Configuration Method Research Articles

Design of polarization-maintaining fiber with uniform doping concentration supporting 14 weakly coupled modes based on discrete point configuration optimization

In this work, we propose a polarization-maintaining, weakly coupled few-mode fiber with a uniform doping concentration, designed via a particle swarm optimization algorithm and a discrete point configuration method. The design features a circular central air hole and an irregular doped boundary. Across the C+L band, all 14 modes exhibit an effective refractive index difference exceeding 1.43×10−4. Compared to conventional polarization-maintaining few-mode fibers, the proposed design offers enhanced flexibility, ensuring mode isolation with a very non-complicated process. This approach can be readily extended to other inhomogeneous optical waveguides, offering a valuable framework for intelligent design in non-uniform waveguide systems.

Reconfigurable Manipulation of Sound with a Multimaterial 3D Printed Origami Metasurface

AbstractThe challenge in reconfigurable manipulation of sound waves using metasurfaces lies in achieving precise control over acoustic behavior while developing efficient and practical tuning methods for structural configurations. However, most studies on reconfigurable acoustic metasurfaces rely on cumbersome and time‐consuming control systems. These approaches often struggle with fabrication techniques, as conventional methods face limitations such as restricted material choices, challenges in achieving complex geometries, and difficulties in incorporating flexible components. This paper proposes a novel approach for developing a reconfigurable metasurface inspired by the Kresling origami, designed for programmable manipulation of acoustic waves at an operating frequency of 2000 Hz. The origami unit cell is fabricated using multimaterial three‐dimensional (3D) printing technology, allowing for the simultaneous printing of two materials with different mechanical properties, thus creating a bistable origami‐based structure. Through optimization, two equilibrium states achieve a reflection phase difference of π through the application of small axial force, F, or torque, T. Various configurations of the metasurface, generated from different combinations of these two equilibria, enable distinct reflective behaviors with switchable and programmable functionalities. The principle of this work simplifies the shaping of acoustic waves through a straightforward mechanical mechanism, eliminating the need for complex control systems and time‐consuming adjustments.

Charge Reversal of the Uppermost Arginine in Sliding Helix S4-I Affects Gating of Cardiac Sodium Channel.

Several mutations of the uppermost arginine, R219, in the voltage-sensing sliding helix S4I of cardiac sodium channel Nav1.5 are reported in the ClinVar databases, but the clinical significance of the respective variants is unknown (VUSs). AlphaFold 3 models predicted a significant downshift of S4I in the R219C VUS. Analogous downshift S4I, upon its in silico deactivation, resulted in a salt bridge between R219 and the uppermost glutamate, E161, in helix S2I. To understand how salt bridge elimination affects biophysical characteristics, we generated mutant channel R219E, expressed it in the HEK293-T cells, and employed the patch-clamp method in a whole-cell configuration. Mutation R219E did not change the peak current density but shortened time to the peak current at several potentials, significantly enhanced activation, enhanced steady-state inactivation and steady-state fast inactivation, and slowed recovery from inactivation. Taken together, these data suggest that mutation R219E destabilized the resting state of Nav1.5. Cardiac syndromes associated with mutations R219P/H/C/P or E161Q/K are consistent with the observed changes of biophysical characteristics of mutant channel R219E suggesting pathogenicity of the respective VUSs, as well as ClinVar-reported VUSs involving arginine or glutamate in homologous positions of several Nav1.5 paralogs.

Effect of Hilbert Fractal Acoustic Metamaterials on Ventilation Noise Control

Abstract Ventilation noise control devices often involve a trade-off between their size and ventilating performance, which limits the ability to reduce low-frequency sound in high-ventilation conditions. To address this challenge, the present study explores the use of Hilbert fractal-based design in ventilated metamaterials for improved acoustic performance. The sound transmission loss (STL) of these metamaterials is compared to that of a simple expansion chamber, which serves as the base case. Various parameters, including Hilbert order (O), channel width (K), ventilated space (l), unit cell thickness (H), and the number of unit cells (N) are investigated. Initially, the transfer matrix method evaluates STL without considering thermoviscous effects, which are later incorporated in numerical simulations and impedance tube experiments. The parametric study reveals that increasing the Hilbert curve order decreases the fundamental frequency, while a higher K value increases it. Additionally, more unit cells enhance STL but reduce its broadband nature. Through the finite element method, band diagrams and eigenmodes of Hilbert and base configurations indicate that increased Hilbert orders result in more bands and correspondence between transmission loss spectra and band gaps. The study also identifies dipole resonance modes in the Hilbert structure, which induce a negative effective bulk modulus that contributes to STL. Real-time performance testing in a twin reverberation chamber demonstrates that the Hilbert structure achieves a 5-dB improvement in STL compared to the base configuration across the 700- to 1400-Hz range. These findings are essential for achieving broadband low-frequency noise reduction while allowing airflow.

Design of a high transmission illumination optics for anamorphic EUV lithography optics using deep reinforcement learning.

The design of the illumination optics for high numerical aperture (NA) anamorphic extreme ultraviolet (EUV) projection optics is a critical challenge to EUV lithography in advanced technology node. However, the EUV illumination optics design using conventional methods have flaws in illumination efficiency and illumination uniformity due to the limitations of relay configuration and matching method that can only consider one factor affecting illumination uniformity. One-mirror configuration can improve illumination efficiency by reducing the number of mirrors. Deep reinforcement learning (RL) can solve the limitations by considering multiple factors simultaneously. In this paper, a design method for a high transmission relay system and a matching method for double facets using deep RL are proposed to design NA 0.55 anamorphic EUV illumination optics. The one-mirror relay system is designed by first calculating its coaxial spherical initial configuration using matrix optics; then the one off-axis relay mirror, which is tilted and decentered to eliminate ray obscuration, is fitted into a conic surface. To satisfy the requirements of multiple factors that affect illumination uniformity, the assignment relationships between the field facets and the pupil facets are determined using deep RL under a certain illumination mode. Simulation results show that the illumination efficiency is 26.24%, and the illumination uniformity can reach 99% on the mask under different illumination modes.

Capacity optimization of photovoltaic storage hydrogen power generation system with peak shaving and frequency regulation

To solve the problem of power imbalance caused by the large-scale integration of photovoltaic new energy into the power grid, an improved optimization configuration method for the capacity of a hydrogen storage system power generation system used for grid peak shaving and frequency regulation is proposed. A hydrogen storage power generation system model is established, and the photovoltaic power generation and hydrogen fuel cell power generation is calculated. A two-layer hydrogen storage power generation system capacity optimization configuration model was established, an improved particle swarm optimization algorithm was used to solve the improved hydrogen storage power generation system capacity optimization configuration model, and the capacity optimization configuration results were obtained. The experimental results show that this method can obtain the best capacity allocation result based on local lighting conditions and the cost of the power generation system, whereas the improved particle swarm optimization algorithm can solve the model faster and reduce the cost of the power generation system. After applying this method, the net income of the solar hydrogen storage power generation system has almost doubled.

Limits of immersion-free recording of holographic waveguide displays.

This Letter discusses the limitations of immersion-free recording schemes for holographic waveguide displays. Traditional holographic recording of waveguides requires recording angles exceeding the critical angle of the hologram-cladding interface. Achieving these angles necessitates edge-lit exposure using prisms and immersion liquids, which are challenging for roll-to-roll mass production and hinder widespread adoption. We present analytical equations describing the set of holographic gratings that can be recorded immersion-free by utilizing recently proposed methods of wavelength and recording configuration shifting. We show that by appropriately choosing the recording polarization, superficial holograms can be mitigated and large effective index modulation can be achieved. Our results demonstrate that all relevant red, green, and blue (RGB) incouplers for typical reflective holographic waveguide displays in a photopolymer can be recorded and copied immersion-free, reducing manufacturing complexity and costs. However, typical expanders cannot. These findings contribute to the industrialization of holographic waveguides for augmented reality (AR).

Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation.

CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure and parameters for these models to achieve the desired results. In this article, an adaptive method for CNN automatic configuration for neonatal brain image segmentation is presented based on the encoder-decoder structure, in which the hyperparameters of this network, i.e., size, length, and width of the filter in each layer along with the type of pooling functions with a reinforcement learning approach and an LA model are determined. These LA models determine the optimal configuration for the CNN model by using DICE and ASD segmentation quality evaluation criteria, so that the segmentation quality can be maximized based on the goal criteria. The effectiveness of the proposed method has been evaluated using a database of infant MRI images and the results have been compared with previous methods. The results show that by using the proposed method, it is possible to segment NBI with higher quality and accuracy.

A cross-entropy-based synergy method for capacity configuration and SOC management of flywheel energy storage in primary frequency regulation

A cross-entropy-based synergy method for capacity configuration and SOC management of flywheel energy storage in primary frequency regulation

Optimization Configuration Method of Energy Storage Considering Photovoltaic Power Consumption and Source-Load Uncertainty

Optimization Configuration Method of Energy Storage Considering Photovoltaic Power Consumption and Source-Load Uncertainty

A multi-objective optimisation configuration method for photovoltaic access microgrid energy storage capacity based on improved genetic algorithm

A multi-objective optimisation configuration method for photovoltaic access microgrid energy storage capacity based on improved genetic algorithm

Configuration synthesis and screening method for magnetic gear two-speed transmission for electric vehicles

Magnetic field modulated gears have contactless, lubrication-free and highly flexible transmission characteristics, which provide an inherent technological advantage in solving the vibration, jerk and lubrication problems of two-gear transmissions in electric vehicles. To this end, a magnetic gear two-speed transmission drive concept is proposed, and a configuration preference method for two-speed transmissions based on weighted directed topology maps is investigated. A directed weighted graph modulo adjacency matrix and topological evolution rules describing the structure of the system are established. Adjacency matrix ergodic evolution and isomorphism discrimination are investigated for single and two-stage magnetic gear two-speed transmissions, respectively. Through the constructed relative speed link identification algorithm, the speed ratio between any component is calculated quickly. A configuration preference method was established for the defined evaluation objectives via hierarchical analysis. Finally, a prototype was built based on the screening results. The test results showed that the optimal solution could accurately meet the design requirements and realize two-speed transmission. The accuracy of the configuration and screening method of the magnetic gear two-speed transmission system proposed in this article is verified. It provides a theoretical reference for the design of high-performance magnetic gear two-speed transmission.

Effect of land configuration and methods of establishment on pearl millet (Pennisetum glaucam)

The present study examines the impact of land configuration and establishment methods on the growth and productivity of pearl millet. The experiment was conducted using a split-plot design with three main plot treatments, flatbed planting (M1), ridges and furrows (M2) and raised bed paired row planting (M3) and four subplot treatments: direct sowing (S1), 10- day-old seedlings (DOS) (S2), 15 DOS (dapog) (S3) and 20 DOS (S4). Each treatment combination was replicated three times. The results revealed that among the main plot treatments, ridges and furrows (M2) demonstrated superior performance exhibiting greater plant height, a higher total number of tillers, and increased dry matter production (DMP) at harvest. In subplot treatments, direct sowing (S1) resulted in better growth parameters, including greater plant height, higher DMP at harvest, and improved indices such as Leaf Area Index (LAI), Crop Growth Rate (CGR), Relative Growth Rate (RGR) and Net Assimilation Rate (NAR). In contrast, the 20 DOS in raised bed paired row planting (M3) recorded the shortest time to heading and 50% flowering. Among the treatment combinations, direct sowing in ridges and furrows (M2S1) achieved the highest plant height, maximum DMP at harvest and superior grain and straw yield.

Classification features of adaptive wing designs: history of creation, application experience and advanced developments

An overview of the developments of Russian and foreign scientists in the field of designing adaptive wings is presented. The work examines historical periods of development and improvement of methods for transforming the wing of aircraft. Classification features of adaptive wings of aircraft are identified, a detailed classification of transformable wings according to purpose (stationary and mobile) and design features (option and method of changing configuration and shape) is proposed. The results of testing finished structures and prototypes are shown. Patents for advanced adaptive wing designs are described.

Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks

The authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of the heavy load, low voltage, and increased network loss caused by the large number of electric vehicle (EV) charging piles and distributed photovoltaic (PV) access in urban, old and unbalanced distribution networks. At the stage of selecting the location of energy storage, a comprehensive power flow sensitivity variance (CPFSV) is defined to determine the location of the energy storage. At the energy storage capacity configuration stage, the energy storage capacity is optimized by considering the benefits of peak shaving and valley filling, energy storage costs, and distribution network voltage deviations. Finally, simulations are conducted using a modified IEEE-33-node system, and the results obtained using the improved beluga whale optimization algorithm show that the peak-to-valley difference of the system after the addition of energy storage decreased by 43.7% and 51.1% compared to the original system and the system with EV and PV resources added, respectively. The maximum CPFSV of the system decreased by 52% and 75.1%, respectively. In addition, the engineering value of this method is verified through a real-machine system with 199 nodes in a district of Kunming. Therefore, the energy storage configuration method proposed in this article can provide a reference for solving the outstanding problems caused by the large-scale access of EVs and PVs to the distribution network.

Energy Storage Configuration and Benefit Evaluation Method for New Energy Power Plants Based on Game Theory

AbstractIn the context of increasing renewable energy penetration, energy storage configuration plays a critical role in mitigating output volatility, enhancing absorption rates, and ensuring the stable operation of power systems. This paper proposes a benefit evaluation method for self-built, leased, and shared energy storage modes in renewable energy power plants. First, energy storage configuration models for each mode are developed, and the actual benefits are calculated from technical, economic, environmental, and social perspectives. Then, the CRITIC method is applied to determine the weights of benefit indicators, and the TOPSIS method is used to rank the overall benefits of each mode. Simulation results validate the effectiveness of the proposed method and compare the benefits of the three modes, showing that the leased mode provides the highest overall benefit. This study provides a quantitative reference for the rational selection of energy storage modes in renewable energy projects.

Research on Power Smoothing Control of Grid-Connected High Proportion New Energy Power Systems

With the increasing share of renewable energy in power systems, the construction of an integrated "source-grid-load-storage" system in regions rich in renewable energy and areas with concentrated loads has become an effective way to promote large-scale local consumption of renewable energy. However, under scenarios of high renewable energy integration, the lack of synchronous inertia support in the integrated system poses severe challenges to system frequency stability. Therefore, it is crucial to deeply explore the frequency regulation potential of each component in the "source-grid-load-storage" system. This paper studies the frequency response characteristics of new energy power stations and the demand-side response mechanism. Based on the actual operating conditions of the system and expected fault scenarios, reasonable virtual inertia time constants are set, and a frequency response model of the "source-grid-load-storage" integrated system is established. Furthermore, a new optimized power command is proposed, which combines a moving average filtering algorithm to smooth wind power output fluctuations. Finally, an optimized energy storage configuration method considering resource coordinated operation is studied. The research in this paper provides theoretical support and reference for improving system frequency stability and promoting renewable energy consumption under high renewable energy integration scenarios.

Broadband polarization-maintaining anti-resonant fiber design via swarm intelligence.

In this study, we utilized a discrete point configuration method in conjunction with genetic algorithm (GA) and particle swarm optimization (PSO) to design broadband polarization-maintaining anti-resonant fibers (PM-ARFs). The resulting structure features a confinement loss (CL) below 0.17 dB/m, birefringence of approximately 8.2 × 10-5, and a polarization loss ratio (PLR) close to 297 at 1550 nm, providing a usable bandwidth of 340 nm across a range from 1.27 μm to 1.61 μm. This design approach provides additional design freedom beyond parameter optimization and component stacking typical of intelligent design methods. Our study offers a robust design methodology for broadband PM-ARFs, with significant implications for the design of other non-uniform optical waveguides.

Adaptive current protection for three‐phase short‐circuit faults in active distribution networks

AbstractTo address the impact of distributed generation (DG) access on the traditional protection configuration methods of distribution networks (DNs) and to facilitate the swift and effective protection of lines by protection devices under fault conditions, an adaptive current protection method for three‐phase short‐circuit faults in active distribution networks (ADNs), based on local information, is proposed. Initially, the applicability issues of traditional protection are examined, and the limitations of the existing protection scheme in ADNs are highlighted. Subsequently, the fault characteristics of ADNs are analysed, taking into account the output characteristics of DGs under short‐circuit faults, which form the foundation for the proposed protection method. Following this, an adaptive current protection method based on local information is introduced, utilizing a dual criterion of phase and magnitude to adjust line protection, with a specific implementation strategy for the protection scheme provided. Finally, the proposed protection scheme is validated using PSCAD/EMTDC simulation software, with results demonstrating that the scheme enables rapid and efficient protection action under fault conditions across the entire line.

Optimization of photovoltaic and battery energy storage configuration utilizing the JAYA algorithm

To optimize the capacities and locations of newly installed photovoltaic (PV) and battery energy storage (BES) into power systems, a JAYA algorithm-based planning optimization methodology is investigated in this article. For this purpose, a series of mathematical models with constraint conditions are put forward to describe the dynamic properties of PVs and BES systems. Then, a general two-level planning model for maximizing the benefits of society is employed by introducing objective functions at the investment and operational levels from comprehensive influencing factors under different companies. To determine the optimal locations and capacities for configuring renewable energy sources, the proposed planning framework is solved using the JAYA algorithm. Finally, the effectiveness and reliability of the proposed configuration method are validated using an Institute of Electrical and Electronics Engineers (IEEE) 24-bus system with PVs and BES systems. Comparing the results of the various cases, it is obvious that the JAYA-based two-level planning optimization method can find the optimal configuration with minimum cost in shorter convergence times. Hence, the configuration strategy determined via the planning optimization method using the JAYA algorithm offers valuable guidance for the installation capacities and layouts of PVs and BES systems in power systems, which underscores their practical significance in energy management.