Search Results (11,571)

Long-range, high-resolution distance measurement with high ambient-light tolerance has been achieved using a 720 × 488-resolution short-pulse indirect time-of-flight (SP-iToF) image sensor featuring six-tap, one-drain pixels fabricated by a front-side illumination (FSI) process. The sensor performs 30-phase demodulation through six-tap pixels in each subframe, combined with five range-shifted subframe (SF) readouts. The six-tap demodulation pixel, designed with a lateral drift-field pinned photodiode, demonstrates over 90% demodulation contrast for a 20 ns light-pulse width. High-speed column-parallel 12-bit cyclic ADCs enable all six-tap subframe signals to be read within 4.38 ms. This high-speed subframe readout, together with efficient exposure-time allocation across the five subframes, enables a depth-image frame rate of 10 fps. The multi-phase demodulation in SP-iToF measurements, operating with an extremely small duty ratio of 0.2%, effectively suppresses ambient-light charge accumulation and the associated shot noise in the pixel. As a result, distance measurements up to 100 m under 100 klux illumination are achieved, with depth noise maintained below 1%. Full article

This study proposes a novel artificial neural network-based methodology for classifying the incident wave direction during ship navigation using the heave–roll–pitch motion response spectra as input. The proposed model demonstrated a balanced performance with an overall accuracy of approximately 0.888, effectively classifying the wave direction into three major categories: head-sea, beam-sea, and following-sea. The methodology utilizes Response Amplitude Operators derived from linear potential flow theory to generate motion response spectra, which are then used to classify the incident wave direction. The model effectively learns the frequency-distribution characteristics of the response spectrum, enabling wave direction classification without the need for complex inverse analysis procedures. This approach is significant in that it allows wave direction recognition solely based on measurable ship motion responses, without the need for additional external sensors or mathematical modeling. This data-driven approach has strong potential for integration into autonomous ship situational awareness modules and real-time wave monitoring technologies. However, the study simplified the directional domain into three representative groups, and the model was validated primarily using a numerically generated dataset, indicating the need for future improvements. Future research will expand the dataset to include a broader range of sea states, improve directional resolution, and explore continuous wave direction prediction. Additionally, further validation using field-measured data will be conducted to assess the real-time applicability of the proposed model. Full article

The paper presents the experimental results of applying a novel protective coating made from Mn_1.7Cu_1.3-xFe_xO₄, compared to commercial spinels Mn_1.5Co_1.5O₄ and MnCo₂O₄, as a key component responsible for preventing chromium diffusion and slowing the increase in area-specific resistance (ASR) in solid oxide fuel cells (SOFCs). The layers of selected materials were deposited on Crofer 22APU steel by electrophoretic deposition (EPD) on small samples and by roll painting on full-scale interconnects. The coatings were evaluated by measuring the ASR of small samples for short and long runs (1000 h), as well as real-scale interconnects assembled in a SOFC stack composed of three cells, measuring 11 × 11 cm², which operated for 1000 h at 670 °C. The collected data on the electrochemical performance of the stack allowed for estimation of the degradation rates of all the repeating units, revealing benefits from using (Mn,Cu,Fe)₃O₄ as a coating. The results are compared to the literature reports. Post-mortem analysis by the SEM-EDS technique allowed for investigation of Cr diffusion levels. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Inefficient management of dissolved oxygen (DO) in intensive aquaculture systems limits fish welfare and productivity by creating oxygen-deficient zones and promoting hydrodynamic conditions that hinder their dispersion. Because water movement directly influences how oxygen is transported and mixed within the culture unit, inadequate flow management can allow localized hypoxia to persist even when total oxygen input appears sufficient. To address this issue, this study proposes an integrated methodology that combines in situ respirometry measurements with Computational Fluid Dynamics (CFD) simulations to evaluate the spatial distribution of DO and diagnose the operational performance of aquaculture systems. The methodology quantifies oxygen consumption using intermittent-flow respirometry, applies a three-dimensional two-phase CFD model (water–oxygen) incorporating experimental oxygen consumption rates as boundary conditions, and validates the model under real operating conditions, focusing on active metabolism as the most demanding physiological state. The model generates a spatial distribution of DO patterns that are significantly modified by pond geometry, water flow characteristics, the metabolism of the fish and fish positioning. The differences between experimental and simulated values ranged from 7.8% to 10.7%, confirming the accuracy of the proposed method. The integration of in situ metabolic measurements with CFD modeling provides a realistic representation of DO dynamics, enabling system optimization and promoting more efficient and sustainable aquaculture. Full article

Ejector-based cooling systems have gathered scientific interest as a low-cost alternative for solar-assisted cooling applications, especially in regions with solar abundance. This work presents the experimental investigation of a solar ejector cooling prototype system. The system, developed at the National Technical University of Athens, includes a custom-made ejector and is powered by a 48 m² flat plate solar collector field, assisted by an auxiliary natural gas boiler. Experimental testing under varying operating conditions was conducted to assess the system’s performance, focusing on the influence of evaporation and condensation temperatures. The maximum coefficient of performance (COP) was measured at approximately 0.160–0.165, corresponding to an entrainment ratio of 0.19 at an evaporation temperature of 9 °C and condensation temperatures of 26–27 °C. Ejector performance substantially declined with increased condensation temperatures. However, the influence of the evaporator pressure on system performance was less significant. These findings demonstrate the feasibility of ejector-based solar cooling as a sustainable solution for reducing electricity use in cooling applications, highlighting the critical influence of operating parameters in the system’s performance optimization. Full article

To address the state estimation and detection problem in the presence of noisy sensor observations, probing costs, and communication noise, we in this paper propose a soft actor-critic (SAC) deep reinforcement learning (DRL) framework for dynamically scheduling sensors and sequentially probing the state of a stochastic system. Moreover, considering Byzantine attacks, we design a generative adversarial network (GAN)-based framework to identify the Byzantine sensors. The GAN-based Byzantine detector and SAC-DRL-based agent are developed to operate in coordination to detect the state of the system reliably and fast while incurring small sensing cost. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total probing cost needed for detection. Via simulation results, we analyze the performances and demonstrate that soft actor–critic algorithms are flexible and effective in action selection in imperfectly known environments due to the maximum entropy strategy and they can achieve stable performance levels in challenging test cases (e.g., involving jamming attacks, imperfectly known noise power levels, and high sensing cost scenarios). We also provide comparisons between the performances of the proposed soft actor–critic and conventional actor–critic algorithms as well as fixed scheduling strategies. Finally, we analyze the impact of Byzantine attacks and identify the reliability and accuracy improvements achieved by the GAN-based approach when combined with the SAC-DRL-based decision-making agent. Full article

Irradiation embrittlement occurs in the cladding materials of fusion reactors during irradiation. Determining the ductile–brittle transition temperature via Charpy impact testing is the primary method for evaluating irradiation embrittlement. Standard-sized V-shaped Charpy impact specimens (CVN) are too large in size and have high induced radioactivity. Small-sized specimens (KLST) can solve these problems, but the performance data measured from small-sized specimens are different from those of standard specimens. In other words, there is a size effect in impact performance. The notch size and hammer impact speed of KLST specimens are different from those of CVN specimens. The influence of these factors on impact performance requires further study. In response to these issues, on the basis of the previous experiments conducted by the research group, GTN damage models of CVN specimens and KLST specimens are constructed using the inverse operation method. Numerical simulation of the impact on the upper platform area is carried out for KLST specimens and variable-sized KLST specimens. Compared with the test results, the numerical simulation results are in good agreement, verifying the accuracy and reliability of the model. The results show that the notch angle and radius have little influence on the plastic zone. The cross-sectional area of the notch has a significant impact on the plastic zone. The impact velocity within the range of 3.8 m/s to 5.24 m/s affects the impact response process, but does not affect the load–displacement curve, the length of the non-plastic deformation zone, or the volume of the plastic zone. Full article

This work presents the development of a low-cost and high-performance multi-sensory gas detection instrument named the AIMNet Sensor, with the integration of a machine learning-based data processing method. The compact and low-power instrument (8.5 × 11.5 cm, 1.4 W) houses the core sensing hardware module, Senseair K96, that integrates both a non-dispersive infrared (NDIR)-based gas sensing unit and a BME280 environmental sensing unit. To address the outdoor operation challenges caused by environmental fluctuation due to the varying temperature, humidity, and pressure, from the software aspect, multiple machine learning-based regression models were trained in this work on 13,125 calibration data points collected under controlled laboratory conditions. Among ten tested algorithms, the Multilayer Perceptron (MLP) and Elastic Net models achieved the highest accuracy, with R-squared coefficient $R^2>0.8$ on both indoor and outdoor scenarios, and with inter-sensor root mean square error (RMSE) within 1.5 ppm across four identical instruments. Moreover, field mobile validation was performed near a wastewater management facility using this solution, confirming a strong correlation with LI-COR reference measurements and a reliable detection of CH${}_4$ leaks with concentrations up to 18 ppm at the test site. Overall, this machine learning-integrated NDIR sensing solution (i.e., AIMNet) offers a practical and scalable solution towards a more robust distributed CH${}_4$ monitoring network for real-world field-deployable applications. Full article

Mobile Mapping Systems (MMSs) have emerged as indispensable instruments for producing high-precision road maps in recent years. Despite incorporating modern devices, their efficacy may be influenced by operational variables such as vehicle speed or the type of GNSS (Global Navigation Satellite System) differential correction employed. This study assesses the impact of varying vehicle speeds and differential correction settings on the accuracy of point grids acquired with an MMS on a two-lane rural road. The experiment was performed across a 7 km distance, incorporating two speeds (40 and 60 km/h) and two travel directions. Three correction methodologies were examined: a proximate local base (MBS), a network station solution of the National Geographic Institute (NET), and virtual reference stations (VRSs). The methodology encompassed normality analysis, descriptive statistics, mean comparisons, one- and two-factor analysis of variance (ANOVA), and the computation of the root mean square error (RMSE) as a measure of accuracy. The findings indicate that horizontal discrepancies remain steady and unaffected by the correction technique; however, notable changes are seen in the vertical component, with the NET option proving to be the most effective. The acquisition rate is the primary determinant, exacerbating errors at 60 km/h. In conclusion, the dependability of MMS surveys is contingent upon the correction approach and operational conditions, and it is advisable to sustain moderate speeds to guarantee precise three-dimensional models. Full article

Reducing the fuel consumption of tractors has consistently been a critical challenge that the agricultural machinery industry must address. To investigate the energy consumption during the plowing process of tractors and enhance their economic efficiency, this study conducted comparative experiments under varying plowing speeds and depths. In this experiment, the CAN bus protocol was utilized for the collection of engine operational data, such as rotational speed and fuel flow. A GPS positioning system was adopted to measure the plowing speed of the tractor and combined with the data from the tractor coasting test, and then the energy consumption for operating the plow was determined. In addition, a tension sensor was installed on the three-point hitch to measure the horizontal pull force exerted by the five-share plow during plowing, thereby facilitating the calculation of the energy consumption of agricultural machinery. The findings indicate that when the tractor’s plowing speed is maintained at 5.7 km/h, both the average fuel consumption and the fuel consumption per unit area increase as the plowing depth increases. If the plowing depth is fixed at 23 cm, the average fuel consumption rises with an increase in plowing speed, whereas the fuel consumption per unit area decreases. The experimental data show that during the actual tillage operation of the tractor, the brake thermal efficiency of diesel engines ranges from 21.76% to 28.57%. The energy consumed by agricultural implements accounts for only 11.79% to 17.04% of the total fuel energy. The energy consumed in operating the tractor-drawn plow accounts for merely 7.87% to 13.66% of the diesel engine output energy. Approximately 23.24% to 38.69% of the effective power of the diesel engine is lost during the transmission process. This study provides valuable insights for optimizing the performance of tractors during operation. Full article

The primary objective of this study is to enable an unmanned surface vehicle (USV) to autonomously approach the extremum of an unknown scalar field using only real-time field measurements. To this end, a source-seeking method based on timescale separation is developed within a hierarchical control framework that divides the closed-loop system into a slow and a fast subsystem. The slow subsystem governs the gradual evolution of the USV pose and generates reference heading and surge commands from local scalar field information, providing a directional cue toward the field extremum. The fast subsystem applies actuator-level control inputs that ensure these references are tracked with sufficient accuracy through rapid corrective actions. A Lyapunov-based analysis is carried out to study the stability properties of the coupled slow–fast dynamics and to establish conditions under which convergence can be guaranteed in the presence of model nonlinearities and external disturbances. Numerical simulations are conducted to illustrate the resulting system behavior and to verify that the proposed framework maintains stable seeking performance under typical operating conditions. Full article

Oxidation is a critical factor contributing to material wear and the degradation of conductive performance during current-carrying tribological processes. The present study investigated the composite oxidation mechanisms that occurred during current-carrying rolling in mixed atmospheres containing O₂ and H₂O vapor. The results obtained in a dry N₂/O₂ mixture, humid N₂, and humid N₂/O₂ mixture indicated that the oxidation mechanisms on current-carrying rolling surfaces involved thermal oxidation, tribo-oxidation, and anodic oxidation. XPS analysis confirmed that the primary oxidation product was CuO. Conductive atomic force microscopy (C-AFM) revealed that surface oxidation caused a significant reduction in conductive α-spots, leading to an increase in contact resistance. Contact resistance exhibited a quasi-linear relationship with the surface CuO content. Contact angle measurements and adhesion tests showed that the enhanced hydrophilicity of the oxidized surface and the resulting high adhesion contributed to an increase in the macroscopic friction coefficient. In humid N₂/O₂ with 50% relative humidity (RH), the friction coefficient rapidly exceeded 0.8 when the O₂ content surpassed 25%. Wear morphology analysis demonstrated that this abrupt increase in the friction coefficient induced fatigue wear on the surface. Overall, the present study elucidated the composite oxidation mechanisms during current-carrying rolling and clarified the pathways through which oxidation affected current-carrying tribological performance. These findings may contribute to improved failure analysis and the safe, reliable operation of electrical contact pairs. Full article

Ricotta cheese whey (RCW) is a by-product with nutritional potential, but its use in the human diet is limited due to its high salinity. Chlorella vulgaris can use RCW as a substrate to enhance biomass productivity. The aim of this work was to evaluate different conditions for C. vulgaris growth in RCW, during scaling-up analysis. After preliminary assays to select growth conditions, two systems were prepared as follows: 500 mL Erlenmeyer flasks (control-system) and a 3 L Bioreactor. Microfiltrated RCW was used as a substrate for C. vulgaris LPMA39 production. Biomass was measured and productivity at 96 h, cell growth kinetics behaviour, biomass biochemical characterisation, and the efficiency of nutrient removal were determined. Both systems presented the same biomass concentration at 96 h (2.2–2.8 g·L⁻¹) and productivity (0.021–0.027 g·L⁻¹·h⁻¹). Nevertheless, 11 h lag-period for cell adaptation to the 3 L Bioreactor was required; thereafter, cells grew faster (µ_max: 0.32 ± 0.08 h⁻¹) than control-system. Finally, slight but significantly lower C_max: 2.14 ± 0.08 was obtained when comparing it to control-system. Lipids, proteins, and pigment contents decreased by the scaling-up; meanwhile, higher reduction in chemical oxygen demand (COD), total phosphorus, and total nitrogen were recorded in the 3 L Bioreactor. Identifying the operating conditions that improve C. vulgaris performance in non-diluted RCW remains a challenge from a sustainability standpoint. Full article

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article