Search Results (27)

Traditional energy conservation policies have primarily relied on economic incentives and informational campaigns. However, recent insights from behavioral and social sciences indicate that subtle behavioral interventions, particularly social nudges, can significantly influence household electricity use. This paper presents a structured review of 23 recent field studies examining how social nudging strategies, such as peer comparison, group identity, and normative messaging, have contributed to measurable reductions in electricity consumption. By analyzing intervention outcomes across different regions and formats, we identify key success factors, limitations, and policy implications. Special attention is given to ethical considerations, fairness in implementation, and potential challenges in sustaining behavior change. This study offers a framework for integrating social nudges into future energy policies, emphasizing their role as low-cost, scalable tools for promoting sustainable energy behavior. Full article

With the evolution of data collection technologies, sensor-generated data have become the norm. However, decades of manually recorded maintenance data still hold untapped value. Natural Language Processing (NLP) offers new ways to extract insights from these historical records, especially from short, unstructured maintenance texts often accompanying structured database fields. While NLP has shown promise in this area, technical texts pose unique challenges, particularly in preprocessing and manual annotation. This study proposes a novel methodology combining Failure Mode and Effect Analysis (FMEA), a reliability engineering tool, into the NLP pipeline to enhance Named Entity Recognition (NER) in maintenance records. By leveraging the structured and domain-specific knowledge encapsulated in FMEAs, the annotation process becomes more systematic, reducing the need for exhaustive manual effort. A case study using real-world data from a major electrical utility demonstrates the effectiveness of this approach. The custom NER model, trained using FMEA-informed annotations, achieves high precision, recall, and F1 scores, successfully identifying key reliability elements in maintenance text. The integration of FMEA not only improves data quality but also supports more informed asset management decisions. This research introduces a novel cross-disciplinary framework combining reliability engineering and NLP. It highlights how domain expertise can be used to streamline annotation, improve model accuracy, and unlock actionable insights from legacy maintenance data. Full article

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture magnetic field maps (MFMs) around the head. Recent advancements have introduced optically pumped magnetometers (OPMs) as a promising alternative. Unlike SQUIDs, OPMs do not require cooling and can be placed closer to regions of interest (ROIs). This study aims to optimize the layout of OPM-MEG sensors, maximizing information capture with a limited number of sensors. We applied a sequential selection algorithm (SSA), originally developed for body surface potential mapping in electrocardiography, which requires a large database of full-head MFMs. While modern OPM-MEG systems offer full-head coverage, expected future clinical use will benefit from simplified procedures, where handling a lower number of sensors is easier and more efficient. To explore this, we converted full-head SQUID-MEG measurements of auditory-evoked fields (AEFs) into OPM-MEG layouts with 80 sensor sites. System conversion was done by calculating a current distribution on the brain surface using minimum norm estimation (MNE). We evaluated the SSA’s performance under different protocols, for example, using measurements of single or combined OPM components. We assessed the quality of estimated MFMs using metrics, such as the correlation coefficient (CC), root-mean-square error, and relative error. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95, localization error < 1 mm) capture most of the information contained in full-head MFMs. Our main finding is that for event-related fields, such as AEFs, which primarily originate from focal sources, a significantly smaller number of sensors than currently used in conventional MEG systems is sufficient to extract relevant information. Full article

When collecting data using ERT2D (2D electrical resistivity tomography) and TDIPT2D (2D time domain-induced polarization), different phenomena can occur, which can cause natural or anthropogenic noise, contaminating the data and making its processing, analysis, and interpretation difficult. Different techniques have been developed to eliminate or reduce these effects on the data, such as noise filtering or the development of new techniques to improve data collection in the field. In the present work, an iterative, weighted, least-squares filter was employed after voltage normalization using current and geometrical factor correction on data collected in rough topographic terrains. The selected filter basis function should be able to represent the natural behavior of the function to be filtered. Stationary or variable voltages in electrical prospecting decay with the inverse of the distance, which can be represented by an expansion in Legendre polynomials. On the other hand, uneven spacing of the electrodes leads to using the incorrect geometric factor, resulting in an error in the calculation of the electrical anomaly. The efficiency of the proposed technique was analyzed and tested with field examples using different filters and by comparing applying and not applying the proposed correction factor. The results indicated low RMS and L2-Norm errors, and better definition of the inverted resistivity image was obtained. For the TDIP data, a better correspondence between the inverted images of resistivity and chargeability was obtained. Full article

An ocean target electric field signal is an effective approach for analyzing the ocean environment and is widely used for detecting ocean targets, extracting their features, and tracking them. Low-frequency analysis and recording (LOFAR) is a commonly used time–frequency analysis tool that provides the time–frequency spectrum of a signal; however, its reliance on the Fourier transform (FT) results in a low frequency resolution and signal-to-noise ratio (SNR), which limits its target detection capabilities. To address this problem, we propose a method called low-frequency analysis and recording based on basis pursuit (LOFAR-BP) for analyzing and detecting ocean target electric field signals. LOFAR-BP uses basis pursuit (BP) with the L1 norm for frequency analysis, whereas LOFAR utilizes the FT. We demonstrate that the FT is the L2 norm mathematically. LOFAR-BP generates the time–frequency spectrum in the same way that LOFAR does. By extracting characteristic values from the time–frequency spectrum, targets can be detected using an appropriate threshold. Both simulation and ocean experiments showed that LOFAR-BP effectively enhances target signals and suppresses noise. Compared with LOFAR, LOFAR-BP improved the frequency resolution by 60% in both experiments and increased the SNR by 54.82 dB in the simulation experiment and by 39.59 dB in the ocean experiment. When applied to target detection, LOFAR-BP can detect targets 6 s earlier than LOFAR can. Full article

To address the difficulty in detecting workers’ violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject–predicate–object relationship. These triplets are embedded into the model using an adaptive connection network, which dynamically weights the relevance of external knowledge to enhance detection accuracy. Furthermore, to enhance the model’s performance, the paper designs a progressive multi-level distillation strategy. On one hand, knowledge transfer is conducted at the object level, region level, and global level, significantly reducing the loss of contextual information during distillation. On the other hand, two teacher models of different scales are introduced, employing a two-stage distillation strategy where the advanced teacher guides the primary teacher in the first stage, and the primary teacher subsequently distills this knowledge to the student model in the second stage, effectively bridging the scale differences between the teacher and student models. Experimental results demonstrate that under the proposed method, the model size is reduced from 14.5 MB to 3.8 MB, and the floating-point operations (FLOPs) are reduced from 15.8 GFLOPs to 5.9 GFLOPs. Despite these optimizations, the AP50 reaches 92.4%, showing a 1.8% improvement compared to the original model. These results highlight the method’s effectiveness in accurately detecting workers’ violation behaviors, providing a quantitative basis for its superiority and offering a novel approach for safety management and monitoring at construction sites. Full article

This paper presents the findings of the research aimed at developing computer models to determine the operating conditions in electric power systems (EPSs) feeding DC and AC railway substations. The object of the research is an EPS with a predominant traction load whose high-voltage power lines are connected to transformer and converter substations with 3 kV and 27.5 kV traction networks. The supply network includes 110 kV and 220 kV power lines. The EPS operating parameters are calculated based on the decomposition of the system into alternating and direct current segments. Calculations are performed for the fundamental frequency and high harmonic frequencies. The modeling technique is universal and can be used to determine the operating parameters and power quality indices for any configuration of an EPS and various designs of traction networks. With this technique, one can solve numerous additional problems, such as calculating the processes of ice melting in traction networks and power lines, determining electromagnetic field strengths, and assessing the heating of power line wires and catenary suspensions. The results obtained show that the voltages on the current collectors are within acceptable limits for all AC and DC electric locomotives. The levels of asymmetry on the 110 and 220 kV tires of traction substations (TP) do not exceed the normally permissible values. The values of the asymmetry coefficients for DC TP are tenths of a percent. With an increase in the size of traffic and in post-emergency conditions caused by the disconnection of communication between one of the support substations and the EPS, the asymmetry indicators on the 220 kV buses of AC substations may exceed the permissible limits. Phase-controlled reactive power sources can be used to reduce them. The analysis of the results of the determination of non-sinusoidal modes allows us to formulate the conclusion that the values of harmonic distortion go beyond the normative limits. Passive and active filters of higher harmonics can be used to normalize them. Calculations of thermal modes of traction transformers show that the temperatures of the most heated points do not exceed acceptable values. Full article

In this paper, we address the approximation of the coupling problem for the wave equation and Maxwell’s equations of electromagnetism in the time domain in terms of electric field by means of a nodal linear finite element discretization in space, combined with a classical explicit finite difference scheme for time discretization. Our study applies to a particular case where the dielectric permittivity has a constant value outside a subdomain, whose closure does not intersect the boundary of the domain where the problem is defined. Inside this subdomain, Maxwell’s equations hold. Outside this subdomain, the wave equation holds, which may correspond to Maxwell’s equations with a constant permittivity under certain conditions. We consider as a model the case of first-order absorbing boundary conditions. First-order error estimates are proven in the sense of two norms involving first-order time and space derivatives under reasonable assumptions, among which lies a CFL condition for hyperbolic equations. The theoretical estimates are validated by numerical computations, which also show that the scheme is globally of the second order in the maximum norm in time and in the least-squares norm in space. Full article

Tests on the content of the electrical component of the electromagnetic field (EMF) were carried out with an NHT3DL broadband meter by Microrad using a 01E (100 kHz ÷ 6.5 GHz) measuring probe. Measurements were made during training flights (Cessna C172, Cessna C152, Aero AT3, and Technam P2006T aircrafts). A neural network was used, the task of which was to learn to predict the successive values of average (E_RMS) and instantaneous (E_PEAK) electromagnetic fields used here. Such a solution would make it possible to determine the most favorable routes for all aircrafts. This article presents a model of an artificial neural network which aims to predict the intensity of the electrical component of the electromagnetic field. In order to create the developed model, that is, to create a training sequence for the model, a series of measurements was carried out on four types of aircraft (Cessna C172, Cessna C152, Aero AT3, and Technam P2006T). The model was based on long short-term memory (LSTM) layers. The tests carried out showed that the accuracy of the model was higher than that of the reference method. The developed model was able to estimate the electrical component for the vicinity of the routes on which it was trained in order to optimize the exposure of the aircraft to the electrical component of the electromagnetic field. In addition, it allowed for data analysis of the same training flight routes. The reference point for the obtained electric energy results were the normative limits of the electromagnetic field that may affect the crew and passengers during a flight. Monitoring and measuring the electromagnetic field generated by devices is important from an environmental point of view, as well as for the purposes of human body protection and electromagnetic compatibility. In order to improve reliability in general aviation and to adapt to the proposed requirements, aviation training centers are obliged to introduce systems for supervising and analyzing flight parameters. Full article

High power electromagnetics (HPEMs) pose a potential threatening risk to the wireless communication system, especially according to the main coupling path of the RF front-end channel. SPICE modeling of the responses coupled on the RF channel is crucial for the EM risk assessment, which helps us learn more about how the pulse conducts on the RF channel. A simplified linear RF channel with pyramid horn antenna is taken as an example by the selection of the key electronic modules of the actual wireless system. This paper proposes a system-level SPICE circuit model for the simplified RF channel according to the hybrid methods of the antenna electromagnetic simulation and SPICE modeling of the RF circuit. The equivalent circuits of the horn antenna illuminated by HPEMs are established with the Vector Fitting method based on Thevenin and Norton theorems. The short current response as the excitation files for the SPICE models are obtained by the commercial electromagnetic simulation of the horn antenna illuminated by Multiple HPEM environments. Equivalent circuits of a micro-strip bandpass filter are also derived with $\pi$ type circuit structure based on the measured admittance data. Then we analyze the HPEM risk faced by the RF channel by considering multiple HPEM environments. The norm theory is utilized to analyze the waveform characteristics from electric fields of HPEMs to the responses of the RF channel. The ratios of the responses versus electric field for each norm are computed and the EM risk degree is ranked based on those results. The results demonstrate that high power microwave is the highest threatening risk for the linear RF channel compared to the other two HPEMs such as ultra-wide band, high altitude electromagnetic pulse. Finally, the flowchart of EM risk assessment is presented based on a previous analysis, which will benefit the EMC design in engineering. Full article

In system-level high-altitude electromagnetic pulse (HEMP) illumination tests, it is common to perform the test in two orthogonal polarizations of the incident electric field. With the judgment standard of the electromagnetic norm, this paper evaluated, improved, generalized, and verified this method. The evaluated result shows that the maximum error of the maximum coupling in this method is less than 3 dB. Meanwhile, this method is improved by serving the 1.2 period of the larger coupling of the 2 illuminations as the maximum coupling. The maximum error can be controlled within 1.5 dB. Moreover, this method is generalized to non-orthogonal conditions. Expressions of the deviations of this method are strictly derived. Based on this method, the application and some extended thinking are discussed. At last, a current coupling test is designed and carried out to verify the methods and conclusions. The methods introduced in this paper can be applied to any linear system in the illumination test under approximate transverse electromagnetic (TEM) waves. Full article

Phonon and spintronic structures of monolayered Janus vanadium-dichalcogenide compounds are calculated by the first-principles schemes of pseudopotential plane-wave based on spin-density functional theory, to study dynamic structural stability and electronic spin-splitting due to spin-orbit coupling (SOC) and spin polarization. Geometry optimizations and phonon-dispersion spectra demonstrate that vanadium-dichalcogenide monolayers possess a high enough cohesive energy, while VSTe and VTe₂ monolayers specially possess a relatively higher in-plane elastic coefficient and represent a dynamically stable structure without any virtual frequency of atomic vibration modes. Atomic population charges and electron density differences demonstrate that V–Te covalent bonds cause a high electrostatic potential gradient perpendicular to layer-plane internal VSTe and VSeTe monolayers. The spin polarization of vanadium 3d-orbital component causes a pronounced energetic spin-splitting of electronic-states near the Fermi level, leading to a semimetal band-structure and increasing optoelectronic band-gap. Rashba spin-splitting around G point in Brillouin zone can be specifically introduced into Janus VSeTe monolayer by strong chalcogen SOC together with a high intrinsic electric field (potential gradient) perpendicular to layer-plane. The vertical splitting of band-edge at K point can be enhanced by a stronger SOC of the chalcogen elements with larger atom numbers for constituting Janus V-dichalcogenide monolayers. The collinear spin-polarization causes the band-edge spin-splitting across Fermi level and leads to a ferrimagnetic order in layer-plane between V and chalcogen cations with higher α and β spin densities, respectively, which accounts for a large net spin as manifested more apparently in VSeTe monolayer. In a conclusion for Janus vanadium-dichalcogenide monolayers, the significant Rashba splitting with an enhanced K-point vertical splitting can be effectively introduced by a strong SOC in VSeTe monolayer, which simultaneously represents the largest net spin of 1.64 (ћ/2) per unit cell. The present study provides a normative scheme for first-principles electronic structure calculations of spintronic low-dimensional materials, and suggests a prospective extension of two-dimensional compound materials applied to spintronics. Full article

Currently, ensuring the correct functioning of the electrical grid is an important issue in terms of maintaining the normative voltage parameters and local line overloads. The unpredictability of Renewable Energy Sources (RES), the occurrence of the phenomenon of peak demand, as well as exceeding the voltage level above the nominal values in a smart grid makes it justifiable to conduct further research in this field. The article presents the results of simulation tests and experimental laboratory tests of an electricity management system in order to reduce excessively high grid load or reduce excessively high grid voltage values resulting from increased production of prosumer RES. The research is based on the Elastic Energy Management (EEM) algorithm for smart appliances (SA) using IoT (Internet of Things) technology. The data for the algorithm was obtained from a message broker that implements the Message Queue Telemetry Transport (MQTT) protocol. The complexity of selecting power settings for SA in the EEM algorithm required the use of a solution that is applied to the NP difficult problem class. For this purpose, the Greedy Randomized Adaptive Search Procedure (GRASP) was used in the EEM algorithm. The presented results of the simulation and experiment confirmed the possibility of regulating the network voltage by the Elastic Energy Management algorithm in the event of voltage fluctuations related to excessive load or local generation. Full article

We study the effect of oblique illumination on the functioning of a plasmonic nanoantenna for chiral light. The antenna is designed to receive a structured beam of light and produce a nanosized near-field distribution that possesses nonzero orbital angular momentum. The design consists of metal (gold) microrods laid on a dielectric surface and is compatible with well-developed nanofabrication techniques. Experimental arrangements often require such an antenna to operate in a tilted geometry, where input light is incident on the antenna at an oblique angle. We analyze the limitations that the angled illumination imposes and discuss approaches to mitigate these limitations. Through our numerical simulations, we find that tilt angles require modifications to the antenna design. Our analysis can guide current and future experimental configurations to push the limits of resolution and sensitivity. Full article

The issues of renewable energy, energy crisis, and carbon reduction have caught people’s attention all over the world, and governments have put forth greater effort to proactively solve these problems. Electric transportation not only benefits the environment, but can also utilize renewable energy to prevent an energy crisis. Based on previous theoretical strands of the literature, this research integrates the technology readiness and acceptance model (TRAM) into the norm activation model (NAM) and proposes an integrated model denoted as TRA-NAM. It takes TRA-NAM as our theoretical foundation and aims to explore the effect of technology readiness and awareness of consequence on the intention toward using an electric scooter (ES). The results display that technology readiness positively influences perceived usefulness and perceived ease of use and further improves consumers’ intention toward adopting ES. In addition, personal norm mediates the relationship between awareness of consequence and intention to adopt ES. This study offers the integrated TRAM-NAM model in order to understand the crucial factors affecting consumers’ intention to adopt electric vehicles (EVs). Overall, this research fills the gap in the field of government policies and transportation and proposes ponderable suggestions, in particular that if they want to encourage or attract consumers to drive an ES, they should not overlook the effect of technology readiness and awareness of consequence. Full article