Search Results (12,689)

Underwater object detection is critical for marine resource utilization, ecological monitoring, and maritime security, yet it remains constrained by optical degradation, high energy consumption, and vulnerability to adversarial perturbations. To address these challenges, this study proposes an Adaptive Vision Transformer (A-ViT)-based detection framework. At the hardware level, a systematic power-modeling and endurance-estimation scheme ensures feasibility across shallow- and deep-water missions. Through the super-resolution reconstruction based on the Hybrid Attention Transformer (HAT) and the staged enhancement with the Deep Initialization and Deep Inception and Channel-wise Attention Module (DICAM), the image quality was significantly improved. Specifically, the Peak Signal-to-Noise Ratio (PSNR) increased by 74.8%, and the Structural Similarity Index (SSIM) improved by 375.8%. Furthermore, the Underwater Image Quality Measure (UIQM) rose from 3.00 to 3.85, while the Underwater Color Image Quality Evaluation (UCIQE) increased from 0.550 to 0.673, demonstrating substantial enhancement in both visual fidelity and color consistency. Detection accuracy is further enhanced by an improved YOLOv11-Coordinate Attention–High-order Spatial Feature Pyramid Network (YOLOv11-CA_HSFPN), which attains a mean Average Precision at Intersection over Union 0.5 (mAP@0.5) of 56.2%, exceeding the baseline YOLOv11 by 1.5 percentage points while maintaining 10.5 ms latency. The proposed A-ViT + ROI reduces inference latency by 27.3% and memory usage by 74.6% when integrated with YOLOv11-CA_HSFPN and achieves up to 48.9% latency reduction and 80.0% VRAM savings in other detectors. An additional Image-stage Attack QuickCheck (IAQ) defense module reduces adversarial-attack-induced latency growth by 33–40%, effectively preventing computational overload. Full article

Grid-forming wind-storage systems can serve as black-start power sources capable of autonomously establishing voltage and frequency references when the external grid is unavailable, thereby providing crucial support for rapid grid restoration. However, during the black-start process, energizing unloaded transformers often induces severe magnetizing inrush currents, which may cause transient overcurrent, damage grid-forming converters, and compromise system stability. To address this issue, this paper proposes a segmented zero-voltage start strategy and a dual-side converter multi-mode switching control scheme based on small-capacity distributed energy storage. First, the formation mechanism of transformer magnetizing inrush under no-load energization is analyzed. A segmented zero-voltage start module is embedded into the outer voltage loop of the virtual synchronous generator (VSG) controller to enable a smooth rise in output voltage, effectively mitigating transient impacts caused by magnetic core saturation. Second, considering the operating requirements during self-start and load restoration stages, a coordinated control framework for dual-side converters is designed to achieve dynamic voltage, frequency, and power regulation with limited energy storage capacity, thereby improving transient stability and energy utilization efficiency. Finally, real-time hardware-in-the-loop (HIL) simulations conducted on an RT-LAB platform verify the feasibility of the proposed control strategy. The results demonstrate that the method can significantly suppress magnetizing inrush current, transient overvoltage, and overcurrent, thus enhancing the success rate and dynamic stability of black-start operations in grid-forming wind-storage systems. Full article

The accurate detection of high-impedance faults (HIFs) in distribution systems is fundamentally dependent on the extraction of weak fault signatures. However, these features are often obscured by complex and high-level noise present in current transformer (CT) measurement data. To address this challenge, an energy-proportion-guided channel-wise attention stacked denoising autoencoder (EPGCA-SDAE) model is proposed. In this model, wavelet decomposition is employed to transform the signal into informative frequency band components. A channel attention mechanism is utilized to adaptively assign weights to each component, thereby enhancing model interpretability. Furthermore, a physics-informed prior, based on energy distribution, is introduced to guide the loss function and regulate the attention learning process. Extensive simulations using both synthetic and real-world $10\mathrm{kV}$ distribution network data are conducted. The superiority of the EPGCA-SDAE over traditional wavelet-based methods, stacked denoising autoencoders (SDAE), denoising convolutional neural network (DnCNN), and Transformer-based networks across various noise conditions is demonstrated. The lowest average mean squared error (MSE) is achieved by the proposed model (simulated: $50.60\times {10}^{-5}\mathrm{p}.\mathrm{u}.$; real: $76.45\times {10}^{-5}\mathrm{p}.\mathrm{u}.$), along with enhanced noise robustness, generalization capability, and physical interpretability. These results verify the method’s feasibility within the tested 10 kV distribution system, providing a reliable data recovery framework for fault diagnosis in noise-contaminated distribution network environments. Full article

Aiming at the drawbacks of the classic CoCrFeNi high-entropy alloy (HEA)—low room-temperature strength and softening above 600 °C, which fail to meet strict material requirements in high-end fields like aerospace—this study used the vacuum hot-pressing sintering process to prepare CoCrFeNiTa_x HEAs (x = 0, 0.5, 1.0, 1.5, 2.0 atom, designated as H4, Ta_0.5, Ta_1.0, Ta_1.5, Ta_2.0, respectively). This process effectively inhibits Ta segregation (a key issue in casting) and facilitates the presence uniform microstructures with relative density ≥ 96%, while this study systematically investigates a broader Ta content range (x = 0–2.0 atom) to quantify phase–property evolution, differing from prior works focusing on limited Ta content or casting/spark plasma sintering (SPS). Via X-ray diffraction (XRD), scanning electron microscopy–energy-dispersive spectroscopy (SEM-EDS), microhardness testing, and room-temperature compression experiments, Ta’s regulatory effect on the alloy’s microstructure and mechanical properties was systematically explored. Results show all alloys have a relative density ≥ 96%, verifying the preparation process’s effectiveness. H4 exhibits a single face-centered cubic (FCC) phase. Ta addition transforms it into a “FCC + hexagonal close-packed (HCP) Laves phase” dual-phase system. Mechanically, the alloy’s inner hardness (reflecting the intrinsic property of the material) increases from 280 HV to 1080 HV, the yield strength from 760 MPa to 1750 MPa, and maximum fracture strength reaches 2280 MPa, while plasticity drops to 12%. Its strengthening mainly comes from the combined action of Ta’s solid-solution strengthening (via lattice distortion hindering dislocation motion) and the Laves phase’s second-phase strengthening (further inhibiting dislocation slip). Full article

Samples of H13 tool steel were produced using the LENS^® laser additive manufacturing technique. Three variants of samples were produced such that during and 2 h after deposition, both the substrate and sample temperatures were maintained at 80, 180, and 350 °C. After the samples were produced, the effect of the substrate temperature on their metallurgical quality, microstructure, and mechanical properties was determined. No segregation of alloying elements was observed. The test results indicate that, depending on the temperature used, the structure of the H13 alloy is martensitic or martensitic-bainitic with a slight residual austenite content of up to 2.1%. Owing to structural changes, the obtained alloy is characterized by lower impact strength compared with conventionally produced alloys and high brittleness, particularly when using an annealing temperature of 350 °C. Isothermal annealing above the martensite start temperature results in extreme brittleness due to a partial structural transformation of martensite into bainite and probable carbide precipitation processes at the nanoscale. Full article

With the accelerating digital transformation of modern society, numerous data center (DC) agents are connected to the distribution power networks (DPNs) via microgrid and engaging in fierce market competition. To address the asymmetric operational risks faced by each data center agent, particularly those arising from market volatility and equipment failures, a novel cooperative game-theoretic approach is proposed in this paper. Firstly, a cooperative operation framework for the microgrid-integrated data centers (MDCs) system is established from two dimensions: joint task allocation across MDCs on the computing side and energy sharing among MDCs on the power side. Moreover, an optimal operating model for MDCs is established, which integrates the task allocation model that takes into account the task processing capacity of MDCs. Then, a cooperative operation model for the MDCs system based on Nash game theory is developed, and a joint solution framework for task allocation and the cooperative operation model is designed. Finally, the proposed cooperative game-theoretic approach is validated in a test system. The results show that the proposed approach ensures the reliable operation of the DPN while avoiding asymmetric operation risks among MDCs. It enhances the stability and security of distributed data processing. Furthermore, the Nash game-theoretic model achieves a symmetric distribution of profits and risks across MDCs, eliminating individual biases and maximizing the overall benefits of the cooperative alliance. Full article

Efficient operational planning in district heating systems (DHSs) is essential for minimizing operating costs and maximizing energy efficiency. However, since practitioners must determine future production plans under unknown future demands and costs in real-world energy systems, it is challenging to solve the production planning problems of DHSs. In this paper, we propose a multi-head attention-based TimesNet (MATN) in which a transformer decoder is incorporated that operates solely on a 24 h lookback window without requiring any future information. Specifically, the model is trained in an end-to-end manner, for which the training dataset was built by solving a mixed integer programming (MIP) model. Experimental results demonstrate that the proposed MATN model significantly outperforms baseline deep learning-based methods. A qualitative analysis of the hourly production plans further indicates that MATN generates robust operational plans that mimic those generated by an MIP model, which suggests the effectiveness of the proposed approach in terms of economic efficiency and operational stability without depending on future information. Full article

Cloud computing has transformed modern IT infrastructure by enabling scalable, on-demand access to virtualized resources. However, the rapid growth of cloud services has intensified energy consumption across data centres, increasing operational costs and carbon footprints. Traditional load-balancing methods, such as Round Robin and First-Fit, often fail to adapt dynamically to fluctuating workloads and heterogeneous resources. To address these limitations, this study introduces a Reinforcement Learning-guided hybrid optimization framework that integrates the Black Eagle Optimizer (BEO) for global exploration with the Pelican Optimization Algorithm (POA) for local refinement. A lightweight RL controller dynamically tunes algorithmic parameters in response to real-time workload and utilization metrics, ensuring adaptive and energy-aware scheduling. The proposed method was implemented in CloudSim 3.0.3 and evaluated under multiple workload scenarios (ranging from 500 to 2000 cloudlets and up to 32 VMs). Compared with state-of-the-art baselines, including PSO-ACO, MS-BWO, and BSO-PSO, the RL-enhanced hybrid BEO–POA achieved up to 30.2% lower energy consumption, 45.6% shorter average response time, 28.4% higher throughput, and 12.7% better resource utilization. These results confirm that combining metaheuristic exploration with RL-based adaptation can significantly improve the energy efficiency, responsiveness, and scalability of cloud scheduling systems, offering a promising pathway toward sustainable, performance-optimized data-centre management. Full article

Post-industrial cities across Europe are undergoing profound transformation, where sustainable building design plays an increasingly strategic role in redefining urban identity and function. The transition toward sustainable urban environments requires innovative construction technologies and performance-driven standards. This study examines the role of sustainable building design in post-industrial urban regeneration, focusing on Katowice, Poland—a city undergoing significant socio-spatial and economic transformation. Through descriptive case studies of selected buildings, the research highlights how high-performance construction techniques, including advanced insulation, energy-efficient ventilation, and integrated daylighting, contribute to prestigious certifications while reducing energy demand for heating, cooling, and lighting. Beyond technical performance, the analyzed projects demonstrate how sustainable buildings can act as catalysts for post-industrial urban renewal, fostering social engagement, environmental responsibility, and architectural innovation. The novelty of this work lies in linking building-scale sustainability interventions with city-scale urban transformation dynamics, offering practical insights for similar post-industrial contexts in Central and Eastern Europe. This research provides the first comparative analysis of certified and non-certified sustainable buildings in the context of post-industrial regeneration in this region. The post-industrial revitalization of Katowice is largely driven by advancements in building energy systems, such as high-efficiency HVAC technologies and other sustainable solutions. The findings demonstrate that sustainable architecture can act as a tangible driver of social, economic, and spatial renewal, providing practical insights for post-industrial regeneration strategies across similar urban contexts. Full article

This study explores the transformative role of Cyber-Physical Power System (CPPS) Digital Twins (DTs) in enhancing the operational resilience, flexibility, and intelligence of modern power grids. By integrating physical system models with real-time cyber elements, CPPS DTs provide a synchronized framework for real-time monitoring, predictive maintenance, energy management, and cybersecurity. A structured literature review was conducted using the ProKnow-C methodology, yielding a curated portfolio of 74 publications from 2017 to 2025. This corpus was analyzed to identify key application areas, enabling technologies, simulation methods, and conceptual maturity levels of CPPS DTs. The study highlights seven primary application domains, including real-time decision support and cybersecurity, while emphasizing essential enablers such as data acquisition systems, cloud/edge computing, and advanced simulation techniques like co-simulation and hardware-in-the-loop testing. Despite significant academic interest, real-world implementations remain limited due to interoperability and integration challenges. The paper identifies gaps in standard definitions, maturity models, and simulation frameworks, underscoring the need for scalable, secure, and interoperable architectures and highlighting key areas for scientific development and real-life application of CPPS DTs, such as grid predictive maintenance, forecasting, fault handling, and power system cybersecurity. Full article

With the increasing demand for high-energy-density lithium batteries, the role of separators has expanded significantly beyond conventional ion conduction and physical isolation. By integrating sensors and introducing functional coatings, separators have gained the ability to monitor internal states in real time and achieve adaptive regulation. This paper systematically reviews the latest research progress on separators modified with functional materials and coatings to achieve information sensing, intelligent response, and multifunctional integration. Notably, an electrochemical sensor based on MXene/MWCNTs-COOH/MOF-808 has been developed for rapid chemical detection; a fully printed ultra-thin flexible multifunctional sensor array has enabled multi-parameter synchronous monitoring; an ion-selective MOF-808-EDTA separator has induced uniform lithium-ion flux; and a PVDF-HFP/LLZTO/PVDF-HFP trilayer separator has maintained structural integrity at 300 °C. These innovative achievements fully demonstrate the enormous potential of intelligent separators in monitoring internal battery states, inhibiting dendrite growth, preventing thermal runaway, and significantly enhancing battery safety, cycle life, and energy density. This points to a transformative development path for the next generation of batteries with higher safety and intelligence. Full article

As artificial intelligence (AI) advances, deep learning models are shifting from convolutional architectures to transformer-based structures, highlighting the importance of accurate floating-point (FP) calculations. Compute-in-memory (CIM) enhances matrix multiplication performance by breaking down the von Neumann architecture. However, many FPCIMs struggle to maintain high precision while achieving efficiency. This work proposes a high-precision hybrid floating-point compute-in-memory (Hy-FPCIM) architecture for Vision Transformer (ViT) through post-alignment with two different CIM macros: Bit-wise Exponent Macro (BEM) and Booth Mantissa Macro (BMM). The high-parallelism BEM efficiently implements exponent calculations in-memory with the Bit-Separated Exponent Summation Unit (BSESU) and the routing-efficient Bit-wise Max Finder (BMF). The high-precision BMM achieves nearly lossless mantissa computation in-memory with efficient Booth 4 encoding and the sensitivity-amplifier-free Flying Mantissa Lookup Table based on 12T Triple Port SRAM. The proposed Hy-FPCIM architecture achieves 23.7 TFLOPS/W energy efficiency and 0.754 TFLOPS/mm² area efficiency, with 617 Kb/mm² memory density in 28 nm technology. With almost lossless architectures, the proposed Hy-FPCIM achieves an accuracy of 81.04% in recognition tasks on the ImageNet dataset using ViT, representing a 0.03% decrease compared to the software baseline. This research presents significant advantages in both accuracy and energy efficiency, providing critical technology for complex deep learning applications. Full article

This study investigated the electrolytic-plasma hardening (EPH) of cast 20GL steel, used for railway spring beams. The main objective was to analyze the spectral characteristics of the cathodic discharge and establish correlations between the plasma parameters, processing regimes, and resulting surface properties. Optical emission spectroscopy revealed that the plasma at 260 V exhibited a high-energy state with an electron density of ~5.3 × 10¹⁶ cm⁻³ and an electron temperature of 10,031 K. Using these parameters, the heat flux from the plasma to the steel surface was estimated at ~1.5 × 10⁷ W/m², confirming that the discharge provides sufficient energy for surface austenitization. Microstructural analysis demonstrated that the electrolyte flow rate, which determines the cooling rate, is the key parameter controlling phase transformations. At low flow rates, ferrite–pearlite and bainitic structures formed, while a fully martensitic structure and maximum hardness (1046 HV) were achieved at 10 L/min. Tribological tests confirmed the superior wear resistance of the martensitic layers, showing a friction coefficient of 0.454 and a wear volume 3.4 times lower than in the as-cast state. These findings verify that EPH offers an energy-efficient, low-cost method for improving the surface performance and service life of 20GL steel components in heavy-duty railway applications. Full article

Conventionally, tomography is an inspection technique in which tomographic images are intended for human perception and interpretation. In this work, we shift this paradigm by transforming tomography into an autonomous estimator of industrial reactor states, enabling fully automated process control. Alcoholic fermentation was employed as an example of a controlled process in the current study. The work presents an original concept utilizing transfer learning in conjunction with a ResNet-type artificial neural network, which converts electrical measurements into a sequence of values correlated with the conductivity of pixels constituting the cross-section of the examined biochemical reactor. The conductivity vector is transformed into a parameter determining substrate concentration, enabling dynamic process regulation in response to signals generated from EIT (Electrical Impedance Tomography). Within the scope of the described research, calibration of the conductivity vector against substrate concentrations was performed, and a Matlab/Simulink-based dynamic Monod kinetics model was developed. The obtained results demonstrate high accuracy in substrate concentration estimation relative to reference values throughout a forty-six-hour process. The same signals enable energy-efficient process control, in which cooling and mixing intensity are regulated according to energy prices and renewable energy availability. This strategy may possess particular application in facilities where fermentation installations are co-located with bioenergy production units. Full article

Urbanization in Cyprus has accelerated significantly over the past 35 years, driven by population growth, infrastructure development, and the expansion of urban centres. This rapid urban transformation has contributed to notable changes in the local climate, primarily through the intensification of the Urban Heat Island (UHI) effect—a phenomenon where urban areas experience significantly higher temperatures than surrounding rural regions. As global climate change continues to influence regional weather patterns, understanding and mitigating local climatic variations such as UHI becomes increasingly critical for sustainable development and public health. In Cyprus, the cities of Nicosia, Limassol, Larnaca, and Paphos have witnessed considerable land use changes, with a growing contrast between densely built urban cores and less developed surrounding areas. This contrast results in uneven energy absorption, reduced vegetation cover, and altered surface temperatures, further exacerbating the effects of climate change at the local level. Full article