. The LSTM model achieved high reconstruction accuracy on normal signals (most errors < 0.02, maximum ≈ 0.08), and successfully detected anomalies exceeding this threshold in complaint cases, where the model anticipated failures up to five days in advance. On the contrary, the linear regression model was limited to detecting only major deviations. These findings highlighted the advantages of AI-based methods in equipment monitoring, minimizing unplanned downtime, and supporting preventive maintenance strategies within dialysis care. Future work will focus on integrating this model into both clinical and home dialysis settings, aiming to develop a scalable, adaptable, and generalizable solution capable of operating effectively across various conditions. Full article

Sensor arrays serve a crucial role in various fields such as environmental monitoring, industrial process control, and medical diagnostics, yet their reliability remains challenged by sensor drift and noise contamination. This study presents a novel framework for real-time data error correction and long-term drift compensation utilizing an iterative random forest-based error correction algorithm paired with an Incremental Domain-Adversarial Network (IDAN). The iterative random forest algorithm leverages the collective data from multiple sensor channels to identify and rectify abnormal sensor responses in real time. The IDAN integrates domain-adversarial learning principles with an incremental adaptation mechanism to effectively manage temporal variations in sensor data. Experiments utilizing the metal oxide semiconductor gas sensor array drift dataset demonstrate that the combination of these approaches significantly enhances data integrity and operational efficiency, achieving a robust and good accuracy even in the presence of severe drift. This study underscores the efficacy of integrating advanced artificial intelligence techniques for the ongoing evolution of sensor array technology, paving the way for enhanced monitoring systems capable of sustaining high levels of performance over extended time periods. Full article