Search Results (62)

Non-contact magnetic sensing technology is widely adopted in transmission line online monitoring scenarios including current measurement and fault location for its non-contact measurement capability, strong environmental robustness and low deployment cost. However, existing magnetic-sensing-based galloping monitoring methods suffer from two critical limitations: no theoretical guidance is provided for sensor placement, and a high false detection rate is observed under current fluctuation conditions. To address these issues, a novel transmission line galloping monitoring method based on spatial magnetic field distribution features is proposed in this paper. A conductor galloping-power frequency magnetic field coupling model is first established to derive the optimal magnetic sensor array arrangement strategy. Subsequently, a galloping detection algorithm fusing multi-node frequency-domain features and phase difference information is proposed to eliminate current fluctuation induced false detection. Simulations conducted based on actual 500 kV transmission line parameters and verification tests carried out on a scaled-down laboratory platform confirm that reliable galloping detection can be realized by the proposed method under both current low-frequency oscillation and random fluctuation scenarios. With advantages of non-contact deployment, high anti-interference performance and detection accuracy, the proposed method has promising application potential in engineering-oriented high-voltage transmission line monitoring. Full article

Sensor-based human activity recognition (HAR) plays a fundamental role in healthcare monitoring, sports analytics, and ambient-assisted living. Although deep learning has substantially advanced HAR performance, two practical issues still limit its real-world deployment: (i) the distribution shift caused by changes in users or sensor placements can degrade generalization, and (ii) the quadratic $\mathcal{O}\left(L^2\right)$ complexity of standard self-attention hinders efficient long-sequence modeling on resource-constrained wearable devices. To address these issues, we propose DSHformer, which is an accuracy-oriented HAR framework that combines compact channel–temporal encoding with locality-sensitive hashing (LSH)-based attention. Specifically, DSHformer (i) employs a low-parameter patch-based graph-attention encoder to jointly model latent relationships among sensor channel–temporal dynamics; (ii) introduces a trainable prototype pool together with a multi-layer decomposition network to improve intra-class compactness and inter-class separability via prototype alignment; and (iii) introduces a decomposition-stable LSH-based attention mechanism tailored for HAR, whose core design couples prototype-guided feature decomposition with locality-sensitive hashing to ensure that semantically related tokens remain consistently grouped in the same hash bucket even after decomposition-induced attenuation. The mechanism thereby operates at $\mathcal{O}(LlogL)$ attention complexity on longer sensor sequences. Extensive experiments on five public benchmarks (WISDM, UCI-HAR, PAMAP2, Opportunity, and UniMiB-SHAR) show that DSHformer achieves accuracies of 98.6%, 93.7%, 98.4%, 88.5%, and 96.6%, respectively, achieving competitive or superior performance compared with both Transformer variants and HAR-specific baselines under the adopted benchmark protocols. Ablation studies further confirm the complementary contribution of each component. Full article

Flexible strain sensors have attracted considerable attention in gait recognition owing to their ability to adhere directly to the skin near joints and transduce local deformation. In existing work, however, sensor placement and orientation are largely determined by anatomical experience, while multi-channel classification still relies on back-end digital processors, whose power consumption and latency constrain system practicality in wearable scenarios. This paper presents an integrated design path that proceeds from skin-mechanics theory through sensor-layout optimization to analog-domain front-end inference. On the layout side, the lines-of-non-extension (LoNE) theory is employed to convert the selection of sensor attachment angles from empirical judgment into a calculable mechanics problem; guided by the spatial course of LoNE in the ankle and knee regions, the positions and angles of the nine sensors are determined individually—channels perpendicular to the LoNE capture maximum strain, channels offset by 45 degrees supplement non-sagittal-plane information, and a channel aligned along the LoNE provides a near-zero-strain reference. On the circuit side, the mathematical equivalence between the weighted summation of a linear classifier and Kirchhoff’s current law (KCL) nodal current superposition is exploited to map the classification operation onto current aggregation in an analog circuit, yielding an in-sensor computing (ISC) front end in which the nine-channel weighted summation is completed in a single analog step. The sensors are fabricated by screen-printing a liquid-metal–polymer composite conductive ink onto a TPU film substrate, with a gauge factor RSD of $6.8\%$ and a tensile linearity $R^2>0.99$. Using walking, running, and stair descent as verification targets, the analog classifier reaches 99% accuracy at the circuit-level functional-verification stage. On real multi-subject data, it achieves $87.0\%\pm 8.4\%$ accuracy under intra-subject cross-session validation, with an analog-domain inference response faster than $100\mathsf{\mu }\mathrm{s}$. This design path is not bound to a specific joint or sensor material; when the layout methodology is extended to additional joint regions and the circuit architecture incorporates multiple outputs to cover more classification categories, the same workflow remains applicable, offering a promising low-power, lightweight technical solution for wearable motion monitoring. Full article

This study proposes a reproducible methodology for optimizing occupancy sensor placement and assessing the sustainable energy performance of smart lighting systems in university classrooms. The research was conducted in Block H of the South Campus of the Universidad Politécnica Salesiana, Quito, using one representative classroom for detailed geometric analysis and extending the optimization to eight classrooms with different dimensions, areas, and installed lighting configurations. The proposed framework integrates Voronoi-based spatial analysis, genetic algorithm optimization, and dynamic occupancy-based lighting control simulation as a retrofit-oriented strategy for existing educational buildings. For the representative classroom, the optimized sensor position was located near the geometric center of the room and achieved an estimated spatial coverage of 94.7% under the adopted sampling-based geometric model and an effective detection radius of 6 m. The multi-classroom analysis showed that the required number of sensors depends on classroom geometry and the adopted sensing radius; at R = 6 m, most classrooms satisfied the 90% coverage criterion with one sensor, while the largest classroom required two sensors. Based on occupancy schedules and automatic control rules, the dynamic simulation showed reductions in lighting operating time of 48% and 52% for 10 h and 12 h daily scenarios, respectively. These reductions were translated into lower daily and monthly energy consumption across different lighting configurations. The results indicate that optimized occupancy-based control can support sustainability-oriented energy management in university buildings by reducing unnecessary electricity use while preserving the existing lighting infrastructure. However, the results are limited to occupancy-based control and do not include daylight harvesting, photometric validation, or a complete economic payback assessment. Full article

Perception-oriented evaluation of automotive headlamps still relies mainly on human-vision photometric criteria, although forward-facing cameras are increasingly safety-critical sensing elements for night driving. This paper benchmarks 16 measured production headlamp light distributions with a simulation chain that combines headlamp spectra and beam patterns, diffuse scene reflection, an imaging-transfer model, and an EMVA-based camera model. The quantitative chain maps scene radiance to sensor-domain signal-to-noise ratio, derives task-specific required signal-to-noise curves from a six-network object-recognition ensemble, and aggregates local threshold satisfaction as region-of-interest coverage across three target reflectances and five driving speeds using WLTP moving-time weights. For the baseline RGB camera, WLTP-weighted coverage ranges from 18.95% to 53.48% across the evaluated light distributions, corresponding to a factor of 2.82 between the weakest and strongest distribution. The camera-parameter sweeps show that favorable beam placement can deliver comparable benchmark coverage with roughly 60% smaller pixel pitch than the weakest distribution, corresponding to an 84% reduction in pixel area, or at materially shorter exposure times. The WLTP-weighted coverage score correlates positively with the established Headlamp Safety Performance Rating, with Pearson $r=0.68$ for the RGB configuration, indicating partial alignment between human-centric and camera-centric illumination needs while confirming that the metrics are not interchangeable. The results identify headlamp light distribution as a primary design parameter for nighttime camera perception and provide a quantitative basis for co-design of automotive lighting and camera-based systems. Full article

Wearable sensors like inertial measurement units (IMUs) can quantify sport technique in natural settings, yet field hockey-specific skill analyses remain limited. This exploratory study investigated how relative foot placement, stick orientation, and lower body kinematics at impact relate to performance of the field hockey sweep skill. Eight experienced female participants performed sweeps under three foot positions relative to the ball (in front, in line, and behind) while IMUs recorded body segment and stick motion. Sweep performance was characterized by accuracy, bounciness, and ball speed. Placing the foot in front of the ball was associated with reduced ball speed and a trend toward lower accuracy relative to the in-line reference, whereas placing the foot behind the ball did not differ from in line on any outcome. Stick roll at impact emerged as a consistent trial-level predictor, with a more open face associated with a greater likelihood of a bouncy sweep and slightly increasing ball speed. Stick pitch and lower limb joint angles were not significant within-participant predictors. However, between-participant analyses indicated that larger knee angles and smaller hip angles were associated with greater accuracy, while smaller average pitch was associated with faster ball speed. Together, these findings indicate that some aspects of sweep performance are amenable to immediate technique adjustments whereas others reflect stable individual movement tendencies. These findings provide a foundation for future work on offering evidence-based guidance for technique refinement and potential implications for injury risk reduction. Full article

Background: Accurate three-dimensional positioning of dental implants is critical for ensuring biomechanical stability, prosthetic passivity, and long-term clinical success. While computer-assisted navigation systems achieve high precision, their complexity and cost often limit accessibility. This study presents the development and preclinical experimental validation of a low-cost prototype designed to enhance angular accuracy in dental implant placement within a controlled 3D haptic simulation environment. Methods: A preclinical experimental design was implemented using a 3D haptic simulator (Virteasy, Montpellier, France). The prototype incorporated high-precision inertial measurement units (IMUs) and an Extended Kalman Filter (EKF) for real-time angular feedback. Ninety-seven simulated implant placements were performed—both freehand and with prototype assistance—under identical virtual conditions by a single experienced operator. Angular deviations in mesiodistal and buccolingual planes were recorded, combined into a composite 3D index, and analyzed using paired t-tests and linear mixed-effects models. The study was conducted in a controlled simulation environment, which does not fully replicate clinical conditions. Results: The prototype significantly reduced angular deviation from 13.49° to 2.99° in the mesiodistal plane (−77.8%) and from 13.56° to 5.59° in the buccolingual plane (−58.8%), achieving an overall 67% improvement in three-dimensional orientation (p < 0.001; Cohen’s d = 1.47). Agreement with an optical reference system (OptiTrack) was excellent (bias = +0.36°, RMSE = 0.39°). Intra-operator reliability exceeded 0.95 (ICC), confirming strong reproducibility and measurement stability. Conclusions: The proposed inertial sensor-based prototype achieved angular accuracy within the range reported for computer-guided systems while maintaining advantages of portability, low cost, and usability. Its integration into haptic simulators provides a valid tool for both educational and preclinical applications, offering real-time feedback that enhances spatial perception and psychomotor learning. Future clinical studies should validate its performance in cadaveric and patient-based contexts to determine its practical impact on surgical precision and implant success. Full article

Advancing environmental monitoring through coordinated autonomous systems is central to sustainable smart region governance and data-driven territorial management. The article presents an engineering-oriented architecture and deployment methodology for an integrated wildfire monitoring and response system that combines unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and stationary sensor networks (SNs). We formalise hub-and-spoke infrastructure placement as a mixed-integer optimisation problem that accounts for platform types, endurance, travel times and logistical constraints, and propose a practical pre-processing pipeline (confidence scoring, resampling, Kalman/median filtering, strategy fusion) for heterogeneous telemetry and imagery. The system couples multimodal neural network processing (image backbones, clustering and time-series models) with online resource-allocation and mission-planning mechanisms to prioritise UAV/UGV sorties and dynamically select launch sites. The article describes scenario-driven operational modes (early warning, alarm verification, autonomous local extinguishing, post-fire recovery, sensor-gap compensation, and inter-hub reinforcement), defines validation protocols (synthetic experiments, precision/recall/F1, and hardware-in-the-loop testing), and proposes KPIs to assess environmental, social, and economic impacts for smart regions. The contribution is a reproducible, deployment-focused blueprint that bridges conceptual UAV–UGV–SN research and practical implementation, highlighting trade-offs in reliability, communication redundancy, and sustainability, and outlining directions for simulation, field pilots and algorithmic refinement. Full article

Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation. Full article

Optimal sensor placement (OSP) for structural health monitoring of large-scale space grid structures must enable reliable identification of localized member deterioration with sparse instrumentation. Modal-based OSP criteria optimize observability of a healthy model but do not directly minimize uncertainty in substructure-level damage parameters. We partition the structure into substructures, simulate axial and biaxial bending stiffness-loss cases, and compute truncated modal flexibility. Each element is encoded by stacked end-node flexibility submatrices over $m=6$ modes. A multi-task, zero-anchored multi-layer perceptron is trained to regress three nonnegative damage parameters and classify damage presence using losses tailored for small-damage accuracy. Sensor sensitivities are obtained by automatic differentiation of the surrogate with respect to flexibility features and aggregated with scenario weights emphasizing critical bending and neighbor-substructure interference scenarios. A greedy D-optimal design then maximizes the log-determinant of a regularized Fisher information matrix under practical coverage constraints; substructure selections are merged into a globally feasible layout. On a representative space grid, the method improves task-oriented identifiability over EFI and MKE across budgets $K_{\mathrm{tot}}=30$–60 (higher-damage D-optimality, lower A-optimality trace, and reduced proxy variance indicators), while yielding lower modal log-determinants. These findings indicate risk-weighted, substructure-first task design as an alternative to purely modal criteria for substructure-level damage-parameter identification. Full article

In many IIoT-based yard operations, accurately identifying the spatial position of containers is becoming increasingly relevant as operators try to automate stacking and retrieval processes by technologies like Container Spatial Localization (CSL). Despite this automation in IIoT, RTK-GPS–based container stacker positioning frequently lacks precision, which causes disruptions in stacking and reduces efficiency in space utilisation. Though it offers placement precision accurately up to 3 cm, this is still insufficient in high-volume Yard Management Systems (YMS). Consequently, this yields to variable container orientation, waste of usable space, increased man input is required in handling goods, and potential automated system failures. This research proposes a novel methodology that combines conventional RTK-GPS measurements with angular information captured from a BHI-260AP–based spreader sensor, allowing the system to correct container placement errors arising from orientation rather than only from positioning. In addition to the spatial positioning problem, we found that continuous IIoT operation raises concerns regarding energy use, particularly when micro-controllers remain active throughout the task cycle. As a solution, this integrates a dynamic task scheduling approach that puts the device in sleep modes whenever computation is not required. In our experiments, this strategy improved overall power efficiency by 34.44%, which makes long automated operation more practical. Full article

Autonomous docking is a critical capability for enabling fully automated operations in industrial and logistics environments using Autonomous Mobile Robots (AMRs). Traditional rule-based docking approaches often struggle with generalization and robustness in complex, dynamic scenarios. This paper presents a deep reinforcement learning-based autonomous docking framework that integrates Proximal Policy Optimization (PPO) with multi-sensor fusion. It includes YOLO-based vision detection, depth estimation, and LiDAR-based orientation correction. A concise 4D state vector, comprising relative position and angle indicators, is used to guide a continuous control policy. The outputs are linear and angular velocity commands for smooth and accurate docking. The training is conducted in a Gym-compatible Gazebo simulation, acting as a digital twin of the real-world system, and incorporates realistic variations in lighting, obstacle placement, and marker visibility. A designed reward function encourages alignment accuracy, progress, and safety. The final policy is deployed on a real robot via a sim-to-real transfer pipeline, supported by a ROS-based transfer node. Experimental results demonstrate that the proposed method achieves robust and precise docking behavior under diverse real-world conditions, validating the effectiveness of PPO-based learning and sensor fusion for practical autonomous docking applications. Full article

This paper focuses on optimizing sensor placement in structures for load monitoring applications. Such applications rely on sensor data to track changes in the structure. Monitoring accuracy relies on proper sensor placement. The goal is to maximize load monitoring accuracy under multiple loading scenarios while the number of sensors is kept smaller than the number of load cases. Building on prior work in which machine learning models predicted loads using only sensor readings without information on load location, this study continues that approach. It demonstrates that prediction models perform better when sensor networks are optimized. The novelty lies in a newly formulated objective function, allowing for optimization of sensor number, positions, and orientations across multiple load cases and measurement types. The goal is to minimize the differences between maximal responses of the structure and detected responses by the sensors (for all possible load cases). The method is validated on a numerical model of a composite structure with 1–3 sensors under seven different load cases. Load predictions from sensors in optimized locations are compared to predictions from measurements of randomly positioned sensors. Statistical comparison proved that the developed methods and algorithms allow us to significantly reduce the prediction errors. Full article

Predicted natural ventilation (NV) often diverges from actual performance in dwellings. This discrepancy arises in part because most design tools do not account for how occupants actually operate windows. This study aims to determine how window geometry and orientation should be adjusted when occupant behavior is considered. Survey data from 150 Melbourne residents were converted into two window-operation schedules: Same Behavior (SB), representing average patterns, and Probable Behavior (PB), capturing stochastic responses to comfort, privacy, and climate. Both schedules were embedded in EnergyPlus and applied to over 200 annual simulations across five window-design stories that varied orientations, placements, and window-to-wall ratios (WWRs). Each story was tested across two living room wall dimensions (7 m and 4.5 m) and evaluated for air-change rate per hour (ACH) and solar gains. PB increased annual ACH by 5–12% over SB, with the greatest uplift in north-facing cross-ventilated layouts on the wider wall. Integrating probabilistic occupant behavior into window design remarkably improves NV effectiveness, with peak summer ACH reaching 4.8, indicating high ventilation rates that support thermal comfort and improved IAQ without mechanical assistance. These results highlight the potential of occupant-responsive window configurations to reduce reliance on mechanical cooling and enhance indoor air quality (IAQ). This study contributes a replicable occupant-centered workflow and ready-to-apply design rules for Australian temperate climates, adapted to different climate zones. Future research will extend the method to different climates, housing types, and user profiles and will integrate smart-sensor feedback, adaptive glazing, and hybrid ventilation strategies through multi-objective optimization. Full article

In northern China, radiant floor heating is widely used in multi-apartment residential buildings, with indoor temperature being a key factor in evaluating a user’s heating demands. However, due to variations in building structure, room orientation, and the outdoor environment, identifying the optimal placement of temperature sensors across multiple zones remains challenging. In this study, we propose a data-driven methodology to identify the optimal placement of temperature sensors for a typical apartment with multiple zones. The proposed methodology is based on computational fluid dynamics (CFD) simulations of several typical scenarios and quantifies the relationship between the temperature field and the volume-averaged operating temperature to determine the optimal locations for temperature sensors. Results indicate that the temperature sensors need to be placed on planes ranging from 1.0 m to 1.7 m, with each plane featuring a distinct optimal area. The RMSE analysis reveals that, despite obvious temperature variations across the residence, the root mean square errors (RMSEs) at the designated sensor locations remain consistently low, with a maximum of 0.35 °C and most values below 0.3 °C. The above results indicate that the optimal sensor placement can significantly reduce potential errors between recorded temperatures and volume-averaged operating temperatures, which can be used as input parameters for personal indoor temperature control. Full article