Search Results (1,175)

The preservation and development of traditional villages are often affected by careless and scattered methods. The conservation paradigm has shifted from focusing on individual villages to regional clusters. This paper examines 275 national-level traditional villages in Henan Province, China, and develops an integrated identification–evaluation–strategy framework. First, cluster identification was performed using the three-dimensional indicator system that combined spatial, historical, cultural, and distinctive resources. This study identifies 16 traditional village clusters using K-means clustering and presents a unified spatial structure referred to as “one pole, three cores, four belts, and multiple points.” Based on cluster identification, a dual-dimensional evaluation system, including internal and external elements, was established to assess the development potential of the identified clusters. The external factors, including ecological resources, humanistic resources, and supporting conditions, were evaluated using a suitability evaluation. Simultaneously, the internal factors, i.e., value potential, spatial potential, and functional potential, were evaluated through the Cloud Model. Lastly, according to the evaluation results and unique resource analysis, the clusters were classified into five development typologies, with a corresponding strategy suggested for each. The integrated framework provides a replicable approach for clustered conservation and revitalization of traditional villages, offering scientific support for regionally integrated heritage management and sustainable rural development. Full article

Wound-rotor synchronous machines (WRSMs) offer a promising, magnet-free alternative for safety-critical transportation sectors like electric vehicles (EVs) and marine propulsion. While multiphase structures enhance fault tolerance in these applications, conventional WRSMs still suffer from reliance on maintenance-prone slip rings and brushes. Brushless multiphase self-excitation presents a compelling solution, but it introduces a critical design challenge: ensuring decoupled control between the torque-producing (αβ) and magnetizing () subspaces to prevent severe performance degradation. To address this cross-coupling issue, this paper proposes a 20-slot/18-pole five-phase architecture. By exploiting distinct spatial harmonics, the stator generates two independently controlled magnetic fields with a dedicated rotor harmonic winding. An integrated diode rectifier then seamlessly converts the induced AC voltages into the required DC field excitation. Extensive finite-element analysis (FEA) using ANSYS Maxwell is conducted to validate the design and rigorously evaluate subspace cross-coupling. Simulation results confirm that the proposed machine meets design specifications, demonstrating stable self-excited operation, acceptable efficiency, and representative fault-tolerant operation under a single open-phase condition, thereby confirming the electromagnetic feasibility of the proposed topology as a promising magnet-free candidate for future alternatives to PMSM-based traction solutions. Full article

We present FilterForge, a chat-driven VS Code environment that pulls the synthesis, analysis, simulation, and optimization stages of microwave bandpass filter design, normally coordinated by hand across tools written in different languages, into one workflow. A deployed Model Context Protocol (MCP) server exposes deterministic Python implementations of coupling-matrix synthesis, uniform predistortion, topology reconfiguration, a genetic-algorithm transmission-zero selector, a mode-matching engine for H-plane iris-coupled rectangular waveguide geometries, and a skill that generates PyAEDT/HFSS notebooks for various dimensioning design-curves. A language-model orchestrator turns natural-language requests into typed tool calls, while every reported quantity stays inside the deterministic kernels, so the numerics remain reproducible and model-agnostic. We evaluate the call layer on a 45-task benchmark across the five tool categories: gemini-3-flash reaches $96.3\%$ tool-selection and $94.8\%$ full-call accuracy with an $88.9\%$ pass${}^3$ rate, which an ablation traces to the curated tool-selection prompt rather than to raw model capability. The mode-matching engine is validated against full-wave HFSS on a six-pole 4 GHz Chebyshev filter tuned from the chat panel, and on an 8 GHz WR-112 counterpart taken end-to-end with no engineer in the loop, where a deterministic critique gates each round until a manufacturable geometry is reached. We then exercise the full workflow on two folded six-pole WR-90 cross-coupled filters at $10\mathrm{GHz}$, a high-selectivity design synthesized against a stop-band mask and a group-delay-equalized variant whose positive cross-coupling uses a pair of side-wall irises, the latter settling to a peak-to-peak in-band group-delay ripple below $1.5\mathrm{ns}$ while recovering the synthesized return loss. Full article

We investigate the onset of hyperbolicity in Jensen polynomials $J_{d,n}$ associated with the Riemann $\Xi$-function and identify a robust parity-driven bifurcation with a natural geometric interpretation. Numerical analysis for degrees $5\le d\le 16$ reveals two distinct regimes. For even d, the roots form a compact complex cluster whose imaginary extent decreases smoothly, and the transition to hyperbolicity is governed by a single complex-conjugate pair, consistent with a low-dimensional (tame) geometric structure. For odd d, a hierarchy of more intricate onset mechanisms emerges, including single-event transitions ($d=11$) and intermittent regimes ($d\ge 13$) with decoupled geometric invariants, suggestive of dynamics on decorated (wild) character varieties. We interpret this dichotomy through a connection with the $P_{\mathrm{III}}\left(D_6\right)$ tau-function arising in the Painlevé confluence diagram. Defining $\tau \left(t\right)=\Xi ({\displaystyle \frac{1}{2}}+\sqrt{-t})/\Xi \left({\displaystyle \frac{1}{2}}\right)$, we construct a generating function $B\left(w\right)={\sum }_{j\ge 0}{b}_j{w}^j$ from the cumulants of $log\Xi ({\displaystyle \frac{1}{2}}+z)$ using high-precision Cauchy/DFT methods (280–400-digit arithmetic), without explicit use of the zero expansion. Two independent numerical diagnostics indicate that B exhibits Stieltjes-type behavior: (i) positivity of Hankel determinants up to order $N=30$ and (ii) Padé approximants whose poles converge to ${\gamma }_k^2$ (squares of Riemann-zero ordinates) with stabilizing residues. These results provide strong evidence that the parity bifurcation observed in Jensen polynomial onset reflects a finite-dimensional manifestation of an underlying moment-based positivity structure. Motivated by this correspondence, we formulate a conjecture relating the Stieltjes nature of $B\left(w\right)$ to the eventual hyperbolicity of Jensen polynomials. This conjecture suggests a bridge between finite-dimensional root geometry and an infinite-dimensional kernel-based positivity framework, while leaving open the problem of establishing such positivity independently of the zero expansion. Full article

This work examines a complex-valued data partition within the improved Loewner Framework to enhance the efficiency of modal parameter identification for aerospace structures. The method is applied to the General Dynamics F-16 Ground Vibration Test dataset, assessing accuracy and computational performance against the standard real-valued formulation. The complex-valued approach reduces execution time by an order of magnitude while preserving the quality of the identified poles. The extracted modal parameters align well with established benchmark results, confirming the suitability of the proposed formulation for reliable and scalable modal analysis of aircraft structures. Full article

The influence of double continuous cold rolling followed by annealing on the texture evolution and mechanical properties of a commercial low-carbon ferritic steel was investigated. Ultrathin sheets (final thickness 0.22 mm) were produced through a two-stage cold rolling process with intermediate and final annealing at 690 °C for 35 s, followed by light temper rolling at 100 °C for 20 s. Texture evolution was characterized using Electron Backscatter Diffraction (EBSD) with Orientation Imaging Microscopy (OIM), producing pole figures and orientation distribution functions (ODFs). Mechanical properties were evaluated through Vickers microhardness and ultimate tensile strength measurements obtained from three independent locations per sample. Quantitative ODF analysis (φ₂ = 45°) revealed that γ-fiber ({111}//ND) intensity increased after each cold reduction stage and decreased after annealing due to recrystallization. The α-fiber (110/RD) and cube components (001//RD) showed a slight increase after annealing. The final ultrathin sheet exhibited moderate γ-fiber intensity (≈3 M.R.D), low Vickers microhardness (100–150 HV), and tensile strength (400–450 MPa). These results demonstrate controlled evolution of texture and microstructure during double cold rolling and annealing, providing a basis for future studies on forming-related behavior without directly assessing formability. Full article

This paper presents a method for estimating moisture content (%MC) in power transformers. It primarily relies on the analysis of the statistical properties of relaxation times characterizing the dielectric frequency response (DFR), which is fitted as a sum of rational functions using the Vector Fitting (VF) tool. The DFR is modeled as a sum of Debye terms (accounting for materials exhibiting different relaxation times due to multiple polarization processes) characterized by poles and residues provided by VF. These parameters are then used to derive statistical factors that correlate with the shape of the dielectric response curve in the context of the Havriliak–Negami (HN) model, which is known for its effectiveness in characterizing materials with multiple relaxation times. By correlating the statistical factors with the HN model parameters, substantial insights into the insulation condition can be achieved. A moisture index (MI) is proposed from these parameters, which, when combined with conductivity, allows for accurate %MC estimation in the solid insulation system (cellulose). The combined MI and conductivity capture combined effects on moisture behavior, addressing both conductivity and polarization losses at different frequencies. The proposed method provides an efficient and straightforward non-invasive approach to insulation assessment without complex optimization algorithms. Experimental work on transformers at varying moisture levels provides validation of the proposed approach and demonstrates strong correlation with industry standards. The results confirm its reliability for moisture evaluation in transformer monitoring. Full article

Aiming at the deterioration in Noise, Vibration and Harshness (NVH) performance caused by broadband torsional vibration in the integrated electric drive system (IEDS) of electric vehicles, most existing studies independently focus on electromagnetic excitation suppression or torsional vibration control of mechanical transmissions. Few researchers consider the coupling characteristics between the electromagnetic nonlinearity of motors and the nonlinearity of gear transmissions, making it difficult to realize the coordinated suppression of high- and low-frequency torsional vibration. In this paper, a seven-degree-of-freedom electromechanical coupling dynamic model is firstly established, which incorporates the electromagnetic torque ripple of the motor, the time-varying meshing stiffness of gears, meshing errors, and gear backlash nonlinearity. Through modal analysis and Campbell diagram solution, the natural characteristics and critical speed range of the system are clarified, and the generation mechanism of full-frequency band torsional vibration as well as the high–low frequency coupling characteristics are systematically revealed. On this basis, a coordinated active control strategy based on PD pole placement and harmonic current injection (PD-HCI) is proposed. The PD pole placement controller is adopted to suppress the low-frequency torsional vibration (0–20 Hz) of the transmission system, and the 5th/7th harmonic current injection is used to counteract the high-frequency torque ripple (above 200 Hz) of the motor, thereby achieving the coordinated suppression of broadband torsional vibration. The Matlab/Simulink R2023a simulation results show that the proposed control strategy reduces the torque fluctuation rate from 3.11% to 1.96%, the speed fluctuation rate from 0.10% to 0.03%, and the total harmonic distortion (THD) of stator current from 8.69% to 1.77% under steady-state operating conditions. Under transient operating conditions with sudden load changes, the stabilization time of fluctuations in speed and half-shaft torque is shortened by more than 80%, the impact amplitude is significantly reduced, and there is no loss in the vehicle’s dynamic response and speed tracking performance. Experimental results show that the coefficients of determination R2 of vehicle speed, motor speed, acceleration and torque are 0.9990, 0.9982, 0.9997 and 0.9997, respectively, which verifies the reliability of the established model. Full article

The conventional robotic position control is gradually being replaced by force control, which is commonly used in humanoid robot applications that require force interaction with the environment, force transmission, or contact. A high-back-drive-efficiency actuator with a high-torque-density permanent magnet motor connecting the low-ratio planetary reducer is widely applied in interactive robotic systems without a torque/force sensor. This paper proposes a high-torque-density permanent magnet motor with an external rotor structure, which can realize torque enhancement by the increased air-gap diameter and better space utilization by the internal planetary reducer, i.e., the reducer inside the stator. First, the motor topologies with different slot/pole number combinations are introduced. Then, the optimization of a slot/pole number combination is elaborated for the maximum torque and torque mass density. In addition, the influence of the slot/pole number combination on the torque characteristic and overload capability is investigated by the finite element (FE) method. The experimental results of the prototype motor are provided to verify the analysis. Full article

Vibration-based Structural Health Monitoring (SHM) exploits automated Operational Modal Analysis (OMA) to track changes in modal parameters over time for subsequent statistical pattern recognition and anomaly detection. However, weak excitation, measurement noise, non-stationarities, non-linearities, and model inaccuracies can jeopardize the reliability of automated OMA and pollute the modal parameter time series with a number of outliers or spurious estimates. These have an impact on statistical pattern recognition and consequently, the anomaly detection accuracy. Thus, a preliminary data cleansing to enhance the robustness of modal parameter tracking is imperative to ensure the reliability of SHM outcomes. Clustering techniques represent an attractive solution to automatically identify underlying data patterns and discriminate possible spurious results. However, the curse of dimensionality is often an issue in the application of such techniques to time series of experimentally identified modal parameters. To mitigate this issue and, at the same time, the computational efforts, the present study proposes an innovative approach leveraging clustering techniques coupled with mode-pairing constraints for robust and automatic tracking of modal parameters in the context of vibration-based SHM applications. Different clustering algorithms have been embedded in the proposed data processing strategy and applied to a real dataset collected on a full-scale structure under operational conditions. The comparative performance assessment demonstrated how DBSCAN outperforms other clustering methods in the context of the proposed approach, allowing the effective separation of the physical poles from the spurious ones even in the presence of closely spaced modes and highly polluted feature space. Full article

As power grids transition towards highly renewable generation on a global scale, maintaining dynamic stability is becoming a major challenge. Replacing traditional synchronous generators with inverter-based renewables strips the grid of rotational inertia, leaving active distribution networks highly vulnerable to frequency deviations and voltage spikes. To avoid expensive poles and wires upgrades, Battery Energy Storage Systems (BESS) are increasingly being deployed as Non-Network Solutions (NNS). However, the current literature reveals a distinct gap between the macro-scale economic planning of these storage assets and the micro-scale dynamic control actually required to keep the grid resilient. To address this gap, this review proposes a multi-layer deterministic synthesis framework that links physical renewable modelling, degradation-aware techno-economic planning, deterministic forecasting, and EMS dispatch through offline time-domain control validation for AC-microgrid energy storage integration. The research examines how advanced central control units within battery management systems can rigorously and jointly estimate State of Charge (SoC) and State of Energy (SoE) to ensure accurate grid-aware dispatch. Furthermore, the study explores the integration of degradation-aware economic modelling in HOMER Pro with dynamic transient control in MATLAB/Simulink R2025b, driven by hybrid metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). This analysis demonstrates that integrating energy storage must be treated as a tightly coupled multidimensional optimization problem to successfully deliver the secure and sustainable infrastructure needed to solve the modern energy trilemma. Full article

As the offshore wind industry scales toward 15 MW capacity, the reliability of Direct-Drive Permanent Magnet Synchronous Generators (DD-PMSGs) becomes critical. However, real-world run-to-failure data for these massive, multi-pole machines is virtually non-existent, creating a barrier for developing effective data-driven diagnostic systems. This study proposes a high-fidelity framework for detecting permanent magnet faults in the International Energy Agency (IEA) 15 MW Reference Wind Turbine. Using Finite Element Analysis (FEA), a dataset (magnetic flux and back electromotive-force (EMF)) capturing the electromagnetic signatures of healthy and faulty states of a PMSG under varying severities is generated. To improve the power of computer vision, 1D time-series signals were transformed into 2D images. Specifically, Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) were applied to magnetic flux density signals, while Markov Transition Fields (MTFs) were applied to back-EMF signals. These representations were then fused into multi-channel Red-Green-Blue (RGB) images and processed via a ResNet-18 Deep Transfer Learning model using a strictly non-overlapping, leakage-free dataset partitioning strategy. The proposed framework achieved a classification accuracy of 99.45% on noise-free data. Furthermore, robustness testing under varying levels of Additive White Gaussian Noise (AWGN) (30 dB, 40 dB, and 50 dB Signal-to-Noise Ratio (SNR)) demonstrated sustained high performance, maintaining over 90% accuracy even under severe 30 dB noise conditions. Comparative analysis proved that this multi-channel fusion significantly outperforms single-channel encoding methods, which collapse under heavy noise, validating the scalability of the framework and applicability for next-generation condition monitoring in harsh offshore environments. Full article

Democratic erosion often begins rhetorically before institutions show visible damage. Here we test whether large language models (LLMs) can detect early linguistic signals of authoritarian drift in political speech. Formal speeches by Adolf Hitler (1922–1939), Donald Trump (2017–2025), Nicola Sturgeon (2014–2023), Giorgia Meloni (2022–2025) and Viktor Orban (2022–2025) were scored using an 11-indicator taxonomy derived from the Levitsky–Ziblatt framework and evaluated independently by GPT-4o, Gemini 2.5-Pro and Grok-4-Fast, with near-perfect inter-model agreement. Principal Component Analysis revealed two poles: an authoritarian–populist cluster (Hitler–Trump–Orban) and a democratic-institutional pole (Meloni–Sturgeon). To quantify proximity to an authoritarian reference, we introduce the Authoritarian Reference Index (ARI), defined such that it captures both its alignment and intensity relative to the Hitler gold-standard vector. Trump exhibited the highest proximity to the reference (99.1% alignment, 80.7% intensity), followed by Orban, who mirrored the structural alignment (97.6%) with a moderated intensity (72.4%). In contrast, the democratic-institutional pole was distinguished by significantly lower intensity scores, with Meloni (16.4%) and Sturgeon (22.3%) remaining distant from the authoritarian magnitude despite varying degrees of structural overlap. These results show that extreme rhetorical peaks carry disproportionate diagnostic weight and that LLMs can expose structural authoritarian patterns relevant for democratic monitoring. Full article

Electric power steering (EPS) motors require low torque ripple, low cogging torque, and smooth torque output to ensure precise control and driving comfort. However, consequent pole permanent magnet synchronous motors (CP-PMSMs), although advantageous in reducing permanent magnet usage, exhibit an imbalanced magnetic flux distribution due to the iron poles, resulting in even-order harmonic components in the back electromotive force (BEMF) and significant torque ripple. In this paper, a rotor pole arrangement for CP-PMSMs is proposed to improve torque characteristics for EPS applications. Symmetric and asymmetric pole arrangements are introduced to modify the magnetic flux distribution and suppress harmonic components generated by the iron poles. In addition, the iron pole arc ratio is selected as a key design variable and analyzed for each model to achieve low torque ripple while maintaining torque performance. The electromagnetic characteristics of the proposed structures are evaluated using finite element analysis under identical operating conditions. The results show that the torque ripple of the proposed models is reduced by approximately 33.3%p and 34.1%p compared with the conventional CP-PMSM, and the cogging torque is also significantly reduced. Although average torque decreases, overall torque characteristics improve due to reduced torque ripple and harmonic components. These results demonstrate that the proposed rotor pole arrangement effectively enhances torque quality in CP-PMSMs without increasing axial length or requiring three-dimensional analysis. Full article

This study proposes a geometric procedure for robust controller tuning under parametric uncertainty, based on root-contour analysis of the closed-loop control system. For a fixed candidate controller tuning, the set of possible pole locations induced by the admissible variations of the control plant parameters is constructed. Robust admissibility is formulated as a geometric set-inclusion problem, requiring this set to remain inside a prescribed dynamic performance region in the complex s-plane. A distinction is introduced between nominal admissibility, robust stability, and robust admissibility, showing that stability over the entire uncertainty set is not sufficient to guarantee the desired dynamic performance. To quantify the root contours, several indices are defined, including the dispersion along the real and imaginary axes, the maximum pole displacement with respect to the nominal pole locations, and the geometric margin to the boundary of the performance region. The procedure is applied to the selection and verification of PI controller tunings for an uncertain single-input–single-output (SISO) control system and is further validated through examples with different structures of parametric uncertainty, including a system with a single uncertain parameter and a PID-controlled system with several uncertain control plant parameters. The results show that root-contour analysis can distinguish tunings that are only robustly stable from tunings that preserve the prescribed dynamic performance over the entire uncertainty set. Thus, the method can be used as a practical tool for the diagnosis, comparison, and selection of controller tunings under parametric uncertainty. Full article