Search Results (56)

Creating high-fidelity digital twins (DTs) for Industry 4.0 applications, it is fundamentally reliant on the accurate 3D modeling of physical assets, a task complicated by the inherent imperfections of real-world point cloud data. This paper addresses the challenge of reconstructing accurate, watertight, and topologically sound 3D meshes from sparse, noisy, and incomplete point clouds acquired in complex industrial environments. We introduce a robust two-stage completion-to-reconstruction framework, C2R3D-Net, that systematically tackles this problem. The methodology first employs a pretrained, self-supervised point cloud completion network to infer a dense and structurally coherent geometric representation from degraded inputs. Subsequently, a novel adaptive surface reconstruction network generates the final high-fidelity mesh. This network features a hybrid encoder (FKAConv-LSA-DC), which integrates fixed-kernel and deformable convolutions with local self-attention to robustly capture both coarse geometry and fine details, and a boundary-aware multi-head interpolation decoder, which explicitly models sharp edges and thin structures to preserve geometric fidelity. Comprehensive experiments on the large-scale synthetic ShapeNet benchmark demonstrate state-of-the-art performance across all standard metrics. Crucially, we validate the framework’s strong zero-shot generalization capability by deploying the model—trained exclusively on synthetic data—to reconstruct complex assets from a custom-collected industrial dataset without any additional fine-tuning. The results confirm the method’s suitability as a robust and scalable approach for 3D asset modeling, a critical enabling step for creating high-fidelity DTs in demanding, unseen industrial settings. Full article

This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vibration and sound signals, recorded during the EoL quality inspection process at the end of an industrial manufacturing line. Recorded signals are transformed directly into Mel-frequency spectrograms (MFS) without pre-processing. To remove non-informative frequency bands and increase data relevance, a six-step data reduction procedure was implemented. Furthermore, to improve fault characterization, a reference spectrogram was generated from healthy motors. The neural network was trained on a highly imbalanced dataset, using oversampling and Bayesian hyperparameter optimization. The final classification algorithm achieved classification metrics with high accuracy (99%). Traditional EoL inspection methods often rely on threshold-based criteria and expert analysis, which can be inconsistent, time-consuming, and poorly scalable. These methods struggle to detect complex or subtle patterns associated with early-stage faults. The proposed approach addresses these issues by learning discriminative patterns directly from raw sensor data and automating the classification process. The results confirm that this approach can reduce the need for human expert engagement during commissioning, eliminate redundant inspection steps, and improve fault detection consistency, offering significant production efficiency gains. Full article

Unmanned systems provide significant prospects for improving the efficiency of electromagnetic geophysical exploration in mineral prospecting and geological mapping, as they can significantly increase the productivity of field surveys by accelerating the movement of the measuring system along the site, as well as minimizing problems in cases where the pedestrian walkability of the site is a challenge. Lightweight and cheap UAV systems with a take-off weight in the low tens of kilograms are unable to carry a powerful current source; therefore, semi-airborne systems with a ground transmitter (an ungrounded loop or grounded at the ends of the line) and a measuring system towed on a UAV are becoming more and more widespread. This paper presents the results for a new generation of semi-airborne technology SibGIS UAV-TEMs belonging to the “line-loop” type and capable of realizing the transient/time-domain (TEM) electromagnetics method used for studying a uranium object of the paleovalley type. Objects of this type are characterized by a low resistivity of the ore zone located in relatively high-resistivity host rocks and, from the position of the geoelectric structure, can be considered a good benchmark for assessing the capabilities of different electrical exploration technologies in general. The aeromobile part of the geophysical system created is implemented on the basis of a hexacopter carrying a measuring system with an inductive sensor, an analog of a 50 × 50 m loop, an 18-bit ADC with satellite synchronization, and a transmitter. The ground part consists of a galvanically grounded supply line and a current source with a transmitter creating multipolar pulses of quasi-DC current in the line. The survey is carried out with a terrain drape based on a satellite digital terrain model. The article presents the results obtained from the electromagnetic soundings in comparison with the reference (drilled) profile, convincingly proving the high efficiency of UAV-TEM. This approach to pre-processing UAV–electrospecting data is described with the aim of improving data quality by taking into account the movement and swaying of the measuring system’s sensor. On the basis of the real data obtained, the sensitivity of the created semi-airborne system was modeled by solving a direct problem in the class of 3D models, which allowed us to evaluate the effectiveness of the method in relation to other geological cases. Full article

Hydrogen and some of its derivatives (such as e-methanol, e-methane, and e-ammonia) are promising energy carriers that have the potential to replace conventional fuels, thereby eliminating their harmful environmental impacts. An innovative use of hydrogen as a zero-emission fuel is forming weakly ionized plasma by seeding the combustion products of hydrogen with a small amount of an alkali metal vapor (cesium or potassium). This formed plasma can be used as a working fluid in supersonic open-cycle magnetohydrodynamic (OCMHD) power generators. In these OCMHD generators, direct-current (DC) electricity is generated straightforwardly without rotary turbogenerators. In the current study, we quantitatively and qualitatively explore the levels of electric conductivity and the resultant volumetric electric output power density in a typical OCMHD supersonic channel, where thermal equilibrium plasma is accelerated at a Mach number of two (Mach 2) while being subject to a strong applied magnetic field (applied magnetic-field flux density) of five teslas (5 T), and a temperature of 2300 K (2026.85 °C). We varied the total pressure of the pre-ionization seeded gas mixture between 1/16 atm and 16 atm. We also varied the seed level between 0.0625% and 16% (pre-ionization mole fraction). We also varied the seed type between cesium and potassium. We also varied the oxidizer type between air (oxygen–nitrogen mixture, 21–79% by mole) and pure oxygen. Our results suggest that the ideal power density can reach exceptional levels beyond 1000 MW/m³ (or 1 kW/cm³) provided that the total absolute pressure can be reduced to about 0.1 atm only and cesium is used for seeding rather than potassium. Under atmospheric air–hydrogen combustion (1 atm total absolute pressure) and 1% mole fraction of seed alkali metal vapor, the theoretical volumetric power density is 410.828 MW/m³ in the case of cesium and 104.486 MW/m³ in the case of potassium. The power density can be enhanced using any of the following techniques: (1) reducing the total pressure, (2) using cesium instead of potassium for seeding, and (3) using air instead of oxygen as an oxidizer (if the temperature is unchanged). A seed level between 1% and 4% (pre-ionization mole fraction) is recommended. Much lower or much higher seed levels may harm the OCMHD performance. The seed level that maximizes the electric power is not necessarily the same seed level that maximizes the electric conductivity, and this is due to additional thermochemical changes caused by the additive seed. For example, in the case of potassium seeding and air combustion, the electric conductivity is maximized with about 6% seed mole fraction, while the output power is maximized at a lower potassium level of about 5%. We also present a comprehensive set of computed thermochemical properties of the seeded combustion gases, such as the molecular weight and the speed of sound. Full article

Mesoscale eddies induce significant variations in the temperature and salinity of the upper ocean, thereby exerting a substantial impact on sound propagation. Specifically, they can form or dissipate surface sound channels (SCs) and subsurface sound channels (SSCs). However, the specific impact of mesoscale eddies remains unclear at present. In this paper, META2.0, Argo, and SODA are employed to analyze the sound propagation characteristics of mesoscale eddies in the Kuroshio Extension (KE) by using a composite analysis method and a ray-tracing model. Results demonstrate that cyclonic eddies (CEs) cause the disappearance of the original SCs in winter, while simultaneously generating SSCs. Conversely, anticyclonic eddies (AEs) induce SCs in spring and winter, while in summer they induce SSCs and in autumn create dual-duct sound channels (DCs). This study quantitatively reveals the influence of mesoscale eddies on the genesis and demise of SCs and SSCs, providing technical support for sonar equipment to utilize mesoscale eddies. Full article

This paper presents the development of an energization system prototype for IoT sensor nodes using Peltier cells as energy harvesters; its operation is optimized by applying a maximum power point tracking algorithm (MPPT) to capture as much electrical energy as possible, even if the cell temperature conditions have variations. In the IoT sensor node, a power management algorithm that works in accordance with the measurement and transmission operations can extend the node operating time, to obtain a greater amount of information and reducing the need for battery maintenance. The proposed methodology consists of developing an energization system, as well as the IoT sensor node. The energization system consists of a block of Peltier cells to obtain up to 4 V, a SEPIC-type DC-DC converter, and a 3.7 V lithium battery for energy storage. The converter works in a closed loop with the MPPT algorithm and delivers a voltage that guarantees the maximum power transfer to the battery. The sensor node was developed based on the ESP8266 development board, it allows data acquisition of temperature, humidity, light intensity, presence, and sound. The node transmits this information to the Ubidots platform for real-time visualization; to take advantage of its processing capacity, MPPT and energy management algorithms are also implemented. The results showed that to obtain a minimum voltage of 3.3 V in the energization system, a temperature difference of $59\pm 1$ °C between the plates of the Peltier cells is required. The MPPT algorithm allows working at the maximum power point and keeps the power delivered to the battery stable, with small transients when the information is transmitted; however, the overshoot and the settling time are reduced and do not affect the node operation. Full article

The potential of electric vehicles (EVs) to support the decarbonization of the transportation sector, crucial for meeting greenhouse gas reduction targets under the Paris Agreement, is obvious. Despite their advantages, the adoption of electric vehicles faces limitations, particularly those related to battery range and charging times, which significantly impact the time needed for a trip compared to their combustion engine counterparts. However, recent improvements in fast charging technology have enhanced these aspects, making EVs more suitable for both daily and long-distance trips. EVs can now deal with long trips, with travel times only slightly longer than those of internal combustion engine (ICE) vehicles. Fast charging capabilities and infrastructure, such as 350 kW chargers, are essential for making EV travel times comparable to ICE vehicles, with brief stops every 2–3 h. Additionally, EVs help reduce noise pollution in urban areas, especially in noise-saturated environments, contributing to an overall decrease in urban sound levels. However, this research highlights a downside of DC (Direct Current) fast charging stations: high-frequency noise emissions during fast charging, which can disturb nearby residents, especially in urban and residential areas. This noise, a result of the growing fast charging infrastructure, has led to complaints and even operational restrictions for some charging stations. Noise-related disturbances are a significant urban issue. The World Health Organization identifies noise as a key contributor to health burdens in Europe, even when noise annoyance is subjective, influenced by individual factors like sensitivity, genetics, and lifestyle, as well as by the specific environment. This paper analyzes the sound emission of a broad sample of DC fast charging stations from leading EU market brands. The goal is to provide tools that assist manufacturers, installers, and operators of rapid charging stations in mitigating the aforementioned sound emissions in order to align these infrastructures with Sustainable Development Goals 3 and 11 adopted by all United Nations Member States in 2015. Full article

Edge processing is a trend in developing new technologies that leverage Artificial Intelligence (AI) without transmitting large volumes of data to centralized processing services. This technique is particularly relevant for security applications where there is a need to reduce the probability of intrusion or data breaches and to decentralize alert systems. Although drone detection has received great research attention, the ability to identify helicopters expands the spectrum of aerial threats that can be detected. In this work, we present the development of a perimeter alert system that integrates AI and multiple sensors processed at the edge. The proposed system can be integrated into a LoRa or LoRaWAN network powered by solar energy. The system incorporates a PDM microphone based on an Arduino Nano 33 BLE with a trained model to identify a drone or a UH-60 from an audio spectrogram to demonstrate its functionality. It is complemented by two PIR motion sensors and a microwave sensor with a range of up to 11 m. Additionally, the DC magnetic field is measured to identify possible sensor movements or changes caused by large bodies, and a configurable RGB light signal visually indicates motion or sound detection. The monitoring system communicates with a second MCU integrated with a LoRa or LoRaWAN communication module, enabling information transmission over distances of up to several kilometers. The system is powered by a LiPo battery, which is recharged using solar energy. The perimeter alert system offers numerous advantages, including edge processing for enhanced data privacy and reduced latency, integrating multiple sensors for increased accuracy, and a decentralized approach to improving security. Its compatibility with LoRa or LoRaWAN networks enables long-range communication, while solar-powered operation reduces environmental impact. These features position the perimeter alert system as a versatile and powerful solution for various applications, including border control, private property protection, and critical infrastructure monitoring. The evaluation results show notable progress in the acoustic detection of helicopters and drones under controlled conditions. Finally, all the original data presented in the study are openly available in an OSF repository. Full article

Inspired by the fact that flying insects improve their power conversion efficiency through resonance, many soft robots driven by dielectric elastomer actuators (DEAs) have achieved optimal performance via first-order modal resonance. Besides first-order resonance, DEAs contribute to multiple innovative functions such as pumps that can make sounds when using multimodal resonances. This study presents the multimodal resonance of a rectangular planar DEA (RPDEA) with a central mass bias. Using a combination of experiments and finite element modeling (FEM), it was discerned that under a prestretch of 1.0 × 1.1, the first-, second-, and third-order resonances corresponded to vertical vibration, rotation along the long axis, and rotation along the short axis, respectively. In first-order resonance, superharmonic, harmonic, and subharmonic responses were activated, while only harmonic and subharmonic responses were observed in the second- and third-order resonances. Further investigations revealed that prestretching tended to inhibit third-order resonance but could elevate the resonance frequencies of the first and second orders. Conveniently, both the experimental and FEM results showed that the frequencies and amplitudes of the multimodal resonances could be tuned by adjusting the amplitudes of the excitation signals, referring to the direct current (DC) amplitude and alternating current (AC) amplitude, respectively. Moreover, instead of linear vibration, we found another novel approach that used rotation vibration to drive a robot with soft bristles via hopping locomotion, showcasing a higher speed compared to the first-order resonance in our robot. Full article

While pulse oximetry using remote photoplethysmography (rPPG) is used in medicine and consumer health, sound theoretical foundations for this methodology are not established. Similarly to traditional pulse oximetry, rPPG oximetry uses two wavelengths to calculate the tissue oxygenation using the so-called ratio-of-ratios, R. However, the relationship between R and tissue oxygenation has not been derived analytically. As such, rPPG oximetry relies mostly on empirical methods. This article aimed to build theoretical foundations for pulse oximetry in rPPG geometry. Using the perturbation approach in diffuse approximation for light propagation in tissues, we obtained an explicit expression of the AC/DC ratio for the rPPG signal. Based on this ratio, the explicit expression for “ratio-of-ratios” was obtained. We have simulated the dependence of “ratio-of-ratios” on arterial blood saturation across a wide range (SaO2 = 70–100%) for several commonly used R/IR light sources (660/780, 660/840, 660/880, and 660/940 nm) and found that the obtained relationship can be modeled by linear functions with an extremely good fit (R² = 0.98–0.99) for all considered R/IR pairs. Moreover, the location of the pulsatile volume can be extracted from rPPG data. From experimental data, we found that the depth of blood pulsations in the human forehead can be estimated as 0.6 mm on the arterial side, which points to the papillary dermis/subpapillary vascular plexus origin of the pulsatile volume. Full article

Saltwater intrusion (SWI) is a critical concern affecting coastal groundwater sources due to natural and anthropogenic activities. The health of coastal aquifers is deteriorated by excessive SWI, mainly caused by the disturbance of the freshwater–saltwater equilibrium due to the escalating population, climate change, and the rising demand for freshwater resources for human activities. Therefore, gaining insight into the dynamics of SWI is crucial, particularly concerning the various factors that influence the intrusion mechanism. The present study focuses on the experimental simulation of saltwater in freshwater aquifers, considering boundary conditions and density-dependent effects. Two geological scenarios within coastal environments were investigated: First, a uniform, homogeneous case consisting of only sand, and second, a heterogeneous case in which layers of sand, clay, and sand mixed with pebbles are used. During the experiment, DC resistivity sounding data, as part of a widely recognized geophysical method, were collected and subsequently inverted to determine the depth of the freshwater–saltwater interface (FSWI). A finite element analysis was employed to generate numerical models based on experimental feedback. Further, for validation purposes, electrical resistivity tomography (ERT) data were collected from two distinct locations: near the seacoast and an aquaculture area. The ERT results show the presence of salinity intrusion in the study area, attributed mainly to groundwater overpumping and fish farming practices. The experimental findings indicate that the advancement of saltwater is affected by the geological properties of the media they traverse. The porosity (ϕ) and permeability (k) of the geological layer play a crucial role during the passage of saltwater flux into freshwater aquifers. The FSWI deviated along the clay boundary and hindered the easy passage of saltwater into surrounding layers. The alignment of experimental, numerical, and geophysical data suggests that this integrated approach could be valuable for studying SWI and can be applied in different geological settings, including tidal flats and alluvial plains. Full article

A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems’ charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed. Full article

The increase in industrial activities has raised concerns regarding air quality in urban areas within Malawi. To assess the source apportionment of air quality parameters (AQPs) and noise levels, concentrations of AQPs (CO, TSP, PM _2.5, PM₁₀) and noise levels were monitored at 15 sites in Makata, Limbe, Maselema, Chirimba, and Maone during dry and wet seasons, respectively. Active mobile multi-gas monitors and a Dylos DC1100 PRO Laser Particle Counter (2018 model) were used to monitor AQPs, while Integrated Sound Level Meters were used to measure noise levels. Monitoring and analysis were guided by the World Health Organization (WHO) and Malawi Standards (MS). A Positive Matrix Factorization (PMF) model was used to determine the source apportionment of AQPs, and matrix trajectories analysed air mass movement. In the wet season, the average concentration values of CO, TSP, PM₁₀, and PM_2.5 were 0.49 ± 0.65 mg/m³, 85.03 ± 62.18 µg/m³, 14.65 ± 8.13 µg/m³, and 11.52 ± 7.19 µg/m³, respectively. Dry season average concentration values increased to 1.31 ± 0.81 mg/m³, 99.86± 30.06 µg/m³, 24.35 ± 9.53 µg/m³, and 18.28 ± 7.14 µg/m³. Noise levels remained below public MS and WHO standards (85 dB). Positive correlations between AQPs and noise levels were observed, strengthening from weak in the dry season to moderately strong in the wet season. PMF analysis identified key factors influencing AQP accumulation, emphasizing the need for periodic sampling to monitor seasonal pollution trends, considering potential impacts on public health and environmental sustainability. Further studies should look at factors affecting the dynamics of PMF in Blantyre City. Full article

The article presents the issues related to load-power sharing in direct-current grid and a novel control method has advantages over known solutions. Unlike many similar-sounding papers, this article shows an attempt at creating fully controllable non-isolated system that allows for load-power sharing between a permanent magnet alternator equipped with machine-side converter (MSC) and a dual active bridge (DAB) tied to batteries or supercapacitor. The current-based load-power sharing is an essential feature of parallel-connected direct-current generators, and all types of voltage sources, in this way are contributing power to the system. To keep the optimal efficiency of the alternator, the rotational speed changes rely on proper mapping of the driving combustion engine. System components include a self-excited synchronous generator (SESG), operating at variable shaft speed, as well as batteries and supercapacitors that provide electricity for sudden electrical-load changes on the distribution grid. The core of the presented system is in a power-distribution method that consists of a programmed-controller structure allowing precise current distribution. A novelty of the proposed method is the use of a cascaded system of current and DC voltage regulators that allows for precise power-distribution control. In contrast with previously presented solutions, the proposed system allows for fast and accurate control of currents, loading parallel-connected DC voltage sources for wide-range generator speed changes. In the presented solution, both converters have been equipped with Schottky diodes, preventing the flow of equalizing currents between closed transistors in the parallel mode of operation. An experimental test-stand of the described system is presented with its theoretical basis and experimental results. Full article

Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source. Full article