Search Results (137)

To address the technical challenge where traditional high-pressure, large-flow emulsion pump stations cannot adapt to the drastic flow rate changes in hydraulic supports due to the fixed displacement of their quantitative pumps—leading to frequent system unloading, severe impacts, and damage—this study proposes an intelligent flow control method based on the digital flow distribution principle for actively perceiving and matching support demands. Building on this method, a compact, electro-hydraulically separated prototype with stepless flow regulation was developed. The system integrates high-speed switching solenoid valves, a piston push rod, a plunger pump, sensors, and a controller. By monitoring piston position in real time, the controller employs an optimized combined regulation strategy that integrates adjustable duty cycles across single, dual, and multiple cycles. This dynamically adjusts the switching timing of the pilot solenoid valve, thereby precisely controlling the closure of the inlet valve. As a result, part of the fluid can return to the suction line during the compression phase, fundamentally achieving accurate and smooth matching between the pump output flow and support demand, while significantly reducing system fluctuations and impacts. This research adopts a combined approach of co-simulation and experimental validation to deeply investigate the dynamic coupling relationship between the piston’s extreme position and delayed valve closure. It further establishes a comprehensive dynamic coupling model covering the response of the pilot valve, actuator motion, and backflow control characteristics. By analyzing key parameters such as reset spring stiffness, piston cylinder diameter, and actuator load, the system reliability is optimized. Evaluation of the backflow strategy and delay phase verifies the effectiveness of the multi-mode composite regulation strategy based on digital displacement pump technology, which extends the effective flow range of the pump to 20–100% of its rated flow. Experimental results show that the system achieves a flow regulation range of 83% under load and 57% without load, with energy efficiency improved by 15–20% due to a significant reduction in overflow losses. Compared with traditional unloading methods, this approach demonstrates markedly higher control precision and stability, with substantial reductions in both flow root mean square error (53.4 L/min vs. 357.2 L/min) and fluctuation amplitude (±3.5 L/min vs. ±12.8 L/min). The system can intelligently respond to support conditions, providing high pressure with small flow during the lowering stage and low pressure with large flow during the lifting stage, effectively achieving on-demand and precise supply of dynamic flow and pressure. The proposed “demand feedforward–flow coordination” control architecture, the innovative electro-hydraulically separated structure, and the multi-cycle optimized regulation strategy collectively provide a practical and feasible solution for upgrading the fluid supply system in fully mechanized mining faces toward fast response, high energy efficiency, and intelligent operation. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

The propagation of interfacial acoustic waves (IAWs) along a SiO₂/ZnO/SU-8/SiO₂ multilayer structure is theoretically predicted and experimentally validated. A two-dimensional finite-element analysis was performed using COMSOL Multiphysics, revealing that key IAW characteristics—such as the number of supported modes, propagation losses, and acoustic field distribution—are strongly influenced by the thickness of the intermediate SU-8 adhesive layer. In particular, the presence of the SU-8 layer enables the existence of IAW modes with opposite localization, namely upward- and downward-propagating IAWs. To validate the theoretical predictions, experimental measurements were carried out on delay lines fabricated on SiO₂/ZnO/SU-8/SiO₂ layered structures, revealing the propagation of three distinct IAW modes. The first two modes correspond to the downward and upward fundamental IAWs, while the third mode is a second-order mode identifiable as a downward leaky IAW (LIAW). The experimental results show excellent agreement with the theoretical predictions and establish a solid foundation for the future development of multifrequency IAW-based devices, including package-less acoustic components, microfluidic platforms, and gas and optical sensors designed for operation under harsh environmental conditions. Full article

The present work introduces a 0.6-volt, all-digital, synthesizable temperature sensor characterized by reduced sensitivity to supply voltage variations. The design incorporates two distinct logic delay lines that are distinguished by their equivalent transistor lengths. These variations in transistor lengths result in varying threshold voltages and thermal dependencies. The difference in thermal dependency is detected through the ratio of their charging currents, which are subsequently transformed into digital outputs via their propagation delays. By employing two types of delay lines, the sensor achieves an eightfold reduction in power supply sensitivity compared to configurations utilizing a single delay line and also obviates the necessity for an external clock. Fabricated with 55 nm CMOS technology, the proposed sensor exhibits an inaccuracy of ±1 $°$C, evaluated through global linear fitting and two-point calibration across five chips, within a temperature range of 20 to 90 $°$C. The all-digital temperature sensor consumes 2 nanojoules (nJ) for each conversion, with a conversion duration of 0.8 milliseconds (ms) and a resolution of 0.2 $°$C. The prototype’s physical dimensions are 37 × 31 ${\mathsf{\mu }\mathrm{m}}^2$. Additionally, synthesis on a Cyclone IV FPGA reveals similar characteristics in terms of supply sensitivity reduction. Full article

In this work, the sensor properties of multilayered Langmuir-Blodgett (LB) films of tetra-tert-butylphthalocyaninate zinc (tBuZnPc) were studied using an acoustoelectronic method. The morphology and optical properties of the fabricated films were characterized by atomic force microscopy and ultraviolet-visible spectroscopy, respectively. The LB films were deposited on surface acoustic wave (SAW) delay lines, and their gas-sensing properties were investigated. The films demonstrated high selectivity towards chloroform vapor compared to acetone, methanol, ethanol, and isopropanol. The highest selectivity was observed for the five-layer film, which can be attributed to the specific interaction of chloroform molecules with the hydrophobic cavities formed by the tert-butyl groups. Increasing the film thickness to 41 layers enhanced the absolute response to chloroform to 370 ppm; however, the selectivity decreased due to increased nonspecific adsorption. The results demonstrate the potential of using tBuZnPc LB films as sensitive coatings for the selective detection of chloroform in environmental and industrial monitoring applications. Full article

Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at $v=60\mathrm{km}{\mathrm{h}}^{-1}$. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. Full article

Understanding mixing behavior at river confluences is essential for effective watershed management in response to increasing environmental issues such as algal blooms and chemical pollution. This study focused on the confluence of the Nakdong and Geumho Rivers, employing high-resolution field measurements using an ADCP (M9) and YSI EXO sensors. Water temperature (°C) and electrical conductivity (μS/cm) data were collected under three representative conditions, including flow ratios of 0.91, 0.45, and 0.29, as well as 0.05, with a maximum temperature difference of up to 6 °C. Mixing behavior was three-dimensionally analyzed by integrating cross-sectional and longitudinal data, and the accuracy of visualization was evaluated using IDW and Kriging spatial interpolation techniques. The analysis revealed that under low flow ratio conditions, vertical mixing was delayed; the thermal stratification persisted up to approximately 3 km downstream from the confluence (Line 3), and complete mixing was not achieved until about 7 km downstream (Line 5) due to density currents. Quantitative comparison indicated that IDW (R² = 0.901, RMSE = 31.522) outperformed Kriging (R² = 0.79, RMSE = 35.458). This study provides a quantitative criterion for identifying the mixing completion zone, thereby addressing the limitations of previous studies that relied on numerical models or limited field data, and offering practical evidence for water quality monitoring and sustainable river management. Full article

In satellite altimetry calibration, the atmosphere’s integrated water vapor content has been customarily derived through the Global Navigation Satellite Systems (GNSS), principally over land where the satellite radiometer is not operational. Progressively, several alternative methods have emerged to estimate this wet troposphere component with ground instruments, alternative satellite sensors, and global models. For any ground calibration facility, integration of various approaches is required to arrive at an optimum value of a calibration constituent and in accordance with the strategy of Fiducial Reference Measurements (FRM). In this work, different estimation methods and instruments are evaluated for wet troposphere delays, especially when transponder and corner reflectors are employed at the Permanent Facility for Altimetry Calibration of the European Space Agency. Evaluation includes, first, ground instruments with microwave radiometers and radiosondes; second, satellite sensors with the Ocean Land Color Instrument (OLCI) and the Sea Land Surface Temperature Radiometer (SLSTR) of the Copernicus Sentinel-3 altimeter, as well as the TROPOMI spectrometer on the Sentinel-5P satellite; and finally with global atmospheric models, such as the European Center for Medium-Range Weather Forecasts. Along these lines, multi-sensor and redundant values for the troposphere delays are thus integrated and used for the calibration of Sentinel-6 MF and Sentinel-3A/B satellite altimeters. All in all, the integrated water vapor value of the troposphere is estimated with an FRM uncertainty of ±15 mm. In the absence of GNSS stations, it is recommended that the OLCI and SLSTR measurements be used for determining tropospheric delays in daylight and night operations, respectively. Ground microwave radiometers can also be used to retrieve tropospheric data with high temporal resolution and accuracy, provided that they are properly installed and calibrated and operated with site-specific parameters. Finally, the synergy of ground radiometers with instruments on board other Copernicus satellites should be further investigated to ensure redundancy and diversity of the produced values for the integrated water vapor. Full article

This article reports on the interrogation of fiber Bragg grating (FBG)-based sensors that have been multiplexed in a ladder topology. In each line of this topology, FBGs with different wavelengths are connected. In addition, delay fibers have been inserted between each line to enable reflections from different lines to be distinguished. Seven FBGs are interrogated simultaneously by applying time- and wavelength-division multiplexing techniques. To improve the signal-to-noise ratio of the weak reflected signals, the heterodyne detection technique is applied. Through the simulation of three different failure cases, we evaluate the fault detection capability of our method. Full article

In the field of modern optical communication, radar signal processing and optical sensors, true time delay technology, as a key means of signal processing, can achieve the accurate control of the time delay of optical signals. This study presents a novel design that integrates a 2 × 2 Multi-Mode Interference (MMI) structure with a Mach–Zehnder modulator on a silicon nitride–lithium niobate (SiN-LiNbO₃) heterogeneous integrated optical platform. This configuration enables the selective interruption of optical wave paths. The upper path passes through an ultralow-loss Archimedes’ spiral waveguide delay line made of silicon nitride, where the five spiral structures provide delays of 10 ps, 20 ps, 40 ps, 80 ps, and 160 ps, respectively. In contrast, the lower path is straight through, without introducing an additional delay. By applying an electrical voltage, the state of the SiN-LiNbO₃ switch can be altered, facilitating the switching and reconfiguration of optical paths and ultimately enabling the combination of various delay values. Simulation results demonstrate that the proposed optical true delay line achieves a discrete, adjustable delay ranging from 10 ps to 310 ps with a step size of 10 ps. The delay loss is less than 0.013 dB/ps, the response speed reaches the order of ns, and the 3 dB-EO bandwidth is broader than 67 GHz. In comparison to other optical switches optical true delay lines in terms of the parameters of delay range, minimum adjustable delay, and delay loss, the proposed optical waveguide digital adjustable true delay line, which is based on an optical switch and an Archimedes’ spiral structure, has outstanding advantages in response speed and delay loss. Full article

Industrial plants now stream thousands of temperature, pressure, flow rate, and composition measurements at minute-level intervals. These multi-sensor records often contain variable transport or residence time delays that hinder accurate disturbance analysis. This study applies lag-specific transfer entropy (LSTE) to historical sensor logs to identify the instrument that first deviates from normal operation and the time required for that deviation to appear at downstream points. A self-prediction optimization step removes each sensor’s own information storage, after which LSTE is computed at candidate lags and tested against time-shifted surrogates for statistical significance. The method is benchmarked on a nonlinear simulation, the Tennessee Eastman plant, a three-phase separator test rig, and a full-scale blast furnace line. Across all cases, LSTE locates the disturbance origin and reports propagation times that match known process physics, while significantly reducing false links compared to classical transfer entropy. Full article

Precision Livestock Farming (PLF) applies a complex of sensor technology, algorithms, and multiple tools for individual, real-time livestock monitoring. In intensive livestock systems, PLF is now quite widespread, allowing for the optimisation of management, thanks to the early recognition of diseases and the possibility of monitoring animals’ feeding and reproductive behaviour, with an overall improvement of their welfare. Similarly, PLF systems represent an opportunity to improve the profitability and sustainability of extensive farming systems, including those of small ruminants, rationalising the use of pastures by avoiding overgrazing and controlling animals. Despite the livestock distribution in several parts of the world, the low profit and the relatively high cost of the devices cause delays in implementing PLF systems in small ruminants compared to those in dairy cows. Applying these tools to animals in extensive systems requires customisation compared to their use in intensive systems. In many cases, the unit prices of sensors for small ruminants are higher than those developed for large animals due to miniaturisation and higher production costs associated with lower production numbers. Sheep and goat farms are often in mountainous and remote areas with poor technological infrastructure and ineffective electricity, telephone, and internet services. Moreover, small ruminant farming is usually associated with advanced age in farmers, contributing to poor local initiatives and delays in PLF implementation. A targeted literature analysis was carried out to identify technologies already applied or at an advanced stage of development for the management of grazing animals, particularly sheep and goats, and their effects on nutrition, production, and animal welfare. The current technological developments include wearable, non-wearable, and network technologies. The review of the technologies involved and the main fields of application can help identify the most suitable systems for managing grazing sheep and goats and contribute to selecting more sustainable and efficient solutions in line with current environmental and welfare concerns. Full article

Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–computer interaction. However, several challenges arise in outdoor settings, including varying lighting conditions, occlusions caused by obstacles, environmental noise, and the complexity of differentiating between similar activities. This study presents a hybrid deep learning approach that integrates human activity recognition and localization in outdoor environments using Wi-Fi signal data. The study focuses on applying the hybrid long short-term memory–bi-gated recurrent unit (LSTM-BIGRU) architecture, designed to enhance the accuracy of activity recognition and location estimation. Moreover, experiments were conducted using a real-world dataset collected with the PicoScene Wi-Fi sensing device, which captures both magnitude and phase information. The results demonstrated a significant improvement in accuracy for both activity recognition and localization tasks. To mitigate data scarcity, this study utilized the conditional tabular generative adversarial network (CTGAN) to generate synthetic channel state information (CSI) data. Additionally, carrier frequency offset (CFO) and cyclic shift delay (CSD) preprocessing techniques were implemented to mitigate phase fluctuations. The experiments were conducted in a line-of-sight (LoS) outdoor environment, where CSI data were collected using the PicoScene Wi-Fi sensor platform across four different activities at outdoor locations. Finally, a comparative analysis of the experimental results highlights the superior performance of the proposed hybrid LSTM-BIGRU model, achieving 99.81% and 98.93% accuracy for activity recognition and location prediction, respectively. Full article

A surface acoustic wave-based NO₂ sensor and its interface electronics, utilizing monolayered two-dimensional sensing materials, were developed for internal pollution monitoring in spacecraft. The sensor system consists of a two-port SAW delay line with monolayered graphene/MoS₂ flakes in the cavity region between two interdigital transducers, along with the interface electronics. A microheater was integrated adjacent to the sensor to maintain a stable temperature field on the sensor surface, thereby enhancing sensitivity, response/recovery times, and selectivity. The monolayered graphene/MoS₂ sensing material, with its high surface-to-volume ratio, excellent mobility, and moderate bonding force with target molecules, enables the rapid response and recovery times of less than 2.5 and 8 s, respectively—among the fastest reported in SAW gas sensor technology. The developed sensor combines the conductivity changes, the mass loading effect, and a synergistic effect that promotes carrier separation caused by a built-in potential barrier between the two monolayers, providing exceptionally high sensitivity of 578 Hz/ppm. Additionally, the sensor’s interface electronics were engineered to mitigate the effects of external factors, such as temperature and humidity, ensuring a stable and reliable performance under varying harsh conditions. Full article

Vast amounts of data are continuously collected through sensors fitted into various pieces of equipment and processes in semiconductor production lines. These integrated datasets often encompass tens of thousands of dimensions, making it challenging to identify complex relationships among data dimensions for diagnosing defects and achieving high yield rates. Parallel Coordinate Plots (PCPs) are effective for visually analyzing multi-dimensional data, but traditional axis reordering methods struggle with superhigh-dimensional datasets. To address these challenges, we propose SDPC, an interactive PCP-based visual analysis system specifically tailored to the unique requirements of semiconductor production lines. SDPC employs a server–client architecture that efficiently visualizes sensor data in real time by dynamically selecting dimensions and down-sampling data based on user interactions. This enables engineers to explore high-dimensional sensor data without noticeable delays, enhancing their ability to identify defects quickly. By integrating user-defined filter conditions and focusing on defect-relevant dimensions, SDPC enhances interpretability and accelerates root cause identification. An evaluation with semiconductor production engineers demonstrated SPDC’s ability to facilitate real-time exploratory analysis, boost operational efficiency, reduce visual analysis time by two-thirds for on-site engineers, and ultimately lead to more effective production processes. Full article