Search Results (670)

This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO₂ emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navigation app designed to predict CO₂ emissions and determine low-carbon routes using a hybrid CNN-LSTM model integrated into a complete pipeline for the ingestion and processing of large, heterogeneous geospatial and road data. Our study quantifies the end-to-end execution time, cumulative CPU load, and maximum RAM consumption for each library when applied to the GreenNav pipeline; it then converts these metrics into energy consumption and CO₂ equivalents. Experiments conducted on datasets ranging from 100 MB to 8 GB demonstrate that Polars in lazy mode offers substantial gains, reducing the processing time by a factor of more than twenty, memory consumption by about two-thirds, and energy consumption by about 60%, while maintaining the predictive accuracy of the model (R² ≈ 0.91). These results clearly show that the careful selection of data processing libraries can reconcile high computing performance and environmental sustainability in large-scale machine learning applications. Full article

This paper presents a microcontroller-based technique for accurately measuring resistive sensors over a wide dynamic range using an adaptive constant current source. Unlike conventional voltage dividers or fixed-current methods—often limited by reduced resolution and saturation when sensor resistance varies across several decades—the proposed system dynamically adjusts the excitation current to maintain optimal Analog-to-Digital Converter (ADC) input conditions. The measurement circuit employs a fixed reference resistor and an inverting amplifier configuration, where the excitation current is generated by one or more pulse-width modulated (PWM) signals filtered through low-pass RC networks. A microcontroller selects the appropriate PWM channel to ensure that the output voltage remains within the ADC’s linear range. To support multiple sensors, an analog switch enables sequential measurements using the same dual-PWM current source. The full experimental implementation uses four op-amps to support modularity, buffering, and dual-range operation. Experimental results show accurate measurement of resistances from 1 kΩ to 100 kΩ, with maximum relative errors of 0.15% in the 1–10 kΩ range and 0.33% in the 10–100 kΩ range. The method provides a low-cost, scalable, and digitally controlled solution suitable for embedded resistive sensing applications without the need for high-resolution ADCs or programmable gain amplifiers. Full article

As the penetration of intermittent renewable energy sources continues to increase, ensuring reliable power system and frequency stability is of importance. Battery energy storage systems (BESSs) have emerged as an important solution to mitigate these challenges by providing essential grid support services. In this context, a state-of-charge (SOC)-frequency control strategy for grid-forming BESSs is proposed to enhance their role in stabilizing grid frequency and improving overall system performance. In the system, the DC-link capacitor is regulated to maintain the angular frequency through a matching control scheme, emulating the characteristics of the rotor dynamics of a synchronous generator (SG). Thereby, the active power control is implemented in the control of the DC/DC converter to further regulate the grid frequency. More specifically, the relationship between the active power and the frequency is established through the SOC of the battery. In addition, owing to the inevitable presence of differential operators in the control loop, a high-gain observer (HGO) is employed, and the corresponding parameter design of the proposed method is elaborated. The proposed strategy simultaneously achieves frequency regulation and implicit energy management by autonomously balancing power output with available battery capacity, demonstrating a novel dual benefit for sustainable grid operation. To verify the effectiveness of the proposed control strategy, a 0.5-Hz frequency change and a 10% power change are carried out through simulations and also on a hardware-in-the-loop (HIL) platform. Full article

Dual-active bridge (DAB) converters have emerged as a preferred topology in electric vehicle charging and energy storage applications, owing to their structurally symmetric configuration and intrinsic galvanic isolation capabilities. However, conventional triple-phase shift (TPS) control strategies face significant challenges in maintaining high efficiency across ultra-wide output voltage and load ranges. To exploit the inherent structural symmetry of the DAB topology, a symmetric optimization strategy based on triple-phase shift (SOS-TPS) is proposed. The method specifically targets the forward buck operating mode, where an optimization framework is established to minimize the root mean square (RMS) current of the inductor, thereby addressing both switching and conduction losses. The formulation explicitly incorporates zero-voltage switching (ZVS) constraints and operating mode conditions. By employing the Karush–Kuhn–Tucker (KKT) conditions in conjunction with the Lagrange multiplier method (LMM), the refined control trajectories corresponding to various power levels are analytically derived, enabling efficient modulation across the entire operating range. In the medium-power region, full-switch ZVS is inherently satisfied. In the low-power operation, full-switch ZVS is achieved by introducing a modulation factor λ, and a selection principle for λ is established. For high-power operation, the strategy transitions to a conventional single-phase shift (SPS) modulation. Furthermore, by exploiting the inherent symmetry of the DAB topology, the proposed method reveals the symmetric property of modulation control. The modulation strategy for the forward boost mode can be efficiently derived through a duty cycle and voltage gain mapping, eliminating the need for re-derivation. To validate the effectiveness of the proposed SOS-TPS strategy, a 2.3 kW experimental prototype was developed. The measured results demonstrate that the method ensures ZVS for all switches under the full load range, supports ultra-wide voltage conversion capability, substantially suppresses RMS current, and achieves a maximum efficiency of 97.3%. Full article

Banana Fusarium wilt (BFW, also known as Panama disease) is a highly infectious and destructive disease that threatens global banana production, requiring early recognition for timely prevention and control. Current monitoring methods primarily rely on continuous variable features—such as band reflectances (BRs) and vegetation indices (VIs)—collectively referred to as basic features (BFs)—which are prone to noise during the early stages of infection and struggle to capture subtle spectral variations, thus limiting the recognition accuracy. To address this limitation, this study proposes a discretized enhanced feature (EF) construction method, the automated kernel density segmentation-based feature construction algorithm (AutoKDFC). By analyzing the differences in the kernel density distributions between healthy and diseased samples, the AutoKDFC automatically determines the optimal segmentation threshold, converting continuous BFs into binary features with higher discriminative power for early-stage recognition. Using UAV-based multi-spectral imagery, BFW recognition models are developed and tested with the random forest (RF), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms. The results show that EFs exhibit significantly stronger correlations with BFW’s presence than original BFs. Feature importance analysis via RF further confirms that EFs contribute more to the model performance, with VI-derived features outperforming BR-based ones. The integration of EFs results in average performance gains of 0.88%, 2.61%, and 3.07% for RF, SVM, and GNB, respectively, with SVM achieving the best performance, averaging over 90%. Additionally, the generated BFW distribution map closely aligns with ground observations and captures spectral changes linked to disease progression, validating the method’s practical utility. Overall, the proposed AutoKDFC method demonstrates high effectiveness and generalizability for BFW recognition. Its core concept of “automatic feature enhancement” has strong potential for broader applications in crop disease monitoring and supports the development of intelligent early warning systems in plant health management. Full article

Transcranial direct current stimulation (tDCS) can modulate cortical excitability in a polarity-specific manner, yet identical protocols often produce inconsistent outcomes across sessions or individuals. This narrative review proposes that much of this variability arises from the brain’s intrinsic temporal landscape. Integrating evidence from chronobiology, sleep research, and non-invasive brain stimulation, we argue that tDCS produces reliable, polarity-specific after-effects only within a circadian–homeostatic “window of efficacy”. On the circadian (Process C) axis, intrinsic alertness, membrane depolarisation, and glutamatergic gain rise in the late biological morning and early evening, whereas pre-dawn phases are marked by reduced excitability and heightened inhibition. On the homeostatic (Process S) axis, consolidated sleep renormalises synaptic weights, widening the capacity for further potentiation, whereas prolonged wakefulness saturates plasticity and can even reverse the usual anodal/cathodal polarity rules. Human stimulation studies mirror this two-process fingerprint: sleep deprivation abolishes anodal long-term-potentiation-like effects and converts cathodal inhibition into facilitation, while stimulating at each participant’s chronotype-aligned (phase-aligned) peak time amplifies and prolongs after-effects even under equal sleep pressure. From these observations we derive practical recommendations: (i) schedule excitatory tDCS after restorative sleep and near the individual wake-maintenance zone; (ii) avoid sessions at high sleep pressure or circadian troughs; (iii) log melatonin phase, chronotype, recent sleep and, where feasible, core temperature; and (iv) consider mild pre-heating or time-restricted feeding as physiological primers. By viewing Borbély’s two-process model and allied metabolic clocks as adjustable knobs for plasticity engineering, this review provides a conceptual scaffold for personalised, time-sensitive tDCS protocols that could improve reproducibility in research and therapeutic gain in the clinic. Full article

Cropland is a critical resource for safeguarding food security. Ensuring both the quantity and quality of cropland is essential for achieving zero hunger and promoting sustainable agriculture. However, whether urbanization-induced cropland loss poses a substantial threat to regional food security remains a key concern. This study examines the central region of the Yangtze River Delta (YRD) in China, integrating CLCD (China Land Cover Dataset) land use/cover data (2001–2023), MOD17A2H net primary productivity (NPP) data, and statistical records to evaluate the impacts of urban expansion on grain yield. The analysis focuses on three components: (1) grain yield loss due to cropland conversion, (2) compensatory yield from newly added cropland under the requisition–compensation policy, (3) yield increases from stable cropland driven by agricultural enhancement strategies. Using Sen’s slope analysis, the Mann–Kendall trend test, and hot/coldspot analysis, we revealed that urban expansion converted approximately 14,598 km² of cropland, leading to a grain production loss of around 3.49 million tons, primarily in the economically developed cities of Yancheng, Nantong, Suzhou, and Shanghai. Meanwhile, 8278 km² of new cropland was added through land reclamation, contributing only 1.43 million tons of grain—offsetting just 41% of the loss. In contrast, stable cropland (102,188 km²) contributed an increase of approximately 9.84 million tons, largely attributed to policy-driven productivity gains in areas such as Chuzhou, Hefei, and Ma’anshan. These findings suggest that while compensatory cropland alone is insufficient to mitigate the food security risks from urbanization, the combined strategy of “Safeguarding Grain in the Land and in Technology” can more than compensate for production losses. This study underscores the importance of optimizing land use policy, strengthening technological interventions, and promoting high-efficiency land management. It provides both theoretical insight and policy guidance for balancing urban development with regional food security and sustainable land use governance. Full article

In this paper, RF sub-modules with millimeter-wave functionality are considered and verified for designing an ultra-wideband receiver (18–40 GHz) required in the electronic support measure (ESM) field. The pre-design of an ultra-wideband super heterodyne receiver (SHR) requires a front-end module (FEM) with four units in the system. Each FEM has four channels with the same path, while the quadrature millimeter down-converter (QMDC) needs to have a converting function that uses a broadband mixer. The FEM includes the ability to provide built-in test (BIT) path functionality to the antenna ports prior to system field installation. Each path of the QMDC requires the consideration of several factors, such as down-converting, broadband gain flatness, and high isolation. As this is an RF module requiring high frequency and wideband characteristics, it is necessary to identify risk factors in advance within a predictable range. Accordingly, the blind-mate A (BMA) connector connection method, the phase-alignment test method in the down-conversion structure, and the LO signal, IF path inflow-blocking method were analyzed and designed. Full article

This paper proposes a family of high-efficiency DC-DC boost converters employing voltage-doubling coupled-inductor technology with a low component count. By varying the homonymous winding connections of the coupled inductor, three topologies are developed: parallel (PWCDVD-CLBC), series (SWCDVD-CLBC), and flipped-parallel (FPWCDVD-CLBC). These converters achieve high-voltage gain, continuous input current, and low-voltage stress across components. The PWCDVD-CLBC and FPWCDVD-CLBC configurations exhibit voltage gains proportional to the turn ratio, while the SWCDVD-CLBC shows an inverse relation, enabling reduced turn ratios. Detailed operational principles, mathematical analysis, and performance advantages are presented. A comparative evaluation demonstrates a higher voltage gain, realizes continuous input current, and has lower voltage stresses. The experimental results validate the theoretical analysis and confirm the feasibility and efficiency of the proposed designs. Full article

Power quality is a key issue in cold ironing (CI) systems, where a stable, clean power supply is essential to meet the needs of moored vessels. According to IEC/ISO/IEEE 80005-1, these systems must deliver high power at standardized voltages (6.6 kV or 11 kV) with minimal harmonic distortion in the presence of vessel load variability. This study proposes a model-free control strategy based on an intelligent proportional–integral (iPI) corrector with adaptive gain, applied to a three-phase modular multilevel converter (MMC) equipped with an LC filter. This architecture, adapted to distributed infrastructures, reduces the number of transformers required while guaranteeing high output voltages. The iPI strategy improves system robustness, dynamically compensates for disturbances, and ensures better power quality. A comparative analysis of three control strategies, proportional–integral (PI), intelligent proportional–integral (iPI), and intelligent proportional–integral adaptive (iPIa), performed in MATLAB/Simulink and complemented by experimental tests on the OPAL-RT platform, revealed a significant THD reduction of 1.18%, in accordance with the IEC/ISO/IEEE 80005-1 standard. These results confirm the effectiveness of the proposed method in meeting the requirements of CI systems. Full article

This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque—representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics. Full article

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

Photovoltaic (PV) systems are prone to different types of faults, primarily electrical faults such as line-to-ground (L-G) and line-to-line (L-L) faults, which can significantly reduce system performance, efficiency, and lead to increased power losses. Moreover, mechanical damage caused by environmental stressors (such as wind, hail, or temperature variations), aging, or improper installation also contribute to system degradation. This study specifically focuses on electrical faults and proposes a method that not only enables the isolation of faulty modules but also ensures the uninterrupted operation of the remaining healthy modules and also assists in the localization of faults. Unlike benchmarked techniques-based boost converters, the Quasi-Z-Source Boost Converter (QZBC) topology offers improved voltage boosting with high gain values, reduced component stress, and enhanced reliability when the PV system is undergoing fault identification and localization algorithms. A 600-watt PV system connected with a Quasi-Z-Source Boost Converter was implemented and tested under different fault conditions using a hardware-in-the-loop (HIL) setup with Typhoon HIL. All the component values of the QZBC were calculated based on the system requirements rather than assumed, ensuring both practical feasibility and design accuracy. The experimental results show that the converter achieved an efficiency of over 96% under electrical-fault conditions, confirming the effectiveness of the quasi-Z-source boost converter in maintaining a stable power output when the PV system is undergoing fault identification and localization algorithms. The study further highlights the benefits of HIL-based testing for evaluating PV-system resilience and fault-handling capabilities in real-time conditions using a Typhoon HIL 404 environment. Full article

This paper introduces a non-isolated ultra-high voltage gain topology using the combination of the coupled-inductor-based inverting buck-boost converter (IBB) and voltage multiplier (VM) structure. In the proposed converter, an ultra-high step-up voltage gain can be achieved with a small duty cycle thanks to a coupled inductor and VMs. The voltage stress and the losses of the switches in the proposed converter are even less than other conventional topologies. Unlike other coupled-inductor topologies, a large voltage spike caused by the leakage inductance of the coupled inductor is smoothed by the capacitor in the voltage multiplier. In addition, zero-voltage switching (ZVS) turn-on for the switches and zero-current switching (ZCS) turn-off for the diodes can be achieved with the energy stored in the leakage inductance. A 360 W (40 V/380 V) prototype converter is implemented to prove the advantages of the proposed converter, with a maximum efficiency of 98.4%. Full article

The permanent magnet synchronous motor (PMSM) is a critical device that converts kinetic energy into mechanical energy. However, it faces issues such as nonlinearity, time-varying uncertainties, and external disturbances, which may degrade the system control performance. To address these challenges, this paper proposes a prescribed performance model-free adaptive fast integral terminal sliding mode control (PP-MFA-FITSMC) method. This approach replaces conventional techniques such as parameter identification, function approximation, and model reduction, offering advantages such as quantitative constraints on the PMSM tracking error, reduced chattering, strong disturbance rejection, and ease of engineering implementation. The method establishes a compact dynamic linearized data model for the PMSM system. Then, it uses a discrete small-gain extended state observer to estimate the composite disturbances in the PMSM online, effectively compensating for their adverse effects. Meanwhile, an improved prescribed performance function and error transformation function are designed, and a fast integral terminal sliding surface is constructed along with a discrete approach law that adaptively adjusts the switching gain. This ensures finite-time convergence of the control system, forming a model-free, low-complexity, high-performance control approach. Finally, response surface methodology is applied to conduct a sensitivity analysis of the controller’s critical parameters. Finally, controller parameter sensitivity experiments and comparative experiments were conducted. In the parameter sensitivity experiments, the response surface methodology was employed to design the tests, revealing the impact of individual parameters and parameter interactions on system performance. In the comparative experiments, under various operating conditions, the proposed strategy consistently constrained the tracking error within ±0.0028 rad, demonstrating superior robustness compared to other control methods. Full article