Search Results (1,224)

Due to the low inertia and small internal resistance of the DC line, the short-circuit fault is more harmful to the DC microgrid than the AC microgrid. Therefore, rapid and accurate detection of faults in DC microgrids plays an important role in ensuring the stable operation of DC microgrids. In this paper, the residual generator is designed based on the state observer, and the fault diagnosis of the DC microgrid is achieved by analyzing and processing the residual signal. Firstly, a mathematical model is established for a single line, and the corresponding residual generator is designed by using the unknown input observer to achieve the fault detection of a single key protection line. Secondly, considering the high cost of fault detection for each line alone, a residual generator is established for the entire DC microgrid to achieve fault detection of the entire DC microgrid, which effectively reduces the cost of fault detection. Finally, the radial DC microgrid and the ring DC microgrid are simulated and verified respectively to ensure that the designed fault diagnosis method is applicable to both topologies. Full article

The increasing penetration of distributed generation (DG) in distribution systems poses significant operational challenges, including increased power losses, voltage profile deviations, and variations in short-circuit currents. These issues may compromise network safety, reliability, and the selectivity of protection schemes under different operating scenarios. This paper proposes a hybrid optimization methodology for the optimal placement and sizing of DG, aiming to minimize active power losses while ensuring voltage regulation and keeping short-circuit currents within permissible limits. An integrated approach is proposed that combines a mesh-to-radial network reconfiguration strategy with a modified Ant Lion Optimization algorithm, known as ALO-DG, enabling the simultaneous optimization of network topology and the allocation of distributed generators at candidate buses. The problem is formulated taking into account power balance constraints, voltage limits, distribution network capacity limits, and short-circuit current limits. The proposed methodology achieved substantial reductions in active power losses in the IEEE 33-bus and 69-bus test systems, reaching 84.42% and 91.56%, respectively. These improvements were accompanied by enhanced voltage profiles while preserving the radial operating structure of the distribution networks. Furthermore, the proposed hybrid methodology serves as a tool for the planning and operation of distribution systems with high DG penetration, particularly in scenarios where grid security and protection coordination are critical considerations. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

Standard cryptocurrency transaction cost models assume flat geometry and assign execution cost as a proportional fee. This paper proposes GEODEX, a framework that models execution slippage as the geodesic arc length on the Fisher information manifold of a Markov-switching GARCH maximum-entropy model, augmented by a joint curvature–topological fragmentation alarm. The Curvature-Fragmentation Law (Proposition 2) is an analytically derived heuristic. Its empirical validity is confirmed across four crisis episodes. Ablation confirms that each geometric component contributes uniquely: removing the geodesic increases mean squared prediction error by 2.9%, removing topological data analysis by 2.1%, and removing curvature by 1.5%. On five cryptocurrency markets (BTC, ETH, XRP, LTC, and BCH), over 2253 daily observations, the framework achieves competitive prediction error and is the only single-signal model retained in the Model Confidence Set at $\alpha =0.10$ against eight benchmarks. A joint curvature–topological alarm fires a median of two days before price-based circuit breaker thresholds across four crisis episodes, including the Terra collapse (May 2022) and FTX bankruptcy (November 2022). Online inference requires under one second; full offline calibration requires approximately 28 h. The framework requires no additional data beyond the upstream estimation pipeline and supports SDG 10 (Reduced Inequalities) and SDG 16 (Strong Institutions) by enabling accessible geometric liquidity intelligence for regulators and smaller market participants. Full article

Intelligent packet routing in sixth-generation (6G) heterogeneous wireless networks must contend with stochastic link failures, heterogeneous delay profiles, and the severe memory constraints of edge nodes. We propose a quantum deep Q-network (Q-DQN) that replaces the multi-layer perceptron in a standard DQN agent with a six-qubit variational quantum circuit (VQC) employing ring-topology entanglement and angle embedding. The total trainable parameter count follows the closed-form expression $\left|\mathsf{\varphi }\right|=12L+7n$, growing at only seven parameters per additional network node. On a 10-node heterogeneous topology with stochastic link failures, Q-DQN achieves an average end-to-end delay of $54.29\pm 1.72$ ms with only 106 parameters, a 49.6× reduction relative to the MLP-based DQN baseline (5258 parameters, $52.89\pm 2.67$ ms). A three-seed scalability evaluation across $n\in \{6,8,10,12\}$ nodes shows that under a limited 200-episode training budget DQN converges more consistently, while Q-DQN matches DQN performance under full 500-episode training at a fraction of the parameter cost. Ablation experiments confirm that local-topology entanglement substantially outperforms full-connection alternatives. These results indicate that VQC-based routing agents can match classical counterparts at a fraction of the parameter cost, providing a path toward ultra-lightweight intelligent routing in 6G edge deployments. Full article

We develop a comparative synthesis of quantum-information geometry beyond complex numbers, with emphasis on what different algebraic frameworks contribute to information-processing structure rather than on their formal novelty alone. The organizing idea is a layer-by-layer test of the standard complex Hilbert-space formalism: each non-complex or deformed framework modifies the scalar field, phase group, projective state space, Born-probability semantics, composition rule, measurement geometry, symmetry algebra or representation category. The central thesis is that such frameworks are physically meaningful when they identify which assumptions make complex quantum mechanics operationally stable: positive probabilities, associative multipartite composition, reversible dynamics, experimentally testable phases, locality constraints, informationally complete measurements, error bases and clear operational semantics. Real quantum theory probes the necessity of complex phases and local tomography; quaternionic quantum mechanics probes non-Abelian phase while retaining associativity and admitting complex embeddings; octonionic proposals probe the boundary where exceptional geometry survives but generic circuit composition is obstructed by non-associativity; Jordan algebras test ordered probabilistic state spaces; Clifford algebras and Bott periodicity provide the spinorial and topological grammar connecting gates, Hopf maps and periodic dimensions; and quantum-group or q-deformed constructions probe coproducts, braiding and representation categories rather than scalar amplitudes. We distinguish three roles that are often conflated: genuine hypercomplex kinematics, Hopf-fibration coordinates for ordinary complex multipartite entanglement, and deformed algebraic or categorical structures. The resulting map separates established equivalence and experimental-constraint results from useful representation tools and speculative programs, while identifying concrete open problems for non-complex quantum information. Full article

Nowadays, numerical simulation methods are advanced and widely used in industry, enabling the modeling of complex systems from printed circuit boards (PCBs) to full power converters. Among many isolated topologies, the phase-shift full-bridge (PSFB) topology is a well-established solution for isolated DC–DC conversion in electric vehicles. Therefore, this paper proposes a broadband electromagnetic compatibility (EMC) modeling methodology for a custom-designed 1 kW gallium nitride (GaN)-based PSFB converter intended for an electric vehicle (EV) DC powertrain. Moreover, the approach combines full-wave electromagnetic simulation with circuit-level simulation, including parasitic effects from PCB layout, power harnesses, and discrete components. Thus, the virtual prototype is assessed within a complete virtual test bench compliant with the standard Comité International Spécial des Perturbations Radioélectriques (CISPR) 25 over the 150 kHz–108 MHz range to capture common-mode (CM) and differential-mode (DM) conducted electromagnetic interference (EMI). Results show that the converter achieves efficiencies of 97.26% in standalone mode and 97.03% when integrated into the full DC powertrain. However, the conducted EMI assessment reveals that both CM and DM emissions exceed CISPR 25 Class 2 limits across the entire spectrum, with excess levels reaching up to 72 dBµV. Therefore, power harnesses significantly increase EMI levels at low frequencies due to the distributed inductance and stray capacitance. Finally, this study demonstrates the value of virtual prototyping for simulation-based EMI prediction in early-stage power converter design. Full article

To address the issues of low voltage levels and insufficient reliability in dynamic regulation and voltage stabilization in existing high-voltage power supplies for electron-curtain accelerators, this paper presents a 150 kV/30 kW DC high-voltage power supply specifically designed for electron-curtain accelerators. The main circuit employs an LC high-frequency resonant topology and a step-up transformer with eight secondary windings, utilizing a parallel step-up and series output architecture to increase the output voltage level. During the charging phase, a dual-closed-loop frequency conversion scheme combined with duty cycle feedforward is employed to accelerate charging speed, while the voltage stabilization phase utilizes hysteresis burst control to improve accuracy. Simulation results indicate that the system can charge to 155 kV in 102 ms, with a voltage ripple less than 0.1%, a linear regulation of 0.01%, and a load regulation of 0.5%. Tests on a low-voltage prototype confirmed that the power devices can achieve zero-current soft switching, with a resonant current peak of 40 A and overall efficiency reaching 96%. The accompanying filament power supply can stably output 24 V/20 A, and the closed-loop voltage regulation is stable and reliable, providing technical support for the engineering application of high-voltage power supplies in high-power electron beam accelerators. Full article

We present a family of compact, programmable wavelength demultiplexers enabled by an etchless silicon nitride platform integrated with the low-loss phase-change material Sb₂Se₃. Using topology optimization (LumOpt) with a p-norm (p = 2) figure-of-merit defined over a 10 nm bandwidth, we design several devices within a common 24 × 24 μm² design region: single-wavelength routers (1530, 1550, 1570, 1590 nm), two-channel (1550/1570 nm), three-channel (1530/1550/1570 nm), and four-channel (1530–1590 nm) coarse wavelength-division demultiplexers, all sharing the same input/output waveguide configuration. Simulation results show that all devices achieve low insertion loss at target wavelengths (peak transmission better than −1.21 dB across all channels), high average transmission over the respective 10 nm bands (typically within 0.1 dB of the peak), and suppressed crosstalk (worst case below −11.52 dB). Leveraging the reversible amorphous-to-crystalline phase transition of Sb₂Se₃ via laser pulses, all devices support post-fabrication reconfiguration, overcoming the static functionality of conventional etched photonic circuits. This work establishes a scalable, software-defined platform that combines inverse design and phase-change materials for high-density, reconfigurable wavelength-routing photonic integrated circuits. Full article

The NMR resonator is the sensor responsible for transmitting RF pulses and receiving detection signals, and its tuning and matching are crucial to acquiring high-sensitivity NMR signals. Automated tuning and matching (ATM) is therefore essential for rapid, accurate, and continuously efficient testing. Existing NMR ATM methods still primarily rely on iterative search strategies, whose dominant cost arises from repeated hardware measurements and waiting periods, often requiring multiple measurement cycles before convergence. The emergence of in situ NMR detection of high-concentration ionic samples has further increased the demand for real-time, rapid ATM with a large dynamic range, posing a major challenge to conventional approaches. This paper proposes a physics-informed few-shot learning method for automatic tuning and matching over wideband and multi-resonance-frequency NMR scenarios. The tuning-and-matching problem is formulated as a structure and frequency-conditioned function regression task, and a conditional neural process (CNP) is introduced to learn cross-task priors and directly predict the states of tunable components from only a small number of real-machine context measurements. A physics regularizer based on the local sensitivity of the input impedance is further designed to impose stronger penalties on errors under high-Q narrowband operating conditions without relying on proprietary analytical circuit models. Simulation studies and real NMR experiments are conducted on multiple circuit topologies and multiple target frequencies using only a small number of NMR samples. The results demonstrate consistent improvements in key metrics, including accuracy of tuning and matching and the number of collected real-machine samples required per task. In particular, with only 100 sampled tuning/matching capacitor points and 20 on-hardware collected samples, the proposed method already delivers satisfactory tuning-and-matching performance. The method achieves an attractive accuracy–cost tradeoff across both cross-topology and cross-frequency scenarios, and shows strong potential for few-shot, rapid, real-time detection. Full article

This paper proposes a source-level electromagnetic interference suppression strategy for high-voltage inverters that uses a series-connected IGBT topology and discrete staircase voltage shaping. From an information-theoretic perspective, the staircase shaping transforms chaotic wideband switching noise into a deterministic harmonic structure, thereby reducing the spectral entropy of the EMI source. This information optimization is achieved using a CPLD-based sequential gate drive circuit, which eliminates the need for complex active gate profiling algorithms. Experimental results obtained using a 1140 V explosion-proof motor drive platform demonstrate harmonic attenuation of 4–16 dB μV within a 2 MHz band. Importantly, this targeted entropy reduction occurs alongside a 68.7% reduction in active-region switching losses, suggesting a concurrent decrease in local thermodynamic entropy production during switching transients. Increasing spectral determinism and relaxing requirements for subsequent physical filters effectively lower the conditional entropy of the overall electromagnetic environment. Leveraging the structural flexibility of series IGBTs, this method provides a practical, low-complexity solution and establishes a novel framework between power electronics and information theory for electromagnetic compatibility. Full article

In the field of power management circuits (PMC) for low-power thermoelectric generators (TEG) intended for autonomous sensors, this article experimentally evaluates the alternatives commercially available. Considering their limitations in terms of minimum input voltage and power efficiency, this article also proposes and experimentally characterizes a circuit topology that combines and interconnects two different PMC alternatives so as to achieve the benefits of both. Thanks to this interconnection, the resulting circuit can operate from a low input voltage (to be precise, an open-circuit voltage of the TEG equal to 40 mV), which is really attractive for TEGs under low thermal gradients, with a satisfactory power efficiency (i.e., up to 78%). Full article

Existing ground-slotted coplanar waveguide (CPW) spoof surface plasmon polariton (SSPP) low-pass filters (LPFs) remain constrained by the difficulty of achieving a wide stopband while maintaining a compact size, as well as by undesired radiation leakage arising from their open-aperture slot configuration. To address these issues, a grounded coplanar waveguide spoof surface plasmon polariton (GCPW-SSPP) low-pass filter based on a multi-arm star-shaped slot (MASS) loading topology is proposed. An equivalent-circuit interpretation and full-wave dispersion analysis show that the multi-arm slots introduce enhanced distributed reactive loading, thereby lowering the asymptotic frequency and enabling compact SSPP implementations. The near-field characteristics further demonstrate tighter electromagnetic confinement, as reflected by an approximately 48% reduction in the electric-field confinement width along the z-direction. To alleviate the trade-off between miniaturization and wide-stopband performance in cascaded SSPP LPFs, the single-cell S-parameters of the proposed topology are investigated. A single MASS unit exhibits a sharp cutoff and a deep transmission notch, allowing a wide stopband to be obtained with fewer cascaded cells. Radiation characteristics are subsequently quantified by a loss-decomposition method, and the MASS topology is found to suppress the radiation leakage of open-aperture ground-slotted structures, yielding a maximum radiation-loss reduction of approximately 75%. To validate the design methodology, a MASS-loaded GCPW-SSPP LPF is designed, fabricated, and measured. The measured results are in good agreement with the simulated ones, confirming the effectiveness of the proposed scheme. By simultaneously achieving a wide stopband, compact size, and suppressed radiation leakage, the proposed filter offers a promising low-interference filtering solution for highly integrated microwave and RF front-end systems. Full article

Industrial integrated circuit (IC) chip defect inspection is hindered by diverse topologies, micro-scale structures, and the insufficient precision of traditional vision paradigms. While the Segment Anything Model (SAM) offers strong zero-shot capabilities, its heavy reliance on manual prompts and lack of domain adaptability limit its viability in automated production lines. This paper proposes IC-SAM, a highly automated framework tailored for electronic manufacturing quality control. IC-SAM synergistically integrates three core modules: Process Prior Knowledge (PPK), which embeds semiconductor domain constraints to suppress background noise; Self-Driven Semantic Prompting, which leverages CLIP to align visual features with process descriptions for autonomous target localization; and Global Feature Fusion (GFF), which optimizes boundary localization through multi-scale interaction. Extensive experiments demonstrate that IC-SAM outperforms baseline models by approximately 15% in both mIoU and mBIoU across SIC, CIC, and IGBT datasets. The framework achieves 9.6 FPS under the tested 1024 × 1024 input setting while introducing only 2.3 M learnable parameters, indicating a parameter-efficient adaptation strategy with quantified computational cost for precision IC inspection. Full article

Focusing on the self-powering demand of aircraft engine bladed disks (blisks), this paper investigates piezoelectric vibration energy-harvesting modeling and non-linear circuit performance. A multi-sector electromechanical coupled model is established to analyze the frequency splitting and vibration localization induced by minor structural mistuning. By breaking the cyclic symmetry, mistuning severely concentrates vibration energy into a specific sector, providing a localized high-energy concentration region for optimal energy extraction. To enhance recovery efficiency and load adaptability, three interface circuit topologies—Standard Energy-Harvesting (SEH), Parallel Synchronized Switch Harvesting on Inductor (P-SSHI), and Double Synchronized Switch Harvesting (D-SSHI)—are comparatively analyzed. Through wideband spatial–spectral dynamic response and steady-state impedance matching analyses, the non-linear energy conversion and transfer mechanisms are systematically characterized. Results demonstrate that synchronized switching circuits significantly improve energy transmission via forced voltage inversion, accompanied by a notable equivalent stiffness enhancement effect induced by electromechanical coupling. Furthermore, the D-SSHI topology not only exhibits substantial advantages in peak power extraction, but also, owing to its internal LC energy decoupling mechanism, forms a broad load-independent power plateau across an extremely wide impedance range. This research provides robust theoretical foundations for designing highly resilient self-powered intelligent blades under extreme operating conditions. Full article